Genetic publications
Last updated Mar 15, 2018
Below you will find all the published papers using UK Biobank genetic data. You can also view recent news stories of UK Biobank genetics research online here: Genetics in the news
2019 |
Amanda G.Wills; ChristianHopfer Phenotypic and genetic relationship between BMI and cigarette smoking in a sample of UK adults Journal Article Addictive behaviours, 2019. @article{G.Wills2019, title = {Phenotypic and genetic relationship between BMI and cigarette smoking in a sample of UK adults}, author = {Amanda G.Wills and ChristianHopfer}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0306460318305094}, year = {2019}, date = {2019-02-01}, journal = {Addictive behaviours}, abstract = {In addition to the health hazards posed individually by cigarette smoking and obesity, the combination of these conditions poses a particular impairment to health. Genetic factors have been shown to influence both traits and, to understand the connection between these conditions, we examined both the observed and genetic relationship between adiposity (an electrical impedance measure of body mass index (BMI)) and cigarettes smoked per day (CPD) in a large sample of current, former, and never smokers in the United Kingdom. In former smokers, BMI was positively associated with cigarettes formerly smoked; further, the genetic factors related to a greater number of cigarettes smoked were also responsible for a higher BMI. In current smokers, there was a positive association between BMI and number of cigarettes smoked, though this relationship did not appear to be influenced by similar genetic factors. We found a positive genetic relationship between smoking in current/former smokers and BMI in never smokers (who would be unmarred by the effects of nicotine). In addition to CPD, in current smokers, we looked at two variables, time from waking to first cigarette and difficulty not smoking for a day, that may align better with cigarette and food ‘craving.’ However, these smoking measures provided mixed findings with respect to their relationship with BMI. Overall, the positive relationships between the genetic factors that influence CPD in smokers and the genetic factors that influence BMI in former and never smokers point to common biological influences behind smoking and obesity.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In addition to the health hazards posed individually by cigarette smoking and obesity, the combination of these conditions poses a particular impairment to health. Genetic factors have been shown to influence both traits and, to understand the connection between these conditions, we examined both the observed and genetic relationship between adiposity (an electrical impedance measure of body mass index (BMI)) and cigarettes smoked per day (CPD) in a large sample of current, former, and never smokers in the United Kingdom. In former smokers, BMI was positively associated with cigarettes formerly smoked; further, the genetic factors related to a greater number of cigarettes smoked were also responsible for a higher BMI. In current smokers, there was a positive association between BMI and number of cigarettes smoked, though this relationship did not appear to be influenced by similar genetic factors. We found a positive genetic relationship between smoking in current/former smokers and BMI in never smokers (who would be unmarred by the effects of nicotine). In addition to CPD, in current smokers, we looked at two variables, time from waking to first cigarette and difficulty not smoking for a day, that may align better with cigarette and food ‘craving.’ However, these smoking measures provided mixed findings with respect to their relationship with BMI. Overall, the positive relationships between the genetic factors that influence CPD in smokers and the genetic factors that influence BMI in former and never smokers point to common biological influences behind smoking and obesity. |
2018 |
Aiden Doherty; Karl Smith-Byrne; Teresa Ferreira; Michael V. Holmes; Chris Holmes; Sara L. Pulit; Cecilia M. Lindgren GWAS identifies 14 loci for device-measured physical activity and sleep duration Journal Article Nature Communications, 2018. @article{Doherty2018, title = {GWAS identifies 14 loci for device-measured physical activity and sleep duration}, author = {Aiden Doherty and Karl Smith-Byrne and Teresa Ferreira and Michael V. Holmes and Chris Holmes and Sara L. Pulit and Cecilia M. Lindgren}, url = {https://www.nature.com/articles/s41467-018-07743-4}, year = {2018}, date = {2018-12-10}, journal = {Nature Communications}, abstract = {Physical activity and sleep duration are established risk factors for many diseases, but their aetiology is poorly understood, partly due to relying on self-reported evidence. Here we report a genome-wide association study (GWAS) of device-measured physical activity and sleep duration in 91,105 UK Biobank participants, finding 14 significant loci (7 novel). These loci account for 0.06% of activity and 0.39% of sleep duration variation. Genome-wide estimates of ~ 15% phenotypic variation indicate high polygenicity. Heritability is higher in women than men for overall activity (23 vs. 20%, p = 1.5 × 10−4) and sedentary behaviours (18 vs. 15%, p = 9.7 × 10−4). Heritability partitioning, enrichment and pathway analyses indicate the central nervous system plays a role in activity behaviours. Two-sample Mendelian randomisation suggests that increased activity might causally lower diastolic blood pressure (beta mmHg/SD: −0.91, SE = 0.18, p = 8.2 × 10−7), and odds of hypertension (Odds ratio/SD: 0.84, SE = 0.03, p = 4.9 × 10−8). Our results advocate the value of physical activity for reducing blood pressure.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Physical activity and sleep duration are established risk factors for many diseases, but their aetiology is poorly understood, partly due to relying on self-reported evidence. Here we report a genome-wide association study (GWAS) of device-measured physical activity and sleep duration in 91,105 UK Biobank participants, finding 14 significant loci (7 novel). These loci account for 0.06% of activity and 0.39% of sleep duration variation. Genome-wide estimates of ~ 15% phenotypic variation indicate high polygenicity. Heritability is higher in women than men for overall activity (23 vs. 20%, p = 1.5 × 10−4) and sedentary behaviours (18 vs. 15%, p = 9.7 × 10−4). Heritability partitioning, enrichment and pathway analyses indicate the central nervous system plays a role in activity behaviours. Two-sample Mendelian randomisation suggests that increased activity might causally lower diastolic blood pressure (beta mmHg/SD: −0.91, SE = 0.18, p = 8.2 × 10−7), and odds of hypertension (Odds ratio/SD: 0.84, SE = 0.03, p = 4.9 × 10−8). Our results advocate the value of physical activity for reducing blood pressure. |
Michael D. Morgan; Erola Pairo-Castineira; Konrad Rawlik; Oriol Canela-Xandri; Jonathan Rees; David Sims; Albert Tenesa; Ian J. Jackson Genome-wide study of hair colour in UK Biobank explains most of the SNP heritability Journal Article Nature Communications, 2018. @article{Morgan2018, title = {Genome-wide study of hair colour in UK Biobank explains most of the SNP heritability}, author = {Michael D. Morgan and Erola Pairo-Castineira and Konrad Rawlik and Oriol Canela-Xandri and Jonathan Rees and David Sims and Albert Tenesa and Ian J. Jackson }, url = {https://www.nature.com/articles/s41467-018-07691-z}, year = {2018}, date = {2018-12-10}, journal = {Nature Communications}, abstract = {Natural hair colour within European populations is a complex genetic trait. Previous work has established that MC1R variants are the principal genetic cause of red hair colour, but with variable penetrance. Here, we have extensively mapped the genes responsible for hair colour in the white, British ancestry, participants in UK Biobank. MC1R only explains 73% of the SNP heritability for red hair in UK Biobank, and in fact most individuals with two MC1R variants have blonde or light brown hair. We identify other genes contributing to red hair, the combined effect of which accounts for ~90% of the SNP heritability. Blonde hair is associated with over 200 genetic variants and we find a continuum from black through dark and light brown to blonde and account for 73% of the SNP heritability of blonde hair. Many of the associated genes are involved in hair growth or texture, emphasising the cellular connections between keratinocytes and melanocytes in the determination of hair colour.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Natural hair colour within European populations is a complex genetic trait. Previous work has established that MC1R variants are the principal genetic cause of red hair colour, but with variable penetrance. Here, we have extensively mapped the genes responsible for hair colour in the white, British ancestry, participants in UK Biobank. MC1R only explains 73% of the SNP heritability for red hair in UK Biobank, and in fact most individuals with two MC1R variants have blonde or light brown hair. We identify other genes contributing to red hair, the combined effect of which accounts for ~90% of the SNP heritability. Blonde hair is associated with over 200 genetic variants and we find a continuum from black through dark and light brown to blonde and account for 73% of the SNP heritability of blonde hair. Many of the associated genes are involved in hair growth or texture, emphasising the cellular connections between keratinocytes and melanocytes in the determination of hair colour. |
Rachel Moore; Francesco Paolo Casale; Marc Jan Bonder; Danilo Horta; BIOS Consortium; Lude Franke; Inês Barroso; Oliver Stegle A linear mixed-model approach to study multivariate gene–environment interactions Journal Article Nature Genetics, 2018. @article{Moore2018, title = {A linear mixed-model approach to study multivariate gene–environment interactions}, author = {Rachel Moore and Francesco Paolo Casale and Marc Jan Bonder and Danilo Horta and BIOS Consortium and Lude Franke and Inês Barroso and Oliver Stegle }, url = {https://www.nature.com/articles/s41588-018-0271-0}, year = {2018}, date = {2018-11-26}, journal = {Nature Genetics}, abstract = {Different exposures, including diet, physical activity, or external conditions can contribute to genotype–environment interactions (G×E). Although high-dimensional environmental data are increasingly available and multiple exposures have been implicated with G×E at the same loci, multi-environment tests for G×E are not established. Here, we propose the structured linear mixed model (StructLMM), a computationally efficient method to identify and characterize loci that interact with one or more environments. After validating our model using simulations, we applied StructLMM to body mass index in the UK Biobank, where our model yields previously known and novel G×E signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Different exposures, including diet, physical activity, or external conditions can contribute to genotype–environment interactions (G×E). Although high-dimensional environmental data are increasingly available and multiple exposures have been implicated with G×E at the same loci, multi-environment tests for G×E are not established. Here, we propose the structured linear mixed model (StructLMM), a computationally efficient method to identify and characterize loci that interact with one or more environments. After validating our model using simulations, we applied StructLMM to body mass index in the UK Biobank, where our model yields previously known and novel G×E signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables. |
M Nethander; L Vandenput; AL Eriksson; S Windahl; T Funck-Brentano; C Ohlsson Evidence of a Causal Effect of Estradiol on Fracture Risk in Men Journal Article The Journal of Clinical Endocrinology & Metabolism, 2018. @article{Nethander2018, title = {Evidence of a Causal Effect of Estradiol on Fracture Risk in Men}, author = {M Nethander and L Vandenput and AL Eriksson and S Windahl and T Funck-Brentano and C Ohlsson }, url = {https://www.ncbi.nlm.nih.gov/pubmed/30215726}, year = {2018}, date = {2018-11-09}, journal = {The Journal of Clinical Endocrinology & Metabolism}, abstract = {Context: Observational studies indicate that serum estradiol (E2) is more strongly associated with bone mineral density (BMD) than serum testosterone (T) while both E2 and T associate with fracture risk in men. Objective: To evaluate the possible causal effect of serum E2 and T on fracture risk in men. Design, Setting, and Participants: A Mendelian Randomization (MR) approach was undertaken using individual-level data of genotypes, BMD as estimated by quantitative ultrasound of the heel (eBMD), fractures (n=17,650), and relevant covariates of 175,583 unrelated men of European origin from the UK Biobank. The genetic instruments for serum E2 and T were taken from the most recent large scale GWAS meta-analyses on these hormones in men. Results: MR analyses demonstrated a causal effect of serum E2 on eBMD and fracture risk. A 1 SD (or 9.6 pg/ml) genetically instrumented decrease in serum E2 was associated with a 0.38 SD decrease in eBMD (p-value 9.7 x 10-74) and an increased risk of any fracture (OR 1.35, 95% CI, 1.18-1.55), non-vertebral major osteoporotic fractures (OR 1.75, 95% CI, 1.35-2.27) and wrist fractures (OR 2.27, 95% CI, 1.62-3.16). These causal effects of serum E2 on fracture risk were robust in sensitivity analyses and remained unchanged in stratified analyses for age, BMI, eBMD, smoking status, and physical activity. MR analyses revealed no evidence of a causal effect of T levels on fracture risk. Conclusion: Our findings provide the first evidence of a robust causal effect of serum E2, but not T, on fracture risk in men.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Context: Observational studies indicate that serum estradiol (E2) is more strongly associated with bone mineral density (BMD) than serum testosterone (T) while both E2 and T associate with fracture risk in men. Objective: To evaluate the possible causal effect of serum E2 and T on fracture risk in men. Design, Setting, and Participants: A Mendelian Randomization (MR) approach was undertaken using individual-level data of genotypes, BMD as estimated by quantitative ultrasound of the heel (eBMD), fractures (n=17,650), and relevant covariates of 175,583 unrelated men of European origin from the UK Biobank. The genetic instruments for serum E2 and T were taken from the most recent large scale GWAS meta-analyses on these hormones in men. Results: MR analyses demonstrated a causal effect of serum E2 on eBMD and fracture risk. A 1 SD (or 9.6 pg/ml) genetically instrumented decrease in serum E2 was associated with a 0.38 SD decrease in eBMD (p-value 9.7 x 10-74) and an increased risk of any fracture (OR 1.35, 95% CI, 1.18-1.55), non-vertebral major osteoporotic fractures (OR 1.75, 95% CI, 1.35-2.27) and wrist fractures (OR 2.27, 95% CI, 1.62-3.16). These causal effects of serum E2 on fracture risk were robust in sensitivity analyses and remained unchanged in stratified analyses for age, BMI, eBMD, smoking status, and physical activity. MR analyses revealed no evidence of a causal effect of T levels on fracture risk. Conclusion: Our findings provide the first evidence of a robust causal effect of serum E2, but not T, on fracture risk in men. |
Rainer Malik; Kristiina Rannikmäe; Matthew Traylor; Marios K Georgakis; Muralidharan Sargurupremraj; Hugh S Markus; Jemma C Hopewell; Stephanie Debette; Cathie LM Sudlow; Martin Dichgans Genome‐Wide Meta‐analysis identifies three novel loci associated with stroke Journal Article Annals of neurology, 2018. @article{Malik2018, title = {Genome‐Wide Meta‐analysis identifies three novel loci associated with stroke}, author = {Rainer Malik and Kristiina Rannikmäe and Matthew Traylor and Marios K Georgakis and Muralidharan Sargurupremraj and Hugh S Markus and Jemma C Hopewell and Stephanie Debette and Cathie LM Sudlow and Martin Dichgans}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ana.25369}, year = {2018}, date = {2018-11-01}, journal = {Annals of neurology}, abstract = {We conducted a European‐only and trans‐ancestral genome‐wide association meta‐analysis in 72,147 stroke patients and 823,869 controls using data from UK Biobank (UKB) and the MEGASTROKE consortium. We identified an exonic polymorphism in NOS3 (rs1799983, p.Glu298Asp; p=2.2E‐8; OR=1.05[1.04‐1.07]) and variants in an intron of COL4A1 (rs9521634, p‐value=3.8E‐8, OR=1.04[1.03‐1.06]) and near DYRK1A (rs720470, p=6.1E‐9; OR=1.05[1.03‐1.07]) at genome‐wide significance for stroke. Effect sizes of known stroke loci were highly correlated between UKB and MEGASTROKE. Using Mendelian Randomization we further show that genetic variation in the nitric oxide synthase (NOS) – nitric oxide (NO) pathway in part affects stroke risk via variation in blood pressure. }, keywords = {}, pubstate = {published}, tppubtype = {article} } We conducted a European‐only and trans‐ancestral genome‐wide association meta‐analysis in 72,147 stroke patients and 823,869 controls using data from UK Biobank (UKB) and the MEGASTROKE consortium. We identified an exonic polymorphism in NOS3 (rs1799983, p.Glu298Asp; p=2.2E‐8; OR=1.05[1.04‐1.07]) and variants in an intron of COL4A1 (rs9521634, p‐value=3.8E‐8, OR=1.04[1.03‐1.06]) and near DYRK1A (rs720470, p=6.1E‐9; OR=1.05[1.03‐1.07]) at genome‐wide significance for stroke. Effect sizes of known stroke loci were highly correlated between UKB and MEGASTROKE. Using Mendelian Randomization we further show that genetic variation in the nitric oxide synthase (NOS) – nitric oxide (NO) pathway in part affects stroke risk via variation in blood pressure. |
Loes CA Rutten-Jacobs; Susanna C Larsson; Rainer Malik; Kristiina Rannikmäe; MEGASTROKE consortium; International Stroke Genetics Consortium; Cathie L Sudlow; Martin Dichgans; Hugh S Markus; and Matthew Traylor Genetic risk, incident stroke, and the benefits of adhering to a healthy lifestyle: cohort study of 306 473 UK Biobank participants Journal Article BMJ, 2018. @article{Rutten-Jacobs2018c, title = {Genetic risk, incident stroke, and the benefits of adhering to a healthy lifestyle: cohort study of 306 473 UK Biobank participants}, author = {Loes CA Rutten-Jacobs and Susanna C Larsson and Rainer Malik and Kristiina Rannikmäe and MEGASTROKE consortium and International Stroke Genetics Consortium and Cathie L Sudlow and Martin Dichgans and Hugh S Markus and and Matthew Traylor}, url = {https://www.bmj.com/content/363/bmj.k4168}, year = {2018}, date = {2018-10-24}, journal = {BMJ}, abstract = {Objective To evaluate the associations of a polygenic risk score and healthy lifestyle with incident stroke. Design Prospective population based cohort study. Setting UK Biobank Study, UK. Participants 306 473 men and women, aged 40-73 years, recruited between 2006 and 2010. Main outcome measure Hazard ratios for a first stroke, estimated using Cox regression. A polygenic risk score of 90 single nucleotide polymorphisms previously associated with stroke was constructed at P<1×10−5 to test for an association with incident stroke. Adherence to a healthy lifestyle was determined on the basis of four factors: non-smoker, healthy diet, body mass index <30 kg/m2, and regular physical exercise. Results During a median follow-up of 7.1 years (2 138 443 person years), 2077 incident strokes (1541 ischaemic stroke, 287 intracerebral haemorrhage, and 249 subarachnoid haemorrhage) were ascertained. The risk of incident stroke was 35% higher among those at high genetic risk (top third of polygenic score) compared with those at low genetic risk (bottom third): hazard ratio 1.35 (95% confidence interval 1.21 to 1.50), P=3.9×10−8. Unfavourable lifestyle (0 or 1 healthy lifestyle factors) was associated with a 66% increased risk of stroke compared with a favourable lifestyle (3 or 4 healthy lifestyle factors): 1.66 (1.45 to 1.89), P=1.19×10−13. The association with lifestyle was independent of genetic risk stratums. Conclusion In this cohort study, genetic and lifestyle factors were independently associated with incident stroke. These results emphasise the benefit of entire populations adhering to a healthy lifestyle, independent of genetic risk.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Objective To evaluate the associations of a polygenic risk score and healthy lifestyle with incident stroke. Design Prospective population based cohort study. Setting UK Biobank Study, UK. Participants 306 473 men and women, aged 40-73 years, recruited between 2006 and 2010. Main outcome measure Hazard ratios for a first stroke, estimated using Cox regression. A polygenic risk score of 90 single nucleotide polymorphisms previously associated with stroke was constructed at P<1×10−5 to test for an association with incident stroke. Adherence to a healthy lifestyle was determined on the basis of four factors: non-smoker, healthy diet, body mass index <30 kg/m2, and regular physical exercise. Results During a median follow-up of 7.1 years (2 138 443 person years), 2077 incident strokes (1541 ischaemic stroke, 287 intracerebral haemorrhage, and 249 subarachnoid haemorrhage) were ascertained. The risk of incident stroke was 35% higher among those at high genetic risk (top third of polygenic score) compared with those at low genetic risk (bottom third): hazard ratio 1.35 (95% confidence interval 1.21 to 1.50), P=3.9×10−8. Unfavourable lifestyle (0 or 1 healthy lifestyle factors) was associated with a 66% increased risk of stroke compared with a favourable lifestyle (3 or 4 healthy lifestyle factors): 1.66 (1.45 to 1.89), P=1.19×10−13. The association with lifestyle was independent of genetic risk stratums. Conclusion In this cohort study, genetic and lifestyle factors were independently associated with incident stroke. These results emphasise the benefit of entire populations adhering to a healthy lifestyle, independent of genetic risk. |
Sandra Sanchez-Roige; Abraham A. Palmer; Pierre Fontanillas; Sarah L. Elson; 23andMe Research Team and the Substance Use Disorder Working Group of the Psychiatric Genomics Consortium and, Mark J. Adams; David M. Howard; Howard J. Edenberg; Gail Davies andRichard C. Crist Ian J. Deary; Andrew M. McIntosh , M.D., Toni-Kim Clarke Genome-Wide Association Study Meta-Analysis of the Alcohol Use Disorders Identification Test (AUDIT) in Two Population-Based Cohorts Journal Article The American Journal of Pyschiatry, 2018. @article{Sanchez-Roige2018, title = {Genome-Wide Association Study Meta-Analysis of the Alcohol Use Disorders Identification Test (AUDIT) in Two Population-Based Cohorts}, author = {Sandra Sanchez-Roige and Abraham A. Palmer and Pierre Fontanillas and Sarah L. Elson and 23andMe Research Team and the Substance Use Disorder Working Group of the Psychiatric Genomics Consortium and, Mark J. Adams and David M. Howard and Howard J. Edenberg and Gail Davies andRichard C. Crist Ian J. Deary and Andrew M. McIntosh , M.D., Toni-Kim Clarke}, url = {https://ajp.psychiatryonline.org/doi/abs/10.1176/appi.ajp.2018.18040369?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub%3Dpubmed&}, year = {2018}, date = {2018-10-19}, journal = {The American Journal of Pyschiatry}, abstract = {Alcohol use disorders are common conditions that have enormous social and economic consequences. Genome-wide association analyses were performed to identify genetic variants associated with a proxy measure of alcohol consumption and alcohol misuse and to explore the shared genetic basis between these measures and other substance use, psychiatric, and behavioral traits. Method: This study used quantitative measures from the Alcohol Use Disorders Identification Test (AUDIT) from two population-based cohorts of European ancestry (UK Biobank [N=121,604] and 23andMe [N=20,328]) and performed a genome-wide association study (GWAS) meta-analysis. Two additional GWAS analyses were performed, a GWAS for AUDIT scores on items 1–3, which focus on consumption (AUDIT-C), and for scores on items 4–10, which focus on the problematic consequences of drinking (AUDIT-P). Results: The GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; this study also replicated previously identified signals in the genes ADH1B, ADH1C, KLB, and GCKR. The dimensions of AUDIT showed positive genetic correlations with alcohol consumption (rg=0.76–0.92) and DSM-IV alcohol dependence (rg=0.33–0.63). AUDIT-P and AUDIT-C scores showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P score was significantly positively genetically correlated with schizophrenia (rg=0.22), major depressive disorder (rg=0.26), and attention deficit hyperactivity disorder (rg=0.23), whereas AUDIT-C score was significantly negatively genetically correlated with major depressive disorder (rg=−0.24) and ADHD (rg=−0.10). This study also used the AUDIT data in the UK Biobank to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total scores ≤4 as control subjects and those with scores ≥12 as case subjects produced a significant high genetic correlation with DSM-IV alcohol dependence (rg=0.82) while retaining most subjects. Conclusions: AUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and alcohol use disorders.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Alcohol use disorders are common conditions that have enormous social and economic consequences. Genome-wide association analyses were performed to identify genetic variants associated with a proxy measure of alcohol consumption and alcohol misuse and to explore the shared genetic basis between these measures and other substance use, psychiatric, and behavioral traits. Method: This study used quantitative measures from the Alcohol Use Disorders Identification Test (AUDIT) from two population-based cohorts of European ancestry (UK Biobank [N=121,604] and 23andMe [N=20,328]) and performed a genome-wide association study (GWAS) meta-analysis. Two additional GWAS analyses were performed, a GWAS for AUDIT scores on items 1–3, which focus on consumption (AUDIT-C), and for scores on items 4–10, which focus on the problematic consequences of drinking (AUDIT-P). Results: The GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; this study also replicated previously identified signals in the genes ADH1B, ADH1C, KLB, and GCKR. The dimensions of AUDIT showed positive genetic correlations with alcohol consumption (rg=0.76–0.92) and DSM-IV alcohol dependence (rg=0.33–0.63). AUDIT-P and AUDIT-C scores showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P score was significantly positively genetically correlated with schizophrenia (rg=0.22), major depressive disorder (rg=0.26), and attention deficit hyperactivity disorder (rg=0.23), whereas AUDIT-C score was significantly negatively genetically correlated with major depressive disorder (rg=−0.24) and ADHD (rg=−0.10). This study also used the AUDIT data in the UK Biobank to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total scores ≤4 as control subjects and those with scores ≥12 as case subjects produced a significant high genetic correlation with DSM-IV alcohol dependence (rg=0.82) while retaining most subjects. Conclusions: AUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and alcohol use disorders. |
Sandra Sanchez-Roige; Abraham A. Palmer; Pierre Fontanillas; Sarah L. Elson; the 23andMe Research Team and the Substance Use Disorder Working Group of the Psychiatric Genomics Consortium; Mark J. Adams; David M. Howard; Howard J. Edenberg; Gail Davies; Richard C. Crist; Ian J. Deary; Andrew M. McIntosh and Toni-Kim Clarke Genome-Wide Association Study Meta-Analysis of the Alcohol Use Disorders Identification Test (AUDIT) in Two Population-Based Cohorts Journal Article The American Journal of Pyschiatry, 2018. @article{Sanchez-Roige2018b, title = {Genome-Wide Association Study Meta-Analysis of the Alcohol Use Disorders Identification Test (AUDIT) in Two Population-Based Cohorts}, author = {Sandra Sanchez-Roige and Abraham A. Palmer and Pierre Fontanillas and Sarah L. Elson and the 23andMe Research Team and the Substance Use Disorder Working Group of the Psychiatric Genomics Consortium and Mark J. Adams and David M. Howard and Howard J. Edenberg and Gail Davies and Richard C. Crist and Ian J. Deary and Andrew M. McIntosh and Toni-Kim Clarke}, url = {https://ajp.psychiatryonline.org/doi/abs/10.1176/appi.ajp.2018.18040369?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed}, year = {2018}, date = {2018-10-19}, journal = {The American Journal of Pyschiatry}, abstract = {Alcohol use disorders are common conditions that have enormous social and economic consequences. Genome-wide association analyses were performed to identify genetic variants associated with a proxy measure of alcohol consumption and alcohol misuse and to explore the shared genetic basis between these measures and other substance use, psychiatric, and behavioral traits. Method: This study used quantitative measures from the Alcohol Use Disorders Identification Test (AUDIT) from two population-based cohorts of European ancestry (UK Biobank [N=121,604] and 23andMe [N=20,328]) and performed a genome-wide association study (GWAS) meta-analysis. Two additional GWAS analyses were performed, a GWAS for AUDIT scores on items 1–3, which focus on consumption (AUDIT-C), and for scores on items 4–10, which focus on the problematic consequences of drinking (AUDIT-P). Results: The GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; this study also replicated previously identified signals in the genes ADH1B, ADH1C, KLB, and GCKR. The dimensions of AUDIT showed positive genetic correlations with alcohol consumption (rg=0.76–0.92) and DSM-IV alcohol dependence (rg=0.33–0.63). AUDIT-P and AUDIT-C scores showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P score was significantly positively genetically correlated with schizophrenia (rg=0.22), major depressive disorder (rg=0.26), and attention deficit hyperactivity disorder (rg=0.23), whereas AUDIT-C score was significantly negatively genetically correlated with major depressive disorder (rg=−0.24) and ADHD (rg=−0.10). This study also used the AUDIT data in the UK Biobank to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total scores ≤4 as control subjects and those with scores ≥12 as case subjects produced a significant high genetic correlation with DSM-IV alcohol dependence (rg=0.82) while retaining most subjects. Conclusions: AUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and alcohol use disorders.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Alcohol use disorders are common conditions that have enormous social and economic consequences. Genome-wide association analyses were performed to identify genetic variants associated with a proxy measure of alcohol consumption and alcohol misuse and to explore the shared genetic basis between these measures and other substance use, psychiatric, and behavioral traits. Method: This study used quantitative measures from the Alcohol Use Disorders Identification Test (AUDIT) from two population-based cohorts of European ancestry (UK Biobank [N=121,604] and 23andMe [N=20,328]) and performed a genome-wide association study (GWAS) meta-analysis. Two additional GWAS analyses were performed, a GWAS for AUDIT scores on items 1–3, which focus on consumption (AUDIT-C), and for scores on items 4–10, which focus on the problematic consequences of drinking (AUDIT-P). Results: The GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; this study also replicated previously identified signals in the genes ADH1B, ADH1C, KLB, and GCKR. The dimensions of AUDIT showed positive genetic correlations with alcohol consumption (rg=0.76–0.92) and DSM-IV alcohol dependence (rg=0.33–0.63). AUDIT-P and AUDIT-C scores showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P score was significantly positively genetically correlated with schizophrenia (rg=0.22), major depressive disorder (rg=0.26), and attention deficit hyperactivity disorder (rg=0.23), whereas AUDIT-C score was significantly negatively genetically correlated with major depressive disorder (rg=−0.24) and ADHD (rg=−0.10). This study also used the AUDIT data in the UK Biobank to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total scores ≤4 as control subjects and those with scores ≥12 as case subjects produced a significant high genetic correlation with DSM-IV alcohol dependence (rg=0.82) while retaining most subjects. Conclusions: AUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and alcohol use disorders. |
Michael Inouye; Nilesh J. Samani; for the UK Biobank CardioMetabolic Consortium CHD Working Group Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults Journal Article Journal of the American College of Cardiology, 2018, (Michael Inouye and Gad Abraham and Christopher P. Nelson and Angela M. Wood and Michael J. Sweeting and Frank Dudbridge and Florence Y. Lai and Stephen Kaptoge and Marta Brozynska and Tingting Wang and Shu Ye and Thomas R. Webb and Martin K. Rutter and Ioanna Tzoulaki and Riyaz S. Patel and Ruth J.F. Loos and Bernard Keavney and Harry Hemingway and John Thompson and Hugh Watkins and Panos Deloukas and Emanuele Di Angelantonio and Adam S. Butterworth and John Danesh and Nilesh J. Samani and for the UK Biobank CardioMetabolic Consortium CHD Working Group). @article{Inouye2018, title = {Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults}, author = {Michael Inouye and Nilesh J. Samani and for the UK Biobank CardioMetabolic Consortium CHD Working Group}, url = {http://www.onlinejacc.org/content/72/16/1883}, year = {2018}, date = {2018-10-16}, journal = {Journal of the American College of Cardiology}, abstract = {Background Coronary artery disease (CAD) has substantial heritability and a polygenic architecture. However, the potential of genomic risk scores to help predict CAD outcomes has not been evaluated comprehensively, because available studies have involved limited genomic scope and limited sample sizes. Objectives This study sought to construct a genomic risk score for CAD and to estimate its potential as a screening tool for primary prevention. Methods Using a meta-analytic approach to combine large-scale, genome-wide, and targeted genetic association data, we developed a new genomic risk score for CAD (metaGRS) consisting of 1.7 million genetic variants. We externally tested metaGRS, both by itself and in combination with available data on conventional risk factors, in 22,242 CAD cases and 460,387 noncases from the UK Biobank. Results The hazard ratio (HR) for CAD was 1.71 (95% confidence interval [CI]: 1.68 to 1.73) per SD increase in metaGRS, an association larger than any other externally tested genetic risk score previously published. The metaGRS stratified individuals into significantly different life course trajectories of CAD risk, with those in the top 20% of metaGRS distribution having an HR of 4.17 (95% CI: 3.97 to 4.38) compared with those in the bottom 20%. The corresponding HR was 2.83 (95% CI: 2.61 to 3.07) among individuals on lipid-lowering or antihypertensive medications. The metaGRS had a higher C-index (C = 0.623; 95% CI: 0.615 to 0.631) for incident CAD than any of 6 conventional factors (smoking, diabetes, hypertension, body mass index, self-reported high cholesterol, and family history). For men in the top 20% of metaGRS with >2 conventional factors, 10% cumulative risk of CAD was reached by 48 years of age. Conclusions The genomic score developed and evaluated here substantially advances the concept of using genomic information to stratify individuals with different trajectories of CAD risk and highlights the potential for genomic screening in early life to complement conventional risk prediction.}, note = {Michael Inouye and Gad Abraham and Christopher P. Nelson and Angela M. Wood and Michael J. Sweeting and Frank Dudbridge and Florence Y. Lai and Stephen Kaptoge and Marta Brozynska and Tingting Wang and Shu Ye and Thomas R. Webb and Martin K. Rutter and Ioanna Tzoulaki and Riyaz S. Patel and Ruth J.F. Loos and Bernard Keavney and Harry Hemingway and John Thompson and Hugh Watkins and Panos Deloukas and Emanuele Di Angelantonio and Adam S. Butterworth and John Danesh and Nilesh J. Samani and for the UK Biobank CardioMetabolic Consortium CHD Working Group}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background Coronary artery disease (CAD) has substantial heritability and a polygenic architecture. However, the potential of genomic risk scores to help predict CAD outcomes has not been evaluated comprehensively, because available studies have involved limited genomic scope and limited sample sizes. Objectives This study sought to construct a genomic risk score for CAD and to estimate its potential as a screening tool for primary prevention. Methods Using a meta-analytic approach to combine large-scale, genome-wide, and targeted genetic association data, we developed a new genomic risk score for CAD (metaGRS) consisting of 1.7 million genetic variants. We externally tested metaGRS, both by itself and in combination with available data on conventional risk factors, in 22,242 CAD cases and 460,387 noncases from the UK Biobank. Results The hazard ratio (HR) for CAD was 1.71 (95% confidence interval [CI]: 1.68 to 1.73) per SD increase in metaGRS, an association larger than any other externally tested genetic risk score previously published. The metaGRS stratified individuals into significantly different life course trajectories of CAD risk, with those in the top 20% of metaGRS distribution having an HR of 4.17 (95% CI: 3.97 to 4.38) compared with those in the bottom 20%. The corresponding HR was 2.83 (95% CI: 2.61 to 3.07) among individuals on lipid-lowering or antihypertensive medications. The metaGRS had a higher C-index (C = 0.623; 95% CI: 0.615 to 0.631) for incident CAD than any of 6 conventional factors (smoking, diabetes, hypertension, body mass index, self-reported high cholesterol, and family history). For men in the top 20% of metaGRS with >2 conventional factors, 10% cumulative risk of CAD was reached by 48 years of age. Conclusions The genomic score developed and evaluated here substantially advances the concept of using genomic information to stratify individuals with different trajectories of CAD risk and highlights the potential for genomic screening in early life to complement conventional risk prediction. |
Michael Inouye; Gad Abraham; Christopher P. Nelson; Angela M. Wood; Michael J. Sweeting; Frank Dudbridge; Florence Y. Lai; Stephen Kaptoge; Marta Brozynska; Tingting Wang; Shu Ye; Thomas R. Webb; Martin K. Rutter; Ioanna Tzoulaki; Riyaz S. Patel; Ruth J.F. Loos; Bernard Keavney; Harry Hemingway; John Thompson; Hugh Watkins; Panos Deloukas; Emanuele Di Angelantonio; Adam S. Butterworth; John Danesh; Nilesh J. Samani; for the UK Biobank CardioMetabolic Consortium CHD Working Group Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults: Implications for Primary Prevention Journal Article Journal of the American College of Cardiology, 2018. @article{Inouye2018b, title = {Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults: Implications for Primary Prevention}, author = {Michael Inouye and Gad Abraham and Christopher P. Nelson and Angela M. Wood and Michael J. Sweeting and Frank Dudbridge and Florence Y. Lai and Stephen Kaptoge and Marta Brozynska and Tingting Wang and Shu Ye and Thomas R. Webb and Martin K. Rutter and Ioanna Tzoulaki and Riyaz S. Patel and Ruth J.F. Loos and Bernard Keavney and Harry Hemingway and John Thompson and Hugh Watkins and Panos Deloukas and Emanuele Di Angelantonio and Adam S. Butterworth and John Danesh and Nilesh J. Samani and for the UK Biobank CardioMetabolic Consortium CHD Working Group}, url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6176870/}, year = {2018}, date = {2018-10-16}, journal = {Journal of the American College of Cardiology}, abstract = {Background Coronary artery disease (CAD) has substantial heritability and a polygenic architecture. However, the potential of genomic risk scores to help predict CAD outcomes has not been evaluated comprehensively, because available studies have involved limited genomic scope and limited sample sizes. Objectives This study sought to construct a genomic risk score for CAD and to estimate its potential as a screening tool for primary prevention. Methods Using a meta-analytic approach to combine large-scale, genome-wide, and targeted genetic association data, we developed a new genomic risk score for CAD (metaGRS) consisting of 1.7 million genetic variants. We externally tested metaGRS, both by itself and in combination with available data on conventional risk factors, in 22,242 CAD cases and 460,387 noncases from the UK Biobank. Results The hazard ratio (HR) for CAD was 1.71 (95% confidence interval [CI]: 1.68 to 1.73) per SD increase in metaGRS, an association larger than any other externally tested genetic risk score previously published. The metaGRS stratified individuals into significantly different life course trajectories of CAD risk, with those in the top 20% of metaGRS distribution having an HR of 4.17 (95% CI: 3.97 to 4.38) compared with those in the bottom 20%. The corresponding HR was 2.83 (95% CI: 2.61 to 3.07) among individuals on lipid-lowering or antihypertensive medications. The metaGRS had a higher C-index (C = 0.623; 95% CI: 0.615 to 0.631) for incident CAD than any of 6 conventional factors (smoking, diabetes, hypertension, body mass index, self-reported high cholesterol, and family history). For men in the top 20% of metaGRS with >2 conventional factors, 10% cumulative risk of CAD was reached by 48 years of age. Conclusions The genomic score developed and evaluated here substantially advances the concept of using genomic information to stratify individuals with different trajectories of CAD risk and highlights the potential for genomic screening in early life to complement conventional risk prediction.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background Coronary artery disease (CAD) has substantial heritability and a polygenic architecture. However, the potential of genomic risk scores to help predict CAD outcomes has not been evaluated comprehensively, because available studies have involved limited genomic scope and limited sample sizes. Objectives This study sought to construct a genomic risk score for CAD and to estimate its potential as a screening tool for primary prevention. Methods Using a meta-analytic approach to combine large-scale, genome-wide, and targeted genetic association data, we developed a new genomic risk score for CAD (metaGRS) consisting of 1.7 million genetic variants. We externally tested metaGRS, both by itself and in combination with available data on conventional risk factors, in 22,242 CAD cases and 460,387 noncases from the UK Biobank. Results The hazard ratio (HR) for CAD was 1.71 (95% confidence interval [CI]: 1.68 to 1.73) per SD increase in metaGRS, an association larger than any other externally tested genetic risk score previously published. The metaGRS stratified individuals into significantly different life course trajectories of CAD risk, with those in the top 20% of metaGRS distribution having an HR of 4.17 (95% CI: 3.97 to 4.38) compared with those in the bottom 20%. The corresponding HR was 2.83 (95% CI: 2.61 to 3.07) among individuals on lipid-lowering or antihypertensive medications. The metaGRS had a higher C-index (C = 0.623; 95% CI: 0.615 to 0.631) for incident CAD than any of 6 conventional factors (smoking, diabetes, hypertension, body mass index, self-reported high cholesterol, and family history). For men in the top 20% of metaGRS with >2 conventional factors, 10% cumulative risk of CAD was reached by 48 years of age. Conclusions The genomic score developed and evaluated here substantially advances the concept of using genomic information to stratify individuals with different trajectories of CAD risk and highlights the potential for genomic screening in early life to complement conventional risk prediction. |
Todd Smith; Marc J. Gunter; Ioanna Tzoulaki; David C. Muller British Journal of Cancer, 2018. @article{Smith2018c, title = {The added value of genetic information in colorectal cancer risk prediction models: development and evaluation in the UK Biobank prospective cohort study}, author = {Todd Smith and Marc J. Gunter and Ioanna Tzoulaki and David C. Muller }, url = {https://www.nature.com/articles/s41416-018-0282-8}, year = {2018}, date = {2018-10-16}, journal = {British Journal of Cancer}, abstract = {Colorectal cancer (CRC) risk prediction models could be used to risk-stratify the population to provide individually tailored screening provision. Using participants from the UK Biobank prospective cohort study, we evaluated whether the addition of a genetic risk score (GRS) could improve the performance of two previously validated models. Inclusion of the GRS did not appreciably improve discrimination of either model, and led to substantial miscalibration. Following recalibration the discrimination did not change, but good calibration for models incorporating the GRS was recovered. Comparing predictions between models with and without the GRS, 5% of participants or fewer changed their absolute risk by ±0.3% or more in either model. In summary, addition of a GRS did not meaningfully improve the performance of validated CRC-risk prediction models. At present, provision of genetic information is not useful for risk stratification for CRC}, keywords = {}, pubstate = {published}, tppubtype = {article} } Colorectal cancer (CRC) risk prediction models could be used to risk-stratify the population to provide individually tailored screening provision. Using participants from the UK Biobank prospective cohort study, we evaluated whether the addition of a genetic risk score (GRS) could improve the performance of two previously validated models. Inclusion of the GRS did not appreciably improve discrimination of either model, and led to substantial miscalibration. Following recalibration the discrimination did not change, but good calibration for models incorporating the GRS was recovered. Comparing predictions between models with and without the GRS, 5% of participants or fewer changed their absolute risk by ±0.3% or more in either model. In summary, addition of a GRS did not meaningfully improve the performance of validated CRC-risk prediction models. At present, provision of genetic information is not useful for risk stratification for CRC |
Loic Yengo; Julia Sidorenko; Kathryn E Kemper; Zhili Zheng; Andrew R Wood; Michael N Weedon; Timothy M Frayling; Joel Hirschhorn; Jian Yang; Peter M Visscher Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry Journal Article Human molecular genetics, 2018. @article{Yengo2018, title = {Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry}, author = {Loic Yengo and Julia Sidorenko and Kathryn E Kemper and Zhili Zheng and Andrew R Wood and Michael N Weedon and Timothy M Frayling and Joel Hirschhorn and Jian Yang and Peter M Visscher}, url = {https://academic.oup.com/hmg/article-abstract/27/20/3641/5067845?redirectedFrom=fulltext}, year = {2018}, date = {2018-10-15}, journal = {Human molecular genetics}, abstract = {Recent genome-wide association studies (GWAS) of height and body mass index (BMI) in ∼250000 European participants have led to the discovery of ∼700 and ∼100 nearly independent single nucleotide polymorphisms (SNPs) associated with these traits, respectively. Here we combine summary statistics from those two studies with GWAS of height and BMI performed in ∼450000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches N ∼700000 individuals and substantially increases the number of GWAS signals associated with these traits. We identified 3290 and 941 near-independent SNPs associated with height and BMI, respectively (at a revised genome-wide significance threshold of P < 1 × 10−8), including 1185 height-associated SNPs and 751 BMI-associated SNPs located within loci not previously identified by these two GWAS. The near-independent genome-wide significant SNPs explain ∼24.6% of the variance of height and ∼6.0% of the variance of BMI in an independent sample from the Health and Retirement Study (HRS). Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were ∼0.44 and ∼0.22, respectively. From analyses of integrating GWAS and expression quantitative trait loci (eQTL) data by summary-data-based Mendelian randomization, we identified an enrichment of eQTLs among lead height and BMI signals, prioritizing 610 and 138 genes, respectively. Our study demonstrates that, as previously predicted, increasing GWAS sample sizes continues to deliver, by the discovery of new loci, increasing prediction accuracy and providing additional data to achieve deeper insight into complex trait biology. All summary statistics are made available for follow-up studies.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Recent genome-wide association studies (GWAS) of height and body mass index (BMI) in ∼250000 European participants have led to the discovery of ∼700 and ∼100 nearly independent single nucleotide polymorphisms (SNPs) associated with these traits, respectively. Here we combine summary statistics from those two studies with GWAS of height and BMI performed in ∼450000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches N ∼700000 individuals and substantially increases the number of GWAS signals associated with these traits. We identified 3290 and 941 near-independent SNPs associated with height and BMI, respectively (at a revised genome-wide significance threshold of P < 1 × 10−8), including 1185 height-associated SNPs and 751 BMI-associated SNPs located within loci not previously identified by these two GWAS. The near-independent genome-wide significant SNPs explain ∼24.6% of the variance of height and ∼6.0% of the variance of BMI in an independent sample from the Health and Retirement Study (HRS). Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were ∼0.44 and ∼0.22, respectively. From analyses of integrating GWAS and expression quantitative trait loci (eQTL) data by summary-data-based Mendelian randomization, we identified an enrichment of eQTLs among lead height and BMI signals, prioritizing 610 and 138 genes, respectively. Our study demonstrates that, as previously predicted, increasing GWAS sample sizes continues to deliver, by the discovery of new loci, increasing prediction accuracy and providing additional data to achieve deeper insight into complex trait biology. All summary statistics are made available for follow-up studies. |
Alexander I. Young; Fabian L. Wauthier; Peter Donnelly Identifying loci affecting trait variability and detecting interactions in genome-wide association studies Journal Article Nature Genetics, 2018. @article{Young2018, title = {Identifying loci affecting trait variability and detecting interactions in genome-wide association studies}, author = {Alexander I. Young and Fabian L. Wauthier and Peter Donnelly}, url = {https://www.nature.com/articles/s41588-018-0225-6}, year = {2018}, date = {2018-10-15}, journal = {Nature Genetics}, abstract = {Identification of genetic variants with effects on trait variability can provide insights into the biological mechanisms that control variation and can identify potential interactions. We propose a two-degree-of-freedom test for jointly testing mean and variance effects to identify such variants. We implement the test in a linear mixed model, for which we provide an efficient algorithm and software. To focus on biologically interesting settings, we develop a test for dispersion effects, that is, variance effects not driven solely by mean effects when the trait distribution is non-normal. We apply our approach to body mass index in the subsample of the UK Biobank population with British ancestry (n ~408,000) and show that our approach can increase the power to detect associated loci. We identify and replicate novel associations with significant variance effects that cannot be explained by the non-normality of body mass index, and we provide suggestive evidence for a connection between leptin levels and body mass index variability.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Identification of genetic variants with effects on trait variability can provide insights into the biological mechanisms that control variation and can identify potential interactions. We propose a two-degree-of-freedom test for jointly testing mean and variance effects to identify such variants. We implement the test in a linear mixed model, for which we provide an efficient algorithm and software. To focus on biologically interesting settings, we develop a test for dispersion effects, that is, variance effects not driven solely by mean effects when the trait distribution is non-normal. We apply our approach to body mass index in the subsample of the UK Biobank population with British ancestry (n ~408,000) and show that our approach can increase the power to detect associated loci. We identify and replicate novel associations with significant variance effects that cannot be explained by the non-normality of body mass index, and we provide suggestive evidence for a connection between leptin levels and body mass index variability. |
Clare Bycroft; Colin Freeman; Desislava Petkova; Gavin Band; Lloyd T. Elliott; Kevin Sharp; Allan Motyer; Damjan Vukcevic; Olivier Delaneau; Jared O’Connell; Adrian Cortes; Samantha Welsh; Alan Young; Mark Effingham; Gil McVean; Stephen Leslie; Naomi Allen; Peter Donnelly; Jonathan Marchini The UK Biobank resource with deep phenotyping and genomic data Journal Article Nature , 2018. @article{Bycroft2018, title = {The UK Biobank resource with deep phenotyping and genomic data}, author = {Clare Bycroft and Colin Freeman and Desislava Petkova and Gavin Band and Lloyd T. Elliott and Kevin Sharp and Allan Motyer and Damjan Vukcevic and Olivier Delaneau and Jared O’Connell and Adrian Cortes and Samantha Welsh and Alan Young and Mark Effingham and Gil McVean and Stephen Leslie and Naomi Allen and Peter Donnelly and Jonathan Marchini }, url = {https://www.nature.com/articles/s41586-018-0579-z}, year = {2018}, date = {2018-10-10}, journal = {Nature }, abstract = {The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases. |
Lloyd T. Elliott; Kevin Sharp; Fidel Alfaro-Almagro; Sinan Shi; Karla L. Miller; Gwenaëlle Douaud; Jonathan Marchini; Stephen M. Smith Genome-wide association studies of brain imaging phenotypes in UK Biobank Journal Article Nature, 2018. @article{Elliott2018, title = {Genome-wide association studies of brain imaging phenotypes in UK Biobank}, author = {Lloyd T. Elliott and Kevin Sharp and Fidel Alfaro-Almagro and Sinan Shi and Karla L. Miller and Gwenaëlle Douaud and Jonathan Marchini and Stephen M. Smith }, url = {https://www.nature.com/articles/s41586-018-0571-7}, year = {2018}, date = {2018-10-10}, journal = {Nature}, abstract = {The genetic architecture of brain structure and function is largely unknown. To investigate this, we carried out genome-wide association studies of 3,144 functional and structural brain imaging phenotypes from UK Biobank (discovery dataset 8,428 subjects). Here we show that many of these phenotypes are heritable. We identify 148 clusters of associations between single nucleotide polymorphisms and imaging phenotypes that replicate at P < 0.05, when we would expect 21 to replicate by chance. Notable significant, interpretable associations include: iron transport and storage genes, related to magnetic susceptibility of subcortical brain tissue; extracellular matrix and epidermal growth factor genes, associated with white matter micro-structure and lesions; genes that regulate mid-line axon development, associated with organization of the pontine crossing tract; and overall 17 genes involved in development, pathway signalling and plasticity. Our results provide insights into the genetic architecture of the brain that are relevant to neurological and psychiatric disorders, brain development and ageing.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The genetic architecture of brain structure and function is largely unknown. To investigate this, we carried out genome-wide association studies of 3,144 functional and structural brain imaging phenotypes from UK Biobank (discovery dataset 8,428 subjects). Here we show that many of these phenotypes are heritable. We identify 148 clusters of associations between single nucleotide polymorphisms and imaging phenotypes that replicate at P < 0.05, when we would expect 21 to replicate by chance. Notable significant, interpretable associations include: iron transport and storage genes, related to magnetic susceptibility of subcortical brain tissue; extracellular matrix and epidermal growth factor genes, associated with white matter micro-structure and lesions; genes that regulate mid-line axon development, associated with organization of the pontine crossing tract; and overall 17 genes involved in development, pathway signalling and plasticity. Our results provide insights into the genetic architecture of the brain that are relevant to neurological and psychiatric disorders, brain development and ageing. |
Lloyd T. Elliott; Kevin Sharp; Fidel Alfaro-Almagro; Sinan Shi; Karla L. Miller; Gwenaëlle Douaud; Jonathan Marchini; Stephen M. Smith Genome-wide association studies of brain imaging phenotypes in UK Biobank Journal Article Nature, 2018. @article{Elliott2018, title = {Genome-wide association studies of brain imaging phenotypes in UK Biobank}, author = {Lloyd T. Elliott and Kevin Sharp and Fidel Alfaro-Almagro and Sinan Shi and Karla L. Miller and Gwenaëlle Douaud and Jonathan Marchini and Stephen M. Smith }, url = {https://www.nature.com/articles/s41586-018-0571-7}, year = {2018}, date = {2018-10-10}, journal = {Nature}, abstract = {The genetic architecture of brain structure and function is largely unknown. To investigate this, we carried out genome-wide association studies of 3,144 functional and structural brain imaging phenotypes from UK Biobank (discovery dataset 8,428 subjects). Here we show that many of these phenotypes are heritable. We identify 148 clusters of associations between single nucleotide polymorphisms and imaging phenotypes that replicate at P < 0.05, when we would expect 21 to replicate by chance. Notable significant, interpretable associations include: iron transport and storage genes, related to magnetic susceptibility of subcortical brain tissue; extracellular matrix and epidermal growth factor genes, associated with white matter micro-structure and lesions; genes that regulate mid-line axon development, associated with organization of the pontine crossing tract; and overall 17 genes involved in development, pathway signalling and plasticity. Our results provide insights into the genetic architecture of the brain that are relevant to neurological and psychiatric disorders, brain development and ageing.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The genetic architecture of brain structure and function is largely unknown. To investigate this, we carried out genome-wide association studies of 3,144 functional and structural brain imaging phenotypes from UK Biobank (discovery dataset 8,428 subjects). Here we show that many of these phenotypes are heritable. We identify 148 clusters of associations between single nucleotide polymorphisms and imaging phenotypes that replicate at P < 0.05, when we would expect 21 to replicate by chance. Notable significant, interpretable associations include: iron transport and storage genes, related to magnetic susceptibility of subcortical brain tissue; extracellular matrix and epidermal growth factor genes, associated with white matter micro-structure and lesions; genes that regulate mid-line axon development, associated with organization of the pontine crossing tract; and overall 17 genes involved in development, pathway signalling and plasticity. Our results provide insights into the genetic architecture of the brain that are relevant to neurological and psychiatric disorders, brain development and ageing. |
Jessica A Rhodes; Jacqueline M Lane; Irma M Vlasac; Martin K Rutter; Charles Czeisler; Richa Saxena Association of DAT1 Genetic Variants with Habitual Sleep Duration in the UK Biobank Journal Article Sleep, 2018. @article{Rhodes2018, title = {Association of DAT1 Genetic Variants with Habitual Sleep Duration in the UK Biobank}, author = {Jessica A Rhodes and Jacqueline M Lane and Irma M Vlasac and Martin K Rutter and Charles Czeisler and Richa Saxena }, url = {https://academic.oup.com/sleep/advance-article-abstract/doi/10.1093/sleep/zsy193/5123695?redirectedFrom=fulltext}, year = {2018}, date = {2018-10-08}, journal = {Sleep}, abstract = {Short sleep duration has been linked to negative health effects, but is a complex phenotype with many contributing factors, including genetic. We evaluated 27 common SNPs in candidate genes previously reported to be associated with other sleep variables for association with self-reported habitual sleep duration in the UK Biobank in 111,975 individuals of European ancestry. Genetic variation in DAT1 (rs464049) was significantly associated with sleep duration after correction for multiple testing (p=4.00x10-5), whereas SNPs correlated to a previously studied VNTR in DAT1 were not significant in this population. We also replicated a previously reported association in DRD2. Independent replication of these associations and a second signal in DRD2 (rs11214607) was observed in an additional 261,870 participants of European ancestry from the UK Biobank. Meta-analysis confirmed genome-wide significant association of DAT1 rs464049 (G, beta (se) = -0.96 (0.18) mins/allele, p = 5.71x10-10) and study-wide significant association of DRD2 (rs17601612, C, beta (SE) = -0.66 (0.18) mins/allele, p = 1.77x10-5; rs11214607, C, beta (SE) = 1.08 (0.24) mins/allele, p = 1.39x10-6). Overall, SNPs in two dopamine-related genes were significantly associated with sleep duration, highlighting the important link of the dopamine system with adult sleep duration in humans.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Short sleep duration has been linked to negative health effects, but is a complex phenotype with many contributing factors, including genetic. We evaluated 27 common SNPs in candidate genes previously reported to be associated with other sleep variables for association with self-reported habitual sleep duration in the UK Biobank in 111,975 individuals of European ancestry. Genetic variation in DAT1 (rs464049) was significantly associated with sleep duration after correction for multiple testing (p=4.00x10-5), whereas SNPs correlated to a previously studied VNTR in DAT1 were not significant in this population. We also replicated a previously reported association in DRD2. Independent replication of these associations and a second signal in DRD2 (rs11214607) was observed in an additional 261,870 participants of European ancestry from the UK Biobank. Meta-analysis confirmed genome-wide significant association of DAT1 rs464049 (G, beta (se) = -0.96 (0.18) mins/allele, p = 5.71x10-10) and study-wide significant association of DRD2 (rs17601612, C, beta (SE) = -0.66 (0.18) mins/allele, p = 1.77x10-5; rs11214607, C, beta (SE) = 1.08 (0.24) mins/allele, p = 1.39x10-6). Overall, SNPs in two dopamine-related genes were significantly associated with sleep duration, highlighting the important link of the dopamine system with adult sleep duration in humans. |
Steven Gazal; Po-Ru Loh; Hilary K. Finucane; Andrea Ganna; Armin Schoech; Shamil Sunyaev; Alkes L. Price Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations Journal Article Nature Genetics, 2018. @article{Gazal2018, title = {Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations}, author = {Steven Gazal and Po-Ru Loh and Hilary K. Finucane and Andrea Ganna and Armin Schoech and Shamil Sunyaev and Alkes L. Price }, url = {https://www.nature.com/articles/s41588-018-0231-8}, year = {2018}, date = {2018-10-08}, journal = {Nature Genetics}, abstract = {Common variant heritability has been widely reported to be concentrated in variants within cell-type-specific non-coding functional annotations, but little is known about low-frequency variant functional architectures. We partitioned the heritability of both low-frequency (0.5%≤ minor allele frequency <5%) and common (minor allele frequency ≥5%) variants in 40 UK Biobank traits across a broad set of functional annotations. We determined that non-synonymous coding variants explain 17 ± 1% of low-frequency variant heritability (h2lf) versus 2.1 ± 0.2% of common variant heritability (h2c). Cell-type-specific non-coding annotations that were significantly enriched for h2c of corresponding traits were similarly enriched for h2lf for most traits, but more enriched for brain-related annotations and traits. For example, H3K4me3 marks in brain dorsolateral prefrontal cortex explain 57 ± 12% of h2lf versus 12 ± 2% of h2c for neuroticism. Forward simulations confirmed that low-frequency variant enrichment depends on the mean selection coefficient of causal variants in the annotation, and can be used to predict effect size variance of causal rare variants (minor allele frequency <0.5%).}, keywords = {}, pubstate = {published}, tppubtype = {article} } Common variant heritability has been widely reported to be concentrated in variants within cell-type-specific non-coding functional annotations, but little is known about low-frequency variant functional architectures. We partitioned the heritability of both low-frequency (0.5%≤ minor allele frequency <5%) and common (minor allele frequency ≥5%) variants in 40 UK Biobank traits across a broad set of functional annotations. We determined that non-synonymous coding variants explain 17 ± 1% of low-frequency variant heritability (h2lf) versus 2.1 ± 0.2% of common variant heritability (h2c). Cell-type-specific non-coding annotations that were significantly enriched for h2c of corresponding traits were similarly enriched for h2lf for most traits, but more enriched for brain-related annotations and traits. For example, H3K4me3 marks in brain dorsolateral prefrontal cortex explain 57 ± 12% of h2lf versus 12 ± 2% of h2c for neuroticism. Forward simulations confirmed that low-frequency variant enrichment depends on the mean selection coefficient of causal variants in the annotation, and can be used to predict effect size variance of causal rare variants (minor allele frequency <0.5%). |
Thomas J. Hoffmann; Hélène Choquet; Jie Yin; Yambazi Banda; Mark N. Kvale; Maria Glymour; Catherine Schaefer; Neil Risch; Eric Jorgenson A Large Multiethnic Genome-Wide Association Study of Adult Body Mass Index Identifies Novel Loci Journal Article Genetics, 2018. @article{Hoffmann2018, title = {A Large Multiethnic Genome-Wide Association Study of Adult Body Mass Index Identifies Novel Loci}, author = {Thomas J. Hoffmann and Hélène Choquet and Jie Yin and Yambazi Banda and Mark N. Kvale and Maria Glymour and Catherine Schaefer and Neil Risch and Eric Jorgenson}, url = {http://www.genetics.org/content/210/2/499.long}, year = {2018}, date = {2018-10-01}, journal = {Genetics}, abstract = {Body mass index (BMI), a proxy measure for obesity, is determined by both environmental (including ethnicity, age, and sex) and genetic factors, with > 400 BMI-associated loci identified to date. However, the impact, interplay, and underlying biological mechanisms among BMI, environment, genetics, and ancestry are not completely understood. To further examine these relationships, we utilized 427,509 calendar year-averaged BMI measurements from 100,418 adults from the single large multiethnic Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. We observed substantial independent ancestry and nationality differences, including ancestry principal component interactions and nonlinear effects. To increase the list of BMI-associated variants before assessing other differences, we conducted a genome-wide association study (GWAS) in GERA, with replication in the Genetic Investigation of Anthropomorphic Traits (GIANT) consortium combined with the UK Biobank (UKB), followed by GWAS in GERA combined with GIANT, with replication in the UKB. We discovered 30 novel independent BMI loci (P < 5.0 × 10−8) that replicated. We then assessed the proportion of BMI variance explained by sex in the UKB using previously identified loci compared to previously and newly identified loci and found slight increases: from 3.0 to 3.3% for males and from 2.7 to 3.0% for females. Further, the variance explained by previously and newly identified variants decreased with increasing age in the GERA and UKB cohorts, echoed in the variance explained by the entire genome, which also showed gene–age interaction effects. Finally, we conducted a tissue expression QTL enrichment analysis, which revealed that GWAS BMI-associated variants were enriched in the cerebellum, consistent with prior work in humans and mice.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Body mass index (BMI), a proxy measure for obesity, is determined by both environmental (including ethnicity, age, and sex) and genetic factors, with > 400 BMI-associated loci identified to date. However, the impact, interplay, and underlying biological mechanisms among BMI, environment, genetics, and ancestry are not completely understood. To further examine these relationships, we utilized 427,509 calendar year-averaged BMI measurements from 100,418 adults from the single large multiethnic Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. We observed substantial independent ancestry and nationality differences, including ancestry principal component interactions and nonlinear effects. To increase the list of BMI-associated variants before assessing other differences, we conducted a genome-wide association study (GWAS) in GERA, with replication in the Genetic Investigation of Anthropomorphic Traits (GIANT) consortium combined with the UK Biobank (UKB), followed by GWAS in GERA combined with GIANT, with replication in the UKB. We discovered 30 novel independent BMI loci (P < 5.0 × 10−8) that replicated. We then assessed the proportion of BMI variance explained by sex in the UKB using previously identified loci compared to previously and newly identified loci and found slight increases: from 3.0 to 3.3% for males and from 2.7 to 3.0% for females. Further, the variance explained by previously and newly identified variants decreased with increasing age in the GERA and UKB cohorts, echoed in the variance explained by the entire genome, which also showed gene–age interaction effects. Finally, we conducted a tissue expression QTL enrichment analysis, which revealed that GWAS BMI-associated variants were enriched in the cerebellum, consistent with prior work in humans and mice. |
Tian Ge; Chia-Yen Chen; Alysa E Doyle; Richard Vettermann; Lauri J Tuominen; Daphne J Holt; Mert R Sabuncu; Jordan W Smoller The Shared Genetic Basis of Educational Attainment and Cerebral Cortical Morphology Journal Article Cerebral Cortex, 2018. @article{Ge2018e, title = {The Shared Genetic Basis of Educational Attainment and Cerebral Cortical Morphology}, author = {Tian Ge and Chia-Yen Chen and Alysa E Doyle and Richard Vettermann and Lauri J Tuominen and Daphne J Holt and Mert R Sabuncu and Jordan W Smoller }, url = {https://academic.oup.com/cercor/advance-article-abstract/doi/10.1093/cercor/bhy216/5112930?redirectedFrom=fulltext}, year = {2018}, date = {2018-10-01}, journal = {Cerebral Cortex}, abstract = {Individual differences in educational attainment are linked to differences in intelligence, and predict important social, economic, and health outcomes. Previous studies have found common genetic factors that influence educational achievement, cognitive performance and total brain volume (i.e., brain size). Here, in a large sample of participants from the UK Biobank, we investigate the shared genetic basis between educational attainment and fine-grained cerebral cortical morphological features, and associate this genetic variation with a related aspect of cognitive ability. Importantly, we execute novel statistical methods that enable high-dimensional genetic correlation analysis, and compute high-resolution surface maps for the genetic correlations between educational attainment and vertex-wise morphological measurements. We conduct secondary analyses, using the UK Biobank verbal–numerical reasoning score, to confirm that variation in educational attainment that is genetically correlated with cortical morphology is related to differences in cognitive performance. Our analyses relate the genetic overlap between cognitive ability and cortical thickness measurements to bilateral primary motor cortex as well as predominantly left superior temporal cortex and proximal regions. These findings extend our understanding of the neurobiology that connects genetic variation to individual differences in educational attainment and cognitive performance.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Individual differences in educational attainment are linked to differences in intelligence, and predict important social, economic, and health outcomes. Previous studies have found common genetic factors that influence educational achievement, cognitive performance and total brain volume (i.e., brain size). Here, in a large sample of participants from the UK Biobank, we investigate the shared genetic basis between educational attainment and fine-grained cerebral cortical morphological features, and associate this genetic variation with a related aspect of cognitive ability. Importantly, we execute novel statistical methods that enable high-dimensional genetic correlation analysis, and compute high-resolution surface maps for the genetic correlations between educational attainment and vertex-wise morphological measurements. We conduct secondary analyses, using the UK Biobank verbal–numerical reasoning score, to confirm that variation in educational attainment that is genetically correlated with cortical morphology is related to differences in cognitive performance. Our analyses relate the genetic overlap between cognitive ability and cortical thickness measurements to bilateral primary motor cortex as well as predominantly left superior temporal cortex and proximal regions. These findings extend our understanding of the neurobiology that connects genetic variation to individual differences in educational attainment and cognitive performance. |
Yingjie Ji, Andrianos; M. Yiorkas; Francesca Frau; Dennis Mook-Kanamori; Harald Staiger; E. Louise Thomas; Naeimeh Atabaki-Pasdar; Archie Campbell; Jessica Tyrrell; Samuel E. Jones; Robin N. Beaumont; Andrew R. Wood; Marcus A. Tuke; Katherine S. Ruth; Anubha Mahajan; Anna Murray; Rachel M. Freathy; Michael N. Weedon; Andrew T. Hattersley; Caroline Hayward; Jürgen Machann; Hans-Ulrich Häring; Paul Franks; Renée de Mutsert; Ewan Pearson; Norbert Stefan; Timothy M. Frayling; Karla V. Allebrandt; Jimmy D. Bell; Alexandra I. Blakemore; Hanieh Yaghootkar Diabetes, 2018. @article{Ji2018, title = {Genome-Wide and Abdominal MRI-Imaging Data Provides Evidence that a Genetically Determined Favourable Adiposity Phenotype is Characterized by Lower Ectopic Liver Fat and Lower Risk of Type 2 Diabetes, Heart Disease and Hypertension}, author = {Yingjie Ji, Andrianos and M. Yiorkas and Francesca Frau and Dennis Mook-Kanamori and Harald Staiger and E. Louise Thomas and Naeimeh Atabaki-Pasdar and Archie Campbell and Jessica Tyrrell and Samuel E. Jones and Robin N. Beaumont and Andrew R. Wood and Marcus A. Tuke and Katherine S. Ruth and Anubha Mahajan and Anna Murray and Rachel M. Freathy and Michael N. Weedon and Andrew T. Hattersley and Caroline Hayward and Jürgen Machann and Hans-Ulrich Häring and Paul Franks and Renée de Mutsert and Ewan Pearson and Norbert Stefan and Timothy M. Frayling and Karla V. Allebrandt and Jimmy D. Bell and Alexandra I. Blakemore and Hanieh Yaghootkar}, url = {http://diabetes.diabetesjournals.org/content/early/2018/10/16/db18-0708.long}, year = {2018}, date = {2018-10-01}, journal = {Diabetes}, abstract = {Recent genetic studies have identified alleles associated with opposite effects on adiposity and risk of type 2 diabetes. We aimed to identify more of these variants and test the hypothesis that such “favourable adiposity” alleles are associated with higher subcutaneous fat and lower ectopic fat. We combined magnetic resonance imaging (MRI) data with genome-wide association studies (GWAS) of body fat % and metabolic traits. We report 14 alleles, including 7 newly characterized alleles, associated with higher adiposity, but a favourable metabolic profile. Consistent with previous studies, individuals carrying more “favourable adiposity” alleles had higher body fat % and higher BMI, but lower risk of type 2 diabetes, heart disease and hypertension. These individuals also had higher subcutaneous fat, but lower liver fat and lower visceral-to-subcutaneous adipose tissue ratio. Individual alleles associated with higher body fat % but lower liver fat and lower risk of type 2 diabetes included those in PPARG, GRB14 and IRS1, whilst the allele in ANKRD55 was paradoxically associated with higher visceral fat but lower risk of type 2 diabetes. Most identified “favourable adiposity” alleles are associated with higher subcutaneous and lower liver fat, a mechanism consistent with the beneficial effects of storing excess triglyceride in metabolically low risk depots.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Recent genetic studies have identified alleles associated with opposite effects on adiposity and risk of type 2 diabetes. We aimed to identify more of these variants and test the hypothesis that such “favourable adiposity” alleles are associated with higher subcutaneous fat and lower ectopic fat. We combined magnetic resonance imaging (MRI) data with genome-wide association studies (GWAS) of body fat % and metabolic traits. We report 14 alleles, including 7 newly characterized alleles, associated with higher adiposity, but a favourable metabolic profile. Consistent with previous studies, individuals carrying more “favourable adiposity” alleles had higher body fat % and higher BMI, but lower risk of type 2 diabetes, heart disease and hypertension. These individuals also had higher subcutaneous fat, but lower liver fat and lower visceral-to-subcutaneous adipose tissue ratio. Individual alleles associated with higher body fat % but lower liver fat and lower risk of type 2 diabetes included those in PPARG, GRB14 and IRS1, whilst the allele in ANKRD55 was paradoxically associated with higher visceral fat but lower risk of type 2 diabetes. Most identified “favourable adiposity” alleles are associated with higher subcutaneous and lower liver fat, a mechanism consistent with the beneficial effects of storing excess triglyceride in metabolically low risk depots. |
Pradeep Suri; Melody R. Palmer; Yakov A. Tsepilov; Maxim B. Freidin; Cindy G. Boer; Michelle S. Yau; Daniel S. Evans; Andrea Gelemanovic; Traci M. Bartz; Maria Nethander; Liubov Arbeeva; Lennart Karssen; Tuhina Neogi; Frances M. K. Williams Genome-wide meta-analysis of 158,000 individuals of European ancestry identifies three loci associated with chronic back pain Journal Article PLoS Genetics, 2018. @article{Suri2018, title = {Genome-wide meta-analysis of 158,000 individuals of European ancestry identifies three loci associated with chronic back pain}, author = { Pradeep Suri and Melody R. Palmer and Yakov A. Tsepilov and Maxim B. Freidin and Cindy G. Boer and Michelle S. Yau and Daniel S. Evans and Andrea Gelemanovic and Traci M. Bartz and Maria Nethander and Liubov Arbeeva and Lennart Karssen and Tuhina Neogi and Frances M. K. Williams}, url = {https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007601}, year = {2018}, date = {2018-09-27}, journal = {PLoS Genetics}, abstract = {Back pain is the #1 cause of years lived with disability worldwide, yet surprisingly little is known regarding the biology underlying this symptom. We conducted a genome-wide association study (GWAS) meta-analysis of chronic back pain (CBP). Adults of European ancestry were included from 15 cohorts in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, and from the UK Biobank interim data release. CBP cases were defined as those reporting back pain present for ≥3–6 months; non-cases were included as comparisons (“controls”). Each cohort conducted genotyping using commercially available arrays followed by imputation. GWAS used logistic regression models with additive genetic effects, adjusting for age, sex, study-specific covariates, and population substructure. The threshold for genome-wide significance in the fixed-effect inverse-variance weighted meta-analysis was p<5×10−8. Suggestive (p<5×10−7) and genome-wide significant (p<5×10−8) variants were carried forward for replication or further investigation in the remaining UK Biobank participants not included in the discovery sample. The discovery sample comprised 158,025 individuals, including 29,531 CBP cases. A genome-wide significant association was found for the intronic variant rs12310519 in SOX5 (OR 1.08, p = 7.2×10−10). This was subsequently replicated in 283,752 UK Biobank participants not included in the discovery sample, including 50,915 cases (OR 1.06, p = 5.3×10−11), and exceeded genome-wide significance in joint meta-analysis (OR 1.07, p = 4.5×10−19). We found suggestive associations at three other loci in the discovery sample, two of which exceeded genome-wide significance in joint meta-analysis: an intergenic variant, rs7833174, located between CCDC26 and GSDMC (OR 1.05, p = 4.4×10−13), and an intronic variant, rs4384683, in DCC (OR 0.97, p = 2.4×10−10). In this first reported meta-analysis of GWAS for CBP, we identified and replicated a genetic locus associated with CBP (SOX5). We also identified 2 other loci that reached genome-wide significance in a 2-stage joint meta-analysis (CCDC26/GSDMC and DCC}, keywords = {}, pubstate = {published}, tppubtype = {article} } Back pain is the #1 cause of years lived with disability worldwide, yet surprisingly little is known regarding the biology underlying this symptom. We conducted a genome-wide association study (GWAS) meta-analysis of chronic back pain (CBP). Adults of European ancestry were included from 15 cohorts in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, and from the UK Biobank interim data release. CBP cases were defined as those reporting back pain present for ≥3–6 months; non-cases were included as comparisons (“controls”). Each cohort conducted genotyping using commercially available arrays followed by imputation. GWAS used logistic regression models with additive genetic effects, adjusting for age, sex, study-specific covariates, and population substructure. The threshold for genome-wide significance in the fixed-effect inverse-variance weighted meta-analysis was p<5×10−8. Suggestive (p<5×10−7) and genome-wide significant (p<5×10−8) variants were carried forward for replication or further investigation in the remaining UK Biobank participants not included in the discovery sample. The discovery sample comprised 158,025 individuals, including 29,531 CBP cases. A genome-wide significant association was found for the intronic variant rs12310519 in SOX5 (OR 1.08, p = 7.2×10−10). This was subsequently replicated in 283,752 UK Biobank participants not included in the discovery sample, including 50,915 cases (OR 1.06, p = 5.3×10−11), and exceeded genome-wide significance in joint meta-analysis (OR 1.07, p = 4.5×10−19). We found suggestive associations at three other loci in the discovery sample, two of which exceeded genome-wide significance in joint meta-analysis: an intergenic variant, rs7833174, located between CCDC26 and GSDMC (OR 1.05, p = 4.4×10−13), and an intronic variant, rs4384683, in DCC (OR 0.97, p = 2.4×10−10). In this first reported meta-analysis of GWAS for CBP, we identified and replicated a genetic locus associated with CBP (SOX5). We also identified 2 other loci that reached genome-wide significance in a 2-stage joint meta-analysis (CCDC26/GSDMC and DCC |
Xikun Han; Emmanuelle Souzeau; Jue-Sheng Ong et al Myocilin Gene Gln368Ter Variant Penetrance and Association With Glaucoma in Population-Based and Registry-Based Studies Journal Article JAMA Ophthalmology, 2018. @article{Han2018, title = {Myocilin Gene Gln368Ter Variant Penetrance and Association With Glaucoma in Population-Based and Registry-Based Studies}, author = {Xikun Han and Emmanuelle Souzeau and Jue-Sheng Ong et al}, url = {https://jamanetwork.com/journals/jamaophthalmology/article-abstract/2704062}, year = {2018}, date = {2018-09-27}, journal = {JAMA Ophthalmology}, abstract = {Importance The p.Gln368Ter (rs74315329) risk allele in the myocilin gene (MYOC) was initially reported to have high penetrance in glaucoma registry-based studies, but much lower estimates were recently obtained from population-based studies. We investigated this disparity using data from Australia and the United Kingdom. Objectives To examine the penetrance and effect size of the MYOC p.Gln368Ter variant with glaucoma and ocular hypertension (OHT). Design, Setting, and Participants This cross-sectional study within the UK Biobank (UKBB) included participants of white British ancestry. Glaucoma cases were defined by International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10) diagnoses and self-reported questionnaires. Carriers of the MYOC p.Gln368Ter variant were identified using genotype imputation from arrays. In contrast, 2 Australian registry-based studies, the Australian and New Zealand Registry of Advanced Glaucoma and the Glaucoma Inheritance Study in Tasmania, ascertained glaucoma cases referred by eye care clinicians, with historic control participants recruited from other Australian studies. Samples were either directly sequenced or had genotypes determined by imputation (for the Australian registry and historic control participants). Recruitment to the UKBB occurred between 2006 and 2010, and data analysis occurred from September 2017 to July 2018. Main Outcomes and Measures The penetrance and odds ratio (OR) were estimated for the MYOC p.Gln368Ter variants in participants with glaucoma and OHT. Results A total of 411 337 UKBB participants of white British ancestry (mean [SD] age, 56.6 [8.0] years) were included, plus 3071 Australian registry and 6750 historic control participants. In the UKBB, the minor allele frequency of the MYOC p.Gln368Ter variant was 1 in 786 individuals (0.13%). The odds ratio of p.Gln368Ter in patients with primary open-angle glaucoma (POAG) was 6.76 (95% CI, 4.05-11.29); glaucoma (POAG, self-reported glaucoma, and unspecified glaucoma), 4.40 (95% CI, 3.38-5.71); OHT, 3.56 (95% CI, 2.53-4.92); and OHT and glaucoma combined, 4.18 (95% CI, 3.05-5.67). The penetrance of the MYOC p.Gln368Ter variant was 7.6% in patients with glaucoma, 24.3% in patients with OHT, and 30.8% in patients with OHT and glaucoma combined. In the Australian registry studies, the odds of MYOC p.Gln368Ter variant were 12.16 (95% CI, 6.34-24.97) in patients with advanced glaucoma and 3.97 (95% CI, 1.55-9.75) in those with nonadvanced glaucoma; the penetrance of glaucoma was 56.1%, and penetrance in those considered to have glaucoma or be glaucoma suspects was 69.5%. Conclusions and Relevance The MYOC p.Gln368Ter variant confers a very high-risk effect size for advanced glaucoma; the risk is lower in nonadvanced glaucoma and OHT. In the general population sample, approximately 50% of MYOC p.Gln368Ter carriers 65 years and older had glaucoma or OHT, with higher prevalence in the Australian registry studies.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Importance The p.Gln368Ter (rs74315329) risk allele in the myocilin gene (MYOC) was initially reported to have high penetrance in glaucoma registry-based studies, but much lower estimates were recently obtained from population-based studies. We investigated this disparity using data from Australia and the United Kingdom. Objectives To examine the penetrance and effect size of the MYOC p.Gln368Ter variant with glaucoma and ocular hypertension (OHT). Design, Setting, and Participants This cross-sectional study within the UK Biobank (UKBB) included participants of white British ancestry. Glaucoma cases were defined by International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10) diagnoses and self-reported questionnaires. Carriers of the MYOC p.Gln368Ter variant were identified using genotype imputation from arrays. In contrast, 2 Australian registry-based studies, the Australian and New Zealand Registry of Advanced Glaucoma and the Glaucoma Inheritance Study in Tasmania, ascertained glaucoma cases referred by eye care clinicians, with historic control participants recruited from other Australian studies. Samples were either directly sequenced or had genotypes determined by imputation (for the Australian registry and historic control participants). Recruitment to the UKBB occurred between 2006 and 2010, and data analysis occurred from September 2017 to July 2018. Main Outcomes and Measures The penetrance and odds ratio (OR) were estimated for the MYOC p.Gln368Ter variants in participants with glaucoma and OHT. Results A total of 411 337 UKBB participants of white British ancestry (mean [SD] age, 56.6 [8.0] years) were included, plus 3071 Australian registry and 6750 historic control participants. In the UKBB, the minor allele frequency of the MYOC p.Gln368Ter variant was 1 in 786 individuals (0.13%). The odds ratio of p.Gln368Ter in patients with primary open-angle glaucoma (POAG) was 6.76 (95% CI, 4.05-11.29); glaucoma (POAG, self-reported glaucoma, and unspecified glaucoma), 4.40 (95% CI, 3.38-5.71); OHT, 3.56 (95% CI, 2.53-4.92); and OHT and glaucoma combined, 4.18 (95% CI, 3.05-5.67). The penetrance of the MYOC p.Gln368Ter variant was 7.6% in patients with glaucoma, 24.3% in patients with OHT, and 30.8% in patients with OHT and glaucoma combined. In the Australian registry studies, the odds of MYOC p.Gln368Ter variant were 12.16 (95% CI, 6.34-24.97) in patients with advanced glaucoma and 3.97 (95% CI, 1.55-9.75) in those with nonadvanced glaucoma; the penetrance of glaucoma was 56.1%, and penetrance in those considered to have glaucoma or be glaucoma suspects was 69.5%. Conclusions and Relevance The MYOC p.Gln368Ter variant confers a very high-risk effect size for advanced glaucoma; the risk is lower in nonadvanced glaucoma and OHT. In the general population sample, approximately 50% of MYOC p.Gln368Ter carriers 65 years and older had glaucoma or OHT, with higher prevalence in the Australian registry studies. |
Sara L Pulit; Charli Stoneman; Andrew P Morris; Andrew R Wood; Craig A Glastonbury; Jessica Tyrrell; Loïc Yengo; Teresa Ferreira; Eirini Marouli; Yingjie Ji; Jian Yang; Samuel Jones; Robin Beaumont; Damien C Croteau-Chonka; Thomas W Winkler; Consortium GIANT; Andrew T Hattersley; Ruth J F Loos; Joel N Hirschhorn; Peter M Visscher; Timothy M Frayling; Hanieh Yaghootkar; Cecilia M Lindgren Meta-analysis of genome-wide association studies for body fat distribution in 694,649 individuals of European ancestry Journal Article Human molecular genetics, 2018. @article{Pulit2018, title = {Meta-analysis of genome-wide association studies for body fat distribution in 694,649 individuals of European ancestry }, author = {Sara L Pulit and Charli Stoneman and Andrew P Morris and Andrew R Wood and Craig A Glastonbury and Jessica Tyrrell and Loïc Yengo and Teresa Ferreira and Eirini Marouli and Yingjie Ji and Jian Yang and Samuel Jones and Robin Beaumont and Damien C Croteau-Chonka and Thomas W Winkler and Consortium GIANT and Andrew T Hattersley and Ruth J F Loos and Joel N Hirschhorn and Peter M Visscher and Timothy M Frayling and Hanieh Yaghootkar and Cecilia M Lindgren}, url = {https://academic.oup.com/hmg/advance-article/doi/10.1093/hmg/ddy327/5098227}, year = {2018}, date = {2018-09-14}, journal = {Human molecular genetics}, abstract = {One in four adults worldwide are either overweight or obese. Epidemiological studies indicate that the location and distribution of excess fat, rather than general adiposity, is most informative for predicting risk of obesity sequellae, including cardiometabolic disease and cancer. We performed a genome-wide association study meta-analysis of body fat distribution, measured by waist-to-hip ratio adjusted for BMI (WHRadjBMI), and identified 463 signals in 346 loci. Heritability and variant effects were generally stronger in women than men, and we found approximately one-third of all signals to be sexually dimorphic. The 5% of individuals carrying the most WHRadjBMI-increasing alleles were 1.62 times more likely than the bottom 5% to have a WHR above the thresholds used for metabolic syndrome. These data, made publicly available, will inform the biology of body fat distribution and its relationship with disease.}, keywords = {}, pubstate = {published}, tppubtype = {article} } One in four adults worldwide are either overweight or obese. Epidemiological studies indicate that the location and distribution of excess fat, rather than general adiposity, is most informative for predicting risk of obesity sequellae, including cardiometabolic disease and cancer. We performed a genome-wide association study meta-analysis of body fat distribution, measured by waist-to-hip ratio adjusted for BMI (WHRadjBMI), and identified 463 signals in 346 loci. Heritability and variant effects were generally stronger in women than men, and we found approximately one-third of all signals to be sexually dimorphic. The 5% of individuals carrying the most WHRadjBMI-increasing alleles were 1.62 times more likely than the bottom 5% to have a WHR above the thresholds used for metabolic syndrome. These data, made publicly available, will inform the biology of body fat distribution and its relationship with disease. |
CA de Leeuw; S Stringer; IA Dekkers; T Heskes; D Posthuma Conditional and interaction gene-set analysis reveals novel functional pathways for blood pressure Journal Article Nature Communications, 2018. @article{deLeeuw2018, title = {Conditional and interaction gene-set analysis reveals novel functional pathways for blood pressure}, author = {CA de Leeuw and S Stringer and IA Dekkers and T Heskes and D Posthuma }, url = {https://www.ncbi.nlm.nih.gov/pubmed/30218068}, year = {2018}, date = {2018-09-14}, journal = {Nature Communications}, abstract = {Gene-set analysis provides insight into which functional and biological properties of genes are aetiologically relevant for a particular phenotype. But genes have multiple properties, and these properties are often correlated across genes. This can cause confounding in a gene-set analysis, because one property may be statistically associated even if biologically irrelevant to the phenotype, by being correlated with gene properties that are relevant. To address this issue we present a novel conditional and interaction gene-set analysis approach, which attains considerable functional refinement of its conclusions compared to traditional gene-set analysis. We applied our approach to blood pressure phenotypes in the UK Biobank data (N = 360,243), the results of which we report here. We confirm and further refine several associations with multiple processes involved in heart and blood vessel formation but also identify novel interactions, among others with cardiovascular tissues involved in regulatory pathways of blood pressure homoeostasis.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Gene-set analysis provides insight into which functional and biological properties of genes are aetiologically relevant for a particular phenotype. But genes have multiple properties, and these properties are often correlated across genes. This can cause confounding in a gene-set analysis, because one property may be statistically associated even if biologically irrelevant to the phenotype, by being correlated with gene properties that are relevant. To address this issue we present a novel conditional and interaction gene-set analysis approach, which attains considerable functional refinement of its conclusions compared to traditional gene-set analysis. We applied our approach to blood pressure phenotypes in the UK Biobank data (N = 360,243), the results of which we report here. We confirm and further refine several associations with multiple processes involved in heart and blood vessel formation but also identify novel interactions, among others with cardiovascular tissues involved in regulatory pathways of blood pressure homoeostasis. |
Mary E. Haas; Krishna G. Aragam; Connor A. Emdin; Alexander G. Bick; International Consortium for Blood Pressure; Gibran Hemani; George Davey Smith; Sekar Kathiresan Genetic Association of Albuminuria with Cardiometabolic Disease and Blood Pressure Journal Article American Journal of Human Genetics, 2018. @article{Haas2018, title = {Genetic Association of Albuminuria with Cardiometabolic Disease and Blood Pressure}, author = {Mary E. Haas and Krishna G. Aragam and Connor A. Emdin and Alexander G. Bick and International Consortium for Blood Pressure and Gibran Hemani and George Davey Smith and Sekar Kathiresan }, url = {https://www.cell.com/ajhg/fulltext/S0002-9297(18)30270-2}, year = {2018}, date = {2018-09-13}, journal = {American Journal of Human Genetics}, abstract = {Excretion of albumin in urine, or albuminuria, is associated with the development of multiple cardiovascular and metabolic diseases. However, whether pathways leading to albuminuria are causal for cardiometabolic diseases is unclear. We addressed this question using a Mendelian randomization framework in the UK Biobank, a large population-based cohort. We first performed a genome-wide association study for albuminuria in 382,500 individuals and identified 32 new albuminuria loci. We constructed albuminuria genetic risk scores and tested for association with cardiometabolic diseases. Genetically elevated albuminuria was strongly associated with increased risk of hypertension (1.38 OR; 95% CI, 1.27–1.50 per 1 SD predicted increase in albuminuria, p = 7.01 × 10 −14). We then examined bidirectional associations of albuminuria with blood pressure which suggested that genetically elevated albuminuria led to higher blood pressure (2.16 mmHg systolic blood pressure; 95% CI, 1.51–2.82 per 1 SD predicted increase in albuminuria, p = 1.22 × 10 −10) and that genetically elevated blood pressure led to more albuminuria (0.005 SD; 95% CI 0.004–0.006 per 1 mmHg predicted increase in systolic blood pressure, p = 2.45 × 10 −13). These results support the existence of a feed-forward loop between albuminuria and blood pressure and imply that albuminuria could increase risk of cardiovascular disease through blood pressure. Moreover, they suggest therapies that target albuminuria-increasing processes could have antihypertensive effects that are amplified through inhibition of this feed-forward loop. }, keywords = {}, pubstate = {published}, tppubtype = {article} } Excretion of albumin in urine, or albuminuria, is associated with the development of multiple cardiovascular and metabolic diseases. However, whether pathways leading to albuminuria are causal for cardiometabolic diseases is unclear. We addressed this question using a Mendelian randomization framework in the UK Biobank, a large population-based cohort. We first performed a genome-wide association study for albuminuria in 382,500 individuals and identified 32 new albuminuria loci. We constructed albuminuria genetic risk scores and tested for association with cardiometabolic diseases. Genetically elevated albuminuria was strongly associated with increased risk of hypertension (1.38 OR; 95% CI, 1.27–1.50 per 1 SD predicted increase in albuminuria, p = 7.01 × 10 −14). We then examined bidirectional associations of albuminuria with blood pressure which suggested that genetically elevated albuminuria led to higher blood pressure (2.16 mmHg systolic blood pressure; 95% CI, 1.51–2.82 per 1 SD predicted increase in albuminuria, p = 1.22 × 10 −10) and that genetically elevated blood pressure led to more albuminuria (0.005 SD; 95% CI 0.004–0.006 per 1 mmHg predicted increase in systolic blood pressure, p = 2.45 × 10 −13). These results support the existence of a feed-forward loop between albuminuria and blood pressure and imply that albuminuria could increase risk of cardiovascular disease through blood pressure. Moreover, they suggest therapies that target albuminuria-increasing processes could have antihypertensive effects that are amplified through inhibition of this feed-forward loop. |
Marcus A. Tuke; Katherine S. Ruth; Andrew R. Wood; Robin N. Beaumont; Jessica Tyrrell; Samuel E. Jones; Hanieh Yaghootkar; and Claire L. S. Turner; Mollie E. Donohoe; Antonia M. Brooke; Morag N. Collinson; Rachel M. Freathy; Michael N. Weedon; Timothy M. Frayling; Anna Murray PhD Mosaic Turner syndrome shows reduced penetrance in an adult population study Journal Article Genetics in Medicine, 2018. @article{Tuke2018, title = {Mosaic Turner syndrome shows reduced penetrance in an adult population study}, author = {Marcus A. Tuke and Katherine S. Ruth and Andrew R. Wood and Robin N. Beaumont and Jessica Tyrrell and Samuel E. Jones and Hanieh Yaghootkar and and Claire L. S. Turner and Mollie E. Donohoe and Antonia M. Brooke and Morag N. Collinson and Rachel M. Freathy and Michael N. Weedon and Timothy M. Frayling and Anna Murray PhD }, url = {https://www.nature.com/articles/s41436-018-0271-6}, year = {2018}, date = {2018-09-05}, journal = {Genetics in Medicine}, abstract = {Purpose Many women with X chromosome aneuploidy undergo lifetime clinical monitoring for possible complications. However, ascertainment of cases in the clinic may mean that the penetrance has been overestimated. Methods We characterized the prevalence and phenotypic consequences of X chromosome aneuploidy in a population of 244,848 women over 40 years of age from UK Biobank, using single-nucleotide polymorphism (SNP) array data. Results We detected 30 women with 45,X; 186 with mosaic 45,X/46,XX; and 110 with 47,XXX. The prevalence of nonmosaic 45,X (12/100,000) and 47,XXX (45/100,000) was lower than expected, but was higher for mosaic 45,X/46,XX (76/100,000). The characteristics of women with 45,X were consistent with the characteristics of a clinically recognized Turner syndrome phenotype, including short stature and primary amenorrhea. In contrast, women with mosaic 45,X/46,XX were less short, had a normal reproductive lifespan and birth rate, and no reported cardiovascular complications. The phenotype of women with 47,XXX included taller stature (5.3 cm; SD = 5.52 cm; P = 5.8 × 10−20) and earlier menopause age (5.12 years; SD = 5.1 years; P = 1.2 × 10−14). Conclusion Our results suggest that the clinical management of women with 45,X/46,XX mosaicism should be minimal, particularly those identified incidentally.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Purpose Many women with X chromosome aneuploidy undergo lifetime clinical monitoring for possible complications. However, ascertainment of cases in the clinic may mean that the penetrance has been overestimated. Methods We characterized the prevalence and phenotypic consequences of X chromosome aneuploidy in a population of 244,848 women over 40 years of age from UK Biobank, using single-nucleotide polymorphism (SNP) array data. Results We detected 30 women with 45,X; 186 with mosaic 45,X/46,XX; and 110 with 47,XXX. The prevalence of nonmosaic 45,X (12/100,000) and 47,XXX (45/100,000) was lower than expected, but was higher for mosaic 45,X/46,XX (76/100,000). The characteristics of women with 45,X were consistent with the characteristics of a clinically recognized Turner syndrome phenotype, including short stature and primary amenorrhea. In contrast, women with mosaic 45,X/46,XX were less short, had a normal reproductive lifespan and birth rate, and no reported cardiovascular complications. The phenotype of women with 47,XXX included taller stature (5.3 cm; SD = 5.52 cm; P = 5.8 × 10−20) and earlier menopause age (5.12 years; SD = 5.1 years; P = 1.2 × 10−14). Conclusion Our results suggest that the clinical management of women with 45,X/46,XX mosaicism should be minimal, particularly those identified incidentally. |
Rona J. Strawbridge; Joey Ward; Laura M. Lyall; Elizabeth M. Tunbridge; Breda Cullen; Nicholas Graham; Amy Ferguson; Keira J. A. Johnston; Donald M. Lyall; Daniel Mackay; Jonathan Cavanagh; David M. Howard; Mark J. Adams; Ian Deary; Valentina Escott-Price; Michael O’Donovan; Andrew M. McIntosh; Mark E. S. Bailey; Jill P. Pell; Paul J. Harrison; Daniel J. Smith Genetics of self-reported risk-taking behaviour, trans-ethnic consistency and relevance to brain gene expression Journal Article Translational Psychiatry , 2018. @article{Strawbridge2018, title = {Genetics of self-reported risk-taking behaviour, trans-ethnic consistency and relevance to brain gene expression}, author = {Rona J. Strawbridge and Joey Ward and Laura M. Lyall and Elizabeth M. Tunbridge and Breda Cullen and Nicholas Graham and Amy Ferguson and Keira J. A. Johnston and Donald M. Lyall and Daniel Mackay and Jonathan Cavanagh and David M. Howard and Mark J. Adams and Ian Deary and Valentina Escott-Price and Michael O’Donovan and Andrew M. McIntosh and Mark E. S. Bailey and Jill P. Pell and Paul J. Harrison and Daniel J. Smith }, url = {https://www.nature.com/articles/s41398-018-0236-1}, year = {2018}, date = {2018-09-04}, journal = {Translational Psychiatry }, abstract = {Risk-taking behaviour is an important component of several psychiatric disorders, including attention-deficit hyperactivity disorder, schizophrenia and bipolar disorder. Previously, two genetic loci have been associated with self-reported risk taking and significant genetic overlap with psychiatric disorders was identified within a subsample of UK Biobank. Using the white British participants of the full UK Biobank cohort (n = 83,677 risk takers versus 244,662 controls) for our primary analysis, we conducted a genome-wide association study of self-reported risk-taking behaviour. In secondary analyses, we assessed sex-specific effects, trans-ethnic heterogeneity and genetic overlap with psychiatric traits. We also investigated the impact of risk-taking-associated SNPs on both gene expression and structural brain imaging. We identified 10 independent loci for risk-taking behaviour, of which eight were novel and two replicated previous findings. In addition, we found two further sex-specific risk-taking loci. There were strong positive genetic correlations between risk-taking and attention-deficit hyperactivity disorder, bipolar disorder and schizophrenia. Index genetic variants demonstrated effects generally consistent with the discovery analysis in individuals of non-British White, South Asian, African-Caribbean or mixed ethnicity. Polygenic risk scores comprising alleles associated with increased risk taking were associated with lower white matter integrity. Genotype-specific expression pattern analyses highlighted DPYSL5, CGREF1 and C15orf59 as plausible candidate genes. Overall, our findings substantially advance our understanding of the biology of risk-taking behaviour, including the possibility of sex-specific contributions, and reveal consistency across ethnicities. We further highlight several putative novel candidate genes, which may mediate these genetic effects.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Risk-taking behaviour is an important component of several psychiatric disorders, including attention-deficit hyperactivity disorder, schizophrenia and bipolar disorder. Previously, two genetic loci have been associated with self-reported risk taking and significant genetic overlap with psychiatric disorders was identified within a subsample of UK Biobank. Using the white British participants of the full UK Biobank cohort (n = 83,677 risk takers versus 244,662 controls) for our primary analysis, we conducted a genome-wide association study of self-reported risk-taking behaviour. In secondary analyses, we assessed sex-specific effects, trans-ethnic heterogeneity and genetic overlap with psychiatric traits. We also investigated the impact of risk-taking-associated SNPs on both gene expression and structural brain imaging. We identified 10 independent loci for risk-taking behaviour, of which eight were novel and two replicated previous findings. In addition, we found two further sex-specific risk-taking loci. There were strong positive genetic correlations between risk-taking and attention-deficit hyperactivity disorder, bipolar disorder and schizophrenia. Index genetic variants demonstrated effects generally consistent with the discovery analysis in individuals of non-British White, South Asian, African-Caribbean or mixed ethnicity. Polygenic risk scores comprising alleles associated with increased risk taking were associated with lower white matter integrity. Genotype-specific expression pattern analyses highlighted DPYSL5, CGREF1 and C15orf59 as plausible candidate genes. Overall, our findings substantially advance our understanding of the biology of risk-taking behaviour, including the possibility of sex-specific contributions, and reveal consistency across ethnicities. We further highlight several putative novel candidate genes, which may mediate these genetic effects. |
Lillias H. Maguire; Samuel K. Handelman; Xiaomeng Du; Yanhua Chen; Tune H. Pers; Elizabeth K. Speliotes Genome-wide association analyses identify 39 new susceptibility loci for diverticular disease Journal Article Nature Genetics, 2018. @article{Maguire2018, title = {Genome-wide association analyses identify 39 new susceptibility loci for diverticular disease}, author = {Lillias H. Maguire and Samuel K. Handelman and Xiaomeng Du and Yanhua Chen and Tune H. Pers and Elizabeth K. Speliotes }, url = {https://www.nature.com/articles/s41588-018-0203-z}, year = {2018}, date = {2018-09-03}, journal = {Nature Genetics}, abstract = {Diverticular disease is common and has a high morbidity. Treatments are limited owing to the poor understanding of its pathophysiology. Here, to elucidate its etiology, we performed a genome-wide association study of diverticular disease (27,444 cases; 382,284 controls) from the UK Biobank and tested for replication in the Michigan Genomics Initiative (2,572 cases; 28,649 controls). We identified 42 loci associated with diverticular disease; 39 of these loci are novel. Using data-driven expression-prioritized integration for complex traits (DEPICT), we show that genes in these associated regions are significantly enriched for expression in mesenchymal stem cells and multiple connective tissue cell types and are co-expressed with genes that have a role in vascular and mesenchymal biology. Genes in these associated loci have roles in immunity, extracellular matrix biology, cell adhesion, membrane transport and intestinal motility. Phenome-wide association analysis of the 42 variants shows a common etiology of diverticular disease with obesity and hernia. These analyses shed light on the genomic landscape of diverticular disease.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Diverticular disease is common and has a high morbidity. Treatments are limited owing to the poor understanding of its pathophysiology. Here, to elucidate its etiology, we performed a genome-wide association study of diverticular disease (27,444 cases; 382,284 controls) from the UK Biobank and tested for replication in the Michigan Genomics Initiative (2,572 cases; 28,649 controls). We identified 42 loci associated with diverticular disease; 39 of these loci are novel. Using data-driven expression-prioritized integration for complex traits (DEPICT), we show that genes in these associated regions are significantly enriched for expression in mesenchymal stem cells and multiple connective tissue cell types and are co-expressed with genes that have a role in vascular and mesenchymal biology. Genes in these associated loci have roles in immunity, extracellular matrix biology, cell adhesion, membrane transport and intestinal motility. Phenome-wide association analysis of the 42 variants shows a common etiology of diverticular disease with obesity and hernia. These analyses shed light on the genomic landscape of diverticular disease. |
Amy Ferguson; Laura M. Lyall; Joey Ward; Rona J. Strawbridge; Breda Cullen; Nicholas Graham; Claire L. Niedzwiedz; Keira J.A. Johnston; Daniel MacKay; Stephany M. Biello; Jill P. Pell; Jonathan Cavanagh , Andrew M. McIntosh , Aiden Doherty , Mark E.S. Bailey , Donald M. Lyall , Cathy A. Wyse , Daniel J. Smith'Correspondence information about the author Daniel J. Smith Genome-Wide Association Study of Circadian Rhythmicity in 71,500 UK Biobank Participants and Polygenic Association with Mood Instability Journal Article Ebio Medicine, 2018. @article{Ferguson2018b, title = {Genome-Wide Association Study of Circadian Rhythmicity in 71,500 UK Biobank Participants and Polygenic Association with Mood Instability}, author = { Amy Ferguson and Laura M. Lyall and Joey Ward and Rona J. Strawbridge and Breda Cullen and Nicholas Graham and Claire L. Niedzwiedz and Keira J.A. Johnston and Daniel MacKay and Stephany M. Biello and Jill P. Pell and Jonathan Cavanagh , Andrew M. McIntosh , Aiden Doherty , Mark E.S. Bailey , Donald M. Lyall , Cathy A. Wyse , Daniel J. Smith'Correspondence information about the author Daniel J. Smith}, url = {https://www.ebiomedicine.com/article/S2352-3964(18)30292-5/fulltext}, year = {2018}, date = {2018-09-01}, journal = {Ebio Medicine}, abstract = {Background Circadian rhythms are fundamental to health and are particularly important for mental wellbeing. Disrupted rhythms of rest and activity are recognised as risk factors for major depressive disorder and bipolar disorder. Methods We conducted a genome-wide association study (GWAS) of low relative amplitude (RA), an objective measure of rest-activity cycles derived from the accelerometer data of 71,500 UK Biobank participants. Polygenic risk scores (PRS) for low RA were used to investigate potential associations with psychiatric phenotypes. Outcomes Two independent genetic loci were associated with low RA, within genomic regions for Neurofascin (NFASC) and Solute Carrier Family 25 Member 17 (SLC25A17). A secondary GWAS of RA as a continuous measure identified a locus within Meis Homeobox 1 (MEIS1). There were no significant genetic correlations between low RA and any of the psychiatric phenotypes assessed. However, PRS for low RA was significantly associated with mood instability across multiple PRS thresholds (at PRS threshold 0·05: OR = 1·02, 95% CI = 1·01–1·02, p = 9·6 × 10−5), and with major depressive disorder (at PRS threshold 0·1: OR = 1·03, 95% CI = 1·01–1·05, p = 0·025) and neuroticism (at PRS threshold 0·5: Beta = 0·02, 95% CI = 0·007–0·04, p = 0·021). Interpretation Overall, our findings contribute new knowledge on the complex genetic architecture of circadian rhythmicity and suggest a putative biological link between disrupted circadian function and mood disorder phenotypes, particularly mood instability, but also major depressive disorder and neuroticism. Funding Medical Research Council (MR/K501335/1).}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background Circadian rhythms are fundamental to health and are particularly important for mental wellbeing. Disrupted rhythms of rest and activity are recognised as risk factors for major depressive disorder and bipolar disorder. Methods We conducted a genome-wide association study (GWAS) of low relative amplitude (RA), an objective measure of rest-activity cycles derived from the accelerometer data of 71,500 UK Biobank participants. Polygenic risk scores (PRS) for low RA were used to investigate potential associations with psychiatric phenotypes. Outcomes Two independent genetic loci were associated with low RA, within genomic regions for Neurofascin (NFASC) and Solute Carrier Family 25 Member 17 (SLC25A17). A secondary GWAS of RA as a continuous measure identified a locus within Meis Homeobox 1 (MEIS1). There were no significant genetic correlations between low RA and any of the psychiatric phenotypes assessed. However, PRS for low RA was significantly associated with mood instability across multiple PRS thresholds (at PRS threshold 0·05: OR = 1·02, 95% CI = 1·01–1·02, p = 9·6 × 10−5), and with major depressive disorder (at PRS threshold 0·1: OR = 1·03, 95% CI = 1·01–1·05, p = 0·025) and neuroticism (at PRS threshold 0·5: Beta = 0·02, 95% CI = 0·007–0·04, p = 0·021). Interpretation Overall, our findings contribute new knowledge on the complex genetic architecture of circadian rhythmicity and suggest a putative biological link between disrupted circadian function and mood disorder phenotypes, particularly mood instability, but also major depressive disorder and neuroticism. Funding Medical Research Council (MR/K501335/1). |
Pau Bellot; Gustavo de los Campos; Miguel Pérez-Enciso Can Deep Learning Improve Genomic Prediction of Complex Human Traits? Journal Article Genetics , 2018. @article{Bellot2018, title = {Can Deep Learning Improve Genomic Prediction of Complex Human Traits?}, author = {Pau Bellot and Gustavo de los Campos and Miguel Pérez-Enciso}, url = {http://www.genetics.org/content/early/2018/08/31/genetics.118.301298.long}, year = {2018}, date = {2018-08-31}, journal = {Genetics }, abstract = {The genetic analysis of complex traits does not escape the current excitement around artificial intelligence, including a renewed interest in 'deep learning' (DL) techniques such as Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). However, the performance of DL for genomic prediction of complex human traits has not been comprehensively tested. To provide an evaluation of MLPs and CNNs, we used data from distantly related white Caucasian individuals (n ∼ 100k individuals, m ∼ 500k SNPs, k=1000) of the interim release of the UK biobank. We analyzed a total of five phenotypes: height, bone heel mineral density (BHMD), body mass index, systolic blood pressure and waist hip ratio, with genomic heritabilities ranging from ∼ 0.20 to 0.70. After hyperparameter optimization using a genetic algorithm, we considered several configurations, from shallow to deep learners and compared the predictive performance of MLPs and CNNs with that of Bayesian linear regressions across sets of SNPs (from 10k and 50k) that were pre-selected using single marker regression analyses. For height, a highly heritable phenotype, all methods performed similarly, although CNNs were slightly but consistently worse. For the rest of phenotypes, performance of some CNNs was comparable or slightly better than linear methods. Performance of MLPs was highly dependent on SNP set and phenotype. In all, over the range of traits evaluated in this study, CNNs performance was competitive to linear models but we did not find any case where DL outperformed the linear model by a sizable margin. We suggest that more research is needed to adapt CNN methodology, originally motivated by image analysis, to genetic - based problems in order for CNNs to be competitive with linear models.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The genetic analysis of complex traits does not escape the current excitement around artificial intelligence, including a renewed interest in 'deep learning' (DL) techniques such as Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). However, the performance of DL for genomic prediction of complex human traits has not been comprehensively tested. To provide an evaluation of MLPs and CNNs, we used data from distantly related white Caucasian individuals (n ∼ 100k individuals, m ∼ 500k SNPs, k=1000) of the interim release of the UK biobank. We analyzed a total of five phenotypes: height, bone heel mineral density (BHMD), body mass index, systolic blood pressure and waist hip ratio, with genomic heritabilities ranging from ∼ 0.20 to 0.70. After hyperparameter optimization using a genetic algorithm, we considered several configurations, from shallow to deep learners and compared the predictive performance of MLPs and CNNs with that of Bayesian linear regressions across sets of SNPs (from 10k and 50k) that were pre-selected using single marker regression analyses. For height, a highly heritable phenotype, all methods performed similarly, although CNNs were slightly but consistently worse. For the rest of phenotypes, performance of some CNNs was comparable or slightly better than linear methods. Performance of MLPs was highly dependent on SNP set and phenotype. In all, over the range of traits evaluated in this study, CNNs performance was competitive to linear models but we did not find any case where DL outperformed the linear model by a sizable margin. We suggest that more research is needed to adapt CNN methodology, originally motivated by image analysis, to genetic - based problems in order for CNNs to be competitive with linear models. |
Katerina Trajanoska; John A Morris; Ling Oei; Hou-Feng Zheng; and David M Evans; and Douglas P Kiel; Claes Ohlsson; J Brent Richards; Fernando Rivadeneira; the GEFOS/GENOMOS consortium; the 23andMe research team Assessment of the genetic and clinical determinants of fracture risk: genome wide association and mendelian randomisation study Journal Article BMJ, 2018. @article{Trajanoska2018, title = {Assessment of the genetic and clinical determinants of fracture risk: genome wide association and mendelian randomisation study}, author = {Katerina Trajanoska and John A Morris and Ling Oei and Hou-Feng Zheng and and David M Evans and and Douglas P Kiel and Claes Ohlsson and J Brent Richards and Fernando Rivadeneira and the GEFOS/GENOMOS consortium and the 23andMe research team}, url = {https://www.bmj.com/content/362/bmj.k3225}, year = {2018}, date = {2018-08-29}, journal = {BMJ}, abstract = {Objectives To identify the genetic determinants of fracture risk and assess the role of 15 clinical risk factors on osteoporotic fracture risk. Design Meta-analysis of genome wide association studies (GWAS) and a two-sample mendelian randomisation approach. Setting 25 cohorts from Europe, United States, east Asia, and Australia with genome wide genotyping and fracture data. Participants A discovery set of 37 857 fracture cases and 227 116 controls; with replication in up to 147 200 fracture cases and 150 085 controls. Fracture cases were defined as individuals (>18 years old) who had fractures at any skeletal site confirmed by medical, radiological, or questionnaire reports. Instrumental variable analyses were performed to estimate effects of 15 selected clinical risk factors for fracture in a two-sample mendelian randomisation framework, using the largest previously published GWAS meta-analysis of each risk factor. Results Of 15 fracture associated loci identified, all were also associated with bone mineral density and mapped to genes clustering in pathways known to be critical to bone biology (eg, SOST, WNT16, and ESR1) or novel pathways (FAM210A, GRB10, and ETS2). Mendelian randomisation analyses showed a clear effect of bone mineral density on fracture risk. One standard deviation decrease in genetically determined bone mineral density of the femoral neck was associated with a 55% increase in fracture risk (odds ratio 1.55 (95% confidence interval 1.48 to 1.63; P=1.5×10−68). Hand grip strength was inversely associated with fracture risk, but this result was not significant after multiple testing correction. The remaining clinical risk factors (including vitamin D levels) showed no evidence for an effect on fracture. Conclusions This large scale GWAS meta-analysis for fracture identified 15 genetic determinants of fracture, all of which also influenced bone mineral density. Among the clinical risk factors for fracture assessed, only bone mineral density showed a major causal effect on fracture. Genetic predisposition to lower levels of vitamin D and estimated calcium intake from dairy sources were not associated with fracture risk.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Objectives To identify the genetic determinants of fracture risk and assess the role of 15 clinical risk factors on osteoporotic fracture risk. Design Meta-analysis of genome wide association studies (GWAS) and a two-sample mendelian randomisation approach. Setting 25 cohorts from Europe, United States, east Asia, and Australia with genome wide genotyping and fracture data. Participants A discovery set of 37 857 fracture cases and 227 116 controls; with replication in up to 147 200 fracture cases and 150 085 controls. Fracture cases were defined as individuals (>18 years old) who had fractures at any skeletal site confirmed by medical, radiological, or questionnaire reports. Instrumental variable analyses were performed to estimate effects of 15 selected clinical risk factors for fracture in a two-sample mendelian randomisation framework, using the largest previously published GWAS meta-analysis of each risk factor. Results Of 15 fracture associated loci identified, all were also associated with bone mineral density and mapped to genes clustering in pathways known to be critical to bone biology (eg, SOST, WNT16, and ESR1) or novel pathways (FAM210A, GRB10, and ETS2). Mendelian randomisation analyses showed a clear effect of bone mineral density on fracture risk. One standard deviation decrease in genetically determined bone mineral density of the femoral neck was associated with a 55% increase in fracture risk (odds ratio 1.55 (95% confidence interval 1.48 to 1.63; P=1.5×10−68). Hand grip strength was inversely associated with fracture risk, but this result was not significant after multiple testing correction. The remaining clinical risk factors (including vitamin D levels) showed no evidence for an effect on fracture. Conclusions This large scale GWAS meta-analysis for fracture identified 15 genetic determinants of fracture, all of which also influenced bone mineral density. Among the clinical risk factors for fracture assessed, only bone mineral density showed a major causal effect on fracture. Genetic predisposition to lower levels of vitamin D and estimated calcium intake from dairy sources were not associated with fracture risk. |
Joëlle A. Pasman; Karin J. H. Verweij; Jacqueline M. Vink GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia Journal Article Nature Neuroscience , 2018. @article{Pasman2018, title = {GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia}, author = {Joëlle A. Pasman and Karin J. H. Verweij and Jacqueline M. Vink }, url = {https://www.nature.com/articles/s41593-018-0206-1#Ack1}, year = {2018}, date = {2018-08-27}, journal = {Nature Neuroscience }, abstract = {Cannabis use is a heritable trait that has been associated with adverse mental health outcomes. In the largest genome-wide association study (GWAS) for lifetime cannabis use to date (N = 184,765), we identified eight genome-wide significant independent single nucleotide polymorphisms in six regions. All measured genetic variants combined explained 11% of the variance. Gene-based tests revealed 35 significant genes in 16 regions, and S-PrediXcan analyses showed that 21 genes had different expression levels for cannabis users versus nonusers. The strongest finding across the different analyses was CADM2, which has been associated with substance use and risk-taking. Significant genetic correlations were found with 14 of 25 tested substance use and mental health–related traits, including smoking, alcohol use, schizophrenia and risk-taking. Mendelian randomization analysis showed evidence for a causal positive influence of schizophrenia risk on cannabis use. Overall, our study provides new insights into the etiology of cannabis use and its relation with mental health.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Cannabis use is a heritable trait that has been associated with adverse mental health outcomes. In the largest genome-wide association study (GWAS) for lifetime cannabis use to date (N = 184,765), we identified eight genome-wide significant independent single nucleotide polymorphisms in six regions. All measured genetic variants combined explained 11% of the variance. Gene-based tests revealed 35 significant genes in 16 regions, and S-PrediXcan analyses showed that 21 genes had different expression levels for cannabis users versus nonusers. The strongest finding across the different analyses was CADM2, which has been associated with substance use and risk-taking. Significant genetic correlations were found with 14 of 25 tested substance use and mental health–related traits, including smoking, alcohol use, schizophrenia and risk-taking. Mendelian randomization analysis showed evidence for a causal positive influence of schizophrenia risk on cannabis use. Overall, our study provides new insights into the etiology of cannabis use and its relation with mental health. |
S Nagpal; G Gibson; UM Marigorta Pervasive Modulation of Obesity Risk by the Environment and Genomic Background Journal Article Genes, 2018. @article{Nagpal2018, title = {Pervasive Modulation of Obesity Risk by the Environment and Genomic Background}, author = {S Nagpal and G Gibson and UM Marigorta }, url = {https://www.ncbi.nlm.nih.gov/pubmed/30110940}, year = {2018}, date = {2018-08-14}, journal = {Genes}, abstract = {The prevalence of the so-called diseases of affluence, such as type 2 diabetes or hypertension, has increased dramatically in the last two generations. Although genome-wide association studies (GWAS) have discovered hundreds of genes involved in disease etiology, the sudden increase in disease incidence suggests a major role for environmental risk factors. Obesity constitutes a case example of a modern trait shaped by contemporary environment, although with considerable debates about the extent to which gene-by-environment (G×E) interactions accentuate obesity risk in individuals following obesogenic lifestyles. Although interaction effects have been robustly confirmed at the FTO locus, accumulating evidence at the genome-wide level implicates a role for polygenic risk-by-environment interactions. Through a variety of analyses using the UK Biobank, we confirm that the genomic background plays a major role in shaping the expressivity of alleles that increase body mass index (BMI).}, keywords = {}, pubstate = {published}, tppubtype = {article} } The prevalence of the so-called diseases of affluence, such as type 2 diabetes or hypertension, has increased dramatically in the last two generations. Although genome-wide association studies (GWAS) have discovered hundreds of genes involved in disease etiology, the sudden increase in disease incidence suggests a major role for environmental risk factors. Obesity constitutes a case example of a modern trait shaped by contemporary environment, although with considerable debates about the extent to which gene-by-environment (G×E) interactions accentuate obesity risk in individuals following obesogenic lifestyles. Although interaction effects have been robustly confirmed at the FTO locus, accumulating evidence at the genome-wide level implicates a role for polygenic risk-by-environment interactions. Through a variety of analyses using the UK Biobank, we confirm that the genomic background plays a major role in shaping the expressivity of alleles that increase body mass index (BMI). |
Amit V. Khera; Mark Chaffin; Krishna G. Aragam; Mary E. Haas; Carolina Roselli; Seung Hoan Choi; Pradeep Natarajan; Eric S. Lander; Steven A. Lubitz; Patrick T. Ellinor; Sekar Kathiresan Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations Journal Article Nature Genetics, 2018. @article{Khera2018, title = {Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations}, author = {Amit V. Khera and Mark Chaffin and Krishna G. Aragam and Mary E. Haas and Carolina Roselli and Seung Hoan Choi and Pradeep Natarajan and Eric S. Lander and Steven A. Lubitz and Patrick T. Ellinor and Sekar Kathiresan }, url = {https://www.nature.com/articles/s41588-018-0183-z}, year = {2018}, date = {2018-08-13}, journal = {Nature Genetics}, abstract = {A key public health need is to identify individuals at high risk for a given disease to enable enhanced screening or preventive therapies. Because most common diseases have a genetic component, one important approach is to stratify individuals based on inherited DNA variation1. Proposed clinical applications have largely focused on finding carriers of rare monogenic mutations at several-fold increased risk. Although most disease risk is polygenic in nature2,3,4,5, it has not yet been possible to use polygenic predictors to identify individuals at risk comparable to monogenic mutations. Here, we develop and validate genome-wide polygenic scores for five common diseases. The approach identifies 8.0, 6.1, 3.5, 3.2, and 1.5% of the population at greater than threefold increased risk for coronary artery disease, atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer, respectively. For coronary artery disease, this prevalence is 20-fold higher than the carrier frequency of rare monogenic mutations conferring comparable risk6. We propose that it is time to contemplate the inclusion of polygenic risk prediction in clinical care, and discuss relevant issues.}, keywords = {}, pubstate = {published}, tppubtype = {article} } A key public health need is to identify individuals at high risk for a given disease to enable enhanced screening or preventive therapies. Because most common diseases have a genetic component, one important approach is to stratify individuals based on inherited DNA variation1. Proposed clinical applications have largely focused on finding carriers of rare monogenic mutations at several-fold increased risk. Although most disease risk is polygenic in nature2,3,4,5, it has not yet been possible to use polygenic predictors to identify individuals at risk comparable to monogenic mutations. Here, we develop and validate genome-wide polygenic scores for five common diseases. The approach identifies 8.0, 6.1, 3.5, 3.2, and 1.5% of the population at greater than threefold increased risk for coronary artery disease, atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer, respectively. For coronary artery disease, this prevalence is 20-fold higher than the carrier frequency of rare monogenic mutations conferring comparable risk6. We propose that it is time to contemplate the inclusion of polygenic risk prediction in clinical care, and discuss relevant issues. |
Wei Zhou; Jonas B. Nielsen; Lars G. Fritsche; Rounak Dey; Maiken E. Gabrielsen; Brooke N. Wolford; Jonathon LeFaive; Peter VandeHaar; Sarah A. Gagliano; Aliya Gifford; Lisa A. Bastarache; Wei-Qi Wei; Joshua C. Denny; Maoxuan Lin; Kristian Hveem; Hyun Min Kang; Goncalo R. Abecasis; Cristen J. Willer; Seunggeun Lee Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies Journal Article Nature Genetics, 2018. @article{Zhou2018b, title = {Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies}, author = {Wei Zhou and Jonas B. Nielsen and Lars G. Fritsche and Rounak Dey and Maiken E. Gabrielsen and Brooke N. Wolford and Jonathon LeFaive and Peter VandeHaar and Sarah A. Gagliano and Aliya Gifford and Lisa A. Bastarache and Wei-Qi Wei and Joshua C. Denny and Maoxuan Lin and Kristian Hveem and Hyun Min Kang and Goncalo R. Abecasis and Cristen J. Willer and Seunggeun Lee }, url = {https://www.nature.com/articles/s41588-018-0184-y}, year = {2018}, date = {2018-08-13}, journal = {Nature Genetics}, abstract = {In genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, the linear mixed model and the recently proposed logistic mixed model, perform poorly; they produce large type I error rates when used to analyze unbalanced case-control phenotypes. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation to calibrate the distribution of score test statistics. This method, SAIGE (Scalable and Accurate Implementation of GEneralized mixed model), provides accurate P values even when case-control ratios are extremely unbalanced. SAIGE uses state-of-art optimization strategies to reduce computational costs; hence, it is applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 samples from white British participants with European ancestry for > 1,400 binary phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, the linear mixed model and the recently proposed logistic mixed model, perform poorly; they produce large type I error rates when used to analyze unbalanced case-control phenotypes. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation to calibrate the distribution of score test statistics. This method, SAIGE (Scalable and Accurate Implementation of GEneralized mixed model), provides accurate P values even when case-control ratios are extremely unbalanced. SAIGE uses state-of-art optimization strategies to reduce computational costs; hence, it is applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 samples from white British participants with European ancestry for > 1,400 binary phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness. |
Tracy A. O’Mara; Dylan M. Glubb; Deborah J. Thompson Identification of nine new susceptibility loci for endometrial cancer Journal Article Nature Communications, 2018. @article{O’Mara2018, title = {Identification of nine new susceptibility loci for endometrial cancer}, author = {Tracy A. O’Mara and Dylan M. Glubb and Deborah J. Thompson }, url = {https://www.nature.com/articles/s41467-018-05427-7}, year = {2018}, date = {2018-08-09}, journal = {Nature Communications}, abstract = {Endometrial cancer is the most commonly diagnosed cancer of the female reproductive tract in developed countries. Through genome-wide association studies (GWAS), we have previously identified eight risk loci for endometrial cancer. Here, we present an expanded meta-analysis of 12,906 endometrial cancer cases and 108,979 controls (including new genotype data for 5624 cases) and identify nine novel genome-wide significant loci, including a locus on 12q24.12 previously identified by meta-GWAS of endometrial and colorectal cancer. At five loci, expression quantitative trait locus (eQTL) analyses identify candidate causal genes; risk alleles at two of these loci associate with decreased expression of genes, which encode negative regulators of oncogenic signal transduction proteins (SH2B3 (12q24.12) and NF1 (17q11.2)). In summary, this study has doubled the number of known endometrial cancer risk loci and revealed candidate causal genes for future study.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Endometrial cancer is the most commonly diagnosed cancer of the female reproductive tract in developed countries. Through genome-wide association studies (GWAS), we have previously identified eight risk loci for endometrial cancer. Here, we present an expanded meta-analysis of 12,906 endometrial cancer cases and 108,979 controls (including new genotype data for 5624 cases) and identify nine novel genome-wide significant loci, including a locus on 12q24.12 previously identified by meta-GWAS of endometrial and colorectal cancer. At five loci, expression quantitative trait locus (eQTL) analyses identify candidate causal genes; risk alleles at two of these loci associate with decreased expression of genes, which encode negative regulators of oncogenic signal transduction proteins (SH2B3 (12q24.12) and NF1 (17q11.2)). In summary, this study has doubled the number of known endometrial cancer risk loci and revealed candidate causal genes for future study. |
Stuart MacGregor; Jue-Sheng Ong; Jiyuan An; Xikun Han; Tiger Zhou; Owen M. Siggs; Matthew H. Law; Emmanuelle Souzeau; Shiwani Sharma; David J. Lynn; Jonathan Beesley; Bronwyn Sheldrick; Richard A. Mills; John Landers; Jonathan B. Ruddle; Stuart L. Graham; Paul R. Healey; Andrew J. R. White; Robert J. Casson; Stephen Best; John R Grigg; Ivan Goldberg; Joseph E. Powell; David C. Whiteman; Graham L. Radford-Smith; Nicholas G. Martin; Grant W. Montgomery; Kathryn P. Burdon; David A. Mackey; Puya Gharahkhani; Jamie E. Craig; Alex W. Hewitt Genome-wide association study of intraocular pressure uncovers new pathways to glaucoma Journal Article Nature Genetics, 2018. @article{MacGregor2018, title = {Genome-wide association study of intraocular pressure uncovers new pathways to glaucoma}, author = {Stuart MacGregor and Jue-Sheng Ong and Jiyuan An and Xikun Han and Tiger Zhou and Owen M. Siggs and Matthew H. Law and Emmanuelle Souzeau and Shiwani Sharma and David J. Lynn and Jonathan Beesley and Bronwyn Sheldrick and Richard A. Mills and John Landers and Jonathan B. Ruddle and Stuart L. Graham and Paul R. Healey and Andrew J. R. White and Robert J. Casson and Stephen Best and John R Grigg and Ivan Goldberg and Joseph E. Powell and David C. Whiteman and Graham L. Radford-Smith and Nicholas G. Martin and Grant W. Montgomery and Kathryn P. Burdon and David A. Mackey and Puya Gharahkhani and Jamie E. Craig and Alex W. Hewitt }, url = {https://www.nature.com/articles/s41588-018-0176-y}, year = {2018}, date = {2018-07-27}, journal = {Nature Genetics}, abstract = {Intraocular pressure (IOP) is currently the sole modifiable risk factor for primary open-angle glaucoma (POAG), one of the leading causes of blindness worldwide1. Both IOP and POAG are highly heritable2. We report a combined analysis of participants from the UK Biobank (n = 103,914) and previously published data from the International Glaucoma Genetic Consortium (n = 29,578)3,4 that identified 101 statistically independent genome-wide-significant SNPs for IOP, 85 of which have not been previously reported4,5,6,7,8,9,10,11,12. We examined these SNPs in 11,018 glaucoma cases and 126,069 controls, and 53 SNPs showed evidence of association. Gene-based tests implicated an additional 22 independent genes associated with IOP. We derived an allele score based on the IOP loci and loci influencing optic nerve head morphology. In 1,734 people with advanced glaucoma and 2,938 controls, participants in the top decile of the allele score were at increased risk (odds ratio (OR) = 5.6; 95% confidence interval (CI): 4.1–7.6) of glaucoma relative to the bottom decile.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Intraocular pressure (IOP) is currently the sole modifiable risk factor for primary open-angle glaucoma (POAG), one of the leading causes of blindness worldwide1. Both IOP and POAG are highly heritable2. We report a combined analysis of participants from the UK Biobank (n = 103,914) and previously published data from the International Glaucoma Genetic Consortium (n = 29,578)3,4 that identified 101 statistically independent genome-wide-significant SNPs for IOP, 85 of which have not been previously reported4,5,6,7,8,9,10,11,12. We examined these SNPs in 11,018 glaucoma cases and 126,069 controls, and 53 SNPs showed evidence of association. Gene-based tests implicated an additional 22 independent genes associated with IOP. We derived an allele score based on the IOP loci and loci influencing optic nerve head morphology. In 1,734 people with advanced glaucoma and 2,938 controls, participants in the top decile of the allele score were at increased risk (odds ratio (OR) = 5.6; 95% confidence interval (CI): 4.1–7.6) of glaucoma relative to the bottom decile. |
EC Johnson; LM Evans; MC Keller Relationships between estimated autozygosity and complex traits in the UK Biobank Journal Article PLoS Genetics, 2018. @article{Johnson2018, title = {Relationships between estimated autozygosity and complex traits in the UK Biobank}, author = {EC Johnson and LM Evans and MC Keller}, url = {https://www.ncbi.nlm.nih.gov/pubmed/30052639}, year = {2018}, date = {2018-07-27}, journal = {PLoS Genetics}, abstract = {Inbreeding increases the risk of certain Mendelian disorders in humans but may also reduce fitness through its effects on complex traits and diseases. Such inbreeding depression is thought to occur due to increased homozygosity at causal variants that are recessive with respect to fitness. Until recently it has been difficult to amass large enough sample sizes to investigate the effects of inbreeding depression on complex traits using genome-wide single nucleotide polymorphism (SNP) data in population-based samples. Further, it is difficult to infer causation in analyses that relate degree of inbreeding to complex traits because confounding variables (e.g., education) may influence both the likelihood for parents to outbreed and offspring trait values. The present study used runs of homozygosity in genome-wide SNP data in up to 400,000 individuals in the UK Biobank to estimate the proportion of the autosome that exists in autozygous tracts-stretches of the genome which are identical due to a shared common ancestor. After multiple testing corrections and controlling for possible sociodemographic confounders, we found significant relationships in the predicted direction between estimated autozygosity and three of the 26 traits we investigated: age at first sexual intercourse, fluid intelligence, and forced expiratory volume in 1 second. Our findings corroborate those of several published studies. These results may imply that these traits have been associated with Darwinian fitness over evolutionary time. However, some of the autozygosity-trait relationships were attenuated after controlling for background sociodemographic characteristics, suggesting that alternative explanations for these associations have not been eliminated. Care needs to be taken in the design and interpretation of ROH studies in order to glean reliable information about the genetic architecture and evolutionary history of complex traits.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Inbreeding increases the risk of certain Mendelian disorders in humans but may also reduce fitness through its effects on complex traits and diseases. Such inbreeding depression is thought to occur due to increased homozygosity at causal variants that are recessive with respect to fitness. Until recently it has been difficult to amass large enough sample sizes to investigate the effects of inbreeding depression on complex traits using genome-wide single nucleotide polymorphism (SNP) data in population-based samples. Further, it is difficult to infer causation in analyses that relate degree of inbreeding to complex traits because confounding variables (e.g., education) may influence both the likelihood for parents to outbreed and offspring trait values. The present study used runs of homozygosity in genome-wide SNP data in up to 400,000 individuals in the UK Biobank to estimate the proportion of the autosome that exists in autozygous tracts-stretches of the genome which are identical due to a shared common ancestor. After multiple testing corrections and controlling for possible sociodemographic confounders, we found significant relationships in the predicted direction between estimated autozygosity and three of the 26 traits we investigated: age at first sexual intercourse, fluid intelligence, and forced expiratory volume in 1 second. Our findings corroborate those of several published studies. These results may imply that these traits have been associated with Darwinian fitness over evolutionary time. However, some of the autozygosity-trait relationships were attenuated after controlling for background sociodemographic characteristics, suggesting that alternative explanations for these associations have not been eliminated. Care needs to be taken in the design and interpretation of ROH studies in order to glean reliable information about the genetic architecture and evolutionary history of complex traits. |
Kim SK PLoS One, 2018. @article{SK2018, title = {Identification of 613 new loci associated with heel bone mineral density and a polygenic risk score for bone mineral density, osteoporosis and fracture}, author = {Kim SK}, url = {https://www.ncbi.nlm.nih.gov/pubmed/30048462}, year = {2018}, date = {2018-07-26}, journal = {PLoS One}, abstract = {Low bone mineral density (BMD) leads to osteoporosis, and is a risk factor for bone fractures, including stress fractures. Using data from UK Biobank, a genome-wide association study identified 1,362 independent SNPs that clustered into 899 loci of which 613 are new. These data were used to train a genetic algorithm using 22,886 SNPs as predictors and showing a correlation with heel bone mineral density of 0.415. Combining this genetic algorithm with height, weight, age and sex resulted in a correlation with heel bone mineral density of 0.496. Individuals with low scores (2.2% of total) showed a change in BMD of -1.16 T-score units, an increase in risk for osteoporosis of 17.4 fold and an increase in risk for fracture of 1.87 fold. Genetic predictors could assist in the identification of individuals at risk for osteoporosis or fractures.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Low bone mineral density (BMD) leads to osteoporosis, and is a risk factor for bone fractures, including stress fractures. Using data from UK Biobank, a genome-wide association study identified 1,362 independent SNPs that clustered into 899 loci of which 613 are new. These data were used to train a genetic algorithm using 22,886 SNPs as predictors and showing a correlation with heel bone mineral density of 0.415. Combining this genetic algorithm with height, weight, age and sex resulted in a correlation with heel bone mineral density of 0.496. Individuals with low scores (2.2% of total) showed a change in BMD of -1.16 T-score units, an increase in risk for osteoporosis of 17.4 fold and an increase in risk for fracture of 1.87 fold. Genetic predictors could assist in the identification of individuals at risk for osteoporosis or fractures. |
James J. Lee; Robbee Wedow; Aysu Okbay; Edward Kong; Omeed Maghzian; Meghan Zacher; Tuan Anh Nguyen-Viet; Peter Bowers; Julia Sidorenko; Richard Karlsson Linnér; Mark Alan Fontana; Tushar Kundu; Chanwook Lee; Hui Li; Ruoxi Li; Rebecca Royer; Pascal N. Timshel; Raymond K. Walters; Emily A. Willoughby; Loïc Yengo; 23andMe Research Team; COGENT (Cognitive Genomics Consortium); Social Science Genetic Association Consortium; Maris Alver; Yanchun Bao; David W. Clark; Felix R. Day; Nicholas A. Furlotte; Peter K. Joshi; Kathryn E. Kemper; Aaron Kleinman; Claudia Langenberg; Reedik Mägi; Joey W. Trampush; Shefali Setia Verma; Yang Wu; Max Lam; Jing Hua Zhao; Zhili Zheng; Jason D. Boardman; Harry Campbell; Jeremy Freese; Kathleen Mullan Harris; Caroline Hayward; Pamela Herd; Meena Kumari; Todd Lencz; Jian’an Luan; Anil K. Malhotra; Andres Metspalu; Lili Milani; Ken K. Ong; John R. B. Perry; David J. Porteous; Marylyn D. Ritchie; Melissa C. Smart; Blair H. Smith; Joyce Y. Tung; Nicholas J. Wareham; James F. Wilson; Jonathan P. Beauchamp; Dalton C. Conley; Tõnu Esko; Steven F. Lehrer; Patrik K. E. Magnusson; Sven Oskarsson; Tune H. Pers; Matthew R. Robinson; Kevin Thom, Chelsea Watson, Christopher F. Chabris, Michelle N. Meyer, David I. Laibson, Jian Yang, Magnus Johannesson; Philipp D. Koellinger; Patrick Turley; Peter M. Visscher; Daniel J. Benjamin; David Cesarini Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals Journal Article Nature Genetics, 2018. @article{Lee2018, title = {Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals}, author = {James J. Lee and Robbee Wedow and Aysu Okbay and Edward Kong and Omeed Maghzian and Meghan Zacher and Tuan Anh Nguyen-Viet and Peter Bowers and Julia Sidorenko and Richard Karlsson Linnér and Mark Alan Fontana and Tushar Kundu and Chanwook Lee and Hui Li and Ruoxi Li and Rebecca Royer and Pascal N. Timshel and Raymond K. Walters and Emily A. Willoughby and Loïc Yengo and 23andMe Research Team and COGENT (Cognitive Genomics Consortium) and Social Science Genetic Association Consortium and Maris Alver and Yanchun Bao and David W. Clark and Felix R. Day and Nicholas A. Furlotte and Peter K. Joshi and Kathryn E. Kemper and Aaron Kleinman and Claudia Langenberg and Reedik Mägi and Joey W. Trampush and Shefali Setia Verma and Yang Wu and Max Lam and Jing Hua Zhao and Zhili Zheng and Jason D. Boardman and Harry Campbell and Jeremy Freese and Kathleen Mullan Harris and Caroline Hayward and Pamela Herd and Meena Kumari and Todd Lencz and Jian’an Luan and Anil K. Malhotra and Andres Metspalu and Lili Milani and Ken K. Ong and John R. B. Perry and David J. Porteous and Marylyn D. Ritchie and Melissa C. Smart and Blair H. Smith and Joyce Y. Tung and Nicholas J. Wareham and James F. Wilson and Jonathan P. Beauchamp and Dalton C. Conley and Tõnu Esko and Steven F. Lehrer and Patrik K. E. Magnusson and Sven Oskarsson and Tune H. Pers and Matthew R. Robinson and Kevin Thom, Chelsea Watson, Christopher F. Chabris, Michelle N. Meyer, David I. Laibson, Jian Yang, Magnus Johannesson and Philipp D. Koellinger and Patrick Turley and Peter M. Visscher and Daniel J. Benjamin and David Cesarini }, url = {https://www.nature.com/articles/s41588-018-0147-3}, year = {2018}, date = {2018-07-23}, journal = {Nature Genetics}, abstract = {Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11–13% of the variance in educational attainment and 7–10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11–13% of the variance in educational attainment and 7–10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research. |
Bram P. Prins; Timothy J. Mead; Jennifer A. Brody; Gardar Sveinbjornsson; Ioanna Ntalla; Nathan A. Bihlmeyer; Marten van den Berg; Jette Bork-Jensen; Stefania Cappellani; Stefan Van Duijvenboden; Nikolai T. Klena; George C. Gabriel; Xiaoqin Liu; Cagri Gulec; Niels Grarup; Jeffrey Haessler; Leanne M. Hall; Annamaria Iorio; Aaron Isaacs; Ruifang Li-Gao; Honghuang Lin; Ching-Ti Liu; Leo-Pekka Lyytikäinen; Jonathan Marten; Hao Mei; Martina Müller-Nurasyid; Michele Orini; Sandosh Padmanabhan; Farid Radmanesh; Julia Ramirez; Antonietta Robino; Molly Schwartz; Jessica van Setten; Albert V. Smith; Niek Verweij; Helen R. Warren; Stefan Weiss; Alvaro Alonso; David O. Arnar; Michiel L. Bots; Rudolf A. de Boer; Anna F. Dominiczak; Mark Eijgelsheim; Patrick T. Ellinor; Xiuqing Guo; Stephan B. Felix; Tamara B. Harris; Caroline Hayward; Susan R. Heckbert; Paul L. Huang; J. W. Jukema; Mika Kähönen; Jan A. Kors; Pier D. Lambiase; Lenore J. Launer; Man Li, Allan Linneberg; Christopher P. Nelson; Oluf Pedersen; Marco Perez; Annette Peters; Ozren Polasek; Bruce M. Psaty; Olli T. Raitakari; Kenneth M. Rice; Jerome I. Rotter; Moritz F. Sinner; Elsayed Z. Soliman; Tim D. Spector; Konstantin Strauch; Unnur Thorsteinsdottir; Andrew Tinker; Stella Trompet; André Uitterlinden; Ilonca Vaartjes; Peter van der Meer; Uwe Völker; Henry Völzke; Melanie Waldenberger; James G. Wilson; Zhijun Xie; Folkert W. Asselbergs; Marcus Dörr; Cornelia M. van Duijn; Paolo Gasparini; Daniel F. Gudbjartsson; Vilmundur Gudnason; Torben Hansen; Stefan Kääb; Jørgen K. Kanters; Charles Kooperberg; Terho Lehtimäki; Henry J. Lin; Steven A. Lubitz; Dennis O. Mook-Kanamori; Francesco J. Conti; Christopher H. Newton-Cheh; Jonathan Rosand; Igor Rudan; Nilesh J. Samani; Gianfranco Sinagra; Blair H. Smith; Hilma Holm; Bruno H. Stricker; Sheila Ulivi; Nona Sotoodehni; Suneel S. Apte; Pim van der Harst; Kari Stefansson; Patricia B. Munroe; Dan E. Arking; Cecilia W. Lo; Yalda Jamshidi Exome-chip meta-analysis identifies novel loci associated with cardiac conduction, including ADAMTS6 Journal Article Genome Biology , 2018. @article{Prins2018, title = {Exome-chip meta-analysis identifies novel loci associated with cardiac conduction, including ADAMTS6}, author = {Bram P. Prins and Timothy J. Mead and Jennifer A. Brody and Gardar Sveinbjornsson and Ioanna Ntalla and Nathan A. Bihlmeyer and Marten van den Berg and Jette Bork-Jensen and Stefania Cappellani and Stefan Van Duijvenboden and Nikolai T. Klena and George C. Gabriel and Xiaoqin Liu and Cagri Gulec and Niels Grarup and Jeffrey Haessler and Leanne M. Hall and Annamaria Iorio and Aaron Isaacs and Ruifang Li-Gao and Honghuang Lin and Ching-Ti Liu and Leo-Pekka Lyytikäinen and Jonathan Marten and Hao Mei and Martina Müller-Nurasyid and Michele Orini and Sandosh Padmanabhan and Farid Radmanesh and Julia Ramirez and Antonietta Robino and Molly Schwartz and Jessica van Setten and Albert V. Smith and Niek Verweij and Helen R. Warren and Stefan Weiss and Alvaro Alonso and David O. Arnar and Michiel L. Bots and Rudolf A. de Boer and Anna F. Dominiczak and Mark Eijgelsheim and Patrick T. Ellinor and Xiuqing Guo and Stephan B. Felix and Tamara B. Harris and Caroline Hayward and Susan R. Heckbert and Paul L. Huang and J. W. Jukema and Mika Kähönen and Jan A. Kors and Pier D. Lambiase and Lenore J. Launer and Man Li, Allan Linneberg and Christopher P. Nelson and Oluf Pedersen and Marco Perez and Annette Peters and Ozren Polasek and Bruce M. Psaty and Olli T. Raitakari and Kenneth M. Rice and Jerome I. Rotter and Moritz F. Sinner and Elsayed Z. Soliman and Tim D. Spector and Konstantin Strauch and Unnur Thorsteinsdottir and Andrew Tinker and Stella Trompet and André Uitterlinden and Ilonca Vaartjes and Peter van der Meer and Uwe Völker and Henry Völzke and Melanie Waldenberger and James G. Wilson and Zhijun Xie and Folkert W. Asselbergs and Marcus Dörr and Cornelia M. van Duijn and Paolo Gasparini and Daniel F. Gudbjartsson and Vilmundur Gudnason and Torben Hansen and Stefan Kääb and Jørgen K. Kanters and Charles Kooperberg and Terho Lehtimäki and Henry J. Lin and Steven A. Lubitz and Dennis O. Mook-Kanamori and Francesco J. Conti and Christopher H. Newton-Cheh and Jonathan Rosand and Igor Rudan and Nilesh J. Samani and Gianfranco Sinagra and Blair H. Smith and Hilma Holm and Bruno H. Stricker and Sheila Ulivi and Nona Sotoodehni and Suneel S. Apte and Pim van der Harst and Kari Stefansson and Patricia B. Munroe and Dan E. Arking and Cecilia W. Lo and Yalda Jamshidi}, url = {https://genomebiology.biomedcentral.com/articles/10.1186/s13059-018-1457-6}, year = {2018}, date = {2018-07-17}, journal = {Genome Biology }, abstract = { Background Genome-wide association studies conducted on QRS duration, an electrocardiographic measurement associated with heart failure and sudden cardiac death, have led to novel biological insights into cardiac function. However, the variants identified fall predominantly in non-coding regions and their underlying mechanisms remain unclear. Results Here, we identify putative functional coding variation associated with changes in the QRS interval duration by combining Illumina HumanExome BeadChip genotype data from 77,898 participants of European ancestry and 7695 of African descent in our discovery cohort, followed by replication in 111,874 individuals of European ancestry from the UK Biobank and deCODE cohorts. We identify ten novel loci, seven within coding regions, including ADAMTS6, significantly associated with QRS duration in gene-based analyses. ADAMTS6 encodes a secreted metalloprotease of currently unknown function. In vitro validation analysis shows that the QRS-associated variants lead to impaired ADAMTS6 secretion and loss-of function analysis in mice demonstrates a previously unappreciated role for ADAMTS6 in connexin 43 gap junction expression, which is essential for myocardial conduction. Conclusions Our approach identifies novel coding and non-coding variants underlying ventricular depolarization and provides a possible mechanism for the ADAMTS6-associated conduction changes. }, keywords = {}, pubstate = {published}, tppubtype = {article} } Background Genome-wide association studies conducted on QRS duration, an electrocardiographic measurement associated with heart failure and sudden cardiac death, have led to novel biological insights into cardiac function. However, the variants identified fall predominantly in non-coding regions and their underlying mechanisms remain unclear. Results Here, we identify putative functional coding variation associated with changes in the QRS interval duration by combining Illumina HumanExome BeadChip genotype data from 77,898 participants of European ancestry and 7695 of African descent in our discovery cohort, followed by replication in 111,874 individuals of European ancestry from the UK Biobank and deCODE cohorts. We identify ten novel loci, seven within coding regions, including ADAMTS6, significantly associated with QRS duration in gene-based analyses. ADAMTS6 encodes a secreted metalloprotease of currently unknown function. In vitro validation analysis shows that the QRS-associated variants lead to impaired ADAMTS6 secretion and loss-of function analysis in mice demonstrates a previously unappreciated role for ADAMTS6 in connexin 43 gap junction expression, which is essential for myocardial conduction. Conclusions Our approach identifies novel coding and non-coding variants underlying ventricular depolarization and provides a possible mechanism for the ADAMTS6-associated conduction changes. |
Bingxin Zhao; Joseph G Ibrahim; Yun Li Tengfei; Li Yue Wang; Yue Shan; Ziliang Zhu; Fan Zhou; Jingwen Zhang; Chao Huang Heritability of Regional Brain Volumes in Large-Scale Neuroimaging and Genetic Studies Journal Article Cerebral Cortex, 2018. @article{Zhao2018, title = {Heritability of Regional Brain Volumes in Large-Scale Neuroimaging and Genetic Studies }, author = {Bingxin Zhao and Joseph G Ibrahim and Yun Li Tengfei and Li Yue Wang and Yue Shan and Ziliang Zhu and Fan Zhou and Jingwen Zhang and Chao Huang }, url = {https://academic.oup.com/cercor/advance-article/doi/10.1093/cercor/bhy157/5052722}, year = {2018}, date = {2018-07-12}, journal = {Cerebral Cortex}, abstract = {Brain genetics is an active research area. The degree to which genetic variants impact variations in brain structure and function remains largely unknown. We examined the heritability of regional brain volumes (P ~ 100) captured by single-nucleotide polymorphisms (SNPs) in UK Biobank (n ~ 9000). We found that regional brain volumes are highly heritable in this study population and common genetic variants can explain up to 80% of their variabilities (median heritability 34.8%). We observed omnigenic impact across the genome and examined the enrichment of SNPs in active chromatin regions. Principal components derived from regional volume data are also highly heritable, but the amount of variance in brain volume explained by the component did not seem to be related to its heritability. Heritability estimates vary substantially across large-scale functional networks, exhibit a symmetric pattern across left and right hemispheres, and are consistent in females and males (correlation = 0.638). We repeated the main analysis in Alzheimer’s Disease Neuroimaging Initiative (n ~ 1100), Philadelphia Neurodevelopmental Cohort (n ~ 600), and Pediatric Imaging, Neurocognition, and Genetics (n ~ 500) datasets, which demonstrated that more stable estimates can be obtained from the UK Biobank.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Brain genetics is an active research area. The degree to which genetic variants impact variations in brain structure and function remains largely unknown. We examined the heritability of regional brain volumes (P ~ 100) captured by single-nucleotide polymorphisms (SNPs) in UK Biobank (n ~ 9000). We found that regional brain volumes are highly heritable in this study population and common genetic variants can explain up to 80% of their variabilities (median heritability 34.8%). We observed omnigenic impact across the genome and examined the enrichment of SNPs in active chromatin regions. Principal components derived from regional volume data are also highly heritable, but the amount of variance in brain volume explained by the component did not seem to be related to its heritability. Heritability estimates vary substantially across large-scale functional networks, exhibit a symmetric pattern across left and right hemispheres, and are consistent in females and males (correlation = 0.638). We repeated the main analysis in Alzheimer’s Disease Neuroimaging Initiative (n ~ 1100), Philadelphia Neurodevelopmental Cohort (n ~ 600), and Pediatric Imaging, Neurocognition, and Genetics (n ~ 500) datasets, which demonstrated that more stable estimates can be obtained from the UK Biobank. |
Po-Ru Loh; Giulio Genovese; Robert E. Handsaker; Hilary K. Finucane; Yakir A. Reshef; Pier Francesco Palamara; Brenda M. Birmann; Michael E. Talkowski; Samuel F. Bakhoum; Steven A. McCarroll; Alkes L. Price Insights into clonal haematopoiesis from 8,342 mosaic chromosomal alterations Journal Article Nature , 2018. @article{Loh2018, title = {Insights into clonal haematopoiesis from 8,342 mosaic chromosomal alterations}, author = {Po-Ru Loh and Giulio Genovese and Robert E. Handsaker and Hilary K. Finucane and Yakir A. Reshef and Pier Francesco Palamara and Brenda M. Birmann and Michael E. Talkowski and Samuel F. Bakhoum and Steven A. McCarroll and Alkes L. Price }, url = {https://www.nature.com/articles/s41586-018-0321-x}, year = {2018}, date = {2018-07-11}, journal = {Nature }, abstract = {The selective pressures that shape clonal evolution in healthy individuals are largely unknown. Here we investigate 8,342 mosaic chromosomal alterations, from 50 kb to 249 Mb long, that we uncovered in blood-derived DNA from 151,202 UK Biobank participants using phase-based computational techniques (estimated false discovery rate, 6–9%). We found six loci at which inherited variants associated strongly with the acquisition of deletions or loss of heterozygosity in cis. At three such loci (MPL, TM2D3–TARSL2, and FRA10B), we identified a likely causal variant that acted with high penetrance (5–50%). Inherited alleles at one locus appeared to affect the probability of somatic mutation, and at three other loci to be objects of positive or negative clonal selection. Several specific mosaic chromosomal alterations were strongly associated with future haematological malignancies. Our results reveal a multitude of paths towards clonal expansions with a wide range of effects on human health.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The selective pressures that shape clonal evolution in healthy individuals are largely unknown. Here we investigate 8,342 mosaic chromosomal alterations, from 50 kb to 249 Mb long, that we uncovered in blood-derived DNA from 151,202 UK Biobank participants using phase-based computational techniques (estimated false discovery rate, 6–9%). We found six loci at which inherited variants associated strongly with the acquisition of deletions or loss of heterozygosity in cis. At three such loci (MPL, TM2D3–TARSL2, and FRA10B), we identified a likely causal variant that acted with high penetrance (5–50%). Inherited alleles at one locus appeared to affect the probability of somatic mutation, and at three other loci to be objects of positive or negative clonal selection. Several specific mosaic chromosomal alterations were strongly associated with future haematological malignancies. Our results reveal a multitude of paths towards clonal expansions with a wide range of effects on human health. |
Abhiram S. Rao; Daniel Lindholm; Manuel A. Rivas; Joshua W. Knowles; Stephen B. Montgomery; Erik Ingelsson Large-Scale Phenome-Wide Association Study of PCSK9 Variants Demonstrates Protection Against Ischemic Stroke Journal Article Circulation: genomic and precision medicine, 2018. @article{Rao2018, title = {Large-Scale Phenome-Wide Association Study of PCSK9 Variants Demonstrates Protection Against Ischemic Stroke}, author = {Abhiram S. Rao and Daniel Lindholm and Manuel A. Rivas and Joshua W. Knowles and Stephen B. Montgomery and Erik Ingelsson}, url = {https://www.ahajournals.org/doi/10.1161/CIRCGEN.118.002162?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub%3Dpubmed&}, year = {2018}, date = {2018-07-11}, journal = {Circulation: genomic and precision medicine}, abstract = {Background: PCSK9 inhibition is a potent new therapy for hypercholesterolemia and cardiovascular disease. Although short-term clinical trial results have not demonstrated major adverse effects, long-term data will not be available for some time. Genetic studies in large biobanks offer a unique opportunity to predict drug effects and provide context for the evaluation of future clinical trial outcomes. Methods: We tested the association of the PCSK9 missense variant rs11591147 with predefined phenotypes and phenome-wide, in 337 536 individuals of British ancestry in the UK Biobank, with independent discovery and replication. Using a Bayesian statistical method, we leveraged phenotype correlations to evaluate the phenome-wide impact of PCSK9 inhibition with higher power at a finer resolution. Results: The T allele of rs11591147 showed a protective effect on hyperlipidemia (odds ratio, 0.63±0.04; P=2.32×10−38), coronary heart disease (odds ratio, 0.73±0.09; P=1.05×10−6), and ischemic stroke (odds ratio, 0.61±0.18; P=2.40×10−3) and was associated with increased type 2 diabetes mellitus risk adjusted for lipid-lowering medication status (odds ratio, 1.24±0.10; P=1.98×10−7). We did not observe associations with cataracts, heart failure, atrial fibrillation, and cognitive dysfunction. Leveraging phenotype correlations, we observed evidence of a protective association with cerebral infarction and vascular occlusion. These results explore the effects of direct PCSK9 inhibition; off-target effects cannot be predicted using this approach. Conclusions: This result represents the first genetic evidence in a large cohort for the protective effect of PCSK9 inhibition on ischemic stroke and corroborates exploratory evidence from clinical trials. PCSK9 inhibition was not associated with variables other than those related to LDL (low-density lipoprotein) cholesterol, atherosclerosis, and type 2 diabetes mellitus, suggesting that other effects are either small or absent.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background: PCSK9 inhibition is a potent new therapy for hypercholesterolemia and cardiovascular disease. Although short-term clinical trial results have not demonstrated major adverse effects, long-term data will not be available for some time. Genetic studies in large biobanks offer a unique opportunity to predict drug effects and provide context for the evaluation of future clinical trial outcomes. Methods: We tested the association of the PCSK9 missense variant rs11591147 with predefined phenotypes and phenome-wide, in 337 536 individuals of British ancestry in the UK Biobank, with independent discovery and replication. Using a Bayesian statistical method, we leveraged phenotype correlations to evaluate the phenome-wide impact of PCSK9 inhibition with higher power at a finer resolution. Results: The T allele of rs11591147 showed a protective effect on hyperlipidemia (odds ratio, 0.63±0.04; P=2.32×10−38), coronary heart disease (odds ratio, 0.73±0.09; P=1.05×10−6), and ischemic stroke (odds ratio, 0.61±0.18; P=2.40×10−3) and was associated with increased type 2 diabetes mellitus risk adjusted for lipid-lowering medication status (odds ratio, 1.24±0.10; P=1.98×10−7). We did not observe associations with cataracts, heart failure, atrial fibrillation, and cognitive dysfunction. Leveraging phenotype correlations, we observed evidence of a protective association with cerebral infarction and vascular occlusion. These results explore the effects of direct PCSK9 inhibition; off-target effects cannot be predicted using this approach. Conclusions: This result represents the first genetic evidence in a large cohort for the protective effect of PCSK9 inhibition on ischemic stroke and corroborates exploratory evidence from clinical trials. PCSK9 inhibition was not associated with variables other than those related to LDL (low-density lipoprotein) cholesterol, atherosclerosis, and type 2 diabetes mellitus, suggesting that other effects are either small or absent. |
Laura Andreasen; Jonas Ghouse; Morten W. Skov; Christian T. Have; Gustav Ahlberg; Peter V. Rasmussen; Allan Linneberg; Oluf Pedersen; Pyotr G. Platonov; Stig Haunsø; Jesper H. Svendsen and, Torben Hansen; Jørgen K. Kanters; Morten S. Olesen Brugada Syndrome-Associated Genetic Loci Are Associated With J-Point Elevation and an Increased Risk of Cardiac Arrest Journal Article Frontiers Physiology, 2018. @article{Andreasen2018, title = {Brugada Syndrome-Associated Genetic Loci Are Associated With J-Point Elevation and an Increased Risk of Cardiac Arrest}, author = {Laura Andreasen and Jonas Ghouse and Morten W. Skov and Christian T. Have and Gustav Ahlberg and Peter V. Rasmussen and Allan Linneberg and Oluf Pedersen and Pyotr G. Platonov and Stig Haunsø and Jesper H. Svendsen and, Torben Hansen and Jørgen K. Kanters and Morten S. Olesen}, url = {https://www.frontiersin.org/articles/10.3389/fphys.2018.00894/full}, year = {2018}, date = {2018-07-10}, journal = {Frontiers Physiology}, abstract = {Introduction: A previous genome-wide association study found three genetic loci, rs9388451, rs10428132, and rs11708996, to increase the risk of Brugada Syndrome (BrS). Since the effect of these loci in the general population is unknown, we aimed to investigate the effect on electrocardiogram (ECG) parameters and outcomes in the general population. Materials and Methods: A cohort of 6,161 individuals (median age 45 [interquartile range (IQR) 40–50] years, 49% males), with available digital ECGs, was genotyped and subsequently followed for a median period of 13 [IQR 12.6–13.4] years. Data on outcomes were collected from Danish administrative healthcare registries. Furthermore, ~400,000 persons from UK Biobank were investigated for associations between the three loci and cardiac arrest/ventricular fibrillation (VF). Results: Homozygote carriers of the C allele in rs6800541 intronic to SCN10A had a significantly larger J-point elevation (JPE) compared with wildtype carriers (11 vs. 6 μV, P < 0.001). There was an additive effect of carrying multiple BrS-associated risk alleles with an increased JPE in lead V1. None of the BrS-associated genetic loci predisposed to syncope, atrial fibrillation, or total mortality in the general Danish population. The rs9388451 genetic locus adjacent to the HEY2 gene was associated with cardiac arrest/VF in an analysis using the UK Biobank study (odds ratio = 1.13 (95% confidence interval: 1.08–1.18), P = 0.006). Conclusions: BrS-associated risk alleles increase the JPE in lead V1 in an additive manner, but was not associated with increased mortality or syncope in the general population of Denmark. However, the HEY2 risk allele increased the risk of cardiac arrest/VF in the larger population study of UK Biobank indicating an important role of this common genetic locus.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Introduction: A previous genome-wide association study found three genetic loci, rs9388451, rs10428132, and rs11708996, to increase the risk of Brugada Syndrome (BrS). Since the effect of these loci in the general population is unknown, we aimed to investigate the effect on electrocardiogram (ECG) parameters and outcomes in the general population. Materials and Methods: A cohort of 6,161 individuals (median age 45 [interquartile range (IQR) 40–50] years, 49% males), with available digital ECGs, was genotyped and subsequently followed for a median period of 13 [IQR 12.6–13.4] years. Data on outcomes were collected from Danish administrative healthcare registries. Furthermore, ~400,000 persons from UK Biobank were investigated for associations between the three loci and cardiac arrest/ventricular fibrillation (VF). Results: Homozygote carriers of the C allele in rs6800541 intronic to SCN10A had a significantly larger J-point elevation (JPE) compared with wildtype carriers (11 vs. 6 μV, P < 0.001). There was an additive effect of carrying multiple BrS-associated risk alleles with an increased JPE in lead V1. None of the BrS-associated genetic loci predisposed to syncope, atrial fibrillation, or total mortality in the general Danish population. The rs9388451 genetic locus adjacent to the HEY2 gene was associated with cardiac arrest/VF in an analysis using the UK Biobank study (odds ratio = 1.13 (95% confidence interval: 1.08–1.18), P = 0.006). Conclusions: BrS-associated risk alleles increase the JPE in lead V1 in an additive manner, but was not associated with increased mortality or syncope in the general population of Denmark. However, the HEY2 risk allele increased the risk of cardiac arrest/VF in the larger population study of UK Biobank indicating an important role of this common genetic locus. |
G Ni; J Gratten; N Wray; SH Lee Age at first birth in women is genetically associated with increased risk of schizophrenia Journal Article Scientific Reports, 2018. @article{Ni2018d, title = {Age at first birth in women is genetically associated with increased risk of schizophrenia}, author = {G Ni and J Gratten and N Wray and SH Lee }, url = {https://www.ncbi.nlm.nih.gov/pubmed/29977057}, year = {2018}, date = {2018-07-05}, journal = {Scientific Reports}, abstract = {Previous studies have shown an increased risk for mental health problems in children born to both younger and older parents compared to children of average-aged parents. We previously used a novel design to reveal a latent mechanism of genetic association between schizophrenia and age at first birth in women (AFB). Here, we use independent data from the UK Biobank (N = 38,892) to replicate the finding of an association between predicted genetic risk of schizophrenia and AFB in women, and to estimate the genetic correlation between schizophrenia and AFB in women stratified into younger and older groups. We find evidence for an association between predicted genetic risk of schizophrenia and AFB in women (P-value = 1.12E-05), and we show genetic heterogeneity between younger and older AFB groups (P-value = 3.45E-03). The genetic correlation between schizophrenia and AFB in the younger AFB group is -0.16 (SE = 0.04) while that between schizophrenia and AFB in the older AFB group is 0.14 (SE = 0.08). Our results suggest that early, and perhaps also late, age at first birth in women is associated with increased genetic risk for schizophrenia in the UK Biobank sample. These findings contribute new insights into factors contributing to the complex bio-social risk architecture underpinning the association between parental age and offspring mental health.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Previous studies have shown an increased risk for mental health problems in children born to both younger and older parents compared to children of average-aged parents. We previously used a novel design to reveal a latent mechanism of genetic association between schizophrenia and age at first birth in women (AFB). Here, we use independent data from the UK Biobank (N = 38,892) to replicate the finding of an association between predicted genetic risk of schizophrenia and AFB in women, and to estimate the genetic correlation between schizophrenia and AFB in women stratified into younger and older groups. We find evidence for an association between predicted genetic risk of schizophrenia and AFB in women (P-value = 1.12E-05), and we show genetic heterogeneity between younger and older AFB groups (P-value = 3.45E-03). The genetic correlation between schizophrenia and AFB in the younger AFB group is -0.16 (SE = 0.04) while that between schizophrenia and AFB in the older AFB group is 0.14 (SE = 0.08). Our results suggest that early, and perhaps also late, age at first birth in women is associated with increased genetic risk for schizophrenia in the UK Biobank sample. These findings contribute new insights into factors contributing to the complex bio-social risk architecture underpinning the association between parental age and offspring mental health. |
J Hutton; T Fatima; TJ Major; R Topless; LK Stamp; TR Merriman; N Dalbeth Mediation analysis to understand genetic relationships between habitual coffee intake and gout Journal Article Arthritis Research and Therapy, 2018. @article{Hutton2018, title = {Mediation analysis to understand genetic relationships between habitual coffee intake and gout}, author = {J Hutton and T Fatima and TJ Major and R Topless and LK Stamp and TR Merriman and N Dalbeth}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29976226}, year = {2018}, date = {2018-07-05}, journal = {Arthritis Research and Therapy}, abstract = {BACKGROUND: Increased coffee intake is associated with reduced serum urate concentrations and lower risk of gout. Specific alleles of the GCKR, ABCG2, MLXIPL, and CYP1A2 genes have been associated with both reduced coffee intake and increased serum urate in separate genome-wide association studies (GWAS). The aim of this study was to determine whether these single nucleotide polymorphisms (SNPs) influence the risk of gout through their effects on coffee consumption. METHODS: This research was conducted using the UK Biobank Resource. Data were available for 130,966 European participants aged 40-69 years. Gout status and coffee intake were tested for association with four urate-associated SNPs: GCKR (rs1260326), ABCG2 (rs2231142), MLXIPL (rs1178977), and CYP1A2 (rs2472297). Multiple regression and path analysis were used to examine whether coffee consumption mediated the effect of the SNPs on gout risk. RESULTS: Coffee consumption was inversely associated with gout (multivariate adjusted odds ratio (95% confidence interval (CI)) for any coffee consumption 0.75 (0.67-0.84, P = 9 × 10-7)). There was also evidence of a dose-effect with multivariate adjusted odds ratio (95% CI) per cup consumed per day of 0.85 (0.82-0.87, P = 9 × 10-32). The urate-increasing GCKR, ABCG2, MLXIPL, and CYP1A2 alleles were associated with reduced daily coffee consumption, with the strongest associations for CYP1A2 (beta -0.30, P = 8 × 10-40), and MLXIPL (beta -0.17, P = 3 × 10-8), and weaker associations for GCKR (beta -0.07, P = 3 × 10-10) and ABCG2 (beta -0.09, P = 2 × 10-9). The urate-increasing GCKR and ABCG2 alleles were associated with gout (multivariate adjusted p < 5 × 10-8 for both), but the urate-increasing MLXIPL and CYP1A2 alleles were not. In mediation analysis, the direct effects of GCKR and ABCG2 accounted for most of the total effect on gout risk, with much smaller indirect effects mediated by coffee consumption. CONCLUSION: Coffee consumption is inversely associated with risk of gout. Although alleles at several SNPs associate with both lower coffee consumption and higher risk of gout, these SNPs largely influence gout risk directly, rather than indirectly through effects on coffee consumption.}, keywords = {}, pubstate = {published}, tppubtype = {article} } BACKGROUND: Increased coffee intake is associated with reduced serum urate concentrations and lower risk of gout. Specific alleles of the GCKR, ABCG2, MLXIPL, and CYP1A2 genes have been associated with both reduced coffee intake and increased serum urate in separate genome-wide association studies (GWAS). The aim of this study was to determine whether these single nucleotide polymorphisms (SNPs) influence the risk of gout through their effects on coffee consumption. METHODS: This research was conducted using the UK Biobank Resource. Data were available for 130,966 European participants aged 40-69 years. Gout status and coffee intake were tested for association with four urate-associated SNPs: GCKR (rs1260326), ABCG2 (rs2231142), MLXIPL (rs1178977), and CYP1A2 (rs2472297). Multiple regression and path analysis were used to examine whether coffee consumption mediated the effect of the SNPs on gout risk. RESULTS: Coffee consumption was inversely associated with gout (multivariate adjusted odds ratio (95% confidence interval (CI)) for any coffee consumption 0.75 (0.67-0.84, P = 9 × 10-7)). There was also evidence of a dose-effect with multivariate adjusted odds ratio (95% CI) per cup consumed per day of 0.85 (0.82-0.87, P = 9 × 10-32). The urate-increasing GCKR, ABCG2, MLXIPL, and CYP1A2 alleles were associated with reduced daily coffee consumption, with the strongest associations for CYP1A2 (beta -0.30, P = 8 × 10-40), and MLXIPL (beta -0.17, P = 3 × 10-8), and weaker associations for GCKR (beta -0.07, P = 3 × 10-10) and ABCG2 (beta -0.09, P = 2 × 10-9). The urate-increasing GCKR and ABCG2 alleles were associated with gout (multivariate adjusted p < 5 × 10-8 for both), but the urate-increasing MLXIPL and CYP1A2 alleles were not. In mediation analysis, the direct effects of GCKR and ABCG2 accounted for most of the total effect on gout risk, with much smaller indirect effects mediated by coffee consumption. CONCLUSION: Coffee consumption is inversely associated with risk of gout. Although alleles at several SNPs associate with both lower coffee consumption and higher risk of gout, these SNPs largely influence gout risk directly, rather than indirectly through effects on coffee consumption. |
FR Day; KK Ong; JRB Perry Elucidating the genetic basis of social interaction and isolation Journal Article Nature Communications, 2018. @article{Day2018, title = {Elucidating the genetic basis of social interaction and isolation}, author = {FR Day and KK Ong and JRB Perry }, url = {https://www.ncbi.nlm.nih.gov/pubmed/29970889}, year = {2018}, date = {2018-07-03}, journal = {Nature Communications}, abstract = {The negative impacts of social isolation and loneliness on health are well documented. However, little is known about their possible biological determinants. In up to 452,302 UK Biobank study participants, we perform genome-wide association study analyses for loneliness and regular participation in social activities. We identify 15 genomic loci (P < 5 × 10-8) for loneliness, and demonstrate a likely causal association between adiposity and increased susceptibility to loneliness and depressive symptoms. Further loci were identified for regular attendance at a sports club or gym (N = 6 loci), pub or social club (N = 13) or religious group (N = 18). Across these traits there was strong enrichment for genes expressed in brain regions that control emotional expression and behaviour. We demonstrate aetiological mechanisms specific to each trait, in addition to identifying loci that are pleiotropic across multiple complex traits. Further study of these traits may identify novel modifiable risk factors associated with social withdrawal and isolation.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The negative impacts of social isolation and loneliness on health are well documented. However, little is known about their possible biological determinants. In up to 452,302 UK Biobank study participants, we perform genome-wide association study analyses for loneliness and regular participation in social activities. We identify 15 genomic loci (P < 5 × 10-8) for loneliness, and demonstrate a likely causal association between adiposity and increased susceptibility to loneliness and depressive symptoms. Further loci were identified for regular attendance at a sports club or gym (N = 6 loci), pub or social club (N = 13) or religious group (N = 18). Across these traits there was strong enrichment for genes expressed in brain regions that control emotional expression and behaviour. We demonstrate aetiological mechanisms specific to each trait, in addition to identifying loci that are pleiotropic across multiple complex traits. Further study of these traits may identify novel modifiable risk factors associated with social withdrawal and isolation. |
Erikka Loftfield; Marilyn C. Cornelis; Neil Caporaso Association of Coffee Drinking With Mortality by Genetic Variation in Caffeine Metabolism Journal Article JAMA internal medicine, 2018. @article{Loftfield2018, title = {Association of Coffee Drinking With Mortality by Genetic Variation in Caffeine Metabolism}, author = {Erikka Loftfield and Marilyn C. Cornelis and Neil Caporaso}, url = {https://jamanetwork.com/journals/jamainternalmedicine/article-abstract/2686145}, year = {2018}, date = {2018-07-02}, journal = {JAMA internal medicine}, abstract = {Importance Prospective cohorts in North America, Europe, and Asia show consistent inverse associations between coffee drinking and mortality, including deaths from cardiovascular disease and some cancers. However, concerns about coffee, particularly among people with common genetic polymorphisms affecting caffeine metabolism and among those drinking more than 5 cups per day, remain. Objective To evaluate associations of coffee drinking with mortality by genetic caffeine metabolism score. Design, Setting, and Participants The UK Biobank is a population-based study that invited approximately 9.2 million individuals from across the United Kingdom to participate. We used baseline demographic, lifestyle, and genetic data form the UK Biobank cohort, with follow-up beginning in 2006 and ending in 2016, to estimate hazard ratios (HRs) for coffee intake and mortality, using multivariable-adjusted Cox proportional hazards models. We investigated potential effect modification by caffeine metabolism, defined by a genetic score of previously identified polymorphisms in AHR, CYP1A2, CYP2A6, and POR that have an effect on caffeine metabolism. Of the 502 641 participants who consented with baseline data, we included those who were not pregnant and had complete data on coffee intake and smoking status (n = 498 134). Exposures Total, ground, instant, and decaffeinated coffee intake. Main Outcomes and Measures All-cause and cause-specific mortality. Results The mean age of the participants was 57 years (range, 38-73 years); 271 019 (54%) were female, and 387 494 (78%) were coffee drinkers. Over 10 years of follow-up, 14 225 deaths occurred. Coffee drinking was inversely associated with all-cause mortality. Using non–coffee drinkers as the reference group, HRs for drinking less than 1, 1, 2 to 3, 4 to 5, 6 to 7, and 8 or more cups per day were 0.94 (95% CI, 0.88-1.01), 0.92 (95% CI, 0.87-0.97), 0.88 (95% CI, 0.84-0.93), 0.88 (95% CI, 0.83-0.93), 0.84 (95% CI, 0.77-0.92), and 0.86 (95% CI, 0.77-0.95), respectively. Similar associations were observed for instant, ground, and decaffeinated coffee, across common causes of death, and regardless of genetic caffeine metabolism score. For example, the HRs for 6 or more cups per day ranged from 0.70 (95% CI, 0.53-0.94) to 0.92 (95% CI, 0.78-1.10), with no evidence of effect modification across strata of caffeine metabolism score (P = .17 for heterogeneity). Conclusions and Relevance Coffee drinking was inversely associated with mortality, including among those drinking 8 or more cups per day and those with genetic polymorphisms indicating slower or faster caffeine metabolism. These findings suggest the importance of noncaffeine constituents in the coffee-mortality association and provide further reassurance that coffee drinking can be a part of a healthy diet.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Importance Prospective cohorts in North America, Europe, and Asia show consistent inverse associations between coffee drinking and mortality, including deaths from cardiovascular disease and some cancers. However, concerns about coffee, particularly among people with common genetic polymorphisms affecting caffeine metabolism and among those drinking more than 5 cups per day, remain. Objective To evaluate associations of coffee drinking with mortality by genetic caffeine metabolism score. Design, Setting, and Participants The UK Biobank is a population-based study that invited approximately 9.2 million individuals from across the United Kingdom to participate. We used baseline demographic, lifestyle, and genetic data form the UK Biobank cohort, with follow-up beginning in 2006 and ending in 2016, to estimate hazard ratios (HRs) for coffee intake and mortality, using multivariable-adjusted Cox proportional hazards models. We investigated potential effect modification by caffeine metabolism, defined by a genetic score of previously identified polymorphisms in AHR, CYP1A2, CYP2A6, and POR that have an effect on caffeine metabolism. Of the 502 641 participants who consented with baseline data, we included those who were not pregnant and had complete data on coffee intake and smoking status (n = 498 134). Exposures Total, ground, instant, and decaffeinated coffee intake. Main Outcomes and Measures All-cause and cause-specific mortality. Results The mean age of the participants was 57 years (range, 38-73 years); 271 019 (54%) were female, and 387 494 (78%) were coffee drinkers. Over 10 years of follow-up, 14 225 deaths occurred. Coffee drinking was inversely associated with all-cause mortality. Using non–coffee drinkers as the reference group, HRs for drinking less than 1, 1, 2 to 3, 4 to 5, 6 to 7, and 8 or more cups per day were 0.94 (95% CI, 0.88-1.01), 0.92 (95% CI, 0.87-0.97), 0.88 (95% CI, 0.84-0.93), 0.88 (95% CI, 0.83-0.93), 0.84 (95% CI, 0.77-0.92), and 0.86 (95% CI, 0.77-0.95), respectively. Similar associations were observed for instant, ground, and decaffeinated coffee, across common causes of death, and regardless of genetic caffeine metabolism score. For example, the HRs for 6 or more cups per day ranged from 0.70 (95% CI, 0.53-0.94) to 0.92 (95% CI, 0.78-1.10), with no evidence of effect modification across strata of caffeine metabolism score (P = .17 for heterogeneity). Conclusions and Relevance Coffee drinking was inversely associated with mortality, including among those drinking 8 or more cups per day and those with genetic polymorphisms indicating slower or faster caffeine metabolism. These findings suggest the importance of noncaffeine constituents in the coffee-mortality association and provide further reassurance that coffee drinking can be a part of a healthy diet. |
Lisa Gai1; Eleazar Eskin1,2 Finding associated variants in genome-wide association studies on multiple traits Journal Article Bioinformatics, 2018. @article{Gai12018, title = {Finding associated variants in genome-wide association studies on multiple traits}, author = {Lisa Gai1 and Eleazar Eskin1,2}, url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022769/}, year = {2018}, date = {2018-07-01}, journal = {Bioinformatics}, abstract = {Many variants identified by genome-wide association studies (GWAS) have been found to affect multiple traits, either directly or through shared pathways. There is currently a wealth of GWAS data collected in numerous phenotypes, and analyzing multiple traits at once can increase power to detect shared variant effects. However, traditional meta-analysis methods are not suitable for combining studies on different traits. When applied to dissimilar studies, these meta-analysis methods can be underpowered compared to univariate analysis. The degree to which traits share variant effects is often not known, and the vast majority of GWAS meta-analysis only consider one trait at a time. Go to: Results Here, we present a flexible method for finding associated variants from GWAS summary statistics for multiple traits. Our method estimates the degree of shared effects between traits from the data. Using simulations, we show that our method properly controls the false positive rate and increases power when an effect is present in a subset of traits. We then apply our method to the North Finland Birth Cohort and UK Biobank datasets using a variety of metabolic traits and discover novel loci.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Many variants identified by genome-wide association studies (GWAS) have been found to affect multiple traits, either directly or through shared pathways. There is currently a wealth of GWAS data collected in numerous phenotypes, and analyzing multiple traits at once can increase power to detect shared variant effects. However, traditional meta-analysis methods are not suitable for combining studies on different traits. When applied to dissimilar studies, these meta-analysis methods can be underpowered compared to univariate analysis. The degree to which traits share variant effects is often not known, and the vast majority of GWAS meta-analysis only consider one trait at a time. Go to: Results Here, we present a flexible method for finding associated variants from GWAS summary statistics for multiple traits. Our method estimates the degree of shared effects between traits from the data. Using simulations, we show that our method properly controls the false positive rate and increases power when an effect is present in a subset of traits. We then apply our method to the North Finland Birth Cohort and UK Biobank datasets using a variety of metabolic traits and discover novel loci. |
Eric R. Gamazon; Ayellet V. Segrè; Martijn van de Bunt; Xiaoquan Wen; Hualin S. Xi; Farhad Hormozdiari; Halit Ongen; Anuar Konkashbaev; Eske M. Derks; François Aguet; Jie Quan; GTEx Consortium; Dan L. Nicolae; Eleazar Eskin; Manolis Kellis; Gad Getz; Mark I. McCarthy; Emmanouil T. Dermitzakis; Nancy J. Cox; Kristin G. Ardlie Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation Journal Article Nature Genetics, 2018. @article{Gamazon2018, title = {Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation}, author = {Eric R. Gamazon and Ayellet V. Segrè and Martijn van de Bunt and Xiaoquan Wen and Hualin S. Xi and Farhad Hormozdiari and Halit Ongen and Anuar Konkashbaev and Eske M. Derks and François Aguet and Jie Quan and GTEx Consortium and Dan L. Nicolae and Eleazar Eskin and Manolis Kellis and Gad Getz and Mark I. McCarthy and Emmanouil T. Dermitzakis and Nancy J. Cox and Kristin G. Ardlie}, url = {https://www.nature.com/articles/s41588-018-0154-4}, year = {2018}, date = {2018-06-28}, journal = {Nature Genetics}, abstract = {We apply integrative approaches to expression quantitative loci (eQTLs) from 44 tissues from the Genotype-Tissue Expression project and genome-wide association study data. About 60% of known trait-associated loci are in linkage disequilibrium with a cis-eQTL, over half of which were not found in previous large-scale whole blood studies. Applying polygenic analyses to metabolic, cardiovascular, anthropometric, autoimmune, and neurodegenerative traits, we find that eQTLs are significantly enriched for trait associations in relevant pathogenic tissues and explain a substantial proportion of the heritability (40–80%). For most traits, tissue-shared eQTLs underlie a greater proportion of trait associations, although tissue-specific eQTLs have a greater contribution to some traits, such as blood pressure. By integrating information from biological pathways with eQTL target genes and applying a gene-based approach, we validate previously implicated causal genes and pathways, and propose new variant and gene associations for several complex traits, which we replicate in the UK BioBank and BioVU.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We apply integrative approaches to expression quantitative loci (eQTLs) from 44 tissues from the Genotype-Tissue Expression project and genome-wide association study data. About 60% of known trait-associated loci are in linkage disequilibrium with a cis-eQTL, over half of which were not found in previous large-scale whole blood studies. Applying polygenic analyses to metabolic, cardiovascular, anthropometric, autoimmune, and neurodegenerative traits, we find that eQTLs are significantly enriched for trait associations in relevant pathogenic tissues and explain a substantial proportion of the heritability (40–80%). For most traits, tissue-shared eQTLs underlie a greater proportion of trait associations, although tissue-specific eQTLs have a greater contribution to some traits, such as blood pressure. By integrating information from biological pathways with eQTL target genes and applying a gene-based approach, we validate previously implicated causal genes and pathways, and propose new variant and gene associations for several complex traits, which we replicate in the UK BioBank and BioVU. |
Laura M. Winchester; John Powell; Simon Lovestone; Alejo J. Nevado-Holgado Red blood cell indices and anaemia as causative factors for cognitive function deficits and for Alzheimer’s disease Journal Article Genome Medicine, 2018. @article{Winchester2018, title = {Red blood cell indices and anaemia as causative factors for cognitive function deficits and for Alzheimer’s disease}, author = {Laura M. Winchester and John Powell and Simon Lovestone and Alejo J. Nevado-Holgado}, url = {https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-018-0556-z}, year = {2018}, date = {2018-06-28}, journal = {Genome Medicine}, abstract = {Background Studies have shown that low haemoglobin and anaemia are associated with poor cognition, and anaemia is known to be associated with Alzheimer’s disease (AD), but the mechanism of this risk is unknown. Here, we first seek to confirm the association between cognition and anaemia and secondly, in order to further understand the mechanism of this association, to estimate the direction of causation using Mendelian randomisation. Methods Two independent cohorts were used in this analysis: AddNeuroMed, a longitudinal study of 738 subjects including AD and age-matched controls with blood cell measures, cognitive assessments and gene expression data from blood; and UK Biobank, a study of 502,649 healthy participants, aged 40–69 years with cognitive test measures and blood cell indices at baseline. General linear models were calculated using cognitive function as the outcome with correction for age, sex and education. In UK Biobank, SNPs with known blood cell measure associations were analysed with Mendelian randomisation to estimate direction of causality. In AddNeuroMed, gene expression data was used in pathway enrichment analysis to identify associations reflecting biological function. Results Both sample sets evidence a reproducible association between cognitive performance and mean corpuscular haemoglobin (MCH), a measure of average mass of haemoglobin per red blood cell. Furthermore, in the AddNeuroMed cohort, where longitudinal samples were available, we showed a greater decline in red blood cell indices for AD patients when compared to controls (p values between 0.05 and 10−6). In the UK Biobank cohort, we found lower haemoglobin in participants with reduced cognitive function. There was a significant association for MCH and red blood cell distribution width (RDW, a measure of cell volume variability) compared to four cognitive function tests including reaction time and reasoning (p < 0.0001). Using Mendelian randomisation, we then showed a significant effect of MCH on the verbal–numeric and numeric traits, implying that anaemia has causative effect on cognitive performance. Conclusions Lower haemoglobin levels in blood are associated to poor cognitive function and AD. We have used UK Biobank SNP data to determine the relationship between cognitive testing and haemoglobin measures and suggest that haemoglobin level and therefore anaemia does have a primary causal impact on cognitive performance. }, keywords = {}, pubstate = {published}, tppubtype = {article} } Background Studies have shown that low haemoglobin and anaemia are associated with poor cognition, and anaemia is known to be associated with Alzheimer’s disease (AD), but the mechanism of this risk is unknown. Here, we first seek to confirm the association between cognition and anaemia and secondly, in order to further understand the mechanism of this association, to estimate the direction of causation using Mendelian randomisation. Methods Two independent cohorts were used in this analysis: AddNeuroMed, a longitudinal study of 738 subjects including AD and age-matched controls with blood cell measures, cognitive assessments and gene expression data from blood; and UK Biobank, a study of 502,649 healthy participants, aged 40–69 years with cognitive test measures and blood cell indices at baseline. General linear models were calculated using cognitive function as the outcome with correction for age, sex and education. In UK Biobank, SNPs with known blood cell measure associations were analysed with Mendelian randomisation to estimate direction of causality. In AddNeuroMed, gene expression data was used in pathway enrichment analysis to identify associations reflecting biological function. Results Both sample sets evidence a reproducible association between cognitive performance and mean corpuscular haemoglobin (MCH), a measure of average mass of haemoglobin per red blood cell. Furthermore, in the AddNeuroMed cohort, where longitudinal samples were available, we showed a greater decline in red blood cell indices for AD patients when compared to controls (p values between 0.05 and 10−6). In the UK Biobank cohort, we found lower haemoglobin in participants with reduced cognitive function. There was a significant association for MCH and red blood cell distribution width (RDW, a measure of cell volume variability) compared to four cognitive function tests including reaction time and reasoning (p < 0.0001). Using Mendelian randomisation, we then showed a significant effect of MCH on the verbal–numeric and numeric traits, implying that anaemia has causative effect on cognitive performance. Conclusions Lower haemoglobin levels in blood are associated to poor cognitive function and AD. We have used UK Biobank SNP data to determine the relationship between cognitive testing and haemoglobin measures and suggest that haemoglobin level and therefore anaemia does have a primary causal impact on cognitive performance. |
Shiu Lun Au Yeung; Shan Luo; C. Mary Schooling The Impact of Glycated Hemoglobin (HbA1c) on Cardiovascular Disease Risk: A Mendelian Randomization Study Using UK Biobank Journal Article Diabetes Care, 2018. @article{Yeung2018b, title = {The Impact of Glycated Hemoglobin (HbA1c) on Cardiovascular Disease Risk: A Mendelian Randomization Study Using UK Biobank}, author = {Shiu Lun Au Yeung and Shan Luo and C. Mary Schooling}, url = {http://care.diabetesjournals.org/content/early/2018/06/21/dc18-0289.long}, year = {2018}, date = {2018-06-28}, journal = {Diabetes Care}, abstract = {OBJECTIVE Glycated hemoglobin (HbA1c) is positively associated with cardiovascular disease (CVD), although evidence is primarily observational. Mendelian randomization studies have only examined its relation with subtypes of CVD. We examined the relation of HbA1c with CVD and its subtypes in the UK Biobank using Mendelian randomization. RESEARCH DESIGN AND METHODS We used 38 genetic variants strongly and independently related to HbA1c (n = 123,665) applied to the UK Biobank (n = 392,038). We used inverse variance weighting (IVW) to obtain the associations of HbA1c with CVD, coronary artery disease (CAD), stroke, and its subtypes. Sensitivity analyses included Mendelian randomization (MR)-Egger, a weighted median and exclusion of potentially invalid single nucleotide polymorphisms (SNPs). We also applied the same genetic instruments to CARDIoGRAMplusC4D (Coronary ARtery DIsease Genome wide Replication and Meta-analysis (CARDIoGRAM) plus The Coronary Artery Disease (C4D) Genetics) 1000 Genomes–based genome-wide association study (n = 184,305) as a validation for CAD. RESULTS In the UK Biobank, HbA1c was not associated with CVD using IVW (odds ratio (OR) 1.11 per %, 95% CI 0.83–1.48). However, HbA1c was associated with increased CAD risk (OR 1.50 per %, 95% CI 1.08–2.11) with directionally consistent results from MR-Egger and weighted median. The positive association with CAD was more pronounced when we excluded potentially invalid SNPs (OR 2.24 per %, 95% CI 1.55–3.25). The positive association was replicated in CARDIoGRAM (OR 1.52 per %, 95% CI 1.03–2.26). The association of HbA1c with stroke and its subtypes was less clear given the low number of cases. CONCLUSIONS HbA1c likely causes CAD. The underlying mechanisms remain to be elucidated.}, keywords = {}, pubstate = {published}, tppubtype = {article} } OBJECTIVE Glycated hemoglobin (HbA1c) is positively associated with cardiovascular disease (CVD), although evidence is primarily observational. Mendelian randomization studies have only examined its relation with subtypes of CVD. We examined the relation of HbA1c with CVD and its subtypes in the UK Biobank using Mendelian randomization. RESEARCH DESIGN AND METHODS We used 38 genetic variants strongly and independently related to HbA1c (n = 123,665) applied to the UK Biobank (n = 392,038). We used inverse variance weighting (IVW) to obtain the associations of HbA1c with CVD, coronary artery disease (CAD), stroke, and its subtypes. Sensitivity analyses included Mendelian randomization (MR)-Egger, a weighted median and exclusion of potentially invalid single nucleotide polymorphisms (SNPs). We also applied the same genetic instruments to CARDIoGRAMplusC4D (Coronary ARtery DIsease Genome wide Replication and Meta-analysis (CARDIoGRAM) plus The Coronary Artery Disease (C4D) Genetics) 1000 Genomes–based genome-wide association study (n = 184,305) as a validation for CAD. RESULTS In the UK Biobank, HbA1c was not associated with CVD using IVW (odds ratio (OR) 1.11 per %, 95% CI 0.83–1.48). However, HbA1c was associated with increased CAD risk (OR 1.50 per %, 95% CI 1.08–2.11) with directionally consistent results from MR-Egger and weighted median. The positive association with CAD was more pronounced when we excluded potentially invalid SNPs (OR 2.24 per %, 95% CI 1.55–3.25). The positive association was replicated in CARDIoGRAM (OR 1.52 per %, 95% CI 1.03–2.26). The association of HbA1c with stroke and its subtypes was less clear given the low number of cases. CONCLUSIONS HbA1c likely causes CAD. The underlying mechanisms remain to be elucidated. |
SL Au Yeung; S Luo; CM Schooling The Impact of Glycated Hemoglobin (HbA1c) on Cardiovascular Disease Risk: A Mendelian Randomization Study Using UK Biobank. Journal Article Diabetes Care, 2018. @article{Yeung2018, title = {The Impact of Glycated Hemoglobin (HbA1c) on Cardiovascular Disease Risk: A Mendelian Randomization Study Using UK Biobank.}, author = {SL Au Yeung and S Luo and CM Schooling}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29950300}, year = {2018}, date = {2018-06-27}, journal = {Diabetes Care}, abstract = {Glycated hemoglobin (HbA1c) is positively associated with cardiovascular disease (CVD), although evidence is primarily observational. Mendelian randomization studies have only examined its relation with subtypes of CVD. We examined the relation of HbA1c with CVD and its subtypes in the UK Biobank using Mendelian randomization. RESEARCH DESIGN AND METHODS: We used 38 genetic variants strongly and independently related to HbA1c (n = 123,665) applied to the UK Biobank (n = 392,038). We used inverse variance weighting (IVW) to obtain the associations of HbA1c with CVD, coronary artery disease (CAD), stroke, and its subtypes. Sensitivity analyses included Mendelian randomization (MR)-Egger, a weighted median and exclusion of potentially invalid single nucleotide polymorphisms (SNPs). We also applied the same genetic instruments to CARDIoGRAMplusC4D (Coronary ARtery DIsease Genome wide Replication and Meta-analysis (CARDIoGRAM) plus The Coronary Artery Disease (C4D) Genetics) 1000 Genomes-based genome-wide association study (n = 184,305) as a validation for CAD. RESULTS: In the UK Biobank, HbA1c was not associated with CVD using IVW (odds ratio (OR) 1.11 per %, 95% CI 0.83-1.48). However, HbA1c was associated with increased CAD risk (OR 1.50 per %, 95% CI 1.08-2.11) with directionally consistent results from MR-Egger and weighted median. The positive association with CAD was more pronounced when we excluded potentially invalid SNPs (OR 2.24 per %, 95% CI 1.55-3.25). The positive association was replicated in CARDIoGRAM (OR 1.52 per %, 95% CI 1.03-2.26). The association of HbA1c with stroke and its subtypes was less clear given the low number of cases. CONCLUSIONS: HbA1c likely causes CAD. The underlying mechanisms remain to be elucidated.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Glycated hemoglobin (HbA1c) is positively associated with cardiovascular disease (CVD), although evidence is primarily observational. Mendelian randomization studies have only examined its relation with subtypes of CVD. We examined the relation of HbA1c with CVD and its subtypes in the UK Biobank using Mendelian randomization. RESEARCH DESIGN AND METHODS: We used 38 genetic variants strongly and independently related to HbA1c (n = 123,665) applied to the UK Biobank (n = 392,038). We used inverse variance weighting (IVW) to obtain the associations of HbA1c with CVD, coronary artery disease (CAD), stroke, and its subtypes. Sensitivity analyses included Mendelian randomization (MR)-Egger, a weighted median and exclusion of potentially invalid single nucleotide polymorphisms (SNPs). We also applied the same genetic instruments to CARDIoGRAMplusC4D (Coronary ARtery DIsease Genome wide Replication and Meta-analysis (CARDIoGRAM) plus The Coronary Artery Disease (C4D) Genetics) 1000 Genomes-based genome-wide association study (n = 184,305) as a validation for CAD. RESULTS: In the UK Biobank, HbA1c was not associated with CVD using IVW (odds ratio (OR) 1.11 per %, 95% CI 0.83-1.48). However, HbA1c was associated with increased CAD risk (OR 1.50 per %, 95% CI 1.08-2.11) with directionally consistent results from MR-Egger and weighted median. The positive association with CAD was more pronounced when we excluded potentially invalid SNPs (OR 2.24 per %, 95% CI 1.55-3.25). The positive association was replicated in CARDIoGRAM (OR 1.52 per %, 95% CI 1.03-2.26). The association of HbA1c with stroke and its subtypes was less clear given the low number of cases. CONCLUSIONS: HbA1c likely causes CAD. The underlying mechanisms remain to be elucidated. |
M. Abdullah Said; Niek Verweij; Pim van der Harst Associations of Combined Genetic and Lifestyle Risks With Incident Cardiovascular Disease and Diabetes in the UK Biobank Study Journal Article JAMA Cardiology, 2018. @article{Said2018b, title = {Associations of Combined Genetic and Lifestyle Risks With Incident Cardiovascular Disease and Diabetes in the UK Biobank Study}, author = { M. Abdullah Said and Niek Verweij and Pim van der Harst}, url = {https://jamanetwork.com/journals/jamacardiology/article-abstract/2686129}, year = {2018}, date = {2018-06-27}, journal = {JAMA Cardiology}, abstract = {Importance Genetic and lifestyle factors both contribute to the risk of developing cardiovascular disease, but whether poor health behaviors are associated with similar increases in risk among individuals with low, intermediate, or high genetic risk is unknown. Objective To investigate the association of combined health behaviors and factors within genetic risk groups with coronary artery disease, atrial fibrillation, stroke, hypertension, and type 2 diabetes as well as to investigate the interactions between genetic risk and lifestyle. Design, Setting, and Participants The UK Biobank cohort study includes more than 500 000 participants aged 40 to 70 years who were recruited from 22 assessment centers across the United Kingdom from 2006 to 2010. A total of 339 003 unrelated individuals of white British descent with available genotype and matching genetic data and reported sex were included in this study from the UK Biobank population-based sample. Individuals were included in the analyses of 1 or more new-onset diseases. Data were analyzed from April 2006 to March 2015. Main Outcomes and Measures Risks of new-onset cardiovascular disease and diabetes associated with genetic risk and combined health behaviors and factors. Genetic risk was categorized as low (quintile 1), intermediate (quintiles 2-4), or high (quintile 5). Within each genetic risk group, the risks of incident events associated with ideal, intermediate, or poor combined health behaviors and factors were investigated and compared with low genetic risk and ideal lifestyle. Results Of 339 003 individuals, 181 702 (53.6%) were female, and the mean (SD) age was 56.86 (7.99) years. During follow-up, 9771 of 325 133 participants (3.0%) developed coronary artery disease, 7095 of 333 637 (2.1%) developed atrial fibrillation, 3145 of 332 971 (0.9%) developed stroke, 11 358 of 234 651 (4.8%) developed hypertension, and 4379 of 322 014 (1.4%) developed diabetes. Genetic risk and lifestyle were independent predictors of incident events, and there were no interactions for any outcome. Compared with ideal lifestyle in the low genetic risk group, poor lifestyle was associated with a hazard ratio of up to 4.54 (95% CI, 3.72-5.54) for coronary artery disease, 5.41 (95% CI, 4.29-6.81) for atrial fibrillation, 4.68 (95% CI, 3.85-5.69) for hypertension, 2.26 (95% CI, 1.63-3.14) for stroke, and 15.46 (95% CI, 10.82-22.08) for diabetes in the high genetic risk group. Conclusions and Relevance In this large contemporary population, genetic composition and combined health behaviors and factors had a log-additive effect on the risk of developing cardiovascular disease. The relative effects of poor lifestyle were comparable between genetic risk groups. Behavioral lifestyle changes should be encouraged for all through comprehensive, multifactorial approaches, although high-risk individuals may be selected based on the genetic risk.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Importance Genetic and lifestyle factors both contribute to the risk of developing cardiovascular disease, but whether poor health behaviors are associated with similar increases in risk among individuals with low, intermediate, or high genetic risk is unknown. Objective To investigate the association of combined health behaviors and factors within genetic risk groups with coronary artery disease, atrial fibrillation, stroke, hypertension, and type 2 diabetes as well as to investigate the interactions between genetic risk and lifestyle. Design, Setting, and Participants The UK Biobank cohort study includes more than 500 000 participants aged 40 to 70 years who were recruited from 22 assessment centers across the United Kingdom from 2006 to 2010. A total of 339 003 unrelated individuals of white British descent with available genotype and matching genetic data and reported sex were included in this study from the UK Biobank population-based sample. Individuals were included in the analyses of 1 or more new-onset diseases. Data were analyzed from April 2006 to March 2015. Main Outcomes and Measures Risks of new-onset cardiovascular disease and diabetes associated with genetic risk and combined health behaviors and factors. Genetic risk was categorized as low (quintile 1), intermediate (quintiles 2-4), or high (quintile 5). Within each genetic risk group, the risks of incident events associated with ideal, intermediate, or poor combined health behaviors and factors were investigated and compared with low genetic risk and ideal lifestyle. Results Of 339 003 individuals, 181 702 (53.6%) were female, and the mean (SD) age was 56.86 (7.99) years. During follow-up, 9771 of 325 133 participants (3.0%) developed coronary artery disease, 7095 of 333 637 (2.1%) developed atrial fibrillation, 3145 of 332 971 (0.9%) developed stroke, 11 358 of 234 651 (4.8%) developed hypertension, and 4379 of 322 014 (1.4%) developed diabetes. Genetic risk and lifestyle were independent predictors of incident events, and there were no interactions for any outcome. Compared with ideal lifestyle in the low genetic risk group, poor lifestyle was associated with a hazard ratio of up to 4.54 (95% CI, 3.72-5.54) for coronary artery disease, 5.41 (95% CI, 4.29-6.81) for atrial fibrillation, 4.68 (95% CI, 3.85-5.69) for hypertension, 2.26 (95% CI, 1.63-3.14) for stroke, and 15.46 (95% CI, 10.82-22.08) for diabetes in the high genetic risk group. Conclusions and Relevance In this large contemporary population, genetic composition and combined health behaviors and factors had a log-additive effect on the risk of developing cardiovascular disease. The relative effects of poor lifestyle were comparable between genetic risk groups. Behavioral lifestyle changes should be encouraged for all through comprehensive, multifactorial approaches, although high-risk individuals may be selected based on the genetic risk. |
Farhad Hormozdiari; Steven Gazal; Bryce van de Geijn; Hilary K. Finucane; Chelsea J.-T. Ju; Po-Ru Loh; Armin Schoech; Yakir Reshef; Xuanyao Liu; Luke O’Connor; Alexander Gusev; Eleazar Eskin; Alkes L. Price Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits Journal Article Nature Communications, 2018. @article{Hormozdiari2018, title = {Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits}, author = {Farhad Hormozdiari and Steven Gazal and Bryce van de Geijn and Hilary K. Finucane and Chelsea J.-T. Ju and Po-Ru Loh and Armin Schoech and Yakir Reshef and Xuanyao Liu and Luke O’Connor and Alexander Gusev and Eleazar Eskin and Alkes L. Price }, url = {https://www.nature.com/articles/s41588-018-0148-2}, year = {2018}, date = {2018-06-25}, journal = {Nature Communications}, abstract = {There is increasing evidence that many risk loci found using genome-wide association studies are molecular quantitative trait loci (QTLs). Here we introduce a new set of functional annotations based on causal posterior probabilities of fine-mapped molecular cis-QTLs, using data from the Genotype-Tissue Expression (GTEx) and BLUEPRINT consortia. We show that these annotations are more strongly enriched for heritability (5.84× for eQTLs; P = 1.19 × 10−31) across 41 diseases and complex traits than annotations containing all significant molecular QTLs (1.80× for expression (e)QTLs). eQTL annotations obtained by meta-analyzing all GTEx tissues generally performed best, whereas tissue-specific eQTL annotations produced stronger enrichments for blood- and brain-related diseases and traits. eQTL annotations restricted to loss-of-function intolerant genes were even more enriched for heritability (17.06×; P = 1.20 × 10−35). All molecular QTLs except splicing QTLs remained significantly enriched in joint analysis, indicating that each of these annotations is uniquely informative for disease and complex trait architectures.}, keywords = {}, pubstate = {published}, tppubtype = {article} } There is increasing evidence that many risk loci found using genome-wide association studies are molecular quantitative trait loci (QTLs). Here we introduce a new set of functional annotations based on causal posterior probabilities of fine-mapped molecular cis-QTLs, using data from the Genotype-Tissue Expression (GTEx) and BLUEPRINT consortia. We show that these annotations are more strongly enriched for heritability (5.84× for eQTLs; P = 1.19 × 10−31) across 41 diseases and complex traits than annotations containing all significant molecular QTLs (1.80× for expression (e)QTLs). eQTL annotations obtained by meta-analyzing all GTEx tissues generally performed best, whereas tissue-specific eQTL annotations produced stronger enrichments for blood- and brain-related diseases and traits. eQTL annotations restricted to loss-of-function intolerant genes were even more enriched for heritability (17.06×; P = 1.20 × 10−35). All molecular QTLs except splicing QTLs remained significantly enriched in joint analysis, indicating that each of these annotations is uniquely informative for disease and complex trait architectures. |
Lucía Colodro-Conde; Baptiste Couvy-Duchesne; John B. Whitfield; Fabian Streit; Scott Gordon; Kathryn E. Kemper; Loic Yengo; Zhili Zheng; Maciej Trzaskowski; Eveline L. de Zeeuw; Michel G. Nivard; Marjolijn Das; Rachel E. Neale; Stuart MacGregor; Catherine M. Olsen; David C. Whiteman; Dorret I. Boomsma; Jian Yang; Marcella Rietschel; John J. McGrath; Sarah E. Medland; Nicholas G. Martin Association Between Population Density and Genetic Risk for Schizophrenia Journal Article JAMA Psychiatry, 2018. @article{Colodro-Conde2018, title = {Association Between Population Density and Genetic Risk for Schizophrenia}, author = {Lucía Colodro-Conde and Baptiste Couvy-Duchesne and John B. Whitfield and Fabian Streit and Scott Gordon and Kathryn E. Kemper and Loic Yengo and Zhili Zheng and Maciej Trzaskowski and Eveline L. de Zeeuw and Michel G. Nivard and Marjolijn Das and Rachel E. Neale and Stuart MacGregor and Catherine M. Olsen and David C. Whiteman and Dorret I. Boomsma and Jian Yang and Marcella Rietschel and John J. McGrath and Sarah E. Medland and Nicholas G. Martin }, url = {https://jamanetwork.com/journals/jamapsychiatry/article-abstract/2685919}, year = {2018}, date = {2018-06-23}, journal = {JAMA Psychiatry}, abstract = {Importance Urban life has been proposed as an environmental risk factor accounting for the increased prevalence of schizophrenia in urban areas. An alternative hypothesis is that individuals with increased genetic risk tend to live in urban/dense areas. Objective To assess whether adults with higher genetic risk for schizophrenia have an increased probability to live in more populated areas than those with lower risk. Design, Setting, and Participants Four large, cross-sectional samples of genotyped individuals of European ancestry older than 18 years with known addresses in Australia, the United Kingdom, and the Netherlands were included in the analysis. Data were based on the postcode of residence at the time of last contact with the participants. Community-based samples who took part in studies conducted by the Queensland Institute for Medical Research Berghofer Medical Research Institute (QIMR), UK Biobank (UKB), Netherlands Twin Register (NTR), or QSkin Sun and Health Study (QSKIN) were included. Genome-wide association analysis and mendelian randomization (MR) were included. The study was conducted between 2016 and 2018. Exposures Polygenic risk scores for schizophrenia derived from genetic data (genetic risk is independently measured from the occurrence of the disease). Socioeconomic status of the area was included as a moderator in some of the models. Main Outcomes and Measures Population density of the place of residence of the participants determined from census data. Remoteness and socioeconomic status of the area were also tested. Results The QIMR participants (15 544; 10 197 [65.6%] women; mean [SD] age, 54.4 [13.2] years) living in more densely populated areas (people per square kilometer) had a higher genetic loading for schizophrenia (r2 = 0.12%; P = 5.69 × 10−5), a result that was replicated across all 3 other cohorts (UKB: 345 246; 187 469 [54.3%] women; age, 65.7 [8.0] years; NTR: 11 212; 6727 [60.0%] women; age, 48.6 [17.5] years; and QSKIN: 15 726; 8602 [54.7%] women; age, 57.0 [7.9] years). This genetic association could account for 1.7% (95% CI, 0.8%-3.2%) of the schizophrenia risk. Estimates from MR analyses performed in the UKB sample were significant (b = 0.049; P = 3.7 × 10−7 using GSMR), suggesting that the genetic liability to schizophrenia may have a causal association with the tendency to live in urbanized locations. Conclusions and Relevance The results of this study appear to support the hypothesis that individuals with increased genetic risk tend to live in urban/dense areas and suggest the need to refine the social stress model for schizophrenia by including genetics as well as possible gene-environment interactions.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Importance Urban life has been proposed as an environmental risk factor accounting for the increased prevalence of schizophrenia in urban areas. An alternative hypothesis is that individuals with increased genetic risk tend to live in urban/dense areas. Objective To assess whether adults with higher genetic risk for schizophrenia have an increased probability to live in more populated areas than those with lower risk. Design, Setting, and Participants Four large, cross-sectional samples of genotyped individuals of European ancestry older than 18 years with known addresses in Australia, the United Kingdom, and the Netherlands were included in the analysis. Data were based on the postcode of residence at the time of last contact with the participants. Community-based samples who took part in studies conducted by the Queensland Institute for Medical Research Berghofer Medical Research Institute (QIMR), UK Biobank (UKB), Netherlands Twin Register (NTR), or QSkin Sun and Health Study (QSKIN) were included. Genome-wide association analysis and mendelian randomization (MR) were included. The study was conducted between 2016 and 2018. Exposures Polygenic risk scores for schizophrenia derived from genetic data (genetic risk is independently measured from the occurrence of the disease). Socioeconomic status of the area was included as a moderator in some of the models. Main Outcomes and Measures Population density of the place of residence of the participants determined from census data. Remoteness and socioeconomic status of the area were also tested. Results The QIMR participants (15 544; 10 197 [65.6%] women; mean [SD] age, 54.4 [13.2] years) living in more densely populated areas (people per square kilometer) had a higher genetic loading for schizophrenia (r2 = 0.12%; P = 5.69 × 10−5), a result that was replicated across all 3 other cohorts (UKB: 345 246; 187 469 [54.3%] women; age, 65.7 [8.0] years; NTR: 11 212; 6727 [60.0%] women; age, 48.6 [17.5] years; and QSKIN: 15 726; 8602 [54.7%] women; age, 57.0 [7.9] years). This genetic association could account for 1.7% (95% CI, 0.8%-3.2%) of the schizophrenia risk. Estimates from MR analyses performed in the UKB sample were significant (b = 0.049; P = 3.7 × 10−7 using GSMR), suggesting that the genetic liability to schizophrenia may have a causal association with the tendency to live in urbanized locations. Conclusions and Relevance The results of this study appear to support the hypothesis that individuals with increased genetic risk tend to live in urban/dense areas and suggest the need to refine the social stress model for schizophrenia by including genetics as well as possible gene-environment interactions. |
Anttila V Bulik-Sullivan Analysis of shared heritability in common disorders of the brain Journal Article Science , 2018, (Brainstorm Consortium, Anttila V, Bulik-Sullivan B, Finucane HK, Walters RK, Bras J, Duncan L, Escott-Price V, Falcone GJ, Gormley P, Malik R, Patsopoulos NA, Ripke S, Wei Z, Yu D, Lee PH, Turley P, Grenier-Boley B, Chouraki V, Kamatani Y, Berr C, Letenneur L, Hannequin D, Amouyel P, Boland A, Deleuze JF, Duron E, Vardarajan BN, Reitz C, Goate AM, Huentelman MJ, Kamboh MI, Larson EB, Rogaeva E, St George-Hyslop P, Hakonarson H, Kukull WA, Farrer LA, Barnes LL, Beach TG, Demirci FY, Head E, Hulette CM, Jicha GA, Kauwe JSK, Kaye JA, Leverenz JB, Levey AI, Lieberman AP, Pankratz VS, Poon WW, Quinn JF, Saykin AJ, Schneider LS, Smith AG, Sonnen JA, Stern RA, Van Deerlin VM, Van Eldik LJ, Harold D, Russo G, Rubinsztein DC, Bayer A, Tsolaki M, Proitsi P, Fox NC, Hampel H, Owen MJ, Mead S, Passmore P, Morgan K, Nöthen MM, Rossor M, Lupton MK, Hoffmann P, Kornhuber J, Lawlor B, McQuillin A, Al-Chalabi A, Bis JC, Ruiz A, Boada M, Seshadri S, Beiser A, Rice K, van der Lee SJ, De Jager PL, Geschwind DH, Riemenschneider M, Riedel-Heller S, Rotter JI, Ransmayr G, Hyman BT, Cruchaga C, Alegret M, Winsvold B, Palta P, Farh KH, Cuenca-Leon E, Furlotte N, Kurth T, Ligthart L, Terwindt GM, Freilinger T, Ran C, Gordon SD, Borck G, Adams HHH, Lehtimäki T, Wedenoja J, Buring JE, Schürks M, Hrafnsdottir M, Hottenga JJ, Penninx B, Artto V, Kaunisto M, Vepsäläinen S, Martin NG, Montgomery GW, Kurki MI, Hämäläinen E, Huang H, Huang J, Sandor C, Webber C, Muller-Myhsok B, Schreiber S, Salomaa V, Loehrer E, Göbel H, Macaya A, Pozo-Rosich P, Hansen T, Werge T, Kaprio J, Metspalu A, Kubisch C, Ferrari MD, Belin AC, van den Maagdenberg AMJM, Zwart JA, Boomsma D, Eriksson N, Olesen J, Chasman DI, Nyholt DR, Avbersek A, Baum L, Berkovic S, Bradfield J, Buono R, Catarino CB, Cossette P, De Jonghe P, Depondt C, Dlugos D, Ferraro TN, French J, Hjalgrim H, Jamnadas-Khoda J, Kälviäinen R, Kunz WS, Lerche H, Leu C, Lindhout D, Lo W, Lowenstein D, McCormack M, Møller RS, Molloy A, Ng PW, Oliver K, Privitera M, Radtke R, Ruppert AK, Sander T, Schachter S, Schankin C, Scheffer I, Schoch S, Sisodiya SM, Smith P, Sperling M, Striano P, Surges R, Thomas GN, Visscher F, Whelan CD, Zara F, Heinzen EL, Marson A, Becker F, Stroink H, Zimprich F, Gasser T, Gibbs R, Heutink P, Martinez M, Morris HR, Sharma M, Ryten M, Mok KY, Pulit S, Bevan S, Holliday E, Attia J, Battey T, Boncoraglio G, Thijs V, Chen WM, Mitchell B, Rothwell P, Sharma P, Sudlow C, Vicente A, Markus H, Kourkoulis C, Pera J, Raffeld M, Silliman S, Boraska Perica V, Thornton LM, Huckins LM, William Rayner N, Lewis CM, Gratacos M, Rybakowski F, Keski-Rahkonen A, Raevuori A, Hudson JI, Reichborn-Kjennerud T, Monteleone P, Karwautz A, Mannik K, Baker JH, O'Toole JK, Trace SE, Davis OSP, Helder SG, Ehrlich S, Herpertz-Dahlmann B, Danner UN, van Elburg AA, Clementi M, Forzan M, Docampo E, Lissowska J, Hauser J, Tortorella A, Maj M, Gonidakis F, Tziouvas K, Papezova H, Yilmaz Z, Wagner G, Cohen-Woods S, Herms S, Julià A, Rabionet R, Dick DM, Ripatti S, Andreassen OA, Espeseth T, Lundervold AJ, Steen VM, Pinto D, Scherer SW, Aschauer H, Schosser A, Alfredsson L, Padyukov L, Halmi KA, Mitchell J, Strober M, Bergen AW, Kaye W, Szatkiewicz JP, Cormand B, Ramos-Quiroga JA, Sánchez-Mora C, Ribasés M, Casas M, Hervas A, Arranz MJ, Haavik J, Zayats T, Johansson S, Williams N, Dempfle A, Rothenberger A, Kuntsi J, Oades RD, Banaschewski T, Franke B, Buitelaar JK, Arias Vasquez A, Doyle AE, Reif A, Lesch KP, Freitag C, Rivero O, Palmason H, Romanos M, Langley K, Rietschel M, Witt SH, Dalsgaard S, Børglum AD, Waldman I, Wilmot B, Molly N, Bau CHD, Crosbie J, Schachar R, Loo SK, McGough JJ, Grevet EH, Medland SE, Robinson E, Weiss LA, Bacchelli E, Bailey A, Bal V, Battaglia A, Betancur C, Bolton P, Cantor R, Celestino-Soper P, Dawson G, De Rubeis S, Duque F, Green A, Klauck SM, Leboyer M, Levitt P, Maestrini E, Mane S, De-Luca DM, Parr J, Regan R, Reichenberg A, Sandin S, Vorstman J, Wassink T, Wijsman E, Cook E, Santangelo S, Delorme R, Rogé B, Magalhaes T, Arking D, Schulze TG, Thompson RC, Strohmaier J, Matthews K, Melle I, Morris D, Blackwood D, McIntosh A, Bergen SE, Schalling M, Jamain S, Maaser A, Fischer SB, Reinbold CS, Fullerton JM, Guzman-Parra J, Mayoral F, Schofield PR, Cichon S, Mühleisen TW, Degenhardt F, Schumacher J, Bauer M, Mitchell PB, Gershon ES, Rice J, Potash JB, Zandi PP, Craddock N, Ferrier IN, Alda M, Rouleau GA, Turecki G, Ophoff R, Pato C, Anjorin A, Stahl E, Leber M, Czerski PM, Cruceanu C, Jones IR, Posthuma D, Andlauer TFM, Forstner AJ, Streit F, Baune BT, Air T, Sinnamon G, Wray NR, MacIntyre DJ, Porteous D, Homuth G, Rivera M, Grove J, Middeldorp CM, Hickie I, Pergadia M, Mehta D, Smit JH, Jansen R, de Geus E, Dunn E, Li QS, Nauck M, Schoevers RA, Beekman AT, Knowles JA, Viktorin A, Arnold P, Barr CL, Bedoya-Berrio G, Bienvenu OJ, Brentani H, Burton C, Camarena B, Cappi C, Cath D, Cavallini M, Cusi D, Darrow S, Denys D, Derks EM, Dietrich A, Fernandez T, Figee M, Freimer N, Gerber G, Grados M, Greenberg E, Hanna GL, Hartmann A, Hirschtritt ME, Hoekstra PJ, Huang A, Huyser C, Illmann C, Jenike M, Kuperman S, Leventhal B, Lochner C, Lyon GJ, Macciardi F, Madruga-Garrido M, Malaty IA, Maras A, McGrath L, Miguel EC, Mir P, Nestadt G, Nicolini H, Okun MS, Pakstis A, Paschou P, Piacentini J, Pittenger C, Plessen K, Ramensky V, Ramos EM, Reus V, Richter MA, Riddle MA, Robertson MM, Roessner V, Rosário M, Samuels JF, Sandor P, Stein DJ, Tsetsos F, Van Nieuwerburgh F, Weatherall S, Wendland JR, Wolanczyk T, Worbe Y, Zai G, Goes FS, McLaughlin N, Nestadt PS, Grabe HJ, Depienne C, Konkashbaev A, Lanzagorta N, Valencia-Duarte A, Bramon E, Buccola N, Cahn W, Cairns M, Chong SA, Cohen D, Crespo-Facorro B, Crowley J, Davidson M, DeLisi L, Dinan T, Donohoe G, Drapeau E, Duan J, Haan L, Hougaard D, Karachanak-Yankova S, Khrunin A, Klovins J, Kučinskas V, Lee Chee Keong J, Limborska S, Loughland C, Lönnqvist J, Maher B, Mattheisen M, McDonald C, Murphy KC, Nenadic I, van Os J, Pantelis C, Pato M, Petryshen T, Quested D, Roussos P, Sanders AR, Schall U, Schwab SG, Sim K, So HC, Stögmann E, Subramaniam M, Toncheva D, Waddington J, Walters J, Weiser M, Cheng W, Cloninger R, Curtis D, Gejman PV, Henskens F, Mattingsdal M, Oh SY, Scott R, Webb B, Breen G, Churchhouse C, Bulik CM, Daly M, Dichgans M, Faraone SV, Guerreiro R, Holmans P, Kendler KS, Koeleman B, Mathews CA, Price A, Scharf J, Sklar P, Williams J, Wood NW, Cotsapas C, Palotie A, Smoller JW, Sullivan P, Rosand J, Corvin A, Neale BM, Schott JM, Anney R, Elia J, Grigoroiu-Serbanescu M, Edenberg HJ, Murray R.). @article{Bulik-Sullivan2018, title = {Analysis of shared heritability in common disorders of the brain}, author = { Anttila V Bulik-Sullivan}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29930110}, year = {2018}, date = {2018-06-22}, journal = {Science }, abstract = {Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.}, note = {Brainstorm Consortium, Anttila V, Bulik-Sullivan B, Finucane HK, Walters RK, Bras J, Duncan L, Escott-Price V, Falcone GJ, Gormley P, Malik R, Patsopoulos NA, Ripke S, Wei Z, Yu D, Lee PH, Turley P, Grenier-Boley B, Chouraki V, Kamatani Y, Berr C, Letenneur L, Hannequin D, Amouyel P, Boland A, Deleuze JF, Duron E, Vardarajan BN, Reitz C, Goate AM, Huentelman MJ, Kamboh MI, Larson EB, Rogaeva E, St George-Hyslop P, Hakonarson H, Kukull WA, Farrer LA, Barnes LL, Beach TG, Demirci FY, Head E, Hulette CM, Jicha GA, Kauwe JSK, Kaye JA, Leverenz JB, Levey AI, Lieberman AP, Pankratz VS, Poon WW, Quinn JF, Saykin AJ, Schneider LS, Smith AG, Sonnen JA, Stern RA, Van Deerlin VM, Van Eldik LJ, Harold D, Russo G, Rubinsztein DC, Bayer A, Tsolaki M, Proitsi P, Fox NC, Hampel H, Owen MJ, Mead S, Passmore P, Morgan K, Nöthen MM, Rossor M, Lupton MK, Hoffmann P, Kornhuber J, Lawlor B, McQuillin A, Al-Chalabi A, Bis JC, Ruiz A, Boada M, Seshadri S, Beiser A, Rice K, van der Lee SJ, De Jager PL, Geschwind DH, Riemenschneider M, Riedel-Heller S, Rotter JI, Ransmayr G, Hyman BT, Cruchaga C, Alegret M, Winsvold B, Palta P, Farh KH, Cuenca-Leon E, Furlotte N, Kurth T, Ligthart L, Terwindt GM, Freilinger T, Ran C, Gordon SD, Borck G, Adams HHH, Lehtimäki T, Wedenoja J, Buring JE, Schürks M, Hrafnsdottir M, Hottenga JJ, Penninx B, Artto V, Kaunisto M, Vepsäläinen S, Martin NG, Montgomery GW, Kurki MI, Hämäläinen E, Huang H, Huang J, Sandor C, Webber C, Muller-Myhsok B, Schreiber S, Salomaa V, Loehrer E, Göbel H, Macaya A, Pozo-Rosich P, Hansen T, Werge T, Kaprio J, Metspalu A, Kubisch C, Ferrari MD, Belin AC, van den Maagdenberg AMJM, Zwart JA, Boomsma D, Eriksson N, Olesen J, Chasman DI, Nyholt DR, Avbersek A, Baum L, Berkovic S, Bradfield J, Buono R, Catarino CB, Cossette P, De Jonghe P, Depondt C, Dlugos D, Ferraro TN, French J, Hjalgrim H, Jamnadas-Khoda J, Kälviäinen R, Kunz WS, Lerche H, Leu C, Lindhout D, Lo W, Lowenstein D, McCormack M, Møller RS, Molloy A, Ng PW, Oliver K, Privitera M, Radtke R, Ruppert AK, Sander T, Schachter S, Schankin C, Scheffer I, Schoch S, Sisodiya SM, Smith P, Sperling M, Striano P, Surges R, Thomas GN, Visscher F, Whelan CD, Zara F, Heinzen EL, Marson A, Becker F, Stroink H, Zimprich F, Gasser T, Gibbs R, Heutink P, Martinez M, Morris HR, Sharma M, Ryten M, Mok KY, Pulit S, Bevan S, Holliday E, Attia J, Battey T, Boncoraglio G, Thijs V, Chen WM, Mitchell B, Rothwell P, Sharma P, Sudlow C, Vicente A, Markus H, Kourkoulis C, Pera J, Raffeld M, Silliman S, Boraska Perica V, Thornton LM, Huckins LM, William Rayner N, Lewis CM, Gratacos M, Rybakowski F, Keski-Rahkonen A, Raevuori A, Hudson JI, Reichborn-Kjennerud T, Monteleone P, Karwautz A, Mannik K, Baker JH, O'Toole JK, Trace SE, Davis OSP, Helder SG, Ehrlich S, Herpertz-Dahlmann B, Danner UN, van Elburg AA, Clementi M, Forzan M, Docampo E, Lissowska J, Hauser J, Tortorella A, Maj M, Gonidakis F, Tziouvas K, Papezova H, Yilmaz Z, Wagner G, Cohen-Woods S, Herms S, Julià A, Rabionet R, Dick DM, Ripatti S, Andreassen OA, Espeseth T, Lundervold AJ, Steen VM, Pinto D, Scherer SW, Aschauer H, Schosser A, Alfredsson L, Padyukov L, Halmi KA, Mitchell J, Strober M, Bergen AW, Kaye W, Szatkiewicz JP, Cormand B, Ramos-Quiroga JA, Sánchez-Mora C, Ribasés M, Casas M, Hervas A, Arranz MJ, Haavik J, Zayats T, Johansson S, Williams N, Dempfle A, Rothenberger A, Kuntsi J, Oades RD, Banaschewski T, Franke B, Buitelaar JK, Arias Vasquez A, Doyle AE, Reif A, Lesch KP, Freitag C, Rivero O, Palmason H, Romanos M, Langley K, Rietschel M, Witt SH, Dalsgaard S, Børglum AD, Waldman I, Wilmot B, Molly N, Bau CHD, Crosbie J, Schachar R, Loo SK, McGough JJ, Grevet EH, Medland SE, Robinson E, Weiss LA, Bacchelli E, Bailey A, Bal V, Battaglia A, Betancur C, Bolton P, Cantor R, Celestino-Soper P, Dawson G, De Rubeis S, Duque F, Green A, Klauck SM, Leboyer M, Levitt P, Maestrini E, Mane S, De-Luca DM, Parr J, Regan R, Reichenberg A, Sandin S, Vorstman J, Wassink T, Wijsman E, Cook E, Santangelo S, Delorme R, Rogé B, Magalhaes T, Arking D, Schulze TG, Thompson RC, Strohmaier J, Matthews K, Melle I, Morris D, Blackwood D, McIntosh A, Bergen SE, Schalling M, Jamain S, Maaser A, Fischer SB, Reinbold CS, Fullerton JM, Guzman-Parra J, Mayoral F, Schofield PR, Cichon S, Mühleisen TW, Degenhardt F, Schumacher J, Bauer M, Mitchell PB, Gershon ES, Rice J, Potash JB, Zandi PP, Craddock N, Ferrier IN, Alda M, Rouleau GA, Turecki G, Ophoff R, Pato C, Anjorin A, Stahl E, Leber M, Czerski PM, Cruceanu C, Jones IR, Posthuma D, Andlauer TFM, Forstner AJ, Streit F, Baune BT, Air T, Sinnamon G, Wray NR, MacIntyre DJ, Porteous D, Homuth G, Rivera M, Grove J, Middeldorp CM, Hickie I, Pergadia M, Mehta D, Smit JH, Jansen R, de Geus E, Dunn E, Li QS, Nauck M, Schoevers RA, Beekman AT, Knowles JA, Viktorin A, Arnold P, Barr CL, Bedoya-Berrio G, Bienvenu OJ, Brentani H, Burton C, Camarena B, Cappi C, Cath D, Cavallini M, Cusi D, Darrow S, Denys D, Derks EM, Dietrich A, Fernandez T, Figee M, Freimer N, Gerber G, Grados M, Greenberg E, Hanna GL, Hartmann A, Hirschtritt ME, Hoekstra PJ, Huang A, Huyser C, Illmann C, Jenike M, Kuperman S, Leventhal B, Lochner C, Lyon GJ, Macciardi F, Madruga-Garrido M, Malaty IA, Maras A, McGrath L, Miguel EC, Mir P, Nestadt G, Nicolini H, Okun MS, Pakstis A, Paschou P, Piacentini J, Pittenger C, Plessen K, Ramensky V, Ramos EM, Reus V, Richter MA, Riddle MA, Robertson MM, Roessner V, Rosário M, Samuels JF, Sandor P, Stein DJ, Tsetsos F, Van Nieuwerburgh F, Weatherall S, Wendland JR, Wolanczyk T, Worbe Y, Zai G, Goes FS, McLaughlin N, Nestadt PS, Grabe HJ, Depienne C, Konkashbaev A, Lanzagorta N, Valencia-Duarte A, Bramon E, Buccola N, Cahn W, Cairns M, Chong SA, Cohen D, Crespo-Facorro B, Crowley J, Davidson M, DeLisi L, Dinan T, Donohoe G, Drapeau E, Duan J, Haan L, Hougaard D, Karachanak-Yankova S, Khrunin A, Klovins J, Kučinskas V, Lee Chee Keong J, Limborska S, Loughland C, Lönnqvist J, Maher B, Mattheisen M, McDonald C, Murphy KC, Nenadic I, van Os J, Pantelis C, Pato M, Petryshen T, Quested D, Roussos P, Sanders AR, Schall U, Schwab SG, Sim K, So HC, Stögmann E, Subramaniam M, Toncheva D, Waddington J, Walters J, Weiser M, Cheng W, Cloninger R, Curtis D, Gejman PV, Henskens F, Mattingsdal M, Oh SY, Scott R, Webb B, Breen G, Churchhouse C, Bulik CM, Daly M, Dichgans M, Faraone SV, Guerreiro R, Holmans P, Kendler KS, Koeleman B, Mathews CA, Price A, Scharf J, Sklar P, Williams J, Wood NW, Cotsapas C, Palotie A, Smoller JW, Sullivan P, Rosand J, Corvin A, Neale BM, Schott JM, Anney R, Elia J, Grigoroiu-Serbanescu M, Edenberg HJ, Murray R.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology. |
Yann C. Klimentidis; David A. Raichlen; Jennifer Bea; David O. Garcia; Nathan E. Wineinger; Lawrence J. Mandarino; Gene E. Alexander; Zhao Chen; Scott B. Going International Journal of Obesity, 2018. @article{Klimentidis2018, title = {Genome-wide association study of habitual physical activity in over 377,000 UK Biobank participants identifies multiple variants including CADM2 and APOE.}, author = {Yann C. Klimentidis and David A. Raichlen and Jennifer Bea and David O. Garcia and Nathan E. Wineinger and Lawrence J. Mandarino and Gene E. Alexander and Zhao Chen and Scott B. Going }, url = {https://www.nature.com/articles/s41366-018-0120-3}, year = {2018}, date = {2018-06-13}, journal = {International Journal of Obesity}, abstract = {Background/objectives Physical activity (PA) protects against a wide range of diseases. Habitual PA appears to be heritable, motivating the search for specific genetic variants that may inform efforts to promote PA and target the best type of PA for each individual. Subjects/methods We used data from the UK Biobank to perform the largest genome-wide association study of PA to date, using three measures based on self-report (nmax = 377,234) and two measures based on wrist-worn accelerometry data (nmax = 91,084). We examined genetic correlations of PA with other traits and diseases, as well as tissue-specific gene expression patterns. With data from the Atherosclerosis Risk in Communities (ARIC; n = 8,556) study, we performed a meta-analysis of our top hits for moderate-to-vigorous PA (MVPA). Results We identified ten loci across all PA measures that were significant in both a basic and a fully adjusted model (p < 5 × 10−9). Upon meta-analysis of the nine top hits for MVPA with results from ARIC, eight were genome-wide significant. Interestingly, among these, the rs429358 variant in the APOE gene was the most strongly associated with MVPA, whereby the allele associated with higher Alzheimer’s risk was associated with greater MVPA. However, we were not able to rule out possible selection bias underlying this result. Variants in CADM2, a gene previously implicated in obesity, risk-taking behavior and other traits, were found to be associated with habitual PA. We also identified three loci consistently associated (p < 5 × 10−5) with PA across both self-report and accelerometry, including CADM2. We found genetic correlations of PA with educational attainment, chronotype, psychiatric traits, and obesity-related traits. Tissue enrichment analyses implicate the brain and pituitary gland as locations where PA-associated loci may exert their actions. Conclusions These results provide new insight into the genetic basis of habitual PA, and the genetic links connecting PA with other traits and diseases.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background/objectives Physical activity (PA) protects against a wide range of diseases. Habitual PA appears to be heritable, motivating the search for specific genetic variants that may inform efforts to promote PA and target the best type of PA for each individual. Subjects/methods We used data from the UK Biobank to perform the largest genome-wide association study of PA to date, using three measures based on self-report (nmax = 377,234) and two measures based on wrist-worn accelerometry data (nmax = 91,084). We examined genetic correlations of PA with other traits and diseases, as well as tissue-specific gene expression patterns. With data from the Atherosclerosis Risk in Communities (ARIC; n = 8,556) study, we performed a meta-analysis of our top hits for moderate-to-vigorous PA (MVPA). Results We identified ten loci across all PA measures that were significant in both a basic and a fully adjusted model (p < 5 × 10−9). Upon meta-analysis of the nine top hits for MVPA with results from ARIC, eight were genome-wide significant. Interestingly, among these, the rs429358 variant in the APOE gene was the most strongly associated with MVPA, whereby the allele associated with higher Alzheimer’s risk was associated with greater MVPA. However, we were not able to rule out possible selection bias underlying this result. Variants in CADM2, a gene previously implicated in obesity, risk-taking behavior and other traits, were found to be associated with habitual PA. We also identified three loci consistently associated (p < 5 × 10−5) with PA across both self-report and accelerometry, including CADM2. We found genetic correlations of PA with educational attainment, chronotype, psychiatric traits, and obesity-related traits. Tissue enrichment analyses implicate the brain and pituitary gland as locations where PA-associated loci may exert their actions. Conclusions These results provide new insight into the genetic basis of habitual PA, and the genetic links connecting PA with other traits and diseases. |
Rosa B. Thorolfsdottir; Gardar Sveinbjornsson; Patrick Sulem; Jonas B. Nielsen; Stefan Jonsson; Gisli H. Halldorsson; Pall Melsted; Erna V. Ivarsdottir; Olafur B. Davidsson; Ragnar P. Kristjansson; Gudmar Thorleifsson; Anna Helgadottir; Solveig Gretarsdottir; Gudmundur Norddahl; Sridharan Rajamani; Bjarni Torfason; Atli S. Valgardsson; Jon T. Sverrisson; Vinicius Tragante; Oddgeir L. Holmen; Folkert W. Asselbergs; Dan M. Roden; Dawood Darbar; Terje R. Pedersen; Marc S. Sabatine; Cristen J. Willer; Maja-Lisa Løchen; Bjarni V. Halldorsson; Ingileif Jonsdottir; Kristian Hveem; David O. Arnar; Unnur Thorsteinsdottir; Daniel F. Gudbjartsson; Hilma Holm; Kari Stefansson Coding variants in RPL3L and MYZAP increase risk of atrial fibrillation Journal Article Communications Biology, 2018. @article{Thorolfsdottir2018, title = {Coding variants in RPL3L and MYZAP increase risk of atrial fibrillation}, author = {Rosa B. Thorolfsdottir and Gardar Sveinbjornsson and Patrick Sulem and Jonas B. Nielsen and Stefan Jonsson and Gisli H. Halldorsson and Pall Melsted and Erna V. Ivarsdottir and Olafur B. Davidsson and Ragnar P. Kristjansson and Gudmar Thorleifsson and Anna Helgadottir and Solveig Gretarsdottir and Gudmundur Norddahl and Sridharan Rajamani and Bjarni Torfason and Atli S. Valgardsson and Jon T. Sverrisson and Vinicius Tragante and Oddgeir L. Holmen and Folkert W. Asselbergs and Dan M. Roden and Dawood Darbar and Terje R. Pedersen and Marc S. Sabatine and Cristen J. Willer and Maja-Lisa Løchen and Bjarni V. Halldorsson and Ingileif Jonsdottir and Kristian Hveem and David O. Arnar and Unnur Thorsteinsdottir and Daniel F. Gudbjartsson and Hilma Holm and Kari Stefansson}, url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6123807/}, year = {2018}, date = {2018-06-12}, journal = {Communications Biology}, abstract = {Most sequence variants identified hitherto in genome-wide association studies (GWAS) of atrial fibrillation are common, non-coding variants associated with risk through unknown mechanisms. We performed a meta-analysis of GWAS of atrial fibrillation among 29,502 cases and 767,760 controls from Iceland and the UK Biobank with follow-up in samples from Norway and the US, focusing on low-frequency coding and splice variants aiming to identify causal genes. We observe associations with one missense (OR = 1.20) and one splice-donor variant (OR = 1.50) in RPL3L, the first ribosomal gene implicated in atrial fibrillation to our knowledge. Analysis of 167 RNA samples from the right atrium reveals that the splice-donor variant in RPL3L results in exon skipping. We also observe an association with a missense variant in MYZAP (OR = 1.38), encoding a component of the intercalated discs of cardiomyocytes. Both discoveries emphasize the close relationship between the mechanical and electrical function of the heart.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Most sequence variants identified hitherto in genome-wide association studies (GWAS) of atrial fibrillation are common, non-coding variants associated with risk through unknown mechanisms. We performed a meta-analysis of GWAS of atrial fibrillation among 29,502 cases and 767,760 controls from Iceland and the UK Biobank with follow-up in samples from Norway and the US, focusing on low-frequency coding and splice variants aiming to identify causal genes. We observe associations with one missense (OR = 1.20) and one splice-donor variant (OR = 1.50) in RPL3L, the first ribosomal gene implicated in atrial fibrillation to our knowledge. Analysis of 167 RNA samples from the right atrium reveals that the splice-donor variant in RPL3L results in exon skipping. We also observe an association with a missense variant in MYZAP (OR = 1.38), encoding a component of the intercalated discs of cardiomyocytes. Both discoveries emphasize the close relationship between the mechanical and electrical function of the heart. |
Carolina Roselli; Mark D. Chaffin; Patrick T. Ellinor Multi-ethnic genome-wide association study for atrial fibrillation Journal Article Nature Genetics, 2018. @article{Roselli2018, title = {Multi-ethnic genome-wide association study for atrial fibrillation}, author = {Carolina Roselli and Mark D. Chaffin and Patrick T. Ellinor }, url = {https://www.nature.com/articles/s41588-018-0133-9}, year = {2018}, date = {2018-06-11}, journal = {Nature Genetics}, abstract = {Atrial fibrillation (AF) affects more than 33 million individuals worldwide1 and has a complex heritability2. We conducted the largest meta-analysis of genome-wide association studies (GWAS) for AF to date, consisting of more than half a million individuals, including 65,446 with AF. In total, we identified 97 loci significantly associated with AF, including 67 that were novel in a combined-ancestry analysis, and 3 that were novel in a European-specific analysis. We sought to identify AF-associated genes at the GWAS loci by performing RNA-sequencing and expression quantitative trait locus analyses in 101 left atrial samples, the most relevant tissue for AF. We also performed transcriptome-wide analyses that identified 57 AF-associated genes, 42 of which overlap with GWAS loci. The identified loci implicate genes enriched within cardiac developmental, electrophysiological, contractile and structural pathways. These results extend our understanding of the biological pathways underlying AF and may facilitate the development of therapeutics for AF.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Atrial fibrillation (AF) affects more than 33 million individuals worldwide1 and has a complex heritability2. We conducted the largest meta-analysis of genome-wide association studies (GWAS) for AF to date, consisting of more than half a million individuals, including 65,446 with AF. In total, we identified 97 loci significantly associated with AF, including 67 that were novel in a combined-ancestry analysis, and 3 that were novel in a European-specific analysis. We sought to identify AF-associated genes at the GWAS loci by performing RNA-sequencing and expression quantitative trait locus analyses in 101 left atrial samples, the most relevant tissue for AF. We also performed transcriptome-wide analyses that identified 57 AF-associated genes, 42 of which overlap with GWAS loci. The identified loci implicate genes enriched within cardiac developmental, electrophysiological, contractile and structural pathways. These results extend our understanding of the biological pathways underlying AF and may facilitate the development of therapeutics for AF. |
LCA Rutten-Jacobs; DJ Tozer; M Duering; R Malik; M Dichgans; HS Markus M Traylor Genetic Study of White Matter Integrity in UK Biobank (N=8448) and the Overlap With Stroke, Depression, and Dementia Journal Article Stroke, 2018. @article{Rutten-Jacobs2018, title = {Genetic Study of White Matter Integrity in UK Biobank (N=8448) and the Overlap With Stroke, Depression, and Dementia}, author = {LCA Rutten-Jacobs and DJ Tozer and M Duering and R Malik and M Dichgans and HS Markus M Traylor }, url = {https://www.ncbi.nlm.nih.gov/pubmed/29752348}, year = {2018}, date = {2018-06-10}, journal = {Stroke}, abstract = {BACKGROUND AND PURPOSE: Structural integrity of the white matter is a marker of cerebral small vessel disease, which is the major cause of vascular dementia and a quarter of all strokes. Genetic studies provide a way to obtain novel insights in the disease mechanism underlying cerebral small vessel disease. The aim was to identify common variants associated with microstructural integrity of the white matter and to elucidate the relationships of white matter structural integrity with stroke, major depressive disorder, and Alzheimer disease. METHODS: This genome-wide association analysis included 8448 individuals from UK Biobank-a population-based cohort study that recruited individuals from across the United Kingdom between 2006 and 2010, aged 40 to 69 years. Microstructural integrity was measured as fractional anisotropy- (FA) and mean diffusivity (MD)-derived parameters on diffusion tensor images. White matter hyperintensity volumes (WMHV) were assessed on T2-weighted fluid-attenuated inversion recovery images. RESULTS: We identified 1 novel locus at genome-wide significance (VCAN [versican]: rs13164785; P=3.7×10-18 for MD and rs67827860; P=1.3×10-14 for FA). LD score regression showed a significant genome-wide correlation between FA, MD, and WMHV (FA-WMHV rG 0.39 [SE, 0.15]; MD-WMHV rG 0.56 [SE, 0.19]). In polygenic risk score analysis, FA, MD, and WMHV were significantly associated with lacunar stroke, MD with major depressive disorder, and WMHV with Alzheimer disease. CONCLUSIONS: Genetic variants within the VCAN gene may play a role in the mechanisms underlying microstructural integrity of the white matter in the brain measured as FA and MD. Mechanisms underlying white matter alterations are shared with cerebrovascular disease, and inherited differences in white matter microstructure impact on Alzheimer disease and major depressive disorder.}, keywords = {}, pubstate = {published}, tppubtype = {article} } BACKGROUND AND PURPOSE: Structural integrity of the white matter is a marker of cerebral small vessel disease, which is the major cause of vascular dementia and a quarter of all strokes. Genetic studies provide a way to obtain novel insights in the disease mechanism underlying cerebral small vessel disease. The aim was to identify common variants associated with microstructural integrity of the white matter and to elucidate the relationships of white matter structural integrity with stroke, major depressive disorder, and Alzheimer disease. METHODS: This genome-wide association analysis included 8448 individuals from UK Biobank-a population-based cohort study that recruited individuals from across the United Kingdom between 2006 and 2010, aged 40 to 69 years. Microstructural integrity was measured as fractional anisotropy- (FA) and mean diffusivity (MD)-derived parameters on diffusion tensor images. White matter hyperintensity volumes (WMHV) were assessed on T2-weighted fluid-attenuated inversion recovery images. RESULTS: We identified 1 novel locus at genome-wide significance (VCAN [versican]: rs13164785; P=3.7×10-18 for MD and rs67827860; P=1.3×10-14 for FA). LD score regression showed a significant genome-wide correlation between FA, MD, and WMHV (FA-WMHV rG 0.39 [SE, 0.15]; MD-WMHV rG 0.56 [SE, 0.19]). In polygenic risk score analysis, FA, MD, and WMHV were significantly associated with lacunar stroke, MD with major depressive disorder, and WMHV with Alzheimer disease. CONCLUSIONS: Genetic variants within the VCAN gene may play a role in the mechanisms underlying microstructural integrity of the white matter in the brain measured as FA and MD. Mechanisms underlying white matter alterations are shared with cerebrovascular disease, and inherited differences in white matter microstructure impact on Alzheimer disease and major depressive disorder. |
Tom G Richardson; Philip C Haycock; Jie Zheng; Nicholas J Timpson; Tom R Gaunt; George Davey Smith; Caroline L Relton; Gibran Hemani Systematic Mendelian randomization framework elucidates hundreds of CpG sites which may mediate the influence of genetic variants on disease Journal Article Human molecular genetics, 2018. @article{Richardson2018, title = {Systematic Mendelian randomization framework elucidates hundreds of CpG sites which may mediate the influence of genetic variants on disease}, author = {Tom G Richardson and Philip C Haycock and Jie Zheng and Nicholas J Timpson and Tom R Gaunt and George Davey Smith and Caroline L Relton and Gibran Hemani }, url = {https://academic.oup.com/hmg/advance-article/doi/10.1093/hmg/ddy210/5034850}, year = {2018}, date = {2018-06-08}, journal = {Human molecular genetics}, abstract = {We have undertaken a systematic Mendelian randomization (MR) study using methylation quantitative trait loci (meQTL) as genetic instruments to assess the relationship between genetic variation, DNA methylation and 139 complex traits. Using two-sample MR, we identified 1,148 associations across 61 traits where genetic variants were associated with both proximal DNA methylation (i.e. cis-meQTL) and complex trait variation (P < 1.39x10−08). Joint likelihood mapping provided evidence that the genetic variant which influenced DNA methylation levels for 348 of these associations across 47 traits was also responsible for variation in complex traits. These associations showed a high rate of replication in the BIOS QTL and UK Biobank datasets for 14 selected traits, as 101 of the attempted 128 associations survived multiple testing corrections (P < 3.91 x 10−04). Integrating expression quantitative trait loci (eQTL) data suggested that genetic variants responsible for 306 of the 348 refined meQTL associations also influence gene expression, which indicates a coordinated system of effects that are consistent with causality. CpG sites were enriched for histone mark peaks in tissue types relevant to their associated trait and implicated genes were enriched across relevant biological pathways. Though we are unable to distinguish mediation from horizontal pleiotropy in these analyses, our findings should prove valuable in prioritising candidate loci where DNA methylation may influence traits and help develop mechanistic insight into the aetiology of complex disease.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We have undertaken a systematic Mendelian randomization (MR) study using methylation quantitative trait loci (meQTL) as genetic instruments to assess the relationship between genetic variation, DNA methylation and 139 complex traits. Using two-sample MR, we identified 1,148 associations across 61 traits where genetic variants were associated with both proximal DNA methylation (i.e. cis-meQTL) and complex trait variation (P < 1.39x10−08). Joint likelihood mapping provided evidence that the genetic variant which influenced DNA methylation levels for 348 of these associations across 47 traits was also responsible for variation in complex traits. These associations showed a high rate of replication in the BIOS QTL and UK Biobank datasets for 14 selected traits, as 101 of the attempted 128 associations survived multiple testing corrections (P < 3.91 x 10−04). Integrating expression quantitative trait loci (eQTL) data suggested that genetic variants responsible for 306 of the 348 refined meQTL associations also influence gene expression, which indicates a coordinated system of effects that are consistent with causality. CpG sites were enriched for histone mark peaks in tissue types relevant to their associated trait and implicated genes were enriched across relevant biological pathways. Though we are unable to distinguish mediation from horizontal pleiotropy in these analyses, our findings should prove valuable in prioritising candidate loci where DNA methylation may influence traits and help develop mechanistic insight into the aetiology of complex disease. |
Z Zhu; PH Lee; MD Chaffin; W Chung; PR Loh; Q Lu; DC Christiani; L Liang A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases Journal Article Nature Genetics, 2018. @article{Zhu2018, title = {A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases}, author = {Z Zhu and PH Lee and MD Chaffin and W Chung and PR Loh and Q Lu and DC Christiani and L Liang}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29785011}, year = {2018}, date = {2018-06-05}, journal = {Nature Genetics}, abstract = {Clinical and epidemiological data suggest that asthma and allergic diseases are associated and may share a common genetic etiology. We analyzed genome-wide SNP data for asthma and allergic diseases in 33,593 cases and 76,768 controls of European ancestry from UK Biobank. Two publicly available independent genome-wide association studies were used for replication. We have found a strong genome-wide genetic correlation between asthma and allergic diseases (rg = 0.75, P = 6.84 × 10-62). Cross-trait analysis identified 38 genome-wide significant loci, including 7 novel shared loci. Computational analysis showed that shared genetic loci are enriched in immune/inflammatory systems and tissues with epithelium cells. Our work identifies common genetic architectures shared between asthma and allergy and will help to advance understanding of the molecular mechanisms underlying co-morbid asthma and allergic diseases.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Clinical and epidemiological data suggest that asthma and allergic diseases are associated and may share a common genetic etiology. We analyzed genome-wide SNP data for asthma and allergic diseases in 33,593 cases and 76,768 controls of European ancestry from UK Biobank. Two publicly available independent genome-wide association studies were used for replication. We have found a strong genome-wide genetic correlation between asthma and allergic diseases (rg = 0.75, P = 6.84 × 10-62). Cross-trait analysis identified 38 genome-wide significant loci, including 7 novel shared loci. Computational analysis showed that shared genetic loci are enriched in immune/inflammatory systems and tissues with epithelium cells. Our work identifies common genetic architectures shared between asthma and allergy and will help to advance understanding of the molecular mechanisms underlying co-morbid asthma and allergic diseases. |
Saskia P. Hagenaars; Ratko Radaković; Christopher Crockford; Chloe Fawns-Ritchie; International FTD-Genomics Consortium (IFGC); Sarah E. Harris; Catharine R. Gale; Ian J. Deary Genetic risk for neurodegenerative disorders, and its overlap with cognitive ability and physical function Journal Article PLOS one, 2018. @article{Hagenaars2018, title = {Genetic risk for neurodegenerative disorders, and its overlap with cognitive ability and physical function}, author = {Saskia P. Hagenaars and Ratko Radaković and Christopher Crockford and Chloe Fawns-Ritchie and International FTD-Genomics Consortium (IFGC) and Sarah E. Harris and Catharine R. Gale and Ian J. Deary }, url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0198187}, year = {2018}, date = {2018-06-01}, journal = {PLOS one}, abstract = {Neurodegenerative disorders are associated with impaired cognitive function and worse physical health outcomes. This study aims to test whether polygenic risk for Alzheimer’s disease, Amyotrophic Lateral Sclerosis (ALS), or frontotemporal dementia (FTD) is associated with cognitive function and physical health in the UK Biobank, a cohort of healthy individuals. Group-based analyses were then performed to compare the top and bottom 10% for the three neurodegenerative polygenic risk scores; these groups were compared on the cognitive and physical health variables. Higher polygenic risk for AD, ALS, and FTD was associated with lower cognitive performance. Higher polygenic risk for FTD was also associated with increased forced expiratory volume in 1s and peak expiratory flow. A significant group difference was observed on the symbol digit substitution task between individuals with high polygenic risk for FTD and high polygenic risk for ALS. The results suggest some overlap between polygenic risk for neurodegenerative disorders, cognitive function and physical health.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Neurodegenerative disorders are associated with impaired cognitive function and worse physical health outcomes. This study aims to test whether polygenic risk for Alzheimer’s disease, Amyotrophic Lateral Sclerosis (ALS), or frontotemporal dementia (FTD) is associated with cognitive function and physical health in the UK Biobank, a cohort of healthy individuals. Group-based analyses were then performed to compare the top and bottom 10% for the three neurodegenerative polygenic risk scores; these groups were compared on the cognitive and physical health variables. Higher polygenic risk for AD, ALS, and FTD was associated with lower cognitive performance. Higher polygenic risk for FTD was also associated with increased forced expiratory volume in 1s and peak expiratory flow. A significant group difference was observed on the symbol digit substitution task between individuals with high polygenic risk for FTD and high polygenic risk for ALS. The results suggest some overlap between polygenic risk for neurodegenerative disorders, cognitive function and physical health. |
Gail Davies; Max Lam; Ian J. Deary Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function Journal Article Nature Communications, 2018. @article{Davies2018, title = {Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function}, author = {Gail Davies and Max Lam and Ian J. Deary }, url = {https://www.nature.com/articles/s41467-018-04362-x}, year = {2018}, date = {2018-05-29}, journal = {Nature Communications}, abstract = {General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N = 300,486; age 16–102) and find 148 genome-wide significant independent loci (P < 5 × 10−8) associated with general cognitive function. Within the novel genetic loci are variants associated with neurodegenerative and neurodevelopmental disorders, physical and psychiatric illnesses, and brain structure. Gene-based analyses find 709 genes associated with general cognitive function. Expression levels across the cortex are associated with general cognitive function. Using polygenic scores, up to 4.3% of variance in general cognitive function is predicted in independent samples. We detect significant genetic overlap between general cognitive function, reaction time, and many health variables including eyesight, hypertension, and longevity. In conclusion we identify novel genetic loci and pathways contributing to the heritability of general cognitive function.}, keywords = {}, pubstate = {published}, tppubtype = {article} } General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N = 300,486; age 16–102) and find 148 genome-wide significant independent loci (P < 5 × 10−8) associated with general cognitive function. Within the novel genetic loci are variants associated with neurodegenerative and neurodevelopmental disorders, physical and psychiatric illnesses, and brain structure. Gene-based analyses find 709 genes associated with general cognitive function. Expression levels across the cortex are associated with general cognitive function. Using polygenic scores, up to 4.3% of variance in general cognitive function is predicted in independent samples. We detect significant genetic overlap between general cognitive function, reaction time, and many health variables including eyesight, hypertension, and longevity. In conclusion we identify novel genetic loci and pathways contributing to the heritability of general cognitive function. |
Anthony P. Khawaja; Jessica N. Cooke Bailey; Nicholas J. Wareham; Robert A. Scott; Mark Simcoe; Robert P. Igo Jr; Yeunjoo E. Song; Robert Wojciechowski; Ching-Yu Cheng; Peng T. Khaw; Louis R. Pasquale; Jonathan L. Haines; Paul J. Foster; Janey L. Wiggs; Chris J. Hammond; Pirro G. Hysi UK Biobank Eye; Vision Consortium & NEIGHBORHOOD Consortium Genome-wide analyses identify 68 new loci associated with intraocular pressure and improve risk prediction for primary open-angle glaucoma Journal Article Nature Genetics, 2018. @article{Khawaja2018, title = {Genome-wide analyses identify 68 new loci associated with intraocular pressure and improve risk prediction for primary open-angle glaucoma}, author = {Anthony P. Khawaja and Jessica N. Cooke Bailey and Nicholas J. Wareham and Robert A. Scott and Mark Simcoe and Robert P. Igo Jr and Yeunjoo E. Song and Robert Wojciechowski and Ching-Yu Cheng and Peng T. Khaw and Louis R. Pasquale and Jonathan L. Haines and Paul J. Foster and Janey L. Wiggs and Chris J. Hammond and Pirro G. Hysi UK Biobank Eye and Vision Consortium & NEIGHBORHOOD Consortium}, url = {https://www.nature.com/articles/s41588-018-0126-8}, year = {2018}, date = {2018-05-22}, journal = {Nature Genetics}, abstract = {Glaucoma is the leading cause of irreversible blindness globally1. Despite its gravity, the disease is frequently undiagnosed in the community2. Raised intraocular pressure (IOP) is the most important risk factor for primary open-angle glaucoma (POAG)3,4. Here we present a meta-analysis of 139,555 European participants, which identified 112 genomic loci associated with IOP, 68 of which are novel. These loci suggest a strong role for angiopoietin-receptor tyrosine kinase signaling, lipid metabolism, mitochondrial function and developmental processes underlying risk for elevated IOP. In addition, 48 of these loci were nominally associated with glaucoma in an independent cohort, 14 of which were significant at a Bonferroni-corrected threshold. Regression-based glaucoma-prediction models had an area under the receiver operating characteristic curve (AUROC) of 0.76 in US NEIGHBORHOOD study participants and 0.74 in independent glaucoma cases from the UK Biobank. Genetic-prediction models for POAG offer an opportunity to target screening and timely therapy to individuals most at risk.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Glaucoma is the leading cause of irreversible blindness globally1. Despite its gravity, the disease is frequently undiagnosed in the community2. Raised intraocular pressure (IOP) is the most important risk factor for primary open-angle glaucoma (POAG)3,4. Here we present a meta-analysis of 139,555 European participants, which identified 112 genomic loci associated with IOP, 68 of which are novel. These loci suggest a strong role for angiopoietin-receptor tyrosine kinase signaling, lipid metabolism, mitochondrial function and developmental processes underlying risk for elevated IOP. In addition, 48 of these loci were nominally associated with glaucoma in an independent cohort, 14 of which were significant at a Bonferroni-corrected threshold. Regression-based glaucoma-prediction models had an area under the receiver operating characteristic curve (AUROC) of 0.76 in US NEIGHBORHOOD study participants and 0.74 in independent glaucoma cases from the UK Biobank. Genetic-prediction models for POAG offer an opportunity to target screening and timely therapy to individuals most at risk. |
Riccardo E. Marioni; Sarah E. Harris; Qian Zhang; Allan F. McRae; Saskia P. Hagenaars; W. David Hill; Gail Davies; Craig W. Ritchie; Catharine R. Gale; John M. Starr; Alison M. Goate; David J. Porteous; Jian Yang; Kathryn L. Evans; Ian J. Deary; Naomi R. Wray; Peter M. Visscher GWAS on family history of Alzheimer’s disease Journal Article Translational Psychiatry, 2018. @article{Marioni2018, title = {GWAS on family history of Alzheimer’s disease}, author = {Riccardo E. Marioni and Sarah E. Harris and Qian Zhang and Allan F. McRae and Saskia P. Hagenaars and W. David Hill and Gail Davies and Craig W. Ritchie and Catharine R. Gale and John M. Starr and Alison M. Goate and David J. Porteous and Jian Yang and Kathryn L. Evans and Ian J. Deary and Naomi R. Wray and Peter M. Visscher}, url = {https://www.nature.com/articles/s41398-018-0150-6}, year = {2018}, date = {2018-05-18}, journal = {Translational Psychiatry}, abstract = {Alzheimer’s disease (AD) is a public health priority for the 21st century. Risk reduction currently revolves around lifestyle changes with much research trying to elucidate the biological underpinnings. We show that self-report of parental history of Alzheimer’s dementia for case ascertainment in a genome-wide association study of 314,278 participants from UK Biobank (27,696 maternal cases, 14,338 paternal cases) is a valid proxy for an AD genetic study. After meta-analysing with published consortium data (n = 74,046 with 25,580 cases across the discovery and replication analyses), three new AD-associated loci (P < 5 × 10−8) are identified. These contain genes relevant for AD and neurodegeneration: ADAM10, BCKDK/KAT8 and ACE. Novel gene-based loci include drug targets such as VKORC1 (warfarin dose). We report evidence that the association of SNPs in the TOMM40 gene with AD is potentially mediated by both gene expression and DNA methylation in the prefrontal cortex. However, it is likely that multiple variants are affecting the trait and gene methylation/expression. Our discovered loci may help to elucidate the biological mechanisms underlying AD and, as they contain genes that are drug targets for other diseases and disorders, warrant further exploration for potential precision medicine applications.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Alzheimer’s disease (AD) is a public health priority for the 21st century. Risk reduction currently revolves around lifestyle changes with much research trying to elucidate the biological underpinnings. We show that self-report of parental history of Alzheimer’s dementia for case ascertainment in a genome-wide association study of 314,278 participants from UK Biobank (27,696 maternal cases, 14,338 paternal cases) is a valid proxy for an AD genetic study. After meta-analysing with published consortium data (n = 74,046 with 25,580 cases across the discovery and replication analyses), three new AD-associated loci (P < 5 × 10−8) are identified. These contain genes relevant for AD and neurodegeneration: ADAM10, BCKDK/KAT8 and ACE. Novel gene-based loci include drug targets such as VKORC1 (warfarin dose). We report evidence that the association of SNPs in the TOMM40 gene with AD is potentially mediated by both gene expression and DNA methylation in the prefrontal cortex. However, it is likely that multiple variants are affecting the trait and gene methylation/expression. Our discovered loci may help to elucidate the biological mechanisms underlying AD and, as they contain genes that are drug targets for other diseases and disorders, warrant further exploration for potential precision medicine applications. |
C. Mary Schooling; Shan Luo; Shiu Lun Au Yeung; Deborah J. Thompson; Savita Karthikeyan; Thomas R. Bolton; Amy M. Mason; Erik Ingelsson; Stephen Burgess; Stephen Burgess Genetic predictors of testosterone and their associations with cardiovascular disease and risk factors: A Mendelian randomization investigation Journal Article International Journal of Cardiology, 2018. @article{Schooling2018, title = {Genetic predictors of testosterone and their associations with cardiovascular disease and risk factors: A Mendelian randomization investigation}, author = { C. Mary Schooling and Shan Luo and Shiu Lun Au Yeung and Deborah J. Thompson and Savita Karthikeyan and Thomas R. Bolton and Amy M. Mason and Erik Ingelsson and Stephen Burgess and Stephen Burgess}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29804699}, year = {2018}, date = {2018-05-18}, journal = {International Journal of Cardiology}, abstract = {BACKGROUND: Testosterone supplementation has been linked to increased cardiovascular disease risk in some observational studies. The causal role of testosterone can be investigated using a Mendelian randomization approach. METHODS AND RESULTS: We assessed genetic associations of variants in two gene regions (SHBG and JMJD1C) with several cardiovascular risk factors (lipids, adiponectin, blood pressure, anthropometric traits) plus male pattern baldness, including control outcomes and potential mediators. We assessed genetic associations with coronary artery disease (CAD) risk in the CARDIoGRAMplusC4D consortium (171,191 individuals including 60,801 cases), and associations with CAD and ischaemic stroke risk in the UK Biobank (367,643 individuals including 25,352 CAD cases and 3650 ischaemic stroke cases). Genetic predictors of increased serum testosterone were associated with lipids, blood pressure, and height. There was some evidence of an association with risk of CAD (SHBG gene region: odds ratio (OR) 0.95 per 1 unit increase in log-transformed testosterone [95% confidence interval: 0.81-1.12, p = 0.55]; JMJD1C gene region: OR 1.24 [1.01-1.51, p = 0.04]) and ischaemic stroke both overall (SHBG: OR 1.05 [0.64, 1.73, p = 0.83]; JMJD1C: OR 2.52 [1.33, 4.77, p = 0.005]) and in men. However, associations with some control outcomes were in the opposite direction to that expected. CONCLUSIONS: Sex hormone-related mechanisms appear to be relevant to cardiovascular risk factors and for stroke (particularly for men). However, the extent that these findings are specifically informative about endogenous testosterone or testosterone supplementation is unclear. These findings underline a fundamental limitation for the use of Mendelian randomization where biological knowledge about the function of genetic variants is uncertain.}, keywords = {}, pubstate = {published}, tppubtype = {article} } BACKGROUND: Testosterone supplementation has been linked to increased cardiovascular disease risk in some observational studies. The causal role of testosterone can be investigated using a Mendelian randomization approach. METHODS AND RESULTS: We assessed genetic associations of variants in two gene regions (SHBG and JMJD1C) with several cardiovascular risk factors (lipids, adiponectin, blood pressure, anthropometric traits) plus male pattern baldness, including control outcomes and potential mediators. We assessed genetic associations with coronary artery disease (CAD) risk in the CARDIoGRAMplusC4D consortium (171,191 individuals including 60,801 cases), and associations with CAD and ischaemic stroke risk in the UK Biobank (367,643 individuals including 25,352 CAD cases and 3650 ischaemic stroke cases). Genetic predictors of increased serum testosterone were associated with lipids, blood pressure, and height. There was some evidence of an association with risk of CAD (SHBG gene region: odds ratio (OR) 0.95 per 1 unit increase in log-transformed testosterone [95% confidence interval: 0.81-1.12, p = 0.55]; JMJD1C gene region: OR 1.24 [1.01-1.51, p = 0.04]) and ischaemic stroke both overall (SHBG: OR 1.05 [0.64, 1.73, p = 0.83]; JMJD1C: OR 2.52 [1.33, 4.77, p = 0.005]) and in men. However, associations with some control outcomes were in the opposite direction to that expected. CONCLUSIONS: Sex hormone-related mechanisms appear to be relevant to cardiovascular risk factors and for stroke (particularly for men). However, the extent that these findings are specifically informative about endogenous testosterone or testosterone supplementation is unclear. These findings underline a fundamental limitation for the use of Mendelian randomization where biological knowledge about the function of genetic variants is uncertain. |
Momoko Horikoshi; Felix R. Day; Masato Akiyama; Makoto Hirata; Yoichiro Kamatani; Koichi Matsuda and Kazuyoshi Ishigaki; Masahiro Kanai; Hollis Wright; Carlos A. Toro; Sergio R. Ojeda; Alejandro Lomniczi; Michiaki Kubo; Ken K. Ong; John.R.B. Perry Elucidating the genetic architecture of reproductive ageing in the Japanese population Journal Article Nature Communications, 2018. @article{Horikoshi2018, title = {Elucidating the genetic architecture of reproductive ageing in the Japanese population}, author = {Momoko Horikoshi and Felix R. Day and Masato Akiyama and Makoto Hirata and Yoichiro Kamatani and Koichi Matsuda and Kazuyoshi Ishigaki and Masahiro Kanai and Hollis Wright and Carlos A. Toro and Sergio R. Ojeda and Alejandro Lomniczi and Michiaki Kubo and Ken K. Ong and John.R.B. Perry}, url = {https://www.nature.com/articles/s41467-018-04398-z}, year = {2018}, date = {2018-05-17}, journal = {Nature Communications}, abstract = {Population studies elucidating the genetic architecture of reproductive ageing have been largely limited to European ancestries, restricting the generalizability of the findings and overlooking possible key genes poorly captured by common European genetic variation. Here, we report 26 loci (all P < 5 × 10–8) for reproductive ageing, i.e. puberty timing or age at menopause, in a non-European population (up to 67,029 women of Japanese ancestry). Highlighted genes for menopause include GNRH1, which supports a primary, rather than passive, role for hypothalamic-pituitary GnRH signalling in the timing of menopause. For puberty timing, we demonstrate an aetiological role for receptor-like protein tyrosine phosphatases by combining evidence across population genetics and pre- and peri-pubertal changes in hypothalamic gene expression in rodent and primate models. Furthermore, our findings demonstrate widespread differences in allele frequencies and effect estimates between Japanese and European associated variants, highlighting the benefits and challenges of large-scale trans-ethnic approaches.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Population studies elucidating the genetic architecture of reproductive ageing have been largely limited to European ancestries, restricting the generalizability of the findings and overlooking possible key genes poorly captured by common European genetic variation. Here, we report 26 loci (all P < 5 × 10–8) for reproductive ageing, i.e. puberty timing or age at menopause, in a non-European population (up to 67,029 women of Japanese ancestry). Highlighted genes for menopause include GNRH1, which supports a primary, rather than passive, role for hypothalamic-pituitary GnRH signalling in the timing of menopause. For puberty timing, we demonstrate an aetiological role for receptor-like protein tyrosine phosphatases by combining evidence across population genetics and pre- and peri-pubertal changes in hypothalamic gene expression in rodent and primate models. Furthermore, our findings demonstrate widespread differences in allele frequencies and effect estimates between Japanese and European associated variants, highlighting the benefits and challenges of large-scale trans-ethnic approaches. |
Julia Ramírez; Stefan van Duijvenboden; Ioanna Ntalla; Borbala Mifsud; Helen R Warren; Evan Tzanis; Michele Orini; Andrew Tinker; Pier D. Lambiase; Patricia B. Munroe Thirty loci identified for heart rate response to exercise and recovery implicate autonomic nervous system Journal Article Nature Communications, 2018. @article{Ramírez2018, title = {Thirty loci identified for heart rate response to exercise and recovery implicate autonomic nervous system}, author = {Julia Ramírez and Stefan van Duijvenboden and Ioanna Ntalla and Borbala Mifsud and Helen R Warren and Evan Tzanis and Michele Orini and Andrew Tinker and Pier D. Lambiase and Patricia B. Munroe}, url = {https://www.nature.com/articles/s41467-018-04148-1}, year = {2018}, date = {2018-05-16}, journal = {Nature Communications}, abstract = {Impaired capacity to increase heart rate (HR) during exercise (ΔHRex), and a reduced rate of recovery post-exercise (ΔHRrec) are associated with higher cardiovascular mortality rates. Currently, the genetic basis of both phenotypes remains to be elucidated. We conduct genome-wide association studies (GWASs) for ΔHRex and ΔHRrec in ~40,000 individuals, followed by replication in ~27,000 independent samples, all from UK Biobank. Six and seven single-nucleotide polymorphisms for ΔHRex and ΔHRrec, respectively, formally replicate. In a full data set GWAS, eight further loci for ΔHRex and nine for ΔHRrec are genome-wide significant (P ≤ 5 × 10−8). In total, 30 loci are discovered, 8 being common across traits. Processes of neural development and modulation of adrenergic activity by the autonomic nervous system are enriched in these results. Our findings reinforce current understanding of HR response to exercise and recovery and could guide future studies evaluating its contribution to cardiovascular risk prediction.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Impaired capacity to increase heart rate (HR) during exercise (ΔHRex), and a reduced rate of recovery post-exercise (ΔHRrec) are associated with higher cardiovascular mortality rates. Currently, the genetic basis of both phenotypes remains to be elucidated. We conduct genome-wide association studies (GWASs) for ΔHRex and ΔHRrec in ~40,000 individuals, followed by replication in ~27,000 independent samples, all from UK Biobank. Six and seven single-nucleotide polymorphisms for ΔHRex and ΔHRrec, respectively, formally replicate. In a full data set GWAS, eight further loci for ΔHRex and nine for ΔHRrec are genome-wide significant (P ≤ 5 × 10−8). In total, 30 loci are discovered, 8 being common across traits. Processes of neural development and modulation of adrenergic activity by the autonomic nervous system are enriched in these results. Our findings reinforce current understanding of HR response to exercise and recovery and could guide future studies evaluating its contribution to cardiovascular risk prediction. |
NC Peeri; JH Creed; GM Anic; RC Thompson; JJ Olson; RV LaRocca; SA Chowdhary; JD Brockman; TA Gerke; LB Nabors; KM Egan Toenail selenium, genetic variation in selenoenzymes and risk and outcome in glioma Journal Article Cancer Epidemiology, 2018. @article{Peeri2018, title = {Toenail selenium, genetic variation in selenoenzymes and risk and outcome in glioma}, author = {NC Peeri and JH Creed and GM Anic and RC Thompson and JJ Olson and RV LaRocca and SA Chowdhary and JD Brockman and TA Gerke and LB Nabors and KM Egan}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29777993}, year = {2018}, date = {2018-05-16}, journal = {Cancer Epidemiology}, abstract = {BACKGROUND: Selenium is an essential trace element obtained through diet that plays a critical role in DNA synthesis and protection from oxidative damage. Selenium intake and polymorphisms in selenoproteins have been linked to the risk of certain cancers though data for glioma are sparse. METHODS: In a case-control study of glioma, we examined the associations of selenium in toenails and genetic variants in the selenoenzyme pathway with the risk of glioma and patient survival. A total of 423 genetic variants in 29 candidate genes in the selenoenzyme pathway were studied in 1547 glioma cases and 1014 healthy controls. Genetic associations were also examined in the UK Biobank cohort comprised of 313,868 persons with 322 incident glioma cases. Toenail selenium was measured in a subcohort of 300 glioma cases and 300 age-matched controls from the case-control study. RESULTS: None of the 423 variants studied were consistently associated with glioma risk in the case-control and cohort studies. Moreover, toenail selenium in the case-control study had no significant association with glioma risk (p trend = 0.70) or patient survival among 254 patients with high grade tumors (p trend = 0.70). CONCLUSION: The present study offers no support for the hypothesis that selenium plays a role in the onset of glioma or patient outcome.}, keywords = {}, pubstate = {published}, tppubtype = {article} } BACKGROUND: Selenium is an essential trace element obtained through diet that plays a critical role in DNA synthesis and protection from oxidative damage. Selenium intake and polymorphisms in selenoproteins have been linked to the risk of certain cancers though data for glioma are sparse. METHODS: In a case-control study of glioma, we examined the associations of selenium in toenails and genetic variants in the selenoenzyme pathway with the risk of glioma and patient survival. A total of 423 genetic variants in 29 candidate genes in the selenoenzyme pathway were studied in 1547 glioma cases and 1014 healthy controls. Genetic associations were also examined in the UK Biobank cohort comprised of 313,868 persons with 322 incident glioma cases. Toenail selenium was measured in a subcohort of 300 glioma cases and 300 age-matched controls from the case-control study. RESULTS: None of the 423 variants studied were consistently associated with glioma risk in the case-control and cohort studies. Moreover, toenail selenium in the case-control study had no significant association with glioma risk (p trend = 0.70) or patient survival among 254 patients with high grade tumors (p trend = 0.70). CONCLUSION: The present study offers no support for the hypothesis that selenium plays a role in the onset of glioma or patient outcome. |
SM Sodini; KE Kemper; NR Wray; M Trzaskowski Comparison of Genotypic and Phenotypic Correlations: Cheverud's Conjecture in Humans Journal Article Genetics , 2018. @article{Sodini2018, title = {Comparison of Genotypic and Phenotypic Correlations: Cheverud's Conjecture in Humans}, author = {SM Sodini and KE Kemper and NR Wray and M Trzaskowski}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29739817}, year = {2018}, date = {2018-05-08}, journal = {Genetics }, abstract = {Accurate estimation of genetic correlation requires large sample sizes and access to genetically informative data, which are not always available. Accordingly, phenotypic correlations are often assumed to reflect genotypic correlations in evolutionary biology. Cheverud's conjecture asserts that the use of phenotypic correlations as proxies for genetic correlations is appropriate. Empirical evidence of the conjecture has been found across plant and animal species, with results suggesting that there is indeed a robust relationship between the two. Here, we investigate the conjecture in human populations, an analysis made possible by recent developments in availability of human genomic data and computing resources. A sample of 108,035 British European individuals from the UK Biobank was split equally into discovery and replication datasets. 17 traits were selected based on sample size, distribution and heritability. Genetic correlations were calculated using linkage disequilibrium score regression applied to the genome-wide association summary statistics of pairs of traits, and compared within and across datasets. Strong and significant correlations were found for the between-dataset comparison, suggesting that the genetic correlations from one independent sample were able to predict the phenotypic correlations from another independent sample within the same population. Designating the selected traits as morphological or non-morphological indicated little difference in correlation. The results of this study support the existence of a relationship between genetic and phenotypic correlations in humans. This finding is of specific interest in anthropological studies, which use measured phenotypic correlations to make inferences about the genetics of ancient human populations.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Accurate estimation of genetic correlation requires large sample sizes and access to genetically informative data, which are not always available. Accordingly, phenotypic correlations are often assumed to reflect genotypic correlations in evolutionary biology. Cheverud's conjecture asserts that the use of phenotypic correlations as proxies for genetic correlations is appropriate. Empirical evidence of the conjecture has been found across plant and animal species, with results suggesting that there is indeed a robust relationship between the two. Here, we investigate the conjecture in human populations, an analysis made possible by recent developments in availability of human genomic data and computing resources. A sample of 108,035 British European individuals from the UK Biobank was split equally into discovery and replication datasets. 17 traits were selected based on sample size, distribution and heritability. Genetic correlations were calculated using linkage disequilibrium score regression applied to the genome-wide association summary statistics of pairs of traits, and compared within and across datasets. Strong and significant correlations were found for the between-dataset comparison, suggesting that the genetic correlations from one independent sample were able to predict the phenotypic correlations from another independent sample within the same population. Designating the selected traits as morphological or non-morphological indicated little difference in correlation. The results of this study support the existence of a relationship between genetic and phenotypic correlations in humans. This finding is of specific interest in anthropological studies, which use measured phenotypic correlations to make inferences about the genetics of ancient human populations. |
Emma A. D. Clifton; John R. B. Perry; Fumiaki Imamura; Luca A. Lotta; Soren Brage; Nita G. Forouhi; Simon J. Griffin; Nicholas J. Wareham; Ken K. Ong; Felix R. Day Genome-wide association study for risk taking propensity indicates shared pathways with body mass index Journal Article Communications Biology, 2018. @article{Clifton2018, title = {Genome-wide association study for risk taking propensity indicates shared pathways with body mass index}, author = {Emma A. D. Clifton and John R. B. Perry and Fumiaki Imamura and Luca A. Lotta and Soren Brage and Nita G. Forouhi and Simon J. Griffin and Nicholas J. Wareham and Ken K. Ong and Felix R. Day}, url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6123697/}, year = {2018}, date = {2018-05-03}, journal = {Communications Biology}, abstract = {Risk-taking propensity is a trait of significant public health relevance but few specific genetic factors are known. Here we perform a genome-wide association study of self-reported risk-taking propensity among 436,236 white European UK Biobank study participants. We identify genome-wide associations at 26 loci (P < 5 × 10-8), 24 of which are novel, implicating genes enriched in the GABA and GABA receptor pathways. Modelling the relationship between risk-taking propensity and body mass index (BMI) using Mendelian randomisation shows a positive association (0.25 approximate SDs of BMI (SE: 0.06); P = 6.7 × 10-5). The impact of individual SNPs is heterogeneous, indicating a complex relationship arising from multiple shared pathways. We identify positive genetic correlations between risk-taking and waist-hip ratio, childhood obesity, ever smoking, attention-deficit hyperactivity disorder, bipolar disorder and schizophrenia, alongside a negative correlation with women's age at first birth. These findings highlight that behavioural pathways involved in risk-taking propensity may play a role in obesity, smoking and psychiatric disorders.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Risk-taking propensity is a trait of significant public health relevance but few specific genetic factors are known. Here we perform a genome-wide association study of self-reported risk-taking propensity among 436,236 white European UK Biobank study participants. We identify genome-wide associations at 26 loci (P < 5 × 10-8), 24 of which are novel, implicating genes enriched in the GABA and GABA receptor pathways. Modelling the relationship between risk-taking propensity and body mass index (BMI) using Mendelian randomisation shows a positive association (0.25 approximate SDs of BMI (SE: 0.06); P = 6.7 × 10-5). The impact of individual SNPs is heterogeneous, indicating a complex relationship arising from multiple shared pathways. We identify positive genetic correlations between risk-taking and waist-hip ratio, childhood obesity, ever smoking, attention-deficit hyperactivity disorder, bipolar disorder and schizophrenia, alongside a negative correlation with women's age at first birth. These findings highlight that behavioural pathways involved in risk-taking propensity may play a role in obesity, smoking and psychiatric disorders. |
E Tikkanen; S Gustafsson; D Amar; A Shcherbina; D Waggott; EA Ashley; E Ingelsson Biological Insights Into Muscular Strength: Genetic Findings in the UK Biobank Journal Article Scientific Reports, 2018. @article{Tikkanen2018b, title = {Biological Insights Into Muscular Strength: Genetic Findings in the UK Biobank}, author = {E Tikkanen and S Gustafsson and D Amar and A Shcherbina and D Waggott and EA Ashley and E Ingelsson }, url = {https://www.ncbi.nlm.nih.gov/pubmed/29691431}, year = {2018}, date = {2018-04-28}, journal = {Scientific Reports}, abstract = {We performed a large genome-wide association study to discover genetic variation associated with muscular strength, and to evaluate shared genetic aetiology with and causal effects of muscular strength on several health indicators. In our discovery analysis of 223,315 individuals, we identified 101 loci associated with grip strength (P <5 × 10-8). Of these, 64 were associated (P < 0.01 and consistent direction) also in the replication dataset (N = 111,610). eQTL analyses highlighted several genes known to play a role in neuro-developmental disorders or brain function, and the results from meta-analysis showed a significant enrichment of gene expression of brain-related transcripts. Further, we observed inverse genetic correlations of grip strength with cardiometabolic traits, and positive correlation with parents' age of death and education. We also showed that grip strength had shared biological pathways with indicators of frailty, including cognitive performance scores. By use of Mendelian randomization, we provide evidence that higher grip strength is protective of both coronary heart disease (OR = 0.69, 95% CI 0.60-0.79, P < 0.0001) and atrial fibrillation (OR = 0.75, 95% CI 0.62-0.90, P = 0.003). In conclusion, our results show shared genetic aetiology between grip strength, and cardiometabolic and cognitive health; and suggest that maintaining muscular strength could prevent future cardiovascular events.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We performed a large genome-wide association study to discover genetic variation associated with muscular strength, and to evaluate shared genetic aetiology with and causal effects of muscular strength on several health indicators. In our discovery analysis of 223,315 individuals, we identified 101 loci associated with grip strength (P <5 × 10-8). Of these, 64 were associated (P < 0.01 and consistent direction) also in the replication dataset (N = 111,610). eQTL analyses highlighted several genes known to play a role in neuro-developmental disorders or brain function, and the results from meta-analysis showed a significant enrichment of gene expression of brain-related transcripts. Further, we observed inverse genetic correlations of grip strength with cardiometabolic traits, and positive correlation with parents' age of death and education. We also showed that grip strength had shared biological pathways with indicators of frailty, including cognitive performance scores. By use of Mendelian randomization, we provide evidence that higher grip strength is protective of both coronary heart disease (OR = 0.69, 95% CI 0.60-0.79, P < 0.0001) and atrial fibrillation (OR = 0.75, 95% CI 0.62-0.90, P = 0.003). In conclusion, our results show shared genetic aetiology between grip strength, and cardiometabolic and cognitive health; and suggest that maintaining muscular strength could prevent future cardiovascular events. |
Naomi R. Wray; Stephan Ripke; the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression Journal Article Nature Genetics, 2018. @article{Wray2018, title = {Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression}, author = {Naomi R. Wray and Stephan Ripke and the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium}, url = {https://www.nature.com/articles/s41588-018-0090-3}, year = {2018}, date = {2018-04-26}, journal = {Nature Genetics}, abstract = {Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype. |
Connor A. Emdin; Amit V. Khera; Mark Chaffin; Derek Klarin; Pradeep Natarajan; Krishna Aragam; Mary Haas; Alexander Bick; Seyedeh M. Zekavat; Akihiro Nomura; Diego Ardissino; James G. Wilson; Heribert Schunkert; Ruth McPherson; Hugh Watkins; Roberto Elosua; Matthew J. Bown; Nilesh J. Samani; Usman Baber; Jeanette Erdmann; Namrata Gupta; John Danesh; Daniel Chasman; Paul Ridker; Joshua Denny; Lisa Bastarache; Judith H. Lichtman; Gail D’Onofrio; Jennifer Mattera; John A. Spertus; Wayne H.-H. Sheu; Kent D. Taylor; Bruce M. Psaty; Stephen S. Rich; Wendy Post; Jerome I. Rotter; Yii-Der Ida Chen; Harlan Krumholz; Danish Saleheen; Stacey Gabriel; Sekar Kathiresan - Analysis of predicted loss-of-function variants in UK Biobank identifies variants protective for disease Journal Article Nature Communications, 2018. @article{Emdin2018, title = {Analysis of predicted loss-of-function variants in UK Biobank identifies variants protective for disease}, author = {Connor A. Emdin and Amit V. Khera and Mark Chaffin and Derek Klarin and Pradeep Natarajan and Krishna Aragam and Mary Haas and Alexander Bick and Seyedeh M. Zekavat and Akihiro Nomura and Diego Ardissino and James G. Wilson and Heribert Schunkert and Ruth McPherson and Hugh Watkins and Roberto Elosua and Matthew J. Bown and Nilesh J. Samani and Usman Baber and Jeanette Erdmann and Namrata Gupta and John Danesh and Daniel Chasman and Paul Ridker and Joshua Denny and Lisa Bastarache and Judith H. Lichtman and Gail D’Onofrio and Jennifer Mattera and John A. Spertus and Wayne H.-H. Sheu and Kent D. Taylor and Bruce M. Psaty and Stephen S. Rich and Wendy Post and Jerome I. Rotter and Yii-Der Ida Chen and Harlan Krumholz and Danish Saleheen and Stacey Gabriel and Sekar Kathiresan - }, url = {https://www.nature.com/articles/s41467-018-03911-8}, year = {2018}, date = {2018-04-24}, journal = {Nature Communications}, abstract = {Less than 3% of protein-coding genetic variants are predicted to result in loss of protein function through the introduction of a stop codon, frameshift, or the disruption of an essential splice site; however, such predicted loss-of-function (pLOF) variants provide insight into effector transcript and direction of biological effect. In >400,000 UK Biobank participants, we conduct association analyses of 3759 pLOF variants with six metabolic traits, six cardiometabolic diseases, and twelve additional diseases. We identified 18 new low-frequency or rare (allele frequency < 5%) pLOF variant-phenotype associations. pLOF variants in the gene GPR151 protect against obesity and type 2 diabetes, in the gene IL33 against asthma and allergic disease, and in the gene IFIH1 against hypothyroidism. In the gene PDE3B, pLOF variants associate with elevated height, improved body fat distribution and protection from coronary artery disease. Our findings prioritize genes for which pharmacologic mimics of pLOF variants may lower risk for disease.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Less than 3% of protein-coding genetic variants are predicted to result in loss of protein function through the introduction of a stop codon, frameshift, or the disruption of an essential splice site; however, such predicted loss-of-function (pLOF) variants provide insight into effector transcript and direction of biological effect. In >400,000 UK Biobank participants, we conduct association analyses of 3759 pLOF variants with six metabolic traits, six cardiometabolic diseases, and twelve additional diseases. We identified 18 new low-frequency or rare (allele frequency < 5%) pLOF variant-phenotype associations. pLOF variants in the gene GPR151 protect against obesity and type 2 diabetes, in the gene IL33 against asthma and allergic disease, and in the gene IFIH1 against hypothyroidism. In the gene PDE3B, pLOF variants associate with elevated height, improved body fat distribution and protection from coronary artery disease. Our findings prioritize genes for which pharmacologic mimics of pLOF variants may lower risk for disease. |
Christopher DeBoever; Yosuke Tanigawa; Malene E. Lindholm; Greg McInnes; Adam Lavertu; Erik Ingelsson; Chris Chang; Euan A. Ashley; Carlos D. Bustamante; Mark J. Daly; Manuel A. Rivas Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study Journal Article Nature Communications, 2018. @article{DeBoever2018, title = {Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study}, author = {Christopher DeBoever and Yosuke Tanigawa and Malene E. Lindholm and Greg McInnes and Adam Lavertu and Erik Ingelsson and Chris Chang and Euan A. Ashley and Carlos D. Bustamante and Mark J. Daly and Manuel A. Rivas }, url = {https://www.nature.com/articles/s41467-018-03910-9}, year = {2018}, date = {2018-04-24}, journal = {Nature Communications}, abstract = {Protein-truncating variants can have profound effects on gene function and are critical for clinical genome interpretation and generating therapeutic hypotheses, but their relevance to medical phenotypes has not been systematically assessed. Here, we characterize the effect of 18,228 protein-truncating variants across 135 phenotypes from the UK Biobank and find 27 associations between medical phenotypes and protein-truncating variants in genes outside the major histocompatibility complex. We perform phenome-wide analyses and directly measure the effect in homozygous carriers, commonly referred to as “human knockouts,” across medical phenotypes for genes implicated as being protective against disease or associated with at least one phenotype in our study. We find several genes with strong pleiotropic or non-additive effects. Our results illustrate the importance of protein-truncating variants in a variety of diseases.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Protein-truncating variants can have profound effects on gene function and are critical for clinical genome interpretation and generating therapeutic hypotheses, but their relevance to medical phenotypes has not been systematically assessed. Here, we characterize the effect of 18,228 protein-truncating variants across 135 phenotypes from the UK Biobank and find 27 associations between medical phenotypes and protein-truncating variants in genes outside the major histocompatibility complex. We perform phenome-wide analyses and directly measure the effect in homozygous carriers, commonly referred to as “human knockouts,” across medical phenotypes for genes implicated as being protective against disease or associated with at least one phenotype in our study. We find several genes with strong pleiotropic or non-additive effects. Our results illustrate the importance of protein-truncating variants in a variety of diseases. |
Junfeng Liu; Loes Rutten-Jacobs; Ming Liu; Hugh S. Markus; Matthew Traylor Causal Impact of Type 2 Diabetes Mellitus on Cerebral Small Vessel Disease Journal Article Stroke, 2018. @article{Liu2018, title = {Causal Impact of Type 2 Diabetes Mellitus on Cerebral Small Vessel Disease}, author = {Junfeng Liu and Loes Rutten-Jacobs and Ming Liu and Hugh S. Markus and Matthew Traylor}, url = {http://stroke.ahajournals.org/content/49/6/1325.long}, year = {2018}, date = {2018-04-23}, journal = {Stroke}, abstract = {Background and Purpose—The relationship between type 2 diabetes mellitus (T2D) and cerebral small vessel disease (CSVD) is unclear. We aimed to examine the causal effect of T2D, fasting glucose levels, and higher insulin resistance on CSVD using Mendelian randomization. Methods—Five CSVD phenotypes were studied; 2 were clinical outcomes associated with CSVD (lacunar stroke: n=2191/27 297 and intracerebral hemorrhage [ICH]: n=2254/8195 [deep and lobar ICH]), whereas 3 were radiological markers of CSVD (white matter hyperintensities: n=8429; fractional anisotropy [FA]: n=8357; and mean diffusivity: n=8357). We applied 2 complementary analyses to evaluate the association of T2D with CSVD. First, we used summarized data from genome-wide association study to calculate the effects of T2D-related variants on CSVD with inverse-variance weighted and weighted median approaches. Second, we performed a genetic risk score approach to test the effects of T2D-associated variants on white matter hyperintensities, FA, and mean diffusivity using individual-level data in UK Biobank. Results—T2D was associated with higher risk of lacunar stroke (odds ratio [OR], 1.15; 95% confidence interval [CI], 1.04–1.28; P=0.007) and lower mean FA (OR, 0.78; 95% CI, 0.66–0.92; P=0.004) but not white matter hyperintensities volume (OR, 1.01; 95% CI, 0.97–1.04; P=0.626), higher mean diffusivity (OR, 1.04; 95% CI, 0.89–1.23; P=0.612), ICH (OR, 1.07; 95% CI, 0.95–1.20; P=0.269), lobar ICH (OR, 1.07; 95% CI, 0.89–1.28; P=0.466), or deep ICH (OR, 1.16; 95% CI, 0.99–1.36; P=0.074). Weighted median and penalized median weighted analysis showed similar effect estimates of T2D on lacunar stroke and FA, but with wider CIs, meaning they were not significant. The genetic score on individual-level data was significantly associated with FA (OR, 0.63; 95% CI, 0.45–0.89; P=0.008) after adjusting for potential confounders. Conclusions—Our Mendelian randomization study provides evidence to suggest that T2D may be causally associated with CSVD, in particular with lacunar stroke and FA.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background and Purpose—The relationship between type 2 diabetes mellitus (T2D) and cerebral small vessel disease (CSVD) is unclear. We aimed to examine the causal effect of T2D, fasting glucose levels, and higher insulin resistance on CSVD using Mendelian randomization. Methods—Five CSVD phenotypes were studied; 2 were clinical outcomes associated with CSVD (lacunar stroke: n=2191/27 297 and intracerebral hemorrhage [ICH]: n=2254/8195 [deep and lobar ICH]), whereas 3 were radiological markers of CSVD (white matter hyperintensities: n=8429; fractional anisotropy [FA]: n=8357; and mean diffusivity: n=8357). We applied 2 complementary analyses to evaluate the association of T2D with CSVD. First, we used summarized data from genome-wide association study to calculate the effects of T2D-related variants on CSVD with inverse-variance weighted and weighted median approaches. Second, we performed a genetic risk score approach to test the effects of T2D-associated variants on white matter hyperintensities, FA, and mean diffusivity using individual-level data in UK Biobank. Results—T2D was associated with higher risk of lacunar stroke (odds ratio [OR], 1.15; 95% confidence interval [CI], 1.04–1.28; P=0.007) and lower mean FA (OR, 0.78; 95% CI, 0.66–0.92; P=0.004) but not white matter hyperintensities volume (OR, 1.01; 95% CI, 0.97–1.04; P=0.626), higher mean diffusivity (OR, 1.04; 95% CI, 0.89–1.23; P=0.612), ICH (OR, 1.07; 95% CI, 0.95–1.20; P=0.269), lobar ICH (OR, 1.07; 95% CI, 0.89–1.28; P=0.466), or deep ICH (OR, 1.16; 95% CI, 0.99–1.36; P=0.074). Weighted median and penalized median weighted analysis showed similar effect estimates of T2D on lacunar stroke and FA, but with wider CIs, meaning they were not significant. The genetic score on individual-level data was significantly associated with FA (OR, 0.63; 95% CI, 0.45–0.89; P=0.008) after adjusting for potential confounders. Conclusions—Our Mendelian randomization study provides evidence to suggest that T2D may be causally associated with CSVD, in particular with lacunar stroke and FA. |
Edward Mountjoy; Neil M Davies; Denis Plotnikov; George Davey Smith; Santiago Rodriguez; Cathy E Williams; Jeremy A Guggenheim; Denize Atan Education and myopia: assessing the direction of causality by mendelian randomisation Journal Article BMJ Open, 2018. @article{Mountjoy2018, title = {Education and myopia: assessing the direction of causality by mendelian randomisation}, author = { Edward Mountjoy and Neil M Davies and Denis Plotnikov and George Davey Smith and Santiago Rodriguez and Cathy E Williams and Jeremy A Guggenheim and Denize Atan}, url = {https://www.bmj.com/content/361/bmj.k2022.long}, year = {2018}, date = {2018-04-19}, journal = {BMJ Open}, abstract = {Objectives To determine whether more years spent in education is a causal risk factor for myopia, or whether myopia is a causal risk factor for more years in education. Design Bidirectional, two sample mendelian randomisation study. Setting Publically available genetic data from two consortiums applied to a large, independent population cohort. Genetic variants used as proxies for myopia and years of education were derived from two large genome wide association studies: 23andMe and Social Science Genetic Association Consortium (SSGAC), respectively. Participants 67 798 men and women from England, Scotland, and Wales in the UK Biobank cohort with available information for years of completed education and refractive error. Main outcome measures Mendelian randomisation analyses were performed in two directions: the first exposure was the genetic predisposition to myopia, measured with 44 genetic variants strongly associated with myopia in 23andMe, and the outcome was years in education; and the second exposure was the genetic predisposition to higher levels of education, measured with 69 genetic variants from SSGAC, and the outcome was refractive error. Results Conventional regression analyses of the observational data suggested that every additional year of education was associated with a more myopic refractive error of −0.18 dioptres/y (95% confidence interval −0.19 to −0.17; P<2e-16). Mendelian randomisation analyses suggested the true causal effect was even stronger: −0.27 dioptres/y (−0.37 to −0.17; P=4e-8). By contrast, there was little evidence to suggest myopia affected education (years in education per dioptre of refractive error −0.008 y/dioptre, 95% confidence interval −0.041 to 0.025, P=0.6). Thus, the cumulative effect of more years in education on refractive error means that a university graduate from the United Kingdom with 17 years of education would, on average, be at least −1 dioptre more myopic than someone who left school at age 16 (with 12 years of education). Myopia of this magnitude would be sufficient to necessitate the use of glasses for driving. Sensitivity analyses showed minimal evidence for genetic confounding that could have biased the causal effect estimates. Conclusions This study shows that exposure to more years in education contributes to the rising prevalence of myopia. Increasing the length of time spent in education may inadvertently increase the prevalence of myopia and potential future visual disability.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Objectives To determine whether more years spent in education is a causal risk factor for myopia, or whether myopia is a causal risk factor for more years in education. Design Bidirectional, two sample mendelian randomisation study. Setting Publically available genetic data from two consortiums applied to a large, independent population cohort. Genetic variants used as proxies for myopia and years of education were derived from two large genome wide association studies: 23andMe and Social Science Genetic Association Consortium (SSGAC), respectively. Participants 67 798 men and women from England, Scotland, and Wales in the UK Biobank cohort with available information for years of completed education and refractive error. Main outcome measures Mendelian randomisation analyses were performed in two directions: the first exposure was the genetic predisposition to myopia, measured with 44 genetic variants strongly associated with myopia in 23andMe, and the outcome was years in education; and the second exposure was the genetic predisposition to higher levels of education, measured with 69 genetic variants from SSGAC, and the outcome was refractive error. Results Conventional regression analyses of the observational data suggested that every additional year of education was associated with a more myopic refractive error of −0.18 dioptres/y (95% confidence interval −0.19 to −0.17; P<2e-16). Mendelian randomisation analyses suggested the true causal effect was even stronger: −0.27 dioptres/y (−0.37 to −0.17; P=4e-8). By contrast, there was little evidence to suggest myopia affected education (years in education per dioptre of refractive error −0.008 y/dioptre, 95% confidence interval −0.041 to 0.025, P=0.6). Thus, the cumulative effect of more years in education on refractive error means that a university graduate from the United Kingdom with 17 years of education would, on average, be at least −1 dioptre more myopic than someone who left school at age 16 (with 12 years of education). Myopia of this magnitude would be sufficient to necessitate the use of glasses for driving. Sensitivity analyses showed minimal evidence for genetic confounding that could have biased the causal effect estimates. Conclusions This study shows that exposure to more years in education contributes to the rising prevalence of myopia. Increasing the length of time spent in education may inadvertently increase the prevalence of myopia and potential future visual disability. |
Manuel A.R. Ferreira; Judith M. Vonk and, Hansjörg Baurecht; Ingo Marenholz; Chao Tian; Joshua D. Hoffman; Quinta Helmer; Annika Tillander; Vilhelmina Ullemar; Yi Lu; Franz Rüschendorf; the 23andMe Research Team; the collaborators of the SHARE study; David A. Hinds; Norbert Hübner and Stephan Weidinger; Patrik K.E. Magnusson; Eric Jorgenson; Young-Ae Lee; Dorret I. Boomsma; Robert Karlsson; Catarina Almqvist; Gerard H. Koppelman; Lavinia Paternoster Eleven loci with new reproducible genetic associations with allergic disease risk Journal Article The Journal of Allergy and Clinical Immunology, 2018. @article{Ferreira2018, title = {Eleven loci with new reproducible genetic associations with allergic disease risk}, author = {Manuel A.R. Ferreira and Judith M. Vonk and, Hansjörg Baurecht and Ingo Marenholz and Chao Tian and Joshua D. Hoffman and Quinta Helmer and Annika Tillander and Vilhelmina Ullemar and Yi Lu and Franz Rüschendorf and the 23andMe Research Team and the collaborators of the SHARE study and David A. Hinds and Norbert Hübner and Stephan Weidinger and Patrik K.E. Magnusson and Eric Jorgenson and Young-Ae Lee and Dorret I. Boomsma and Robert Karlsson and Catarina Almqvist and Gerard H. Koppelman and Lavinia Paternoster }, url = {https://www.jacionline.org/article/S0091-6749(18)30558-X/fulltext}, year = {2018}, date = {2018-04-19}, journal = {The Journal of Allergy and Clinical Immunology}, abstract = {BACKGROUND: A recent genome-wide association study (GWAS) identified 99 loci that contain genetic risk variants shared between asthma, hay fever, and eczema. Many more risk loci shared between these common allergic diseases remain to be discovered, which could point to new therapeutic opportunities. OBJECTIVE: We sought to identify novel risk loci shared between asthma, hay fever, and eczema by applying a gene-based test of association to results from a published GWAS that included data from 360,838 subjects. METHODS: We used approximate conditional analysis to adjust the results from the published GWAS for the effects of the top risk variants identified in that study. We then analyzed the adjusted GWAS results with the EUGENE gene-based approach, which combines evidence for association with disease risk across regulatory variants identified in different tissues. Novel gene-based associations were followed up in an independent sample of 233,898 subjects from the UK Biobank study. RESULTS: Of the 19,432 genes tested, 30 had a significant gene-based association at a Bonferroni-corrected P value of 2.5 × 10-6. Of these, 20 were also significantly associated (P < .05/30 = .0016) with disease risk in the replication sample, including 19 that were located in 11 loci not reported to contain allergy risk variants in previous GWASs. Among these were 9 genes with a known function that is directly relevant to allergic disease: FOSL2, VPRBP, IPCEF1, PRR5L, NCF4, APOBR, IL27, ATXN2L, and LAT. For 4 genes (eg, ATXN2L), a genetically determined decrease in gene expression was associated with decreased allergy risk, and therefore drugs that inhibit gene expression or function are predicted to ameliorate disease symptoms. The opposite directional effect was observed for 14 genes, including IL27, a cytokine known to suppress TH2 responses. CONCLUSION: Using a gene-based approach, we identified 11 risk loci for allergic disease that were not reported in previous GWASs. Functional studies that investigate the contribution of the 19 associated genes to the pathophysiology of allergic disease and assess their therapeutic potential are warranted.}, keywords = {}, pubstate = {published}, tppubtype = {article} } BACKGROUND: A recent genome-wide association study (GWAS) identified 99 loci that contain genetic risk variants shared between asthma, hay fever, and eczema. Many more risk loci shared between these common allergic diseases remain to be discovered, which could point to new therapeutic opportunities. OBJECTIVE: We sought to identify novel risk loci shared between asthma, hay fever, and eczema by applying a gene-based test of association to results from a published GWAS that included data from 360,838 subjects. METHODS: We used approximate conditional analysis to adjust the results from the published GWAS for the effects of the top risk variants identified in that study. We then analyzed the adjusted GWAS results with the EUGENE gene-based approach, which combines evidence for association with disease risk across regulatory variants identified in different tissues. Novel gene-based associations were followed up in an independent sample of 233,898 subjects from the UK Biobank study. RESULTS: Of the 19,432 genes tested, 30 had a significant gene-based association at a Bonferroni-corrected P value of 2.5 × 10-6. Of these, 20 were also significantly associated (P < .05/30 = .0016) with disease risk in the replication sample, including 19 that were located in 11 loci not reported to contain allergy risk variants in previous GWASs. Among these were 9 genes with a known function that is directly relevant to allergic disease: FOSL2, VPRBP, IPCEF1, PRR5L, NCF4, APOBR, IL27, ATXN2L, and LAT. For 4 genes (eg, ATXN2L), a genetically determined decrease in gene expression was associated with decreased allergy risk, and therefore drugs that inhibit gene expression or function are predicted to ameliorate disease symptoms. The opposite directional effect was observed for 14 genes, including IL27, a cytokine known to suppress TH2 responses. CONCLUSION: Using a gene-based approach, we identified 11 risk loci for allergic disease that were not reported in previous GWASs. Functional studies that investigate the contribution of the 19 associated genes to the pathophysiology of allergic disease and assess their therapeutic potential are warranted. |
David M. Howard, Mark J. Adams, Masoud Shirali, Toni-Kim Clarke, Riccardo E. Marioni, Gail Davies, Jonathan R. I. Coleman, Clara Alloza, Xueyi Shen, Miruna C. Barbu, Eleanor M. Wigmore, Jude Gibson, 23andMe Research Team, Saskia P. Hagenaars, Cathryn M. Lewis, Joey Ward, Daniel J. Smith, Patrick F. Sullivan, Chris S. Haley, Gerome Breen, Ian J. Deary & Andrew M. McIntosh Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways Journal Article Nature Communications, 2018. @article{Howard2018, title = {Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways}, author = {David M. Howard, Mark J. Adams, Masoud Shirali, Toni-Kim Clarke, Riccardo E. Marioni, Gail Davies, Jonathan R. I. Coleman, Clara Alloza, Xueyi Shen, Miruna C. Barbu, Eleanor M. Wigmore, Jude Gibson, 23andMe Research Team, Saskia P. Hagenaars, Cathryn M. Lewis, Joey Ward, Daniel J. Smith, Patrick F. Sullivan, Chris S. Haley, Gerome Breen, Ian J. Deary & Andrew M. McIntosh }, url = {https://www.nature.com/articles/s41467-018-03819-3}, year = {2018}, date = {2018-04-16}, journal = {Nature Communications}, abstract = {Depression is a polygenic trait that causes extensive periods of disability. Previous genetic studies have identified common risk variants which have progressively increased in number with increasing sample sizes of the respective studies. Here, we conduct a genome-wide association study in 322,580 UK Biobank participants for three depression-related phenotypes: broad depression, probable major depressive disorder (MDD), and International Classification of Diseases (ICD, version 9 or 10)-coded MDD. We identify 17 independent loci that are significantly associated (P < 5 × 10−8) across the three phenotypes. The direction of effect of these loci is consistently replicated in an independent sample, with 14 loci likely representing novel findings. Gene sets are enriched in excitatory neurotransmission, mechanosensory behaviour, post synapse, neuron spine and dendrite functions. Our findings suggest that broad depression is the most tractable UK Biobank phenotype for discovering genes and gene sets that further our understanding of the biological pathways underlying depression.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Depression is a polygenic trait that causes extensive periods of disability. Previous genetic studies have identified common risk variants which have progressively increased in number with increasing sample sizes of the respective studies. Here, we conduct a genome-wide association study in 322,580 UK Biobank participants for three depression-related phenotypes: broad depression, probable major depressive disorder (MDD), and International Classification of Diseases (ICD, version 9 or 10)-coded MDD. We identify 17 independent loci that are significantly associated (P < 5 × 10−8) across the three phenotypes. The direction of effect of these loci is consistently replicated in an independent sample, with 14 loci likely representing novel findings. Gene sets are enriched in excitatory neurotransmission, mechanosensory behaviour, post synapse, neuron spine and dendrite functions. Our findings suggest that broad depression is the most tractable UK Biobank phenotype for discovering genes and gene sets that further our understanding of the biological pathways underlying depression. |
Jian Zeng; Ronald de Vlaming; Yang Wu; Matthew R. Robinson; Luke R. Lloyd-Jones; Loic Yengo; Chloe X. Yap; Angli Xue; Julia Sidorenko; Allan F. McRae; Joseph E. Powell; Grant W. Montgomery; Andres Metspalu; Tonu Esko; Greg Gibson; Naomi R. Wray; Peter M. Visscher; Jian Yang Signatures of negative selection in the genetic architecture of human complex traits Journal Article Nature Genetics, 2018. @article{Zeng2018, title = {Signatures of negative selection in the genetic architecture of human complex traits}, author = {Jian Zeng and Ronald de Vlaming and Yang Wu and Matthew R. Robinson and Luke R. Lloyd-Jones and Loic Yengo and Chloe X. Yap and Angli Xue and Julia Sidorenko and Allan F. McRae and Joseph E. Powell and Grant W. Montgomery and Andres Metspalu and Tonu Esko and Greg Gibson and Naomi R. Wray and Peter M. Visscher and Jian Yang }, url = {https://www.nature.com/articles/s41588-018-0101-4}, year = {2018}, date = {2018-04-16}, journal = {Nature Genetics}, abstract = {We develop a Bayesian mixed linear model that simultaneously estimates single-nucleotide polymorphism (SNP)-based heritability, polygenicity (proportion of SNPs with nonzero effects), and the relationship between SNP effect size and minor allele frequency for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752) and show that on average, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (P < 0.05/28) signatures of natural selection in the genetic architecture of 23 traits, including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. The significant estimates of the relationship between effect size and minor allele frequency in complex traits are consistent with a model of negative (or purifying) selection, as confirmed by forward simulation. We conclude that negative selection acts pervasively on the genetic variants associated with human complex traits.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We develop a Bayesian mixed linear model that simultaneously estimates single-nucleotide polymorphism (SNP)-based heritability, polygenicity (proportion of SNPs with nonzero effects), and the relationship between SNP effect size and minor allele frequency for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752) and show that on average, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (P < 0.05/28) signatures of natural selection in the genetic architecture of 23 traits, including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. The significant estimates of the relationship between effect size and minor allele frequency in complex traits are consistent with a model of negative (or purifying) selection, as confirmed by forward simulation. We conclude that negative selection acts pervasively on the genetic variants associated with human complex traits. |
A. Shadrina; Y. Tsepilov; M. Smetanina; E. Voronina; E. Seliverstov; E. Ilyukhin; A. Kirienko; I. Zolotukhin; M. Filipenko Polymorphisms of genes involved in inflammation and blood vessel development influence the risk of varicose veins Journal Article Clinical Genetics , 2018. @article{Shadrina2018, title = {Polymorphisms of genes involved in inflammation and blood vessel development influence the risk of varicose veins }, author = {A. Shadrina and Y. Tsepilov and M. Smetanina and E. Voronina and E. Seliverstov and E. Ilyukhin and A. Kirienko and I. Zolotukhin and M. Filipenko}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cge.13362}, year = {2018}, date = {2018-04-16}, journal = {Clinical Genetics }, abstract = {Heredity plays an important role in the etiology of varicose veins (VVs). However, the genetic basis underlying this condition remains poorly understood. Our aim was to replicate top association signals from genome‐wide association studies (GWASs) for VVs of lower extremities using 2 independent datasets—our sample of ethnic Russian individuals (709 cases and 278 controls) and a large cohort of British residents from UK Biobank (10 861 cases and 397 594 controls). Associations of polymorphisms rs11121615, rs6712038, rs507666, rs966562, rs7111987, rs6062618, and rs6905288 were validated in the UK Biobank individuals at a Bonferroni‐corrected significance level. In Russian cohort, only rs11121615 reached a nominal significance level of P < .05. Results of original GWAS and replication studies were combined by a meta‐analysis, and polymorphisms listed above as well as rs111434909 and rs4463578 passed a genome‐wide significant threshold. Notably, the majority of these polymorphisms were located within or near genes involved in vascular development and remodeling, and regulation of inflammatory response. Our results confirm the role of these polymorphisms in genetic susceptibility to VVs and indicate the revealed genomic regions as good candidates for further fine‐mapping studies and functional analysis. Moreover, our findings implicate inflammation and abnormal vascular architecture in VVs pathogenesis. }, keywords = {}, pubstate = {published}, tppubtype = {article} } Heredity plays an important role in the etiology of varicose veins (VVs). However, the genetic basis underlying this condition remains poorly understood. Our aim was to replicate top association signals from genome‐wide association studies (GWASs) for VVs of lower extremities using 2 independent datasets—our sample of ethnic Russian individuals (709 cases and 278 controls) and a large cohort of British residents from UK Biobank (10 861 cases and 397 594 controls). Associations of polymorphisms rs11121615, rs6712038, rs507666, rs966562, rs7111987, rs6062618, and rs6905288 were validated in the UK Biobank individuals at a Bonferroni‐corrected significance level. In Russian cohort, only rs11121615 reached a nominal significance level of P < .05. Results of original GWAS and replication studies were combined by a meta‐analysis, and polymorphisms listed above as well as rs111434909 and rs4463578 passed a genome‐wide significant threshold. Notably, the majority of these polymorphisms were located within or near genes involved in vascular development and remodeling, and regulation of inflammatory response. Our results confirm the role of these polymorphisms in genetic susceptibility to VVs and indicate the revealed genomic regions as good candidates for further fine‐mapping studies and functional analysis. Moreover, our findings implicate inflammation and abnormal vascular architecture in VVs pathogenesis. |
E Tikkanen; S Gustafsson; E Ingelsson Circulation , 2018. @article{Tikkanen2018, title = {Associations of Fitness, Physical Activity, Strength, and Genetic Risk With Cardiovascular Disease: Longitudinal Analyses in the UK Biobank Study}, author = {E Tikkanen and S Gustafsson and E Ingelsson }, url = {https://www.ncbi.nlm.nih.gov/pubmed/29632216}, year = {2018}, date = {2018-04-09}, journal = {Circulation }, abstract = {Background -Observational studies have shown inverse associations among fitness, physical activity, and cardiovascular disease. However, little is known about these associations in individuals with elevated genetic susceptibility for these diseases. Methods -We estimated associations of grip strength, objective and subjective physical activity, and cardiorespiratory fitness with cardiovascular events and all-cause death in a large cohort of 502635 individuals from the UK Biobank (median follow-up, 6.1 years; interquartile range, 5.4-6.8 years). Then we further examined these associations in individuals with different genetic burden by stratifying individuals based on their genetic risk scores for coronary heart disease and atrial fibrillation. We compared disease risk among individuals in different tertiles of fitness, physical activity, and genetic risk using lowest tertiles as reference. Results -Grip strength, physical activity, and cardiorespiratory fitness showed inverse associations with incident cardiovascular events (coronary heart disease: hazard ratio [HR], 0.79; 95% confidence interval [CI], 0.77- 0.81; HR, 0.95; 95% CI, 0.93-0.97; and HR, 0.68; 95% CI, 0.63-0.74, per SD change, respectively; atrial fibrillation: HR, 0.75; 95% CI, 0.73- 0.76; HR, 0.93; 95% CI, 0.91-0.95; and HR, 0.60; 95% CI, 0.56-0.65, per SD change, respectively). Higher grip strength and cardiorespiratory fitness were associated with lower risk of incident coronary heart disease and atrial fibrillation in each genetic risk score group (Ptrend <0.001 in each genetic risk category). In particular, high levels of cardiorespiratory fitness were associated with 49% lower risk for coronary heart disease (HR, 0.51; 95% CI, 0.38-0.69) and 60% lower risk for atrial fibrillation (HR, 0.40; 95%, CI 0.30-0.55) among individuals at high genetic risk for these diseases. Conclusions - Fitness and physical activity demonstrated inverse associations with incident cardiovascular disease in the general population, as well as in individuals with elevated genetic risk for these diseases.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background -Observational studies have shown inverse associations among fitness, physical activity, and cardiovascular disease. However, little is known about these associations in individuals with elevated genetic susceptibility for these diseases. Methods -We estimated associations of grip strength, objective and subjective physical activity, and cardiorespiratory fitness with cardiovascular events and all-cause death in a large cohort of 502635 individuals from the UK Biobank (median follow-up, 6.1 years; interquartile range, 5.4-6.8 years). Then we further examined these associations in individuals with different genetic burden by stratifying individuals based on their genetic risk scores for coronary heart disease and atrial fibrillation. We compared disease risk among individuals in different tertiles of fitness, physical activity, and genetic risk using lowest tertiles as reference. Results -Grip strength, physical activity, and cardiorespiratory fitness showed inverse associations with incident cardiovascular events (coronary heart disease: hazard ratio [HR], 0.79; 95% confidence interval [CI], 0.77- 0.81; HR, 0.95; 95% CI, 0.93-0.97; and HR, 0.68; 95% CI, 0.63-0.74, per SD change, respectively; atrial fibrillation: HR, 0.75; 95% CI, 0.73- 0.76; HR, 0.93; 95% CI, 0.91-0.95; and HR, 0.60; 95% CI, 0.56-0.65, per SD change, respectively). Higher grip strength and cardiorespiratory fitness were associated with lower risk of incident coronary heart disease and atrial fibrillation in each genetic risk score group (Ptrend <0.001 in each genetic risk category). In particular, high levels of cardiorespiratory fitness were associated with 49% lower risk for coronary heart disease (HR, 0.51; 95% CI, 0.38-0.69) and 60% lower risk for atrial fibrillation (HR, 0.40; 95%, CI 0.30-0.55) among individuals at high genetic risk for these diseases. Conclusions - Fitness and physical activity demonstrated inverse associations with incident cardiovascular disease in the general population, as well as in individuals with elevated genetic risk for these diseases. |
Hilary K. Finucane; Yakir A. Reshef; Verneri Anttila; Kamil Slowikowski; Alexander Gusev; Andrea Byrnes; Steven Gazal; Po-Ru Loh; Caleb Lareau; Noam Shoresh; Giulio Genovese; Arpiar Saunders; Evan Macosko; Samuela Pollack; The Brainstorm Consortium; John R. B. Perry; Jason D. Buenrostro; Bradley E. Bernstein; Soumya Raychaudhuri; Steven McCarroll; Benjamin M. Neale; Alkes L. Price Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types Journal Article Nature Genetics, 2018. @article{Finucane2018, title = {Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types}, author = {Hilary K. Finucane and Yakir A. Reshef and Verneri Anttila and Kamil Slowikowski and Alexander Gusev and Andrea Byrnes and Steven Gazal and Po-Ru Loh and Caleb Lareau and Noam Shoresh and Giulio Genovese and Arpiar Saunders and Evan Macosko and Samuela Pollack and The Brainstorm Consortium and John R. B. Perry and Jason D. Buenrostro and Bradley E. Bernstein and Soumya Raychaudhuri and Steven McCarroll and Benjamin M. Neale and Alkes L. Price }, url = {https://www.nature.com/articles/s41588-018-0081-4}, year = {2018}, date = {2018-04-09}, journal = {Nature Genetics}, abstract = {We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals. |
Ferdinando Bonfiglio; Tenghao Zheng; Koldo Garcia-Etxebarria; Fatemeh Hadizadeh; Luis Bujanda; Francesca Bresso; Lars Agreus; Anna Andreasson; Aldona Dlugosz; Greger Lindberg; Peter T. Schmidt; Pontus Karling; Bodil Ohlsson; Magnus Simren; Susanna Walter; Gerardo Nardone; Rosario Cuomo; Paolo Usai-Satta; Francesca Galeazzi; Matteo Neri; Piero Portincasa; Massimo Bellini; Giovanni Barbara; Anna Latiano Female-specific Association Between Variants on Chromosome 9 and Self-reported Diagnosis of Irritable Bowel Syndrome Journal Article Gastroenterology, 2018. @article{Bonfiglio2018, title = {Female-specific Association Between Variants on Chromosome 9 and Self-reported Diagnosis of Irritable Bowel Syndrome}, author = {Ferdinando Bonfiglio and Tenghao Zheng and Koldo Garcia-Etxebarria and Fatemeh Hadizadeh and Luis Bujanda and Francesca Bresso and Lars Agreus and Anna Andreasson and Aldona Dlugosz and Greger Lindberg and Peter T. Schmidt and Pontus Karling and Bodil Ohlsson and Magnus Simren and Susanna Walter and Gerardo Nardone and Rosario Cuomo and Paolo Usai-Satta and Francesca Galeazzi and Matteo Neri and Piero Portincasa and Massimo Bellini and Giovanni Barbara and Anna Latiano}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29626450}, year = {2018}, date = {2018-04-04}, journal = {Gastroenterology}, abstract = {BACKGROUND & AIMS: Genetic factors are believed to affect risk for irritable bowel syndrome (IBS), but there have been no sufficiently powered and adequately sized studies. To identify DNA variants associated with IBS risk, we performed a genome-wide association study (GWAS) of the large UK Biobank population-based cohort, which includes genotype and health data from 500,000 participants. METHODS: We studied 7,287,191 high-quality single-nucleotide polymorphisms in individuals who self-reported a doctor's diagnosis of IBS (cases; m=9576) compared to the remainder of the cohort (controls; n=336,499) (mean age of study subjects, 40-69 years). Genome-wide significant findings were further investigated in 2045 patients with IBS from tertiary centers and 7955 population controls from Europe and the United States, and a small general population sample from Sweden (n=249). Functional annotation of GWAS results was carried out by integrating data from multiple biorepositories, to obtain biological insights from the observed associations. RESULTS: We identified a genome-wide significant association on chromosome 9q31.2 (SNP rs10512344; P=3.57×10-8), in a region previously linked to age at menarche, and 13 additional loci of suggestive significance (P<5.0×10-6). Sex-stratified analyses revealed that the variants at 9q32.1 affect risk of IBS in only women (P=4.29×10-10 in UK Biobank) and also associate with constipation-predominant IBS in women (P=.015 in the tertiary cohort) and harder stools in women (P=.0012 in the population-based sample). Functional annotation of the 9q32.1 locus identified 8 candidate genes, including the elongator complex protein 1 gene (ELP1 or IKBKAP), which is mutated in patients with familial dysautonomia. CONCLUSIONS: In a sufficiently powered GWAS of IBS, we associated variants at the locus 9q32.1 with risk of IBS in women. This observation may provide additional rationale for investigating the role of sex hormones and autonomic dysfunction in IBS.}, keywords = {}, pubstate = {published}, tppubtype = {article} } BACKGROUND & AIMS: Genetic factors are believed to affect risk for irritable bowel syndrome (IBS), but there have been no sufficiently powered and adequately sized studies. To identify DNA variants associated with IBS risk, we performed a genome-wide association study (GWAS) of the large UK Biobank population-based cohort, which includes genotype and health data from 500,000 participants. METHODS: We studied 7,287,191 high-quality single-nucleotide polymorphisms in individuals who self-reported a doctor's diagnosis of IBS (cases; m=9576) compared to the remainder of the cohort (controls; n=336,499) (mean age of study subjects, 40-69 years). Genome-wide significant findings were further investigated in 2045 patients with IBS from tertiary centers and 7955 population controls from Europe and the United States, and a small general population sample from Sweden (n=249). Functional annotation of GWAS results was carried out by integrating data from multiple biorepositories, to obtain biological insights from the observed associations. RESULTS: We identified a genome-wide significant association on chromosome 9q31.2 (SNP rs10512344; P=3.57×10-8), in a region previously linked to age at menarche, and 13 additional loci of suggestive significance (P<5.0×10-6). Sex-stratified analyses revealed that the variants at 9q32.1 affect risk of IBS in only women (P=4.29×10-10 in UK Biobank) and also associate with constipation-predominant IBS in women (P=.015 in the tertiary cohort) and harder stools in women (P=.0012 in the population-based sample). Functional annotation of the 9q32.1 locus identified 8 candidate genes, including the elongator complex protein 1 gene (ELP1 or IKBKAP), which is mutated in patients with familial dysautonomia. CONCLUSIONS: In a sufficiently powered GWAS of IBS, we associated variants at the locus 9q32.1 with risk of IBS in women. This observation may provide additional rationale for investigating the role of sex hormones and autonomic dysfunction in IBS. |
LR Lloyd-Jones; MR Robinson; J Yang; PM Visscher Transformation of Summary Statistics from Linear Mixed Model Association on All-or-None Traits to Odds Ratio Journal Article Genetics , 2018. @article{Lloyd-Jones2018, title = {Transformation of Summary Statistics from Linear Mixed Model Association on All-or-None Traits to Odds Ratio}, author = {LR Lloyd-Jones and MR Robinson and J Yang and PM Visscher}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29429966}, year = {2018}, date = {2018-04-04}, journal = {Genetics }, abstract = {Genome-wide association studies (GWAS) have identified thousands of loci that are robustly associated with complex diseases. The use of linear mixed model (LMM) methodology for GWAS is becoming more prevalent due to its ability to control for population structure and cryptic relatedness and to increase power. The odds ratio (OR) is a common measure of the association of a disease with an exposure (e.g., a genetic variant) and is readably available from logistic regression. However, when the LMM is applied to all-or-none traits it provides estimates of genetic effects on the observed 0-1 scale, a different scale to that in logistic regression. This limits the comparability of results across studies, for example in a meta-analysis, and makes the interpretation of the magnitude of an effect from an LMM GWAS difficult. In this study, we derived transformations from the genetic effects estimated under the LMM to the OR that only rely on summary statistics. To test the proposed transformations, we used real genotypes from two large, publicly available data sets to simulate all-or-none phenotypes for a set of scenarios that differ in underlying model, disease prevalence, and heritability. Furthermore, we applied these transformations to GWAS summary statistics for type 2 diabetes generated from 108,042 individuals in the UK Biobank. In both simulation and real-data application, we observed very high concordance between the transformed OR from the LMM and either the simulated truth or estimates from logistic regression. The transformations derived and validated in this study improve the comparability of results from prospective and already performed LMM GWAS on complex diseases by providing a reliable transformation to a common comparative scale for the genetic effects.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Genome-wide association studies (GWAS) have identified thousands of loci that are robustly associated with complex diseases. The use of linear mixed model (LMM) methodology for GWAS is becoming more prevalent due to its ability to control for population structure and cryptic relatedness and to increase power. The odds ratio (OR) is a common measure of the association of a disease with an exposure (e.g., a genetic variant) and is readably available from logistic regression. However, when the LMM is applied to all-or-none traits it provides estimates of genetic effects on the observed 0-1 scale, a different scale to that in logistic regression. This limits the comparability of results across studies, for example in a meta-analysis, and makes the interpretation of the magnitude of an effect from an LMM GWAS difficult. In this study, we derived transformations from the genetic effects estimated under the LMM to the OR that only rely on summary statistics. To test the proposed transformations, we used real genotypes from two large, publicly available data sets to simulate all-or-none phenotypes for a set of scenarios that differ in underlying model, disease prevalence, and heritability. Furthermore, we applied these transformations to GWAS summary statistics for type 2 diabetes generated from 108,042 individuals in the UK Biobank. In both simulation and real-data application, we observed very high concordance between the transformed OR from the LMM and either the simulated truth or estimates from logistic regression. The transformations derived and validated in this study improve the comparability of results from prospective and already performed LMM GWAS on complex diseases by providing a reliable transformation to a common comparative scale for the genetic effects. |
Luke M. Evans; Rasool Tahmasbi; Matt Jones; Scott I. Vrieze; Gonçalo R. Abecasis; Sayantan Das; Douglas W. Bjelland; Teresa R. de Candia; Jian Yang; Michael E. Goddard; Peter M. Visscher; Matthew C. Keller; Haplotype Reference Consortium Narrow-sense heritability estimation of complex traits using identity-by-descent information Journal Article Heredity, 2018. @article{Evans2018, title = {Narrow-sense heritability estimation of complex traits using identity-by-descent information}, author = {Luke M. Evans and Rasool Tahmasbi and Matt Jones and Scott I. Vrieze and Gonçalo R. Abecasis and Sayantan Das and Douglas W. Bjelland and Teresa R. de Candia and Jian Yang and Michael E. Goddard and Peter M. Visscher and Matthew C. Keller and Haplotype Reference Consortium}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29588506}, year = {2018}, date = {2018-03-28}, journal = {Heredity}, abstract = {Heritability is a fundamental parameter in genetics. Traditional estimates based on family or twin studies can be biased due to shared environmental or non-additive genetic variance. Alternatively, those based on genotyped or imputed variants typically underestimate narrow-sense heritability contributed by rare or otherwise poorly tagged causal variants. Identical-by-descent (IBD) segments of the genome share all variants between pairs of chromosomes except new mutations that have arisen since the last common ancestor. Therefore, relating phenotypic similarity to degree of IBD sharing among classically unrelated individuals is an appealing approach to estimating the near full additive genetic variance while possibly avoiding biases that can occur when modeling close relatives. We applied an IBD-based approach (GREML-IBD) to estimate heritability in unrelated individuals using phenotypic simulation with thousands of whole-genome sequences across a range of stratification, polygenicity levels, and the minor allele frequencies of causal variants (CVs). In simulations, the IBD-based approach produced unbiased heritability estimates, even when CVs were extremely rare, although precision was low. However, population stratification and non-genetic familial environmental effects shared across generations led to strong biases in IBD-based heritability. We used data on two traits in ~120,000 people from the UK Biobank to demonstrate that, depending on the trait and possible confounding environmental effects, GREML-IBD can be applied to very large genetic datasets to infer the contribution of very rare variants lost using other methods. However, we observed apparent biases in these real data, suggesting that more work may be required to understand and mitigate factors that influence IBD-based heritability estimates.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Heritability is a fundamental parameter in genetics. Traditional estimates based on family or twin studies can be biased due to shared environmental or non-additive genetic variance. Alternatively, those based on genotyped or imputed variants typically underestimate narrow-sense heritability contributed by rare or otherwise poorly tagged causal variants. Identical-by-descent (IBD) segments of the genome share all variants between pairs of chromosomes except new mutations that have arisen since the last common ancestor. Therefore, relating phenotypic similarity to degree of IBD sharing among classically unrelated individuals is an appealing approach to estimating the near full additive genetic variance while possibly avoiding biases that can occur when modeling close relatives. We applied an IBD-based approach (GREML-IBD) to estimate heritability in unrelated individuals using phenotypic simulation with thousands of whole-genome sequences across a range of stratification, polygenicity levels, and the minor allele frequencies of causal variants (CVs). In simulations, the IBD-based approach produced unbiased heritability estimates, even when CVs were extremely rare, although precision was low. However, population stratification and non-genetic familial environmental effects shared across generations led to strong biases in IBD-based heritability. We used data on two traits in ~120,000 people from the UK Biobank to demonstrate that, depending on the trait and possible confounding environmental effects, GREML-IBD can be applied to very large genetic datasets to infer the contribution of very rare variants lost using other methods. However, we observed apparent biases in these real data, suggesting that more work may be required to understand and mitigate factors that influence IBD-based heritability estimates. |
XR Gao; H Huang; DR Nannini; F Fan; H Kim Genome-Wide Association Analyses Identify New Loci Influencing Intraocular Pressure Journal Article Human molecular genetics, 2018. @article{Gao2018, title = {Genome-Wide Association Analyses Identify New Loci Influencing Intraocular Pressure}, author = {XR Gao and H Huang and DR Nannini and F Fan and H Kim}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29617998}, year = {2018}, date = {2018-03-28}, journal = {Human molecular genetics}, abstract = {Elevated intraocular pressure (IOP) is a significant risk factor for glaucoma, the leading cause of irreversible blindness worldwide. While previous studies have identified numerous genetic variants associated with IOP, these loci only explain a fraction of IOP heritability. Recent establishments of biobank repositories have resulted in large amounts of data, enabling the identification of the remaining heritability for complex traits. Here, we describe the largest genome-wide association study of IOP to date using participants of European ancestry from the UK Biobank. We identified 671 directly genotyped variants that are significantly associated with IOP (P < 5 × 10-8). In addition to 103 novel loci, the top ranked novel IOP genes are LMX1B, NR1H3, MADD, and SEPT9. We replicated these findings in an external population and examined the pleiotropic nature of these loci. These discoveries not only further our understanding of the genetic architecture of IOP, but also shed new light on the biological processes underlying glaucoma.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Elevated intraocular pressure (IOP) is a significant risk factor for glaucoma, the leading cause of irreversible blindness worldwide. While previous studies have identified numerous genetic variants associated with IOP, these loci only explain a fraction of IOP heritability. Recent establishments of biobank repositories have resulted in large amounts of data, enabling the identification of the remaining heritability for complex traits. Here, we describe the largest genome-wide association study of IOP to date using participants of European ancestry from the UK Biobank. We identified 671 directly genotyped variants that are significantly associated with IOP (P < 5 × 10-8). In addition to 103 novel loci, the top ranked novel IOP genes are LMX1B, NR1H3, MADD, and SEPT9. We replicated these findings in an external population and examined the pleiotropic nature of these loci. These discoveries not only further our understanding of the genetic architecture of IOP, but also shed new light on the biological processes underlying glaucoma. |
Eleni Zengini; Konstantinos Hatzikotoulas; Ioanna Tachmazidou; Julia Steinberg; Fernando P. Hartwig; Lorraine Southam; Sophie Hackinger; Cindy G. Boer; Unnur Styrkarsdottir; Arthur Gilly; Daniel Suveges; Britt Killian; Thorvaldur Ingvarsson; Helgi Jonsson; George C. Babis; Andrew McCaskie; Andre G. Uitterlinden; Joyce B. J. van Meurs; Unnur Thorsteinsdottir; Kari Stefansson; George Davey Smith; Jeremy M. Wilkinson; Eleftheria Zeggini Genome-wide analyses using UK Biobank data provide insights into the genetic architecture of osteoarthritis Journal Article Nature Genetics, 2018. @article{Zengini2018, title = {Genome-wide analyses using UK Biobank data provide insights into the genetic architecture of osteoarthritis}, author = {Eleni Zengini and Konstantinos Hatzikotoulas and Ioanna Tachmazidou and Julia Steinberg and Fernando P. Hartwig and Lorraine Southam and Sophie Hackinger and Cindy G. Boer and Unnur Styrkarsdottir and Arthur Gilly and Daniel Suveges and Britt Killian and Thorvaldur Ingvarsson and Helgi Jonsson and George C. Babis and Andrew McCaskie and Andre G. Uitterlinden and Joyce B. J. van Meurs and Unnur Thorsteinsdottir and Kari Stefansson and George Davey Smith and Jeremy M. Wilkinson and Eleftheria Zeggini}, url = {https://www.nature.com/articles/s41588-018-0079-y}, year = {2018}, date = {2018-03-20}, journal = {Nature Genetics}, abstract = {Osteoarthritis is a common complex disease imposing a large public-health burden. Here, we performed a genome-wide association study for osteoarthritis, using data across 16.5 million variants from the UK Biobank resource. After performing replication and meta-analysis in up to 30,727 cases and 297,191 controls, we identified nine new osteoarthritis loci, in all of which the most likely causal variant was noncoding. For three loci, we detected association with biologically relevant radiographic endophenotypes, and in five signals we identified genes that were differentially expressed in degraded compared with intact articular cartilage from patients with osteoarthritis. We established causal effects on osteoarthritis for higher body mass index but not for triglyceride levels or genetic predisposition to type 2 diabetes.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Osteoarthritis is a common complex disease imposing a large public-health burden. Here, we performed a genome-wide association study for osteoarthritis, using data across 16.5 million variants from the UK Biobank resource. After performing replication and meta-analysis in up to 30,727 cases and 297,191 controls, we identified nine new osteoarthritis loci, in all of which the most likely causal variant was noncoding. For three loci, we detected association with biologically relevant radiographic endophenotypes, and in five signals we identified genes that were differentially expressed in degraded compared with intact articular cartilage from patients with osteoarthritis. We established causal effects on osteoarthritis for higher body mass index but not for triglyceride levels or genetic predisposition to type 2 diabetes. |
Sebastian E. Beyer; Mihir M. Sanghvi; Nay Aung; Alice Hosking; Jackie A. Cooper; José Miguel Paiva; Aaron M. Lee; Kenneth Fung; Elena Lukaschuk; Valentina Carapella; Murray A. Mittleman; Soren Brage; Stefan K. Piechnik; Stefan Neubauer; Steffen E. Petersen Prospective association between handgrip strength and cardiac structure and function in UK adults Journal Article PLoS One, 2018. @article{Beyer2018, title = {Prospective association between handgrip strength and cardiac structure and function in UK adults}, author = {Sebastian E. Beyer and Mihir M. Sanghvi and Nay Aung and Alice Hosking and Jackie A. Cooper and José Miguel Paiva and Aaron M. Lee and Kenneth Fung and Elena Lukaschuk and Valentina Carapella and Murray A. Mittleman and Soren Brage and Stefan K. Piechnik and Stefan Neubauer and Steffen E. Petersen }, url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193124}, year = {2018}, date = {2018-03-14}, journal = {PLoS One}, abstract = {Handgrip strength, a measure of muscular fitness, is associated with cardiovascular (CV) events and CV mortality but its association with cardiac structure and function is unknown. The goal of this study was to determine if handgrip strength is associated with changes in cardiac structure and function in UK adults. Methods and results Left ventricular (LV) ejection fraction (EF), end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), mass (M), and mass-to-volume ratio (MVR) were measured in a sample of 4,654 participants of the UK Biobank Study 6.3 ± 1 years after baseline using cardiovascular magnetic resonance (CMR). Handgrip strength was measured at baseline and at the imaging follow-up examination. We determined the association between handgrip strength at baseline as well as its change over time and each of the cardiac outcome parameters. After adjustment, higher level of handgrip strength at baseline was associated with higher LVEDV (difference per SD increase in handgrip strength: 1.3ml, 95% CI 0.1–2.4; p = 0.034), higher LVSV (1.0ml, 0.3–1.8; p = 0.006), lower LVM (-1.0g, -1.8 –-0.3; p = 0.007), and lower LVMVR (-0.013g/ml, -0.018 –-0.007; p<0.001). The association between handgrip strength and LVEDV and LVSV was strongest among younger individuals, while the association with LVM and LVMVR was strongest among older individuals. Conclusions Better handgrip strength was associated with cardiac structure and function in a pattern indicative of less cardiac hypertrophy and remodeling. These characteristics are known to be associated with a lower risk of cardiovascular events. }, keywords = {}, pubstate = {published}, tppubtype = {article} } Handgrip strength, a measure of muscular fitness, is associated with cardiovascular (CV) events and CV mortality but its association with cardiac structure and function is unknown. The goal of this study was to determine if handgrip strength is associated with changes in cardiac structure and function in UK adults. Methods and results Left ventricular (LV) ejection fraction (EF), end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), mass (M), and mass-to-volume ratio (MVR) were measured in a sample of 4,654 participants of the UK Biobank Study 6.3 ± 1 years after baseline using cardiovascular magnetic resonance (CMR). Handgrip strength was measured at baseline and at the imaging follow-up examination. We determined the association between handgrip strength at baseline as well as its change over time and each of the cardiac outcome parameters. After adjustment, higher level of handgrip strength at baseline was associated with higher LVEDV (difference per SD increase in handgrip strength: 1.3ml, 95% CI 0.1–2.4; p = 0.034), higher LVSV (1.0ml, 0.3–1.8; p = 0.006), lower LVM (-1.0g, -1.8 –-0.3; p = 0.007), and lower LVMVR (-0.013g/ml, -0.018 –-0.007; p<0.001). The association between handgrip strength and LVEDV and LVSV was strongest among younger individuals, while the association with LVM and LVMVR was strongest among older individuals. Conclusions Better handgrip strength was associated with cardiac structure and function in a pattern indicative of less cardiac hypertrophy and remodeling. These characteristics are known to be associated with a lower risk of cardiovascular events. |
Sébastien Thériault; Nathalie Gaudreault; Maxime Lamontagne; Mickael Rosa; Marie-Chloé Boulanger; David Messika-Zeitoun; Marie-Annick Clavel; Romain Capoulade; François Dagenais; Philippe Pibarot; Patrick Mathieu; Yohan Bossé A transcriptome-wide association study identifies PALMD as a susceptibility gene for calcific aortic valve stenosis Journal Article Nature Communications, 2018. @article{Thériault2018, title = {A transcriptome-wide association study identifies PALMD as a susceptibility gene for calcific aortic valve stenosis}, author = {Sébastien Thériault and Nathalie Gaudreault and Maxime Lamontagne and Mickael Rosa and Marie-Chloé Boulanger and David Messika-Zeitoun and Marie-Annick Clavel and Romain Capoulade and François Dagenais and Philippe Pibarot and Patrick Mathieu and Yohan Bossé}, url = {https://www.nature.com/articles/s41467-018-03260-6}, year = {2018}, date = {2018-03-07}, journal = {Nature Communications}, abstract = {Calcific aortic valve stenosis (CAVS) is a common and life-threatening heart disease and the current treatment options cannot stop or delay its progression. A GWAS on 1009 cases and 1017 ethnically matched controls was combined with a large-scale eQTL mapping study of human aortic valve tissues (n = 233) to identify susceptibility genes for CAVS. Replication was performed in the UK Biobank, including 1391 cases and 352,195 controls. A transcriptome-wide association study (TWAS) reveals PALMD (palmdelphin) as significantly associated with CAVS. The CAVS risk alleles and increasing disease severity are both associated with decreased mRNA expression levels of PALMD in valve tissues. The top variant identified shows a similar effect and strong association with CAVS (P = 1.53 × 10−10) in UK Biobank. The identification of PALMD as a susceptibility gene for CAVS provides insights into the genetic nature of this disease, opens avenues to investigate its etiology and to develop much-needed therapeutic options.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Calcific aortic valve stenosis (CAVS) is a common and life-threatening heart disease and the current treatment options cannot stop or delay its progression. A GWAS on 1009 cases and 1017 ethnically matched controls was combined with a large-scale eQTL mapping study of human aortic valve tissues (n = 233) to identify susceptibility genes for CAVS. Replication was performed in the UK Biobank, including 1391 cases and 352,195 controls. A transcriptome-wide association study (TWAS) reveals PALMD (palmdelphin) as significantly associated with CAVS. The CAVS risk alleles and increasing disease severity are both associated with decreased mRNA expression levels of PALMD in valve tissues. The top variant identified shows a similar effect and strong association with CAVS (P = 1.53 × 10−10) in UK Biobank. The identification of PALMD as a susceptibility gene for CAVS provides insights into the genetic nature of this disease, opens avenues to investigate its etiology and to develop much-needed therapeutic options. |
Robert M. Maier; Zhihong Zhu; Sang Hong Lee; Maciej Trzaskowski; Douglas M. Ruderfer; Eli A. Stahl; Stephan Ripke; Naomi R. Wray; Jian Yang; Peter M. Visscher; Matthew R. Robinson Improving genetic prediction by leveraging genetic correlations among human diseases and traits Journal Article Nature Communications, 2018. @article{Maier2018, title = {Improving genetic prediction by leveraging genetic correlations among human diseases and traits}, author = {Robert M. Maier and Zhihong Zhu and Sang Hong Lee and Maciej Trzaskowski and Douglas M. Ruderfer and Eli A. Stahl and Stephan Ripke and Naomi R. Wray and Jian Yang and Peter M. Visscher and Matthew R. Robinson}, url = {https://www.nature.com/articles/s41467-017-02769-6}, year = {2018}, date = {2018-03-07}, journal = {Nature Communications}, abstract = {Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. |
Valentina Escott-Price; Daniel J. Smith; Kimberley Kendall; Joey Ward; George Kirov; Michael J. Owen; James Walters; Michael C. O'Donovan Polygenic risk for schizophrenia and season of birth within the UK Biobank cohort Journal Article Psychological Medicine, 2018. @article{Escott-Price2018, title = {Polygenic risk for schizophrenia and season of birth within the UK Biobank cohort}, author = {Valentina Escott-Price and Daniel J. Smith and Kimberley Kendall and Joey Ward and George Kirov and Michael J. Owen and James Walters and Michael C. O'Donovan}, url = {https://www.cambridge.org/core/journals/psychological-medicine/article/polygenic-risk-for-schizophrenia-and-season-of-birth-within-the-uk-biobank-cohort/3E9D0050C1E55E2EC14C8284AF682CF6}, year = {2018}, date = {2018-03-04}, journal = {Psychological Medicine}, abstract = { Background There is strong evidence that people born in winter and in spring have a small increased risk of schizophrenia. As this ‘season of birth’ effect underpins some of the most influential hypotheses concerning potentially modifiable risk exposures, it is important to exclude other possible explanations for the phenomenon. Methods Here we sought to determine whether the season of birth effect reflects gene-environment confounding rather than a pathogenic process indexing environmental exposure. We directly measured, in 136 538 participants from the UK Biobank (UKBB), the burdens of common schizophrenia risk alleles and of copy number variants known to increase the risk for the disorder, and tested whether these were correlated with a season of birth. Results Neither genetic measure was associated with season or month of birth within the UKBB sample. Conclusions As our study was highly powered to detect small effects, we conclude that the season of birth effect in schizophrenia reflects a true pathogenic effect of environmental exposure. }, keywords = {}, pubstate = {published}, tppubtype = {article} } Background There is strong evidence that people born in winter and in spring have a small increased risk of schizophrenia. As this ‘season of birth’ effect underpins some of the most influential hypotheses concerning potentially modifiable risk exposures, it is important to exclude other possible explanations for the phenomenon. Methods Here we sought to determine whether the season of birth effect reflects gene-environment confounding rather than a pathogenic process indexing environmental exposure. We directly measured, in 136 538 participants from the UK Biobank (UKBB), the burdens of common schizophrenia risk alleles and of copy number variants known to increase the risk for the disorder, and tested whether these were correlated with a season of birth. Results Neither genetic measure was associated with season or month of birth within the UKBB sample. Conclusions As our study was highly powered to detect small effects, we conclude that the season of birth effect in schizophrenia reflects a true pathogenic effect of environmental exposure. |
Robin N Beaumont; et al. Human Molecular Genetics, 2018, (Robin N Beaumont and Nicole M Warrington and Alana Cavadino and Jessica Tyrrell and Michael Nodzenski and Momoko Horikoshi and Frank Geller and Ronny Myhre and Rebecca C Richmond and Lavinia Paternoster and Jonathan P Bradfield and Eskil Kreiner-Møller and Ville Huikari and Sarah Metrustry and Kathryn L Lunetta and Jodie N Painter and Jouke-Jan Hottenga and Catherine Allard and Sheila J Barton and Ana Espinosa and Julie A Marsh and Catherine Potter and Ge Zhang and Wei Ang and Diane J Berry and Luigi Bouchard and Shikta Das and Hakon Hakonarson and Jani Heikkinen and Øyvind Helgeland and Berthold Hocher and Albert Hofman and Hazel M Inskip and Samuel E Jones and Manolis Kogevinas and Penelope A Lind and Letizia Marullo and Sarah E Medland and Anna Murray and Jeffrey C Murray and Pål R Njølstad and Ellen A Nohr and Christoph Reichetzeder and Susan M Ring and Katherine S Ruth and Loreto Santa-Marina and Denise M Scholtens and Sylvain Sebert and Verena Sengpiel and Marcus A Tuke and Marc Vaudel and Michael N Weedon and Gonneke Willemsen and Andrew R Wood and Hanieh Yaghootkar and Louis J Muglia and Meike Bartels and Caroline L Relton and Craig E Pennell and Leda Chatzi and Xavier Estivill and John W Holloway and Dorret I Boomsma and Grant W Montgomery and Joanne M Murabito and Tim D Spector and Christine Power and Marjo-Ritta and Järvelin Hans Bisgaard and Struan FA Grant and Thorkild IA Sørensen and Vincent W Jaddoe and Bo Jacobsson and Mads Melbye and Mark I McCarthy and Andrew T Hattersley and M Geoffrey Hayes and Timothy M Frayling and Marie-France Hivert and Janine F Felix and Elina Hyppönen and William L Lowe Jr and David M Evans and Debbie A Lawlor ). @article{Beaumont2018, title = {Genome-wide association study of offspring birth weight in 86,577 women identifies five novel loci and highlights maternal genetic effects that are independent of fetal genetics}, author = {Robin N Beaumont and et al. }, url = {https://academic.oup.com/hmg/advance-article/doi/10.1093/hmg/ddx429/4788598}, year = {2018}, date = {2018-03-01}, journal = {Human Molecular Genetics}, abstract = {Genome-wide association studies (GWAS) of birth weight have focused on fetal genetics, while relatively little is known about the role of maternal genetic variation. We aimed to identify maternal genetic variants associated with birth weight that could highlight potentially relevant maternal determinants of fetal growth. We meta-analysed data on up to 8.7 million SNPs in up to 86,577 women of European descent from the Early Growth Genetics (EGG) Consortium and the UK Biobank. We used structural equation modelling (SEM) and analyses of mother-child pairs to quantify the separate maternal and fetal genetic effects. Maternal SNPs at 10 loci (MTNR1B, HMGA2, SH2B3, KCNAB1, L3MBTL3, GCK, EBF1, TCF7L2, ACTL9, CYP3A7) were associated with offspring birth weight at P<5x10−8. In SEM analyses, at least 7 of the 10 associations were consistent with effects of the maternal genotype acting via the intrauterine environment, rather than via effects of shared alleles with the fetus. Variants, or correlated proxies, at many of the loci had been previously associated with adult traits, including fasting glucose (MTNR1B, GCK and TCF7L2) and sex hormone levels (CYP3A7), and one (EBF1) with gestational duration. The identified associations indicate genetic effects on maternal glucose, cytochrome P450 activity and gestational duration, and potentially on maternal blood pressure and immune function, are relevant for fetal growth. Further characterization of these associations in mechanistic and causal analyses will enhance understanding of the potentially modifiable maternal determinants of fetal growth, with the goal of reducing the morbidity and mortality associated with low and high birth weights.}, note = {Robin N Beaumont and Nicole M Warrington and Alana Cavadino and Jessica Tyrrell and Michael Nodzenski and Momoko Horikoshi and Frank Geller and Ronny Myhre and Rebecca C Richmond and Lavinia Paternoster and Jonathan P Bradfield and Eskil Kreiner-Møller and Ville Huikari and Sarah Metrustry and Kathryn L Lunetta and Jodie N Painter and Jouke-Jan Hottenga and Catherine Allard and Sheila J Barton and Ana Espinosa and Julie A Marsh and Catherine Potter and Ge Zhang and Wei Ang and Diane J Berry and Luigi Bouchard and Shikta Das and Hakon Hakonarson and Jani Heikkinen and Øyvind Helgeland and Berthold Hocher and Albert Hofman and Hazel M Inskip and Samuel E Jones and Manolis Kogevinas and Penelope A Lind and Letizia Marullo and Sarah E Medland and Anna Murray and Jeffrey C Murray and Pål R Njølstad and Ellen A Nohr and Christoph Reichetzeder and Susan M Ring and Katherine S Ruth and Loreto Santa-Marina and Denise M Scholtens and Sylvain Sebert and Verena Sengpiel and Marcus A Tuke and Marc Vaudel and Michael N Weedon and Gonneke Willemsen and Andrew R Wood and Hanieh Yaghootkar and Louis J Muglia and Meike Bartels and Caroline L Relton and Craig E Pennell and Leda Chatzi and Xavier Estivill and John W Holloway and Dorret I Boomsma and Grant W Montgomery and Joanne M Murabito and Tim D Spector and Christine Power and Marjo-Ritta and Järvelin Hans Bisgaard and Struan FA Grant and Thorkild IA Sørensen and Vincent W Jaddoe and Bo Jacobsson and Mads Melbye and Mark I McCarthy and Andrew T Hattersley and M Geoffrey Hayes and Timothy M Frayling and Marie-France Hivert and Janine F Felix and Elina Hyppönen and William L Lowe Jr and David M Evans and Debbie A Lawlor }, keywords = {}, pubstate = {published}, tppubtype = {article} } Genome-wide association studies (GWAS) of birth weight have focused on fetal genetics, while relatively little is known about the role of maternal genetic variation. We aimed to identify maternal genetic variants associated with birth weight that could highlight potentially relevant maternal determinants of fetal growth. We meta-analysed data on up to 8.7 million SNPs in up to 86,577 women of European descent from the Early Growth Genetics (EGG) Consortium and the UK Biobank. We used structural equation modelling (SEM) and analyses of mother-child pairs to quantify the separate maternal and fetal genetic effects. Maternal SNPs at 10 loci (MTNR1B, HMGA2, SH2B3, KCNAB1, L3MBTL3, GCK, EBF1, TCF7L2, ACTL9, CYP3A7) were associated with offspring birth weight at P<5x10−8. In SEM analyses, at least 7 of the 10 associations were consistent with effects of the maternal genotype acting via the intrauterine environment, rather than via effects of shared alleles with the fetus. Variants, or correlated proxies, at many of the loci had been previously associated with adult traits, including fasting glucose (MTNR1B, GCK and TCF7L2) and sex hormone levels (CYP3A7), and one (EBF1) with gestational duration. The identified associations indicate genetic effects on maternal glucose, cytochrome P450 activity and gestational duration, and potentially on maternal blood pressure and immune function, are relevant for fetal growth. Further characterization of these associations in mechanistic and causal analyses will enhance understanding of the potentially modifiable maternal determinants of fetal growth, with the goal of reducing the morbidity and mortality associated with low and high birth weights. |
Niek Verweij; Yordi J. van de Vegte; Pim van der Harst Genetic study links components of the autonomous nervous system to heart-rate profile during exercise Journal Article Nature Genetics, 2018. @article{Verweij2018, title = {Genetic study links components of the autonomous nervous system to heart-rate profile during exercise}, author = {Niek Verweij and Yordi J. van de Vegte and Pim van der Harst}, url = {https://www.nature.com/articles/s41467-018-03395-6}, year = {2018}, date = {2018-03-01}, journal = {Nature Genetics}, abstract = {Heart rate (HR) responds to exercise by increasing during exercise and recovering after exercise. As such, HR is an important predictor of mortality that researchers believe is modulated by the autonomic nervous system. However, the mechanistic basis underlying inter-individual differences has yet to be explained. Here, we perform a large-scale genome-wide analysis of HR increase and HR recovery in 58,818 UK Biobank individuals. Twenty-five independent SNPs in 23 loci are identified to be associated (p < 8.3 × 10−9) with HR increase or HR recovery. A total of 36 candidate causal genes are prioritized that are enriched for pathways related to neuron biology. No evidence is found of a causal relationship with mortality or cardiovascular diseases. However, a nominal association with parental lifespan requires further study. In conclusion, the findings provide new biological and clinical insight into the mechanistic underpinnings of HR response to exercise. The results also underscore the role of the autonomous nervous system in HR recovery.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Heart rate (HR) responds to exercise by increasing during exercise and recovering after exercise. As such, HR is an important predictor of mortality that researchers believe is modulated by the autonomic nervous system. However, the mechanistic basis underlying inter-individual differences has yet to be explained. Here, we perform a large-scale genome-wide analysis of HR increase and HR recovery in 58,818 UK Biobank individuals. Twenty-five independent SNPs in 23 loci are identified to be associated (p < 8.3 × 10−9) with HR increase or HR recovery. A total of 36 candidate causal genes are prioritized that are enriched for pathways related to neuron biology. No evidence is found of a causal relationship with mortality or cardiovascular diseases. However, a nominal association with parental lifespan requires further study. In conclusion, the findings provide new biological and clinical insight into the mechanistic underpinnings of HR response to exercise. The results also underscore the role of the autonomous nervous system in HR recovery. |
Dipender Gill; Christopher F. Brewer; Fabiola Del Greco; Prasanthi Sivakumaran; Jack Bowden; Nuala A. Sheehan; Cosetta Minelli Age at menarche and adult body mass index: a Mendelian randomization study Journal Article International Journal of Obesity, 2018. @article{Gill2018, title = {Age at menarche and adult body mass index: a Mendelian randomization study}, author = {Dipender Gill and Christopher F. Brewer and Fabiola Del Greco and Prasanthi Sivakumaran and Jack Bowden and Nuala A. Sheehan and Cosetta Minelli}, url = {https://www.nature.com/articles/s41366-018-0048-7}, year = {2018}, date = {2018-02-26}, journal = {International Journal of Obesity}, abstract = {Pubertal timing has psychological and physical sequelae. While observational studies have demonstrated an association between age at menarche and adult body mass index (BMI), confounding makes it difficult to infer causality. Methods The Mendelian randomization (MR) technique is not limited by traditional confounding and was used to investigate the presence of a causal effect of age at menarche on adult BMI. MR uses genetic variants as instruments under the assumption that they act on BMI only through age at menarche (no pleiotropy). Using a two-sample MR approach, heterogeneity between the MR estimates from individual instruments was used as a proxy for pleiotropy, with sensitivity analyses performed if detected. Genetic instruments and estimates of their association with age at menarche were obtained from a genome-wide association meta-analysis on 182,416 women. The genetic effects on adult BMI were estimated using data on 80,465 women from the UK Biobank. The presence of a causal effect of age at menarche on adult BMI was further investigated using data on 70,692 women from the GIANT Consortium. Results There was evidence of pleiotropy among instruments. Using the UK Biobank data, after removing instruments associated with childhood BMI that were likely exerting pleiotropy, fixed-effect meta-analysis across instruments demonstrated that a 1 year increase in age at menarche reduces adult BMI by 0.38 kg/m2 (95% CI 0.25–0.51 kg/m2). However, evidence of pleiotropy remained. MR-Egger regression did not suggest directional bias, and similar estimates to the fixed-effect meta-analysis were obtained in sensitivity analyses when using a random-effect model, multivariable MR, MR-Egger regression, a weighted median estimator and a weighted mode-based estimator. The direction and significance of the causal effect were replicated using GIANT Consortium data. Conclusion MR provides evidence to support the hypothesis that earlier age at menarche causes higher adult BMI. Complex hormonal and psychological factors may be responsible.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Pubertal timing has psychological and physical sequelae. While observational studies have demonstrated an association between age at menarche and adult body mass index (BMI), confounding makes it difficult to infer causality. Methods The Mendelian randomization (MR) technique is not limited by traditional confounding and was used to investigate the presence of a causal effect of age at menarche on adult BMI. MR uses genetic variants as instruments under the assumption that they act on BMI only through age at menarche (no pleiotropy). Using a two-sample MR approach, heterogeneity between the MR estimates from individual instruments was used as a proxy for pleiotropy, with sensitivity analyses performed if detected. Genetic instruments and estimates of their association with age at menarche were obtained from a genome-wide association meta-analysis on 182,416 women. The genetic effects on adult BMI were estimated using data on 80,465 women from the UK Biobank. The presence of a causal effect of age at menarche on adult BMI was further investigated using data on 70,692 women from the GIANT Consortium. Results There was evidence of pleiotropy among instruments. Using the UK Biobank data, after removing instruments associated with childhood BMI that were likely exerting pleiotropy, fixed-effect meta-analysis across instruments demonstrated that a 1 year increase in age at menarche reduces adult BMI by 0.38 kg/m2 (95% CI 0.25–0.51 kg/m2). However, evidence of pleiotropy remained. MR-Egger regression did not suggest directional bias, and similar estimates to the fixed-effect meta-analysis were obtained in sensitivity analyses when using a random-effect model, multivariable MR, MR-Egger regression, a weighted median estimator and a weighted mode-based estimator. The direction and significance of the causal effect were replicated using GIANT Consortium data. Conclusion MR provides evidence to support the hypothesis that earlier age at menarche causes higher adult BMI. Complex hormonal and psychological factors may be responsible. |
AB Adewoye; Shrine; L Odenthal-Hesse; S Welsh; A Malarstig; S Jelinsky; I Kilty; MD Tobin; EJ Hollox; LV Wain Human CCL3L1 copy number variation, gene expression, and the role of the CCL3L1-CCR5 axis in lung function Journal Article Wellcome open research, 2018. @article{Adewoye2018, title = {Human CCL3L1 copy number variation, gene expression, and the role of the CCL3L1-CCR5 axis in lung function}, author = {AB Adewoye and Shrine and L Odenthal-Hesse and S Welsh and A Malarstig and S Jelinsky and I Kilty and MD Tobin and EJ Hollox and LV Wain }, url = {https://wellcomeopenresearch.org/articles/3-13/v2}, year = {2018}, date = {2018-02-21}, journal = {Wellcome open research}, abstract = {Background: The CCL3L1-CCR5 signaling axis is important in a number of inflammatory responses, including macrophage function, and T-cell-dependent immune responses. Small molecule CCR5 antagonists exist, including the approved antiretroviral drug maraviroc, and therapeutic monoclonal antibodies are in development. Repositioning of drugs and targets into new disease areas can accelerate the availability of new therapies and substantially reduce costs. As it has been shown that drug targets with genetic evidence supporting their involvement in the disease are more likely to be successful in clinical development, using genetic association studies to identify new target repurposing opportunities could be fruitful. Here we investigate the potential of perturbation of the CCL3L1-CCR5 axis as treatment for respiratory disease. Europeans typically carry between 0 and 5 copies of CCL3L1 and this multi-allelic variation is not detected by widely used genome-wide single nucleotide polymorphism studies. Methods: We directly measured the complex structural variation of CCL3L1 using the Paralogue Ratio Test and imputed (with validation) CCR5d32 genotypes in 5,000 individuals from UK Biobank, selected from the extremes of the lung function distribution, and analysed DNA and RNAseq data for CCL3L1 from the 1000 Genomes Project. Results: We confirmed the gene dosage effect of CCL3L1 copy number on CCL3L1 mRNA expression levels. We found no evidence for association of CCL3L1 copy number or CCR5d32 genotype with lung function. Conclusions: These results suggest that repositioning CCR5 antagonists is unlikely to be successful for the treatment of airflow obstruction. }, keywords = {}, pubstate = {published}, tppubtype = {article} } Background: The CCL3L1-CCR5 signaling axis is important in a number of inflammatory responses, including macrophage function, and T-cell-dependent immune responses. Small molecule CCR5 antagonists exist, including the approved antiretroviral drug maraviroc, and therapeutic monoclonal antibodies are in development. Repositioning of drugs and targets into new disease areas can accelerate the availability of new therapies and substantially reduce costs. As it has been shown that drug targets with genetic evidence supporting their involvement in the disease are more likely to be successful in clinical development, using genetic association studies to identify new target repurposing opportunities could be fruitful. Here we investigate the potential of perturbation of the CCL3L1-CCR5 axis as treatment for respiratory disease. Europeans typically carry between 0 and 5 copies of CCL3L1 and this multi-allelic variation is not detected by widely used genome-wide single nucleotide polymorphism studies. Methods: We directly measured the complex structural variation of CCL3L1 using the Paralogue Ratio Test and imputed (with validation) CCR5d32 genotypes in 5,000 individuals from UK Biobank, selected from the extremes of the lung function distribution, and analysed DNA and RNAseq data for CCL3L1 from the 1000 Genomes Project. Results: We confirmed the gene dosage effect of CCL3L1 copy number on CCL3L1 mRNA expression levels. We found no evidence for association of CCL3L1 copy number or CCR5d32 genotype with lung function. Conclusions: These results suggest that repositioning CCR5 antagonists is unlikely to be successful for the treatment of airflow obstruction. |
Céline Vetter; Hassan S. Dashti; Jacqueline M. Lane; Simon G. Anderson; Eva S. Schernhammer; Martin K. Rutter; Richa Saxena; Frank A.J.L. Scheer Night Shift Work, Genetic Risk, and Type 2 Diabetes in the UK Biobank Journal Article Diabetes Care , 2018. @article{Vetter2018, title = {Night Shift Work, Genetic Risk, and Type 2 Diabetes in the UK Biobank}, author = {Céline Vetter and Hassan S. Dashti and Jacqueline M. Lane and Simon G. Anderson and Eva S. Schernhammer and Martin K. Rutter and Richa Saxena and Frank A.J.L. Scheer}, url = {http://care.diabetesjournals.org/content/early/2018/01/31/dc17-1933}, year = {2018}, date = {2018-02-13}, journal = {Diabetes Care }, abstract = {OBJECTIVE To examine the effects of past and current night shift work and genetic type 2 diabetes vulnerability on type 2 diabetes odds. RESEARCH DESIGN AND METHODS In the UK Biobank, we examined associations of current (N = 272,214) and lifetime (N = 70,480) night shift work exposure with type 2 diabetes risk (6,770 and 1,191 prevalent cases, respectively). For 180,704 and 44,141 unrelated participants of European ancestry (4,002 and 726 cases, respectively) with genetic data, we assessed whether shift work exposure modified the relationship between a genetic risk score (comprising 110 single-nucleotide polymorphisms) for type 2 diabetes and prevalent diabetes. RESULTS Compared with day workers, all current night shift workers were at higher multivariable-adjusted odds for type 2 diabetes (none or rare night shifts: odds ratio [OR] 1.15 [95% CI 1.05–1.26]; some nights: OR 1.18 [95% CI 1.05–1.32]; and usual nights: OR 1.44 [95% CI 1.19–1.73]), except current permanent night shift workers (OR 1.09 [95% CI 0.93–1.27]). Considering a person’s lifetime work schedule and compared with never shift workers, working more night shifts per month was associated with higher type 2 diabetes odds (<3/month: OR 1.24 [95% CI 0.90–1.68]; 3–8/month: OR 1.11 [95% CI 0.90–1.37]; and >8/month: OR 1.36 [95% CI 1.14–1.62]; Ptrend = 0.001). The association between genetic type 2 diabetes predisposition and type 2 diabetes odds was not modified by shift work exposure. CONCLUSIONS Our findings show that night shift work, especially rotating shift work including night shifts, is associated with higher type 2 diabetes odds and that the number of night shifts worked per month appears most relevant for type 2 diabetes odds. Also, shift work exposure does not modify genetic risk for type 2 diabetes, a novel finding that warrants replication.}, keywords = {}, pubstate = {published}, tppubtype = {article} } OBJECTIVE To examine the effects of past and current night shift work and genetic type 2 diabetes vulnerability on type 2 diabetes odds. RESEARCH DESIGN AND METHODS In the UK Biobank, we examined associations of current (N = 272,214) and lifetime (N = 70,480) night shift work exposure with type 2 diabetes risk (6,770 and 1,191 prevalent cases, respectively). For 180,704 and 44,141 unrelated participants of European ancestry (4,002 and 726 cases, respectively) with genetic data, we assessed whether shift work exposure modified the relationship between a genetic risk score (comprising 110 single-nucleotide polymorphisms) for type 2 diabetes and prevalent diabetes. RESULTS Compared with day workers, all current night shift workers were at higher multivariable-adjusted odds for type 2 diabetes (none or rare night shifts: odds ratio [OR] 1.15 [95% CI 1.05–1.26]; some nights: OR 1.18 [95% CI 1.05–1.32]; and usual nights: OR 1.44 [95% CI 1.19–1.73]), except current permanent night shift workers (OR 1.09 [95% CI 0.93–1.27]). Considering a person’s lifetime work schedule and compared with never shift workers, working more night shifts per month was associated with higher type 2 diabetes odds (<3/month: OR 1.24 [95% CI 0.90–1.68]; 3–8/month: OR 1.11 [95% CI 0.90–1.37]; and >8/month: OR 1.36 [95% CI 1.14–1.62]; Ptrend = 0.001). The association between genetic type 2 diabetes predisposition and type 2 diabetes odds was not modified by shift work exposure. CONCLUSIONS Our findings show that night shift work, especially rotating shift work including night shifts, is associated with higher type 2 diabetes odds and that the number of night shifts worked per month appears most relevant for type 2 diabetes odds. Also, shift work exposure does not modify genetic risk for type 2 diabetes, a novel finding that warrants replication. |
R Pazoki; A Dehghan; E Evangelou; H Warren; H Gao; M Caulfield; P Elliott; I Tzoulaki Genetic Predisposition to High Blood Pressure and Lifestyle Factors: Associations With Midlife Blood Pressure Levels and Cardiovascular Events. Journal Article Circulation, 2018. @article{Pazoki2018, title = {Genetic Predisposition to High Blood Pressure and Lifestyle Factors: Associations With Midlife Blood Pressure Levels and Cardiovascular Events.}, author = {R Pazoki and A Dehghan and E Evangelou and H Warren and H Gao and M Caulfield and P Elliott and I Tzoulaki }, url = {https://www.ncbi.nlm.nih.gov/pubmed/29254930}, year = {2018}, date = {2018-02-13}, journal = {Circulation}, abstract = {BACKGROUND: High blood pressure (BP) is a major risk factor for cardiovascular diseases (CVDs), the leading cause of mortality worldwide. Both heritable and lifestyle risk factors contribute to elevated BP levels. We aimed to investigate the extent to which lifestyle factors could offset the effect of an adverse BP genetic profile and its effect on CVD risk. METHODS: We constructed a genetic risk score for high BP by using 314 published BP loci in 277 005 individuals without previous CVD from the UK Biobank study, a prospective cohort of individuals aged 40 to 69 years, with a median of 6.11 years of follow-up. We scored participants according to their lifestyle factors including body mass index, healthy diet, sedentary lifestyle, alcohol consumption, smoking, and urinary sodium excretion levels measured at recruitment. We examined the association between tertiles of genetic risk and tertiles of lifestyle score with BP levels and incident CVD by using linear regression and Cox regression models, respectively. RESULTS: Healthy lifestyle score was strongly associated with BP (P<10-320) for systolic and diastolic BP and CVD events regardless of the underlying BP genetic risk. Participants with a favorable in comparison with an unfavorable lifestyle (bottom versus top tertile lifestyle score) had 3.6, 3.5, and 3.6 mm Hg lower systolic BP in low, middle, and high genetic risk groups, respectively (P for interaction=0.0006). Similarly, favorable in comparison with unfavorable lifestyle showed 30%, 31%, and 33% lower risk of CVD among participants in low, middle, and high genetic risk groups, respectively (P for interaction=0.99). CONCLUSIONS: Our data further support population-wide efforts to lower BP in the population via lifestyle modification. The advantages and disadvantages of disclosing genetic predisposition to high BP for risk stratification needs careful evaluation.}, keywords = {}, pubstate = {published}, tppubtype = {article} } BACKGROUND: High blood pressure (BP) is a major risk factor for cardiovascular diseases (CVDs), the leading cause of mortality worldwide. Both heritable and lifestyle risk factors contribute to elevated BP levels. We aimed to investigate the extent to which lifestyle factors could offset the effect of an adverse BP genetic profile and its effect on CVD risk. METHODS: We constructed a genetic risk score for high BP by using 314 published BP loci in 277 005 individuals without previous CVD from the UK Biobank study, a prospective cohort of individuals aged 40 to 69 years, with a median of 6.11 years of follow-up. We scored participants according to their lifestyle factors including body mass index, healthy diet, sedentary lifestyle, alcohol consumption, smoking, and urinary sodium excretion levels measured at recruitment. We examined the association between tertiles of genetic risk and tertiles of lifestyle score with BP levels and incident CVD by using linear regression and Cox regression models, respectively. RESULTS: Healthy lifestyle score was strongly associated with BP (P<10-320) for systolic and diastolic BP and CVD events regardless of the underlying BP genetic risk. Participants with a favorable in comparison with an unfavorable lifestyle (bottom versus top tertile lifestyle score) had 3.6, 3.5, and 3.6 mm Hg lower systolic BP in low, middle, and high genetic risk groups, respectively (P for interaction=0.0006). Similarly, favorable in comparison with unfavorable lifestyle showed 30%, 31%, and 33% lower risk of CVD among participants in low, middle, and high genetic risk groups, respectively (P for interaction=0.99). CONCLUSIONS: Our data further support population-wide efforts to lower BP in the population via lifestyle modification. The advantages and disadvantages of disclosing genetic predisposition to high BP for risk stratification needs careful evaluation. |
NM Warrington; RM Freathy; MC Neale; DM Evans Using structural equation modelling to jointly estimate maternal and fetal effects on birthweight in the UK Biobank Journal Article International Journal of Epidemiology, 2018. @article{Warrington2018, title = {Using structural equation modelling to jointly estimate maternal and fetal effects on birthweight in the UK Biobank}, author = {NM Warrington and RM Freathy and MC Neale and DM Evans}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29447406}, year = {2018}, date = {2018-02-13}, journal = {International Journal of Epidemiology}, abstract = {Background: To date, 60 genetic variants have been robustly associated with birthweight. It is unclear whether these associations represent the effect of an individual's own genotype on their birthweight, their mother's genotype, or both. Methods: We demonstrate how structural equation modelling (SEM) can be used to estimate both maternal and fetal effects when phenotype information is present for individuals in two generations and genotype information is available on the older individual. We conduct an extensive simulation study to assess the bias, power and type 1 error rates of the SEM and also apply the SEM to birthweight data in the UK Biobank study. Results: Unlike simple regression models, our approach is unbiased when there is both a maternal and a fetal effect. The method can be used when either the individual's own phenotype or the phenotype of their offspring is not available, and allows the inclusion of summary statistics from additional cohorts where raw data cannot be shared. We show that the type 1 error rate of the method is appropriate, and that there is substantial statistical power to detect a genetic variant that has a moderate effect on the phenotype and reasonable power to detect whether it is a fetal and/or a maternal effect. We also identify a subset of birthweight-associated single nucleotide polymorphisms (SNPs) that have opposing maternal and fetal effects in the UK Biobank. Conclusions: Our results show that SEM can be used to estimate parameters that would be difficult to quantify using simple statistical methods alone.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background: To date, 60 genetic variants have been robustly associated with birthweight. It is unclear whether these associations represent the effect of an individual's own genotype on their birthweight, their mother's genotype, or both. Methods: We demonstrate how structural equation modelling (SEM) can be used to estimate both maternal and fetal effects when phenotype information is present for individuals in two generations and genotype information is available on the older individual. We conduct an extensive simulation study to assess the bias, power and type 1 error rates of the SEM and also apply the SEM to birthweight data in the UK Biobank study. Results: Unlike simple regression models, our approach is unbiased when there is both a maternal and a fetal effect. The method can be used when either the individual's own phenotype or the phenotype of their offspring is not available, and allows the inclusion of summary statistics from additional cohorts where raw data cannot be shared. We show that the type 1 error rate of the method is appropriate, and that there is substantial statistical power to detect a genetic variant that has a moderate effect on the phenotype and reasonable power to detect whether it is a fetal and/or a maternal effect. We also identify a subset of birthweight-associated single nucleotide polymorphisms (SNPs) that have opposing maternal and fetal effects in the UK Biobank. Conclusions: Our results show that SEM can be used to estimate parameters that would be difficult to quantify using simple statistical methods alone. |
T Skaaby; AE Taylor; RK Jacobsen; LT Møllehave; N Friedrich; BH Thuesen; DM Shabanzadeh; L Paternoster; U Völker; M Nauck; H Völzke; M Munafò; T Hansen; O Pedersen; T Jørgensen; N Grarup; A Linneberg European Journal of Clinical Nutrition, 2018. @article{Skaaby2018, title = {Associations of genetic determinants of serum vitamin B12 and folate concentrations with hay fever and asthma: a Mendelian randomization meta-analysis.}, author = {T Skaaby and AE Taylor and RK Jacobsen and LT Møllehave and N Friedrich and BH Thuesen and DM Shabanzadeh and L Paternoster and U Völker and M Nauck and H Völzke and M Munafò and T Hansen and O Pedersen and T Jørgensen and N Grarup and A Linneberg}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29249824}, year = {2018}, date = {2018-02-07}, journal = {European Journal of Clinical Nutrition}, abstract = {Studies of the effect of vitamin B12 and folate on the risk of asthma and hay fever have shown inconsistent results that may be biased by reverse causation and confounding. We used a Mendelian randomization approach to examine a potential causal effect of vitamin B12 and folate on hay fever, asthma, and selected biomarkers of allergy by using 11 vitamin B12-associated single-nucleotide polymorphisms (SNPs) and 2 folate-associated SNPs as unconfounded markers. SUBJECTS/METHODS: We included 162,736 participants from 9 population-based studies including the UK Biobank. Results were combined in instrumental variable and meta-analyses and effects expressed as odds ratios (ORs) or estimates with 95% confidence interval (CI). RESULTS: Using genetic proxies for B12 and folate, instrumental variable analyses did not show evidence for associations between serum B12 and hay fever: OR = 1.02 (95% CI: 0.98, 1.05), asthma: OR = 0.99 (95% CI: 0.95, 1.04), allergic sensitization: OR = 1.02 (95% CI: 0.74, 1.40), or change in serum IgE: 10.0% (95% CI: -9.6%, 29.6%) per 100 pg/ml B12. Similarly, there was no evidence for association between serum folate and hay fever: OR = 0.74 (95% CI: 0.45, 1.21), asthma: OR = 0.80 (95% CI: 0.43, 1.49), or allergic sensitization: OR = 1.92 (95% CI: 0.11, 33.45), but there was a statistically significant association with change in serum IgE: 2.0% (95% CI: 0.43%, 3.58%) per 0.1 ng/ml serum folate. CONCLUSIONS: Our results did not support the hypothesis that levels of vitamin B12 and folate are causally related to hay fever, asthma, or biomarkers of allergy, but we found evidence of a positive association between serum folate and serum total IgE.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Studies of the effect of vitamin B12 and folate on the risk of asthma and hay fever have shown inconsistent results that may be biased by reverse causation and confounding. We used a Mendelian randomization approach to examine a potential causal effect of vitamin B12 and folate on hay fever, asthma, and selected biomarkers of allergy by using 11 vitamin B12-associated single-nucleotide polymorphisms (SNPs) and 2 folate-associated SNPs as unconfounded markers. SUBJECTS/METHODS: We included 162,736 participants from 9 population-based studies including the UK Biobank. Results were combined in instrumental variable and meta-analyses and effects expressed as odds ratios (ORs) or estimates with 95% confidence interval (CI). RESULTS: Using genetic proxies for B12 and folate, instrumental variable analyses did not show evidence for associations between serum B12 and hay fever: OR = 1.02 (95% CI: 0.98, 1.05), asthma: OR = 0.99 (95% CI: 0.95, 1.04), allergic sensitization: OR = 1.02 (95% CI: 0.74, 1.40), or change in serum IgE: 10.0% (95% CI: -9.6%, 29.6%) per 100 pg/ml B12. Similarly, there was no evidence for association between serum folate and hay fever: OR = 0.74 (95% CI: 0.45, 1.21), asthma: OR = 0.80 (95% CI: 0.43, 1.49), or allergic sensitization: OR = 1.92 (95% CI: 0.11, 33.45), but there was a statistically significant association with change in serum IgE: 2.0% (95% CI: 0.43%, 3.58%) per 0.1 ng/ml serum folate. CONCLUSIONS: Our results did not support the hypothesis that levels of vitamin B12 and folate are causally related to hay fever, asthma, or biomarkers of allergy, but we found evidence of a positive association between serum folate and serum total IgE. |
Elisabetta Casalone; Ioanna Tachmazidou; Eleni Zengini; Konstantinos Hatzikotoulas; Sophie Hackinger; Daniel Suveges; Julia Steinberg; Nigel William Rayner; arcOGEN Consortium; Jeremy Mark Wilkinson; Kalliope Panoutsopoulou; Eleftheria Zeggini A novel variant in GLIS3 is associated with osteoarthritis Journal Article Annals of the Rheumatic diseases, 2018. @article{Casalone2018, title = {A novel variant in GLIS3 is associated with osteoarthritis}, author = { Elisabetta Casalone and Ioanna Tachmazidou and Eleni Zengini and Konstantinos Hatzikotoulas and Sophie Hackinger and Daniel Suveges and Julia Steinberg and Nigel William Rayner and arcOGEN Consortium and Jeremy Mark Wilkinson and Kalliope Panoutsopoulou and Eleftheria Zeggini}, url = {http://ard.bmj.com/content/early/2018/02/06/annrheumdis-2017-211848}, year = {2018}, date = {2018-02-07}, journal = {Annals of the Rheumatic diseases}, abstract = {Objectives Osteoarthritis (OA) is a complex disease, but its genetic aetiology remains poorly characterised. To identify novel susceptibility loci for OA, we carried out a genome-wide association study (GWAS) in individuals from the largest UK-based OA collections to date. Methods We carried out a discovery GWAS in 5414 OA individuals with knee and/or hip total joint replacement (TJR) and 9939 population-based controls. We followed-up prioritised variants in OA subjects from the interim release of the UK Biobank resource (up to 12 658 cases and 50 898 controls) and our lead finding in operated OA subjects from the full release of UK Biobank (17 894 cases and 89 470 controls). We investigated its functional implications in methylation, gene expression and proteomics data in primary chondrocytes from 12 pairs of intact and degraded cartilage samples from patients undergoing TJR. Results We detect a genome-wide significant association at rs10116772 with TJR (P=3.7×10−8; for allele A: OR (95% CI) 0.97 (0.96 to 0.98)), an intronic variant in GLIS3, which is expressed in cartilage. Variants in strong correlation with rs10116772 have been associated with elevated plasma glucose levels and diabetes. Conclusions We identify a novel susceptibility locus for OA that has been previously implicated in diabetes and glycaemic traits.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Objectives Osteoarthritis (OA) is a complex disease, but its genetic aetiology remains poorly characterised. To identify novel susceptibility loci for OA, we carried out a genome-wide association study (GWAS) in individuals from the largest UK-based OA collections to date. Methods We carried out a discovery GWAS in 5414 OA individuals with knee and/or hip total joint replacement (TJR) and 9939 population-based controls. We followed-up prioritised variants in OA subjects from the interim release of the UK Biobank resource (up to 12 658 cases and 50 898 controls) and our lead finding in operated OA subjects from the full release of UK Biobank (17 894 cases and 89 470 controls). We investigated its functional implications in methylation, gene expression and proteomics data in primary chondrocytes from 12 pairs of intact and degraded cartilage samples from patients undergoing TJR. Results We detect a genome-wide significant association at rs10116772 with TJR (P=3.7×10−8; for allele A: OR (95% CI) 0.97 (0.96 to 0.98)), an intronic variant in GLIS3, which is expressed in cartilage. Variants in strong correlation with rs10116772 have been associated with elevated plasma glucose levels and diabetes. Conclusions We identify a novel susceptibility locus for OA that has been previously implicated in diabetes and glycaemic traits. |
Xue Li; Xiangrui Meng; Athina Spiliopoulou; Maria Timofeeva; Wei-Qi Wei; Aliya Gifford; Xia Shen; Yazhou He; Tim Varley; Paul McKeigue; Ioanna Tzoulaki; Alan F Wright; Peter Joshi; Joshua C Denny; Harry Campbell; Evropi Theodoratou MR-PheWAS: exploring the causal effect of SUA level on multiple disease outcomes by using genetic instruments in UK Biobank Journal Article Annals of the Rheumatic diseases , 2018. @article{Li2018, title = {MR-PheWAS: exploring the causal effect of SUA level on multiple disease outcomes by using genetic instruments in UK Biobank}, author = { Xue Li and Xiangrui Meng and Athina Spiliopoulou and Maria Timofeeva and Wei-Qi Wei and Aliya Gifford and Xia Shen and Yazhou He and Tim Varley and Paul McKeigue and Ioanna Tzoulaki and Alan F Wright and Peter Joshi and Joshua C Denny and Harry Campbell and Evropi Theodoratou}, url = {http://ard.bmj.com/content/early/2018/02/06/annrheumdis-2017-212534}, year = {2018}, date = {2018-02-06}, journal = {Annals of the Rheumatic diseases }, abstract = {Objectives We aimed to investigate the role of serum uric acid (SUA) level in a broad spectrum of disease outcomes using data for 120 091 individuals from UK Biobank. Methods We performed a phenome-wide association study (PheWAS) to identify disease outcomes associated with SUA genetic risk loci. We then implemented conventional Mendelian randomisation (MR) analysis to investigate the causal relevance between SUA level and disease outcomes identified from PheWAS. We next applied MR Egger analysis to detect and account for potential pleiotropy, which conventional MR analysis might mistake for causality, and used the HEIDI (heterogeneity in dependent instruments) test to remove cross-phenotype associations that were likely due to genetic linkage. Results Our PheWAS identified 25 disease groups/outcomes associated with SUA genetic risk loci after multiple testing correction (P<8.57e-05). Our conventional MR analysis implicated a causal role of SUA level in three disease groups: inflammatory polyarthropathies (OR=1.22, 95% CI 1.11 to 1.34), hypertensive disease (OR=1.08, 95% CI 1.03 to 1.14) and disorders of metabolism (OR=1.07, 95% CI 1.01 to 1.14); and four disease outcomes: gout (OR=4.88, 95% CI 3.91 to 6.09), essential hypertension (OR=1.08, 95% CI 1.03 to 1.14), myocardial infarction (OR=1.16, 95% CI 1.03 to 1.30) and coeliac disease (OR=1.41, 95% CI 1.05 to 1.89). After balancing pleiotropic effects in MR Egger analysis, only gout and its encompassing disease group of inflammatory polyarthropathies were considered to be causally associated with SUA level. Our analysis highlighted a locus (ATXN2/S2HB3) that may influence SUA level and multiple cardiovascular and autoimmune diseases via pleiotropy. Conclusions Elevated SUA level is convincing to cause gout and inflammatory polyarthropathies, and might act as a marker for the wider range of diseases with which it associates. Our findings support further investigation on the clinical relevance of SUA level with cardiovascular, metabolic, autoimmune and respiratory diseases.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Objectives We aimed to investigate the role of serum uric acid (SUA) level in a broad spectrum of disease outcomes using data for 120 091 individuals from UK Biobank. Methods We performed a phenome-wide association study (PheWAS) to identify disease outcomes associated with SUA genetic risk loci. We then implemented conventional Mendelian randomisation (MR) analysis to investigate the causal relevance between SUA level and disease outcomes identified from PheWAS. We next applied MR Egger analysis to detect and account for potential pleiotropy, which conventional MR analysis might mistake for causality, and used the HEIDI (heterogeneity in dependent instruments) test to remove cross-phenotype associations that were likely due to genetic linkage. Results Our PheWAS identified 25 disease groups/outcomes associated with SUA genetic risk loci after multiple testing correction (P<8.57e-05). Our conventional MR analysis implicated a causal role of SUA level in three disease groups: inflammatory polyarthropathies (OR=1.22, 95% CI 1.11 to 1.34), hypertensive disease (OR=1.08, 95% CI 1.03 to 1.14) and disorders of metabolism (OR=1.07, 95% CI 1.01 to 1.14); and four disease outcomes: gout (OR=4.88, 95% CI 3.91 to 6.09), essential hypertension (OR=1.08, 95% CI 1.03 to 1.14), myocardial infarction (OR=1.16, 95% CI 1.03 to 1.30) and coeliac disease (OR=1.41, 95% CI 1.05 to 1.89). After balancing pleiotropic effects in MR Egger analysis, only gout and its encompassing disease group of inflammatory polyarthropathies were considered to be causally associated with SUA level. Our analysis highlighted a locus (ATXN2/S2HB3) that may influence SUA level and multiple cardiovascular and autoimmune diseases via pleiotropy. Conclusions Elevated SUA level is convincing to cause gout and inflammatory polyarthropathies, and might act as a marker for the wider range of diseases with which it associates. Our findings support further investigation on the clinical relevance of SUA level with cardiovascular, metabolic, autoimmune and respiratory diseases. |
P Turley; RK Walters; O Maghzian; A Okbay; JJ Lee; MA Fontana; TA Nguyen-Viet; R Wedow; M Zacher; NA Furlotte; P Magnusson; S Oskarsson; M Johannesson; PM Visscher; D Laibson; D Cesarini; BM Neale; DJ Benjamin Multi-trait analysis of genome-wide association summary statistics using MTAG. Journal Article Nature Genetics, 2018. @article{Turley2018, title = {Multi-trait analysis of genome-wide association summary statistics using MTAG.}, author = {P Turley and RK Walters and O Maghzian and A Okbay and JJ Lee and MA Fontana and TA Nguyen-Viet and R Wedow and M Zacher and NA Furlotte and P Magnusson and S Oskarsson and M Johannesson and PM Visscher and D Laibson and D Cesarini and BM Neale and DJ Benjamin}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29292387}, year = {2018}, date = {2018-02-05}, journal = {Nature Genetics}, abstract = {We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N eff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N eff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations. |
Rona J. Strawbridge; Joey Ward; Breda Cullen; Elizabeth M. Tunbridge; Sarah Hartz; Laura Bierut; Amy Horton; Mark E. S. Bailey; Nicholas Graham; Amy Ferguson; Donald M. Lyall; Daniel Mackay; Laura M. Pidgeon; Jonathan Cavanagh; Jill P. Pell; Michael O’Donovan; Valentina Escott-Price; Paul J. Harrison; Daniel J. Smith Genome-wide analysis of self-reported risk-taking behaviour and cross-disorder genetic correlations in the UK Biobank cohort Journal Article Translational Psychiatry, 2018. @article{Strawbridge2017, title = {Genome-wide analysis of self-reported risk-taking behaviour and cross-disorder genetic correlations in the UK Biobank cohort}, author = {Rona J. Strawbridge and Joey Ward and Breda Cullen and Elizabeth M. Tunbridge and Sarah Hartz and Laura Bierut and Amy Horton and Mark E. S. Bailey and Nicholas Graham and Amy Ferguson and Donald M. Lyall and Daniel Mackay and Laura M. Pidgeon and Jonathan Cavanagh and Jill P. Pell and Michael O’Donovan and Valentina Escott-Price and Paul J. Harrison and Daniel J. Smith}, url = {https://www.nature.com/articles/s41398-017-0079-1}, year = {2018}, date = {2018-02-02}, journal = {Translational Psychiatry}, abstract = {Risk-taking behaviour is a key component of several psychiatric disorders and could influence lifestyle choices such as smoking, alcohol use, and diet. As a phenotype, risk-taking behaviour therefore fits within a Research Domain Criteria (RDoC) approach, whereby identifying genetic determinants of this trait has the potential to improve our understanding across different psychiatric disorders. Here we report a genome-wide association study in 116,255 UK Biobank participants who responded yes/no to the question “Would you consider yourself a risk taker?” Risk takers (compared with controls) were more likely to be men, smokers, and have a history of psychiatric disorder. Genetic loci associated with risk-taking behaviour were identified on chromosomes 3 (rs13084531) and 6 (rs9379971). The effects of both lead SNPs were comparable between men and women. The chromosome 3 locus highlights CADM2, previously implicated in cognitive and executive functions, but the chromosome 6 locus is challenging to interpret due to the complexity of the HLA region. Risk-taking behaviour shared significant genetic risk with schizophrenia, bipolar disorder, attention-deficit hyperactivity disorder, and post-traumatic stress disorder, as well as with smoking and total obesity. Despite being based on only a single question, this study furthers our understanding of the biology of risk-taking behaviour, a trait that has a major impact on a range of common physical and mental health disorders.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Risk-taking behaviour is a key component of several psychiatric disorders and could influence lifestyle choices such as smoking, alcohol use, and diet. As a phenotype, risk-taking behaviour therefore fits within a Research Domain Criteria (RDoC) approach, whereby identifying genetic determinants of this trait has the potential to improve our understanding across different psychiatric disorders. Here we report a genome-wide association study in 116,255 UK Biobank participants who responded yes/no to the question “Would you consider yourself a risk taker?” Risk takers (compared with controls) were more likely to be men, smokers, and have a history of psychiatric disorder. Genetic loci associated with risk-taking behaviour were identified on chromosomes 3 (rs13084531) and 6 (rs9379971). The effects of both lead SNPs were comparable between men and women. The chromosome 3 locus highlights CADM2, previously implicated in cognitive and executive functions, but the chromosome 6 locus is challenging to interpret due to the complexity of the HLA region. Risk-taking behaviour shared significant genetic risk with schizophrenia, bipolar disorder, attention-deficit hyperactivity disorder, and post-traumatic stress disorder, as well as with smoking and total obesity. Despite being based on only a single question, this study furthers our understanding of the biology of risk-taking behaviour, a trait that has a major impact on a range of common physical and mental health disorders. |
Weihua Meng; Mark J. Adams; Harry L. Heberta; Ian J. Deary; Andrew M. McIntosh; Blair H. Smith A Genome-Wide Association Study Finds Genetic Associations with Broadly-Defined Headache in UK Biobank (N = 223,773) Journal Article EBioMedicine, 2018. @article{Menga2018, title = {A Genome-Wide Association Study Finds Genetic Associations with Broadly-Defined Headache in UK Biobank (N = 223,773)}, author = { Weihua Meng and Mark J. Adams and Harry L. Heberta and Ian J. Deary and Andrew M. McIntosh and Blair H. Smith}, url = {https://www.sciencedirect.com/science/article/pii/S2352396418300288}, year = {2018}, date = {2018-01-31}, journal = {EBioMedicine}, abstract = {Background Headache is the most common neurological symptom and a leading cause of years lived with disability. We sought to identify the genetic variants associated with a broadly-defined headache phenotype in 223,773 subjects from the UK Biobank cohort. Methods We defined headache based on a specific question answered by the UK Biobank participants. We performed a genome-wide association study of headache as a single entity, using 74,461 cases and 149,312 controls. Results We identified 3343 SNPs which reached the genome-wide significance level of P < 5 × 10− 8. The SNPs were located in 28 loci, with the top SNP of rs11172113 in the LRP1 gene having a P value of 4.92 × 10− 47. Of the 28 loci, 14 have previously been associated with migraine. Among 14 new loci, rs77804065 with a P value of 5.87 × 10− 15 in the LINC02210-CRHR1 gene was the top SNP. Significant relationships between multiple brain tissues and genetic associations were identified through tissue expression analysis. We also identified significant positive genetic correlations between headache and many psychological traits. Conclusions Our results suggest that brain function is closely related to broadly-defined headache. In addition, we found that many psychological traits have genetic correlations with headache.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background Headache is the most common neurological symptom and a leading cause of years lived with disability. We sought to identify the genetic variants associated with a broadly-defined headache phenotype in 223,773 subjects from the UK Biobank cohort. Methods We defined headache based on a specific question answered by the UK Biobank participants. We performed a genome-wide association study of headache as a single entity, using 74,461 cases and 149,312 controls. Results We identified 3343 SNPs which reached the genome-wide significance level of P < 5 × 10− 8. The SNPs were located in 28 loci, with the top SNP of rs11172113 in the LRP1 gene having a P value of 4.92 × 10− 47. Of the 28 loci, 14 have previously been associated with migraine. Among 14 new loci, rs77804065 with a P value of 5.87 × 10− 15 in the LINC02210-CRHR1 gene was the top SNP. Significant relationships between multiple brain tissues and genetic associations were identified through tissue expression analysis. We also identified significant positive genetic correlations between headache and many psychological traits. Conclusions Our results suggest that brain function is closely related to broadly-defined headache. In addition, we found that many psychological traits have genetic correlations with headache. |
SC Larsson; H Melhus; K Michaëlsson Circulating Serum 25-Hydroxyvitamin D Levels and Bone Mineral Density: Mendelian Randomization Study. Journal Article 2018. @article{Larsson2018, title = {Circulating Serum 25-Hydroxyvitamin D Levels and Bone Mineral Density: Mendelian Randomization Study.}, author = {SC Larsson and H Melhus and K Michaëlsson}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29338102}, year = {2018}, date = {2018-01-16}, abstract = {here is considerable discussion of the importance for increased serum 25-hydroxyvitamin D (S-25OHD) concentration associated with adequacy for bone health. Accordingly, whether long-term high S-25OHD concentration in general positively affects bone mineral density (BMD) is uncertain. We used a Mendelian randomization design to determine the association between genetically increased S-25OHD concentrations and BMD. Five single-nucleotide polymorphisms (SNPs) in or near genes encoding enzymes and carrier proteins involved in vitamin D synthesis or metabolism were used as instrumental variables to genetically predict 1 standard deviation increase in S-25OHD concentration. Summary statistics data for the associations of the S-25OHD-associated SNPs with dual-energy X-ray absorptiometry (DXA)-derived femoral neck and lumbar spine BMD were obtained from the Genetic Factors for Osteoporosis (GEFOS) Consortium (32,965 individuals) and ultrasound-derived heel estimated BMD from the UK Biobank (142,487 individuals). None of the SNPs were associated with BMD at Bonferroni-corrected significance level, but there was a suggestive association between rs6013897 near CYP24A1 and femoral neck BMD (p = 0.01). In Mendelian randomization analysis, genetically predicted 1 standard deviation increment of S-25OHD was not associated with higher femoral neck BMD (SD change in BMD 0.02; 95% confidence interval [CI] -0.03 to 0.07; p = 0.37), lumbar spine BMD (SD change in BMD 0.02; 95% CI -0.04 to 0.08; p = 0.49), or estimated BMD (g/cm2 change in BMD -0.03; 95% CI -0.05 to -0.01; p = 0.02). This study does not support a causal association between long-term elevated S-25OHD concentrations and higher BMD in generally healthy populations. These results suggest that more emphasis should be placed on the development of evidence-based cut-off points for vitamin D inadequacy rather than a general recommendation to increase S-25OHD.}, keywords = {}, pubstate = {published}, tppubtype = {article} } here is considerable discussion of the importance for increased serum 25-hydroxyvitamin D (S-25OHD) concentration associated with adequacy for bone health. Accordingly, whether long-term high S-25OHD concentration in general positively affects bone mineral density (BMD) is uncertain. We used a Mendelian randomization design to determine the association between genetically increased S-25OHD concentrations and BMD. Five single-nucleotide polymorphisms (SNPs) in or near genes encoding enzymes and carrier proteins involved in vitamin D synthesis or metabolism were used as instrumental variables to genetically predict 1 standard deviation increase in S-25OHD concentration. Summary statistics data for the associations of the S-25OHD-associated SNPs with dual-energy X-ray absorptiometry (DXA)-derived femoral neck and lumbar spine BMD were obtained from the Genetic Factors for Osteoporosis (GEFOS) Consortium (32,965 individuals) and ultrasound-derived heel estimated BMD from the UK Biobank (142,487 individuals). None of the SNPs were associated with BMD at Bonferroni-corrected significance level, but there was a suggestive association between rs6013897 near CYP24A1 and femoral neck BMD (p = 0.01). In Mendelian randomization analysis, genetically predicted 1 standard deviation increment of S-25OHD was not associated with higher femoral neck BMD (SD change in BMD 0.02; 95% confidence interval [CI] -0.03 to 0.07; p = 0.37), lumbar spine BMD (SD change in BMD 0.02; 95% CI -0.04 to 0.08; p = 0.49), or estimated BMD (g/cm2 change in BMD -0.03; 95% CI -0.05 to -0.01; p = 0.02). This study does not support a causal association between long-term elevated S-25OHD concentrations and higher BMD in generally healthy populations. These results suggest that more emphasis should be placed on the development of evidence-based cut-off points for vitamin D inadequacy rather than a general recommendation to increase S-25OHD. |
S Thériault; R Lali; M Chong; JL Velianou; MK Natarajan; G Paré Polygenic Contribution in Individuals With Early-Onset Coronary Artery Disease Journal Article Circulation, Genomic and Precision Medicine, 2018. @article{Thériault2018b, title = {Polygenic Contribution in Individuals With Early-Onset Coronary Artery Disease}, author = {S Thériault and R Lali and M Chong and JL Velianou and MK Natarajan and G Paré}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29874178}, year = {2018}, date = {2018-01-11}, journal = {Circulation, Genomic and Precision Medicine}, abstract = {BACKGROUND: Despite evidence of high heritability, monogenic disorders are identified in a minor fraction of individuals with early-onset coronary artery disease (EOCAD). We hypothesized that some individuals with EOCAD carry a high number of common genetic risk variants, with a combined effect similar to Mendelian forms of coronary artery disease, such as familial hypercholesterolemia. METHODS AND RESULTS: To confirm the polygenic contribution to EOCAD (age of ≤40 years for men and ≤45 years for women), we calculated in 111 418 British participants from the UK Biobank cohort a genetic risk score (GRS) based on the presence of 182 independent variants associated with coronary artery disease (GRS182). Participants with a diagnosis of EOCAD who underwent a revascularization procedure (n=96) had a significantly higher GRS182 (P=3.21×10-9) than those without EOCAD. An increase of 1 SD in GRS182 corresponded to an odds ratio of 1.84 (1.52-2.24) for EOCAD. The prevalence of a polygenic contribution that increased EOCAD risk similar to what is observed in heterozygous familial hypercholesterolemia was estimated at 1 in 53. In a local cohort of individuals with EOCAD (n=30), GRS182 was significantly increased compared with UK Biobank controls (P=0.001). Seven participants (23%) had a GRS182 corresponding to an estimated 2-fold increase in EOCAD risk; none had a rare mutation involved in monogenic dyslipidemia or EOCAD. CONCLUSIONS: These results suggest a significant polygenic contribution in individuals presenting with EOCAD, which could be more prevalent than familial hypercholesterolemia. Determination of the polygenic risk component could be included in the diagnostic workup of patients with EOCAD.}, keywords = {}, pubstate = {published}, tppubtype = {article} } BACKGROUND: Despite evidence of high heritability, monogenic disorders are identified in a minor fraction of individuals with early-onset coronary artery disease (EOCAD). We hypothesized that some individuals with EOCAD carry a high number of common genetic risk variants, with a combined effect similar to Mendelian forms of coronary artery disease, such as familial hypercholesterolemia. METHODS AND RESULTS: To confirm the polygenic contribution to EOCAD (age of ≤40 years for men and ≤45 years for women), we calculated in 111 418 British participants from the UK Biobank cohort a genetic risk score (GRS) based on the presence of 182 independent variants associated with coronary artery disease (GRS182). Participants with a diagnosis of EOCAD who underwent a revascularization procedure (n=96) had a significantly higher GRS182 (P=3.21×10-9) than those without EOCAD. An increase of 1 SD in GRS182 corresponded to an odds ratio of 1.84 (1.52-2.24) for EOCAD. The prevalence of a polygenic contribution that increased EOCAD risk similar to what is observed in heterozygous familial hypercholesterolemia was estimated at 1 in 53. In a local cohort of individuals with EOCAD (n=30), GRS182 was significantly increased compared with UK Biobank controls (P=0.001). Seven participants (23%) had a GRS182 corresponding to an estimated 2-fold increase in EOCAD risk; none had a rare mutation involved in monogenic dyslipidemia or EOCAD. CONCLUSIONS: These results suggest a significant polygenic contribution in individuals presenting with EOCAD, which could be more prevalent than familial hypercholesterolemia. Determination of the polygenic risk component could be included in the diagnostic workup of patients with EOCAD. |
W. D. Hill; R. E. Marioni; O. Maghzian; S. J. Ritchie, S. P. Hagenaars, A. M. McIntosh, C. R. Gale, G. Davies & I. J. Deary A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence Journal Article Molecular Psychiatry, 2018. @article{Hill2018, title = {A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence}, author = {W. D. Hill and R. E. Marioni and O. Maghzian and S. J. Ritchie, S. P. Hagenaars, A. M. McIntosh, C. R. Gale, G. Davies & I. J. Deary}, url = {https://www.nature.com/articles/s41380-017-0001-5}, year = {2018}, date = {2018-01-11}, journal = {Molecular Psychiatry}, abstract = {Intelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including a wide range of physical, and mental health variables. Education is strongly genetically correlated with intelligence (r g = 0.70). We used these findings as foundations for our use of a novel approach—multi-trait analysis of genome-wide association studies (MTAG; Turley et al. 2017)—to combine two large genome-wide association studies (GWASs) of education and intelligence, increasing statistical power and resulting in the largest GWAS of intelligence yet reported. Our study had four goals: first, to facilitate the discovery of new genetic loci associated with intelligence; second, to add to our understanding of the biology of intelligence differences; third, to examine whether combining genetically correlated traits in this way produces results consistent with the primary phenotype of intelligence; and, finally, to test how well this new meta-analytic data sample on intelligence predicts phenotypic intelligence in an independent sample. By combining datasets using MTAG, our functional sample size increased from 199,242 participants to 248,482. We found 187 independent loci associated with intelligence, implicating 538 genes, using both SNP-based and gene-based GWAS. We found evidence that neurogenesis and myelination—as well as genes expressed in the synapse, and those involved in the regulation of the nervous system—may explain some of the biological differences in intelligence. The results of our combined analysis demonstrated the same pattern of genetic correlations as those from previous GWASs of intelligence, providing support for the meta-analysis of these genetically-related phenotypes. Intelligence, also known as general cognitive function or simply g, describes the shared variance that exists between diverse measures of cognitive ability [1]. In a population with a range of cognitive ability, intelligence accounts for around 40% of the variation between individuals in scores on diverse cognitive tests [2]. Intelligence is predictive of health states, including mortality; [3, 4] a lower level of cognitive function in youth is associated with earlier death over the next several decades [5]. Intelligence is a heritable trait, with twin- and family-based estimates of heritability indicating that between 50–80% of differences in intelligence can be explained by genetic factors [6]. These genetic factors make a greater contribution to phenotypic differences as age increases from childhood to adulthood [7]. Heritability estimates derived from molecular genetic data using the GREML-SC [8, 9] method indicate that around 20–30% of variation can be explained by variants in linkage disequilibrium (LD) with genotyped single nucleotide polymorphisms (SNPs) [10]. Some of the association between intelligence and health is due to genetic variants that act across traits [11, 12]. More recent methods to measure heritability, such as GREML-KIN [13], and GREML-MS [14] using imputed SNPs, have found that some of the heritability of intelligence can be found in variants that are in poor LD with genotyped variants; by taking these into consideration, SNP heritability estimates of 0.54 (GREML-KIN) and 0.50 (GREML-MS) [15] have been found. Relatively few genetic variants have reliably been associated with intelligence differences [16]. The sparsity of genome-wide significant SNPs discovered so far, combined with the substantial heritability estimate, suggests a phenotype with a highly polygenic architecture, where the total effect of all associated variants is substantial, but in which each individual variant exerts only a small influence. This is compelling evidence that the number of uncovered genome-wide significant loci associated with intelligence can be increased by raising the sample size—and thus the statistical power—of GWASs, as has been the case for other phenotypes such as height [17] and schizophrenia [18]. Two strategies have emerged in order to maximise power by increasing the sample size for loci discovery in intelligence research. The first involves the meta-analysis of many GWASs conducted on intelligence [19,20,21]. However, these studies are hampered by the fact that each individual sample tends to use different cognitive tests, and these individual sample sizes are often small; thus, even the resulting meta-analysis is underpowered to detect loci associated with intelligence with very small effect sizes [19,20,21]. This problem is ameliorated in studies like UK Biobank, which contain a large number of individuals who have supplied genetic data and taken the same cognitive test [22]. In the case of UK Biobank, a test of verbal and numerical reasoning shows a high genetic correlation with intelligence [23] as derived from psychometrically validated test batteries [16]. The second method is to use a “proxy” phenotype [24] that shows high phenotypic and genetic correlations with intelligence, and should therefore have a similar genetic architecture. Educational attainment has been successfully used as a proxy phenotype for intelligence [24], owing in part to the phenotypic and genetic correlation between the traits [7], and to the ease with which it can be measured relatively consistently, facilitating the larger sample sizes required for loci discovery [25]. Such methods have led to sample sizes of 293,723 for educational attainment, and the discovery of 74 loci attaining genome-wide significance [25]. The genetic correlation between the largest GWAS on intelligence and the largest GWAS on education was 0.70 [16]. In the present study, we combined these two approaches by using MTAG [26], a newly-developed technique that allows the meta-analysis of summary statistics from genetically-related traits. This enabled us effectively to increase the sample size (to add power) to GWASs of intelligence by adding in the genetic variance that is shared with proxy phenotypes. We used summary results from the largest available GWAS on intelligence (n = 78,308) [16]. We performed a meta-analysis using these data, and those from the latest release of the genetic data from UK Biobank to maximise power in our GWAS of intelligence. Finally, we added the Social Science Genetic Association Consortium (SSGAC) GWAS summary results for years of education [25] (n = 329,417, which include individuals from UK Biobank). By combining our meta-analytic dataset on intelligence with the education dataset from the SSGAC, we increased the power to discover loci associated with intelligence. The estimated effective sample size increased from 199,242 to 248,482 participants. We then used bivariate linkage disequilibrium score regression [12] to test whether these meta-analytic results have the same genetic architecture as other measures of intelligence. We used both SNP-based and gene-based GWAS to maximise our ability to discover loci and genes associated with intelligence, before predicting phenotypic intelligence in an independent sample using polygenic profile scoring. We used functional mapping and annotation of genetic associations (FUMA) to identify and annotate independent associations within our data. Finally, we applied gene-set analysis, using 10,891 gene sets sourced from Gene Ontology [27], Reactome [28], and MSigDB [29] to derive biological meaning from our data. Our results indicated that, by drawing on multiple large GWAS datasets all measuring intelligence-related traits, we could attain greater statistical power to detect genetic variants associated with intelligence, facilitate our understanding of the underlying biology of intelligence differences, and make substantial phenotypic predictions of intelligence using SNP data.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Intelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including a wide range of physical, and mental health variables. Education is strongly genetically correlated with intelligence (r g = 0.70). We used these findings as foundations for our use of a novel approach—multi-trait analysis of genome-wide association studies (MTAG; Turley et al. 2017)—to combine two large genome-wide association studies (GWASs) of education and intelligence, increasing statistical power and resulting in the largest GWAS of intelligence yet reported. Our study had four goals: first, to facilitate the discovery of new genetic loci associated with intelligence; second, to add to our understanding of the biology of intelligence differences; third, to examine whether combining genetically correlated traits in this way produces results consistent with the primary phenotype of intelligence; and, finally, to test how well this new meta-analytic data sample on intelligence predicts phenotypic intelligence in an independent sample. By combining datasets using MTAG, our functional sample size increased from 199,242 participants to 248,482. We found 187 independent loci associated with intelligence, implicating 538 genes, using both SNP-based and gene-based GWAS. We found evidence that neurogenesis and myelination—as well as genes expressed in the synapse, and those involved in the regulation of the nervous system—may explain some of the biological differences in intelligence. The results of our combined analysis demonstrated the same pattern of genetic correlations as those from previous GWASs of intelligence, providing support for the meta-analysis of these genetically-related phenotypes. Intelligence, also known as general cognitive function or simply g, describes the shared variance that exists between diverse measures of cognitive ability [1]. In a population with a range of cognitive ability, intelligence accounts for around 40% of the variation between individuals in scores on diverse cognitive tests [2]. Intelligence is predictive of health states, including mortality; [3, 4] a lower level of cognitive function in youth is associated with earlier death over the next several decades [5]. Intelligence is a heritable trait, with twin- and family-based estimates of heritability indicating that between 50–80% of differences in intelligence can be explained by genetic factors [6]. These genetic factors make a greater contribution to phenotypic differences as age increases from childhood to adulthood [7]. Heritability estimates derived from molecular genetic data using the GREML-SC [8, 9] method indicate that around 20–30% of variation can be explained by variants in linkage disequilibrium (LD) with genotyped single nucleotide polymorphisms (SNPs) [10]. Some of the association between intelligence and health is due to genetic variants that act across traits [11, 12]. More recent methods to measure heritability, such as GREML-KIN [13], and GREML-MS [14] using imputed SNPs, have found that some of the heritability of intelligence can be found in variants that are in poor LD with genotyped variants; by taking these into consideration, SNP heritability estimates of 0.54 (GREML-KIN) and 0.50 (GREML-MS) [15] have been found. Relatively few genetic variants have reliably been associated with intelligence differences [16]. The sparsity of genome-wide significant SNPs discovered so far, combined with the substantial heritability estimate, suggests a phenotype with a highly polygenic architecture, where the total effect of all associated variants is substantial, but in which each individual variant exerts only a small influence. This is compelling evidence that the number of uncovered genome-wide significant loci associated with intelligence can be increased by raising the sample size—and thus the statistical power—of GWASs, as has been the case for other phenotypes such as height [17] and schizophrenia [18]. Two strategies have emerged in order to maximise power by increasing the sample size for loci discovery in intelligence research. The first involves the meta-analysis of many GWASs conducted on intelligence [19,20,21]. However, these studies are hampered by the fact that each individual sample tends to use different cognitive tests, and these individual sample sizes are often small; thus, even the resulting meta-analysis is underpowered to detect loci associated with intelligence with very small effect sizes [19,20,21]. This problem is ameliorated in studies like UK Biobank, which contain a large number of individuals who have supplied genetic data and taken the same cognitive test [22]. In the case of UK Biobank, a test of verbal and numerical reasoning shows a high genetic correlation with intelligence [23] as derived from psychometrically validated test batteries [16]. The second method is to use a “proxy” phenotype [24] that shows high phenotypic and genetic correlations with intelligence, and should therefore have a similar genetic architecture. Educational attainment has been successfully used as a proxy phenotype for intelligence [24], owing in part to the phenotypic and genetic correlation between the traits [7], and to the ease with which it can be measured relatively consistently, facilitating the larger sample sizes required for loci discovery [25]. Such methods have led to sample sizes of 293,723 for educational attainment, and the discovery of 74 loci attaining genome-wide significance [25]. The genetic correlation between the largest GWAS on intelligence and the largest GWAS on education was 0.70 [16]. In the present study, we combined these two approaches by using MTAG [26], a newly-developed technique that allows the meta-analysis of summary statistics from genetically-related traits. This enabled us effectively to increase the sample size (to add power) to GWASs of intelligence by adding in the genetic variance that is shared with proxy phenotypes. We used summary results from the largest available GWAS on intelligence (n = 78,308) [16]. We performed a meta-analysis using these data, and those from the latest release of the genetic data from UK Biobank to maximise power in our GWAS of intelligence. Finally, we added the Social Science Genetic Association Consortium (SSGAC) GWAS summary results for years of education [25] (n = 329,417, which include individuals from UK Biobank). By combining our meta-analytic dataset on intelligence with the education dataset from the SSGAC, we increased the power to discover loci associated with intelligence. The estimated effective sample size increased from 199,242 to 248,482 participants. We then used bivariate linkage disequilibrium score regression [12] to test whether these meta-analytic results have the same genetic architecture as other measures of intelligence. We used both SNP-based and gene-based GWAS to maximise our ability to discover loci and genes associated with intelligence, before predicting phenotypic intelligence in an independent sample using polygenic profile scoring. We used functional mapping and annotation of genetic associations (FUMA) to identify and annotate independent associations within our data. Finally, we applied gene-set analysis, using 10,891 gene sets sourced from Gene Ontology [27], Reactome [28], and MSigDB [29] to derive biological meaning from our data. Our results indicated that, by drawing on multiple large GWAS datasets all measuring intelligence-related traits, we could attain greater statistical power to detect genetic variants associated with intelligence, facilitate our understanding of the underlying biology of intelligence differences, and make substantial phenotypic predictions of intelligence using SNP data. |
LS Hall; MJ Adams; A Arnau-Soler; TK Clarke; DM Howard; Y Zeng; G Davies; SP Hagenaars; A Maria Fernandez-Pujals; J Gibson; EM Wigmore; TS Boutin; C Hayward; DJ Porteous; IJ Deary; PA Thomson; CS Haley; AM McIntosh Genome-wide meta-analyses of stratified depression in Generation Scotland and UK Biobank. Journal Article Translational Psychiatry, 2018. @article{Hall2018, title = {Genome-wide meta-analyses of stratified depression in Generation Scotland and UK Biobank.}, author = {LS Hall and MJ Adams and A Arnau-Soler and TK Clarke and DM Howard and Y Zeng and G Davies and SP Hagenaars and A Maria Fernandez-Pujals and J Gibson and EM Wigmore and TS Boutin and C Hayward and DJ Porteous and IJ Deary and PA Thomson and CS Haley and AM McIntosh}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29317602}, year = {2018}, date = {2018-01-10}, journal = {Translational Psychiatry}, abstract = {Few replicable genetic associations for Major Depressive Disorder (MDD) have been identified. Recent studies of MDD have identified common risk variants by using a broader phenotype definition in very large samples, or by reducing phenotypic and ancestral heterogeneity. We sought to ascertain whether it is more informative to maximize the sample size using data from all available cases and controls, or to use a sex or recurrent stratified subset of affected individuals. To test this, we compared heritability estimates, genetic correlation with other traits, variance explained by MDD polygenic score, and variants identified by genome-wide meta-analysis for broad and narrow MDD classifications in two large British cohorts - Generation Scotland and UK Biobank. Genome-wide meta-analysis of MDD in males yielded one genome-wide significant locus on 3p22.3, with three genes in this region (CRTAP, GLB1, and TMPPE) demonstrating a significant association in gene-based tests. Meta-analyzed MDD, recurrent MDD and female MDD yielded equivalent heritability estimates, showed no detectable difference in association with polygenic scores, and were each genetically correlated with six health-correlated traits (neuroticism, depressive symptoms, subjective well-being, MDD, a cross-disorder phenotype and Bipolar Disorder). Whilst stratified GWAS analysis revealed a genome-wide significant locus for male MDD, the lack of independent replication, and the consistent pattern of results in other MDD classifications suggests that phenotypic stratification using recurrence or sex in currently available sample sizes is currently weakly justified. Based upon existing studies and our findings, the strategy of maximizing sample sizes is likely to provide the greater gain.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Few replicable genetic associations for Major Depressive Disorder (MDD) have been identified. Recent studies of MDD have identified common risk variants by using a broader phenotype definition in very large samples, or by reducing phenotypic and ancestral heterogeneity. We sought to ascertain whether it is more informative to maximize the sample size using data from all available cases and controls, or to use a sex or recurrent stratified subset of affected individuals. To test this, we compared heritability estimates, genetic correlation with other traits, variance explained by MDD polygenic score, and variants identified by genome-wide meta-analysis for broad and narrow MDD classifications in two large British cohorts - Generation Scotland and UK Biobank. Genome-wide meta-analysis of MDD in males yielded one genome-wide significant locus on 3p22.3, with three genes in this region (CRTAP, GLB1, and TMPPE) demonstrating a significant association in gene-based tests. Meta-analyzed MDD, recurrent MDD and female MDD yielded equivalent heritability estimates, showed no detectable difference in association with polygenic scores, and were each genetically correlated with six health-correlated traits (neuroticism, depressive symptoms, subjective well-being, MDD, a cross-disorder phenotype and Bipolar Disorder). Whilst stratified GWAS analysis revealed a genome-wide significant locus for male MDD, the lack of independent replication, and the consistent pattern of results in other MDD classifications suggests that phenotypic stratification using recurrence or sex in currently available sample sizes is currently weakly justified. Based upon existing studies and our findings, the strategy of maximizing sample sizes is likely to provide the greater gain. |
CM Astley; JN Todd; RM Salem; S Vedantam; CB Ebbeling; PL Huang; DS Ludwig; JN Hirschhorn; JC Florez Genetic Evidence That Carbohydrate-Stimulated Insulin Secretion Leads to Obesity. Journal Article Clinical Chemistry, 2018. @article{Astley2018, title = {Genetic Evidence That Carbohydrate-Stimulated Insulin Secretion Leads to Obesity.}, author = {CM Astley and JN Todd and RM Salem and S Vedantam and CB Ebbeling and PL Huang and DS Ludwig and JN Hirschhorn and JC Florez}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29295838}, year = {2018}, date = {2018-01-01}, journal = {Clinical Chemistry}, abstract = {BACKGROUND: A fundamental precept of the carbohydrate-insulin model of obesity is that insulin secretion drives weight gain. However, fasting hyperinsulinemia can also be driven by obesity-induced insulin resistance. We used genetic variation to isolate and estimate the potentially causal effect of insulin secretion on body weight. METHODS: Genetic instruments of variation of insulin secretion [assessed as insulin concentration 30 min after oral glucose (insulin-30)] were used to estimate the causal relationship between increased insulin secretion and body mass index (BMI), using bidirectional Mendelian randomization analysis of genome-wide association studies. Data sources included summary results from the largest published metaanalyses of predominantly European ancestry for insulin secretion (n = 26037) and BMI (n = 322154), as well as individual-level data from the UK Biobank (n = 138541). Data from the Cardiology and Metabolic Patient Cohort study at Massachusetts General Hospital (n = 1675) were used to validate genetic associations with insulin secretion and to test the observational association of insulin secretion and BMI. RESULTS: Higher genetically determined insulin-30 was strongly associated with higher BMI (β = 0.098, P = 2.2 × 10-21), consistent with a causal role in obesity. Similar positive associations were noted in sensitivity analyses using other genetic variants as instrumental variables. By contrast, higher genetically determined BMI was not associated with insulin-30. CONCLUSIONS: Mendelian randomization analyses provide evidence for a causal relationship of glucose-stimulated insulin secretion on body weight, consistent with the carbohydrate-insulin model of obesity.}, keywords = {}, pubstate = {published}, tppubtype = {article} } BACKGROUND: A fundamental precept of the carbohydrate-insulin model of obesity is that insulin secretion drives weight gain. However, fasting hyperinsulinemia can also be driven by obesity-induced insulin resistance. We used genetic variation to isolate and estimate the potentially causal effect of insulin secretion on body weight. METHODS: Genetic instruments of variation of insulin secretion [assessed as insulin concentration 30 min after oral glucose (insulin-30)] were used to estimate the causal relationship between increased insulin secretion and body mass index (BMI), using bidirectional Mendelian randomization analysis of genome-wide association studies. Data sources included summary results from the largest published metaanalyses of predominantly European ancestry for insulin secretion (n = 26037) and BMI (n = 322154), as well as individual-level data from the UK Biobank (n = 138541). Data from the Cardiology and Metabolic Patient Cohort study at Massachusetts General Hospital (n = 1675) were used to validate genetic associations with insulin secretion and to test the observational association of insulin secretion and BMI. RESULTS: Higher genetically determined insulin-30 was strongly associated with higher BMI (β = 0.098, P = 2.2 × 10-21), consistent with a causal role in obesity. Similar positive associations were noted in sensitivity analyses using other genetic variants as instrumental variables. By contrast, higher genetically determined BMI was not associated with insulin-30. CONCLUSIONS: Mendelian randomization analyses provide evidence for a causal relationship of glucose-stimulated insulin secretion on body weight, consistent with the carbohydrate-insulin model of obesity. |
2017 |
Jonas B. Nielsen; Lars G. Fritsche; Wei Zhou; Tanya M. Teslovich; Oddgeir L. Holmen; Stefan Gustafsson; Maiken E. Gabrielsen; Ellen M. Schmidt; Robin Beaumont; Brooke N. Wolford; Maoxuan Lin; Chad M. Brummett; Michael H. Preuss; Lena Refsgaard; Erwin P. Bottinger; Sarah E. Graham; Ida Surakka; Yunhan Chu; Anne Heidi Skogholt; Håvard Dalen; Alan P. Boyle American Journal of Human Genetics, 2017. @article{Nielsen2017, title = {Genome-wide Study of Atrial Fibrillation Identifies Seven Risk Loci and Highlights Biological Pathways and Regulatory Elements Involved in Cardiac Development}, author = {Jonas B. Nielsen and Lars G. Fritsche and Wei Zhou and Tanya M. Teslovich and Oddgeir L. Holmen and Stefan Gustafsson and Maiken E. Gabrielsen and Ellen M. Schmidt and Robin Beaumont and Brooke N. Wolford and Maoxuan Lin and Chad M. Brummett and Michael H. Preuss and Lena Refsgaard and Erwin P. Bottinger and Sarah E. Graham and Ida Surakka and Yunhan Chu and Anne Heidi Skogholt and Håvard Dalen and Alan P. Boyle}, url = {https://www.sciencedirect.com/science/article/pii/S0002929717304925}, year = {2017}, date = {2017-12-28}, journal = {American Journal of Human Genetics}, abstract = {Atrial fibrillation (AF) is a common cardiac arrhythmia and a major risk factor for stroke, heart failure, and premature death. The pathogenesis of AF remains poorly understood, which contributes to the current lack of highly effective treatments. To understand the genetic variation and biology underlying AF, we undertook a genome-wide association study (GWAS) of 6,337 AF individuals and 61,607 AF-free individuals from Norway, including replication in an additional 30,679 AF individuals and 278,895 AF-free individuals. Through genotyping and dense imputation mapping from whole-genome sequencing, we tested almost nine million genetic variants across the genome and identified seven risk loci, including two novel loci. One novel locus (lead single-nucleotide variant [SNV] rs12614435; p = 6.76 × 10−18) comprised intronic and several highly correlated missense variants situated in the I-, A-, and M-bands of titin, which is the largest protein in humans and responsible for the passive elasticity of heart and skeletal muscle. The other novel locus (lead SNV rs56202902; p = 1.54 × 10−11) covered a large, gene-dense chromosome 1 region that has previously been linked to cardiac conduction. Pathway and functional enrichment analyses suggested that many AF-associated genetic variants act through a mechanism of impaired muscle cell differentiation and tissue formation during fetal heart development.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Atrial fibrillation (AF) is a common cardiac arrhythmia and a major risk factor for stroke, heart failure, and premature death. The pathogenesis of AF remains poorly understood, which contributes to the current lack of highly effective treatments. To understand the genetic variation and biology underlying AF, we undertook a genome-wide association study (GWAS) of 6,337 AF individuals and 61,607 AF-free individuals from Norway, including replication in an additional 30,679 AF individuals and 278,895 AF-free individuals. Through genotyping and dense imputation mapping from whole-genome sequencing, we tested almost nine million genetic variants across the genome and identified seven risk loci, including two novel loci. One novel locus (lead single-nucleotide variant [SNV] rs12614435; p = 6.76 × 10−18) comprised intronic and several highly correlated missense variants situated in the I-, A-, and M-bands of titin, which is the largest protein in humans and responsible for the passive elasticity of heart and skeletal muscle. The other novel locus (lead SNV rs56202902; p = 1.54 × 10−11) covered a large, gene-dense chromosome 1 region that has previously been linked to cardiac conduction. Pathway and functional enrichment analyses suggested that many AF-associated genetic variants act through a mechanism of impaired muscle cell differentiation and tissue formation during fetal heart development. |
M Luciano; SP Hagenaars; G Davies; WD Hill; TK Clarke; M Shirali; SE Harris; RE Marioni; DC Liewald; C Fawns-Ritchie; MJ Adams; DM Howard; CM Lewis; CR Gale; AM McIntosh IJ Deary Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism. Journal Article Nature Genetics, 2017. @article{Luciano2017b, title = {Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism.}, author = {M Luciano and SP Hagenaars and G Davies and WD Hill and TK Clarke and M Shirali and SE Harris and RE Marioni and DC Liewald and C Fawns-Ritchie and MJ Adams and DM Howard and CM Lewis and CR Gale and AM McIntosh IJ Deary}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29255261}, year = {2017}, date = {2017-12-18}, journal = {Nature Genetics}, abstract = {Neuroticism is a relatively stable personality trait characterized by negative emotionality (for example, worry and guilt) 1 ; heritability estimated from twin studies ranges from 30 to 50% 2 , and SNP-based heritability ranges from 6 to 15% 3-6 . Increased neuroticism is associated with poorer mental and physical health 7,8 , translating to high economic burden 9 . Genome-wide association studies (GWAS) of neuroticism have identified up to 11 associated genetic loci 3,4 . Here we report 116 significant independent loci from a GWAS of neuroticism in 329,821 UK Biobank participants; 15 of these loci replicated at P < 0.00045 in an unrelated cohort (N = 122,867). Genetic signals were enriched in neuronal genesis and differentiation pathways, and substantial genetic correlations were found between neuroticism and depressive symptoms (r g = 0.82, standard error (s.e.) = 0.03), major depressive disorder (MDD; r g = 0.69, s.e. = 0.07) and subjective well-being (r g = -0.68, s.e. = 0.03) alongside other mental health traits. These discoveries significantly advance understanding of neuroticism and its association with MDD.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Neuroticism is a relatively stable personality trait characterized by negative emotionality (for example, worry and guilt) 1 ; heritability estimated from twin studies ranges from 30 to 50% 2 , and SNP-based heritability ranges from 6 to 15% 3-6 . Increased neuroticism is associated with poorer mental and physical health 7,8 , translating to high economic burden 9 . Genome-wide association studies (GWAS) of neuroticism have identified up to 11 associated genetic loci 3,4 . Here we report 116 significant independent loci from a GWAS of neuroticism in 329,821 UK Biobank participants; 15 of these loci replicated at P < 0.00045 in an unrelated cohort (N = 122,867). Genetic signals were enriched in neuronal genesis and differentiation pathways, and substantial genetic correlations were found between neuroticism and depressive symptoms (r g = 0.82, standard error (s.e.) = 0.03), major depressive disorder (MDD; r g = 0.69, s.e. = 0.07) and subjective well-being (r g = -0.68, s.e. = 0.03) alongside other mental health traits. These discoveries significantly advance understanding of neuroticism and its association with MDD. |
Cristina T Vicente; Joana A Revez; Manuel A R Ferreira Lessons from ten years of genome-wide association studies of asthma Journal Article Clinical and Translational Immunology, 2017. @article{Vicente2017, title = {Lessons from ten years of genome-wide association studies of asthma}, author = {Cristina T Vicente and Joana A Revez and Manuel A R Ferreira}, url = {http://onlinelibrary.wiley.com/doi/10.1038/cti.2017.54/abstract}, year = {2017}, date = {2017-12-15}, journal = {Clinical and Translational Immunology}, abstract = {Twenty-five genome-wide association studies (GWAS) of asthma were published between 2007 and 2016, the largest with a sample size of 157242 individuals. Across these studies, 39 genetic variants in low linkage disequilibrium (LD) with each other were reported to associate with disease risk at a significance threshold of P<5 × 10−8, including 31 in populations of European ancestry. Results from analyses of the UK Biobank data (n=380 503) indicate that at least 28 of the 31 associations reported in Europeans represent true-positive findings, collectively explaining 2.5% of the variation in disease liability (median of 0.06% per variant). We identified 49 transcripts as likely target genes of the published asthma risk variants, mostly based on LD with expression quantitative trait loci (eQTL). Of these genes, 16 were previously implicated in disease pathophysiology by functional studies, including TSLP, TNFSF4, ADORA1, CHIT1 and USF1. In contrast, at present, there is limited or no functional evidence directly implicating the remaining 33 likely target genes in asthma pathophysiology. Some of these genes have a known function that is relevant to allergic disease, including F11R, CD247, PGAP3, AAGAB, CAMK4 and PEX14, and so could be prioritized for functional follow-up. We conclude by highlighting three areas of research that are essential to help translate GWAS findings into clinical research or practice, namely validation of target gene predictions, understanding target gene function and their role in disease pathophysiology and genomics-guided prioritization of targets for drug development.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Twenty-five genome-wide association studies (GWAS) of asthma were published between 2007 and 2016, the largest with a sample size of 157242 individuals. Across these studies, 39 genetic variants in low linkage disequilibrium (LD) with each other were reported to associate with disease risk at a significance threshold of P<5 × 10−8, including 31 in populations of European ancestry. Results from analyses of the UK Biobank data (n=380 503) indicate that at least 28 of the 31 associations reported in Europeans represent true-positive findings, collectively explaining 2.5% of the variation in disease liability (median of 0.06% per variant). We identified 49 transcripts as likely target genes of the published asthma risk variants, mostly based on LD with expression quantitative trait loci (eQTL). Of these genes, 16 were previously implicated in disease pathophysiology by functional studies, including TSLP, TNFSF4, ADORA1, CHIT1 and USF1. In contrast, at present, there is limited or no functional evidence directly implicating the remaining 33 likely target genes in asthma pathophysiology. Some of these genes have a known function that is relevant to allergic disease, including F11R, CD247, PGAP3, AAGAB, CAMK4 and PEX14, and so could be prioritized for functional follow-up. We conclude by highlighting three areas of research that are essential to help translate GWAS findings into clinical research or practice, namely validation of target gene predictions, understanding target gene function and their role in disease pathophysiology and genomics-guided prioritization of targets for drug development. |
EbbaDu Rietz; Jonathan Coleman; Kylie Glanville; Shing Wan Choi; Paul F.O’Reilly; JonnaKuntsi Association of Polygenic Risk for Attention-Deficit/Hyperactivity Disorder With Co-occurring Traits and Disorders Journal Article Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2017. @article{Rietz2017, title = {Association of Polygenic Risk for Attention-Deficit/Hyperactivity Disorder With Co-occurring Traits and Disorders}, author = {EbbaDu Rietz and Jonathan Coleman and Kylie Glanville and Shing Wan Choi and Paul F.O’Reilly and JonnaKuntsi}, url = {https://www.sciencedirect.com/science/article/pii/S2451902217302318}, year = {2017}, date = {2017-12-14}, journal = {Biological Psychiatry: Cognitive Neuroscience and Neuroimaging}, abstract = {A recent large-scale mega genome-wide association study identified, for the first time, genetic variants at 12 loci significantly associated with attention-deficit/hyperactivity disorder (ADHD). In this study we use a powerful polygenic approach, with polygenic scores derived from the genome-wide association study, to investigate the etiological overlap between ADHD and frequently co-occurring traits and disorders. Methods Polygenic risk scores for ADHD derived from the mega genome-wide association study (20,183 cases and 35,191 control subjects) were computed in a large-scale adult population sample (N = 135,726) recruited by the UK Biobank. Regression analyses were conducted to investigate whether polygenic risk for ADHD is associated with related traits and disorders in this population sample. The effects of sex were investigated via inclusion of an interaction term in the models. Results Polygenic risk for ADHD significantly and positively predicted body mass index (R2 = .45%; p = 5 × 10−129), neuroticism (R2 = .09%; p = 2 × 10−24), depression (R2 = .11%; p = 2 × 10−13), anxiety (R2 = .06%; p = 3 × 10−4), risk taking (R2 = .12%; p = 9 × 10−25), alcohol intake (R2 = .09%; p = 8 × 10−29), smoking (R2 = .33%; p = 4 × 10−21), alcohol dependency (R2 = .21%; p = 5 × 10−6), and negatively predicted verbal-numerical reasoning (R2 = .38%; p = 5 × 10−36). Polygenic risk scores did not significantly predict schizophrenia or bipolar disorder, although this may be because of the small number of diagnostic cases. We found no interaction effects between polygenic risk for ADHD and sex on any phenotypes. Conclusions Our findings suggest that common genetic variation underlying risk for clinically diagnosed ADHD also contributes to higher body mass index, neuroticism, anxiety and depressive disorders, alcohol and nicotine use, risk taking, and lower general cognitive ability in the general population. These findings suggest that the co-occurrence of several traits with ADHD is partly explained by the same common genetic variants.}, keywords = {}, pubstate = {published}, tppubtype = {article} } A recent large-scale mega genome-wide association study identified, for the first time, genetic variants at 12 loci significantly associated with attention-deficit/hyperactivity disorder (ADHD). In this study we use a powerful polygenic approach, with polygenic scores derived from the genome-wide association study, to investigate the etiological overlap between ADHD and frequently co-occurring traits and disorders. Methods Polygenic risk scores for ADHD derived from the mega genome-wide association study (20,183 cases and 35,191 control subjects) were computed in a large-scale adult population sample (N = 135,726) recruited by the UK Biobank. Regression analyses were conducted to investigate whether polygenic risk for ADHD is associated with related traits and disorders in this population sample. The effects of sex were investigated via inclusion of an interaction term in the models. Results Polygenic risk for ADHD significantly and positively predicted body mass index (R2 = .45%; p = 5 × 10−129), neuroticism (R2 = .09%; p = 2 × 10−24), depression (R2 = .11%; p = 2 × 10−13), anxiety (R2 = .06%; p = 3 × 10−4), risk taking (R2 = .12%; p = 9 × 10−25), alcohol intake (R2 = .09%; p = 8 × 10−29), smoking (R2 = .33%; p = 4 × 10−21), alcohol dependency (R2 = .21%; p = 5 × 10−6), and negatively predicted verbal-numerical reasoning (R2 = .38%; p = 5 × 10−36). Polygenic risk scores did not significantly predict schizophrenia or bipolar disorder, although this may be because of the small number of diagnostic cases. We found no interaction effects between polygenic risk for ADHD and sex on any phenotypes. Conclusions Our findings suggest that common genetic variation underlying risk for clinically diagnosed ADHD also contributes to higher body mass index, neuroticism, anxiety and depressive disorders, alcohol and nicotine use, risk taking, and lower general cognitive ability in the general population. These findings suggest that the co-occurrence of several traits with ADHD is partly explained by the same common genetic variants. |
LC Weng; SH Choi; D Klarin; JG Smith; PR Loh; M Chaffin; C Roselli; OL Hulme; KL Lunetta; J Dupuis; EJ Benjamin; C Newton-Cheh; S Kathiresan; PT Ellinor; SA Lubitz Heritability of Atrial Fibrillation Journal Article Circulation, 2017. @article{Weng2017b, title = {Heritability of Atrial Fibrillation}, author = {LC Weng and SH Choi and D Klarin and JG Smith and PR Loh and M Chaffin and C Roselli and OL Hulme and KL Lunetta and J Dupuis and EJ Benjamin and C Newton-Cheh and S Kathiresan and PT Ellinor and SA Lubitz}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29237688}, year = {2017}, date = {2017-12-10}, journal = {Circulation}, abstract = {BACKGROUND: Previous reports have implicated multiple genetic loci associated with AF, but the contributions of genome-wide variation to AF susceptibility have not been quantified. METHODS AND RESULTS: We assessed the contribution of genome-wide single-nucleotide polymorphism variation to AF risk (single-nucleotide polymorphism heritability, h2g ) using data from 120 286 unrelated individuals of European ancestry (2987 with AF) in the population-based UK Biobank. We ascertained AF based on self-report, medical record billing codes, procedure codes, and death records. We estimated h2g using a variance components method with variants having a minor allele frequency ≥1%. We evaluated h2g in age, sex, and genomic strata of interest. The h2g for AF was 22.1% (95% confidence interval, 15.6%-28.5%) and was similar for early- versus older-onset AF (≤65 versus >65 years of age), as well as for men and women. The proportion of AF variance explained by genetic variation was mainly accounted for by common (minor allele frequency, ≥5%) variants (20.4%; 95% confidence interval, 15.1%-25.6%). Only 6.4% (95% confidence interval, 5.1%-7.7%) of AF variance was attributed to variation within known AF susceptibility, cardiac arrhythmia, and cardiomyopathy gene regions. CONCLUSIONS: Genetic variation contributes substantially to AF risk. The risk for AF conferred by genomic variation is similar to that observed for several other cardiovascular diseases. Established AF loci only explain a moderate proportion of disease risk, suggesting that furt}, keywords = {}, pubstate = {published}, tppubtype = {article} } BACKGROUND: Previous reports have implicated multiple genetic loci associated with AF, but the contributions of genome-wide variation to AF susceptibility have not been quantified. METHODS AND RESULTS: We assessed the contribution of genome-wide single-nucleotide polymorphism variation to AF risk (single-nucleotide polymorphism heritability, h2g ) using data from 120 286 unrelated individuals of European ancestry (2987 with AF) in the population-based UK Biobank. We ascertained AF based on self-report, medical record billing codes, procedure codes, and death records. We estimated h2g using a variance components method with variants having a minor allele frequency ≥1%. We evaluated h2g in age, sex, and genomic strata of interest. The h2g for AF was 22.1% (95% confidence interval, 15.6%-28.5%) and was similar for early- versus older-onset AF (≤65 versus >65 years of age), as well as for men and women. The proportion of AF variance explained by genetic variation was mainly accounted for by common (minor allele frequency, ≥5%) variants (20.4%; 95% confidence interval, 15.1%-25.6%). Only 6.4% (95% confidence interval, 5.1%-7.7%) of AF variance was attributed to variation within known AF susceptibility, cardiac arrhythmia, and cardiomyopathy gene regions. CONCLUSIONS: Genetic variation contributes substantially to AF risk. The risk for AF conferred by genomic variation is similar to that observed for several other cardiovascular diseases. Established AF loci only explain a moderate proportion of disease risk, suggesting that furt |
Pim van der Harst; Niek Verweij The Identification of 64 Novel Genetic Loci Provides an Expanded View on the Genetic Architecture of Coronary Artery Disease Journal Article Circulation Research , 2017. @article{vanderHarst2017, title = {The Identification of 64 Novel Genetic Loci Provides an Expanded View on the Genetic Architecture of Coronary Artery Disease}, author = {Pim van der Harst and Niek Verweij}, url = {http://circres.ahajournals.org/content/early/2017/12/05/CIRCRESAHA.117.312086}, year = {2017}, date = {2017-12-06}, journal = {Circulation Research }, abstract = {Rationale: Coronary artery disease (CAD) is a complex phenotype driven by genetic and environmental factors. 97 genetic risk loci have been identified so far, but the identification of additional susceptibility loci might be important to enhance our understanding of the genetic architecture of CAD. Objective: To expand the number of genome-wide significant loci, catalog functional insights, and enhance our understanding of the genetic architecture of CAD. Methods and Results: We performed a genome-wide association study (GWAS) in 34,541 CAD cases and 261,984 controls of UK biobank Resource followed by replication in 88,192 cases and 162,544 controls from CARDIoGRAMplusC4D. We identified 75 loci that replicated and were genome-wide significant (P<5x10-8) in meta-analysis, 13 of which had not been reported previously. Next, to further identify novel loci we identified all promising (P<0.0001) loci in the CARDIoGRAMplusC4D data and performed reciprocal replication and meta-analyses with UK biobank. This led to the identification of 21 additional novel loci reaching genome-wide significance (P<5x10-8) in meta-analysis. Finally, we performed a genome wide meta-analysis of all available data revealing 30 additional novel loci (P<5x10-8) without further replication. The increase in sample size by UK Biobank raised the number of reconstituted gene-sets from 4.2% to 13.9% of all gene-sets to be involved in CAD. For the 64 novel loci, 155 candidate causal genes were prioritized, many without an obvious connection to CAD. Fine-mapping of the 161 CAD loci generated lists of credible sets of single causal variants and genes for functional follow-up. Genetic risk variants of CAD were linked to development of atrial fibrillation, heart failure and death. Conclusions: We identified 64 novel genetic risk loci for CAD and performed fine-mapping of all 161 risk loci to obtain a credible set of causal variants. The large expansion of reconstituted gene-sets argues in favor of an expanded "omnigenic model" view on the genetic architecture of CAD. }, keywords = {}, pubstate = {published}, tppubtype = {article} } Rationale: Coronary artery disease (CAD) is a complex phenotype driven by genetic and environmental factors. 97 genetic risk loci have been identified so far, but the identification of additional susceptibility loci might be important to enhance our understanding of the genetic architecture of CAD. Objective: To expand the number of genome-wide significant loci, catalog functional insights, and enhance our understanding of the genetic architecture of CAD. Methods and Results: We performed a genome-wide association study (GWAS) in 34,541 CAD cases and 261,984 controls of UK biobank Resource followed by replication in 88,192 cases and 162,544 controls from CARDIoGRAMplusC4D. We identified 75 loci that replicated and were genome-wide significant (P<5x10-8) in meta-analysis, 13 of which had not been reported previously. Next, to further identify novel loci we identified all promising (P<0.0001) loci in the CARDIoGRAMplusC4D data and performed reciprocal replication and meta-analyses with UK biobank. This led to the identification of 21 additional novel loci reaching genome-wide significance (P<5x10-8) in meta-analysis. Finally, we performed a genome wide meta-analysis of all available data revealing 30 additional novel loci (P<5x10-8) without further replication. The increase in sample size by UK Biobank raised the number of reconstituted gene-sets from 4.2% to 13.9% of all gene-sets to be involved in CAD. For the 64 novel loci, 155 candidate causal genes were prioritized, many without an obvious connection to CAD. Fine-mapping of the 161 CAD loci generated lists of credible sets of single causal variants and genes for functional follow-up. Genetic risk variants of CAD were linked to development of atrial fibrillation, heart failure and death. Conclusions: We identified 64 novel genetic risk loci for CAD and performed fine-mapping of all 161 risk loci to obtain a credible set of causal variants. The large expansion of reconstituted gene-sets argues in favor of an expanded "omnigenic model" view on the genetic architecture of CAD. |
Luke C. Pilling; Chia-Ling Kuo; Kamil Sicinski; Jone Tamosauskaite; George A. Kuchel; Lorna W. Harries; Pamela Herd; Robert Wallace; Luigi Ferrucci; David Melzer Human longevity: 25 genetic loci associated in 389,166 UK biobank participants Journal Article Aging , 2017. @article{Pilling2017b, title = {Human longevity: 25 genetic loci associated in 389,166 UK biobank participants}, author = {Luke C. Pilling and Chia-Ling Kuo and Kamil Sicinski and Jone Tamosauskaite and George A. Kuchel and Lorna W. Harries and Pamela Herd and Robert Wallace and Luigi Ferrucci and David Melzer}, url = {http://www.aging-us.com/article/101334}, year = {2017}, date = {2017-12-06}, journal = {Aging }, abstract = {We undertook a genome-wide association study (GWAS) of parental longevity in European descent UK Biobank participants. For combined mothers' and fathers' attained age, 10 loci were associated (p<5*10-8), including 8 previously identified for traits including survival, Alzheimer’s and cardiovascular disease. Of these, 4 were also associated with longest 10% survival (mothers age ≥90 years, fathers ≥87 years), with 2 additional associations including MC2R intronic variants (coding for the adrenocorticotropic hormone receptor). Mother’s age at death was associated with 3 additional loci (2 linked to autoimmune conditions), and 8 for fathers only. An attained age genetic risk score associated with parental survival in the US Health and Retirement Study and the Wisconsin Longitudinal Study and with having a centenarian parent (n=1,181) in UK Biobank. The results suggest that human longevity is highly polygenic with prominent roles for loci likely involved in cellular senescence and inflammation, plus lipid metabolism and cardiovascular conditions. There may also be gender specific routes to longevity.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We undertook a genome-wide association study (GWAS) of parental longevity in European descent UK Biobank participants. For combined mothers' and fathers' attained age, 10 loci were associated (p<5*10-8), including 8 previously identified for traits including survival, Alzheimer’s and cardiovascular disease. Of these, 4 were also associated with longest 10% survival (mothers age ≥90 years, fathers ≥87 years), with 2 additional associations including MC2R intronic variants (coding for the adrenocorticotropic hormone receptor). Mother’s age at death was associated with 3 additional loci (2 linked to autoimmune conditions), and 8 for fathers only. An attained age genetic risk score associated with parental survival in the US Health and Retirement Study and the Wisconsin Longitudinal Study and with having a centenarian parent (n=1,181) in UK Biobank. The results suggest that human longevity is highly polygenic with prominent roles for loci likely involved in cellular senescence and inflammation, plus lipid metabolism and cardiovascular conditions. There may also be gender specific routes to longevity. |
JH Bjørngaard; AT Nordestgaard; AE Taylor; JL Treur; ME Gabrielsen; MR Munafò; BG Nordestgaard; BO Åsvold; Romundstad; G Davey Smith Heavier smoking increases coffee consumption: findings from a Mendelian randomization analysis. Journal Article International Journal of Epidemiology, 2017. @article{Bjørngaard2017, title = {Heavier smoking increases coffee consumption: findings from a Mendelian randomization analysis.}, author = {JH Bjørngaard and AT Nordestgaard and AE Taylor and JL Treur and ME Gabrielsen and MR Munafò and BG Nordestgaard and BO Åsvold and Romundstad and G Davey Smith}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29025033}, year = {2017}, date = {2017-12-01}, journal = {International Journal of Epidemiology}, abstract = {There is evidence for a positive relationship between cigarette and coffee consumption in smokers. Cigarette smoke increases metabolism of caffeine, so this may represent a causal effect of smoking on caffeine intake. Methods: We performed Mendelian randomization analyses in the UK Biobank (N = 114 029), the Norwegian HUNT study (N = 56 664) and the Copenhagen General Population Study (CGPS) (N = 78 650). We used the rs16969968 genetic variant as a proxy for smoking heaviness in all studies and rs4410790 and rs2472297 as proxies for coffee consumption in UK Biobank and CGPS. Analyses were conducted using linear regression and meta-analysed across studies. Results: Each additional cigarette per day consumed by current smokers was associated with higher coffee consumption (0.10 cups per day, 95% CI: 0.03, 0.17). There was weak evidence for an increase in tea consumption per additional cigarette smoked per day (0.04 cups per day, 95% CI: -0.002, 0.07). There was strong evidence that each additional copy of the minor allele of rs16969968 (which increases daily cigarette consumption) in current smokers was associated with higher coffee consumption (0.16 cups per day, 95% CI: 0.11, 0.20), but only weak evidence for an association with tea consumption (0.04 cups per day, 95% CI: -0.01, 0.09). There was no clear evidence that rs16969968 was associated with coffee or tea consumption in never or former smokers or that the coffee-related variants were associated with cigarette consumption. Conclusions: Higher cigarette consumption causally increases coffee intake. This is consistent with faster metabolism of caffeine by smokers, but could also reflect a behavioural effect of smoking on coffee drinking.}, keywords = {}, pubstate = {published}, tppubtype = {article} } There is evidence for a positive relationship between cigarette and coffee consumption in smokers. Cigarette smoke increases metabolism of caffeine, so this may represent a causal effect of smoking on caffeine intake. Methods: We performed Mendelian randomization analyses in the UK Biobank (N = 114 029), the Norwegian HUNT study (N = 56 664) and the Copenhagen General Population Study (CGPS) (N = 78 650). We used the rs16969968 genetic variant as a proxy for smoking heaviness in all studies and rs4410790 and rs2472297 as proxies for coffee consumption in UK Biobank and CGPS. Analyses were conducted using linear regression and meta-analysed across studies. Results: Each additional cigarette per day consumed by current smokers was associated with higher coffee consumption (0.10 cups per day, 95% CI: 0.03, 0.17). There was weak evidence for an increase in tea consumption per additional cigarette smoked per day (0.04 cups per day, 95% CI: -0.002, 0.07). There was strong evidence that each additional copy of the minor allele of rs16969968 (which increases daily cigarette consumption) in current smokers was associated with higher coffee consumption (0.16 cups per day, 95% CI: 0.11, 0.20), but only weak evidence for an association with tea consumption (0.04 cups per day, 95% CI: -0.01, 0.09). There was no clear evidence that rs16969968 was associated with coffee or tea consumption in never or former smokers or that the coffee-related variants were associated with cigarette consumption. Conclusions: Higher cigarette consumption causally increases coffee intake. This is consistent with faster metabolism of caffeine by smokers, but could also reflect a behavioural effect of smoking on coffee drinking. |
Nicholas J Thomas; Samuel E Jones PhD; Michael N Weedon PhD; Beverley M Shields PhD; Richard A Oram PhD; Prof Andrew T Hattersley The Lancet Diabetes and Endocrinology, 2017. @article{Thomas2017, title = {Frequency and phenotype of type 1 diabetes in the first six decades of life: a cross-sectional, genetically stratified survival analysis from UK Biobank}, author = { Nicholas J Thomas and Samuel E Jones PhD and Michael N Weedon PhD and Beverley M Shields PhD and Richard A Oram PhD and Prof Andrew T Hattersley}, url = {http://www.thelancet.com/journals/landia/article/PIIS2213-8587(17)30362-5/fulltext}, year = {2017}, date = {2017-11-30}, journal = {The Lancet Diabetes and Endocrinology}, abstract = {Type 1 diabetes is typically considered a disease of children and young adults. Genetic susceptibility to young-onset type 1 diabetes is well defined and does not predispose to type 2 diabetes. It is not known how frequently genetic susceptibility to type 1 diabetes leads to a diagnosis of diabetes after age 30 years. We aimed to investigate the frequency and phenotype of type 1 diabetes resulting from high genetic susceptibility in the first six decades of life. Methods In this cross-sectional analysis, we used a type 1 diabetes genetic risk score based on 29 common variants to identify individuals of white European descent in UK Biobank in the half of the population with high or low genetic susceptibility to type 1 diabetes. We used Kaplan-Meier analysis to evaluate the number of cases of diabetes in both groups in the first six decades of life. We genetically defined type 1 diabetes as the additional cases of diabetes that occurred in the high genetic susceptibility group compared with the low genetic susceptibility group. All remaining cases were defined as type 2 diabetes. We assessed the clinical characteristics of the groups with genetically defined type 1 or type 2 diabetes. Findings 13 250 (3·5%) of 379 511 white European individuals in UK Biobank had developed diabetes in the first six decades of life. 1286 more cases of diabetes were in the half of the population with high genetic susceptibility to type 1 diabetes than in the half of the population with low genetic susceptibility. These genetically defined cases of type 1 diabetes were distributed across all ages of diagnosis; 537 (42%) were in individuals diagnosed when aged 31–60 years, representing 4% (537/12 233) of all diabetes cases diagnosed after age 30 years. The clinical characteristics of the group diagnosed with type 1 diabetes when aged 31–60 years were similar to the clinical characteristics of the group diagnosed with type 1 diabetes when aged 30 years or younger. For individuals diagnosed with diabetes when aged 31–60 years, the clinical characteristics of type 1 diabetes differed from those of type 2 diabetes: they had a lower BMI (27·4 kg/m2 [95% CI 26·7–28·0] vs 32·4 kg/m2 [32·2–32·5]; p<0·0001), were more likely to use insulin in the first year after diagnosis (89% [476/537] vs 6% [648/11 696]; p<0·0001), and were more likely to have diabetic ketoacidosis (11% [61/537] vs 0·3% [30/11 696]; p<0·0001). Interpretation Genetic susceptibility to type 1 diabetes results in non-obesity-related, insulin-dependent diabetes, which presents throughout the first six decades of life. Our results highlight the difficulty of identifying type 1 diabetes after age 30 years because of the increasing background prevalence of type 2 diabetes. Failure to diagnose late-onset type 1 diabetes can have serious consequences because these patients rapidly develop insulin dependency. Funding Wellcome Trust and Diabetes UK.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Type 1 diabetes is typically considered a disease of children and young adults. Genetic susceptibility to young-onset type 1 diabetes is well defined and does not predispose to type 2 diabetes. It is not known how frequently genetic susceptibility to type 1 diabetes leads to a diagnosis of diabetes after age 30 years. We aimed to investigate the frequency and phenotype of type 1 diabetes resulting from high genetic susceptibility in the first six decades of life. Methods In this cross-sectional analysis, we used a type 1 diabetes genetic risk score based on 29 common variants to identify individuals of white European descent in UK Biobank in the half of the population with high or low genetic susceptibility to type 1 diabetes. We used Kaplan-Meier analysis to evaluate the number of cases of diabetes in both groups in the first six decades of life. We genetically defined type 1 diabetes as the additional cases of diabetes that occurred in the high genetic susceptibility group compared with the low genetic susceptibility group. All remaining cases were defined as type 2 diabetes. We assessed the clinical characteristics of the groups with genetically defined type 1 or type 2 diabetes. Findings 13 250 (3·5%) of 379 511 white European individuals in UK Biobank had developed diabetes in the first six decades of life. 1286 more cases of diabetes were in the half of the population with high genetic susceptibility to type 1 diabetes than in the half of the population with low genetic susceptibility. These genetically defined cases of type 1 diabetes were distributed across all ages of diagnosis; 537 (42%) were in individuals diagnosed when aged 31–60 years, representing 4% (537/12 233) of all diabetes cases diagnosed after age 30 years. The clinical characteristics of the group diagnosed with type 1 diabetes when aged 31–60 years were similar to the clinical characteristics of the group diagnosed with type 1 diabetes when aged 30 years or younger. For individuals diagnosed with diabetes when aged 31–60 years, the clinical characteristics of type 1 diabetes differed from those of type 2 diabetes: they had a lower BMI (27·4 kg/m2 [95% CI 26·7–28·0] vs 32·4 kg/m2 [32·2–32·5]; p<0·0001), were more likely to use insulin in the first year after diagnosis (89% [476/537] vs 6% [648/11 696]; p<0·0001), and were more likely to have diabetic ketoacidosis (11% [61/537] vs 0·3% [30/11 696]; p<0·0001). Interpretation Genetic susceptibility to type 1 diabetes results in non-obesity-related, insulin-dependent diabetes, which presents throughout the first six decades of life. Our results highlight the difficulty of identifying type 1 diabetes after age 30 years because of the increasing background prevalence of type 2 diabetes. Failure to diagnose late-onset type 1 diabetes can have serious consequences because these patients rapidly develop insulin dependency. Funding Wellcome Trust and Diabetes UK. |
Joey Ward; Rona J. Strawbridge; Mark E. S. Bailey; Nicholas Graham; Amy Ferguson; Donald M. Lyall; Breda Cullen; Laura M. Pidgeon; Jonathan Cavanagh; Daniel F. Mackay; Jill P. Pell; Michael O’Donovan; Valentina Escott-Price; Daniel J. Smith Nature Translational Psychiatry, 2017. @article{Ward2017, title = {Genome-wide analysis in UK Biobank identifies four loci associated with mood instability and genetic correlation with major depressive disorder, anxiety disorder and schizophrenia}, author = {Joey Ward and Rona J. Strawbridge and Mark E. S. Bailey and Nicholas Graham and Amy Ferguson and Donald M. Lyall and Breda Cullen and Laura M. Pidgeon and Jonathan Cavanagh and Daniel F. Mackay and Jill P. Pell and Michael O’Donovan and Valentina Escott-Price and Daniel J. Smith}, url = {https://www.nature.com/articles/s41398-017-0012-7}, year = {2017}, date = {2017-11-30}, journal = {Nature Translational Psychiatry}, abstract = {Mood instability is a core clinical feature of affective and psychotic disorders. In keeping with the Research Domain Criteria approach, it may be a useful construct for identifying biology that cuts across psychiatric categories. We aimed to investigate the biological validity of a simple measure of mood instability and evaluate its genetic relationship with several psychiatric disorders, including major depressive disorder (MDD), bipolar disorder (BD), schizophrenia, attention deficit hyperactivity disorder (ADHD), anxiety disorder and post-traumatic stress disorder (PTSD). We conducted a genome-wide association study (GWAS) of mood instability in 53,525 cases and 60,443 controls from UK Biobank, identifying four independently associated loci (on chromosomes 8, 9, 14 and 18), and a common single-nucleotide polymorphism (SNP)-based heritability estimate of ~8%. We found a strong genetic correlation between mood instability and MDD (rg = 0.60, SE = 0.07, p = 8.95 × 10−17) and a small but significant genetic correlation with both schizophrenia (rg = 0.11, SE = 0.04, p = 0.01) and anxiety disorders (rg = 0.28, SE = 0.14, p = 0.04), although no genetic correlation with BD, ADHD or PTSD was observed. Several genes at the associated loci may have a role in mood instability, including the DCC netrin 1 receptor (DCC) gene, eukaryotic translation initiation factor 2B subunit beta (eIF2B2), placental growth factor (PGF) and protein tyrosine phosphatase, receptor type D (PTPRD). Strengths of this study include the very large sample size, but our measure of mood instability may be limited by the use of a single question. Overall, this work suggests a polygenic basis for mood instability. This simple measure can be obtained in very large samples; our findings suggest that doing so may offer the opportunity to illuminate the fundamental biology of mood regulation.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Mood instability is a core clinical feature of affective and psychotic disorders. In keeping with the Research Domain Criteria approach, it may be a useful construct for identifying biology that cuts across psychiatric categories. We aimed to investigate the biological validity of a simple measure of mood instability and evaluate its genetic relationship with several psychiatric disorders, including major depressive disorder (MDD), bipolar disorder (BD), schizophrenia, attention deficit hyperactivity disorder (ADHD), anxiety disorder and post-traumatic stress disorder (PTSD). We conducted a genome-wide association study (GWAS) of mood instability in 53,525 cases and 60,443 controls from UK Biobank, identifying four independently associated loci (on chromosomes 8, 9, 14 and 18), and a common single-nucleotide polymorphism (SNP)-based heritability estimate of ~8%. We found a strong genetic correlation between mood instability and MDD (rg = 0.60, SE = 0.07, p = 8.95 × 10−17) and a small but significant genetic correlation with both schizophrenia (rg = 0.11, SE = 0.04, p = 0.01) and anxiety disorders (rg = 0.28, SE = 0.14, p = 0.04), although no genetic correlation with BD, ADHD or PTSD was observed. Several genes at the associated loci may have a role in mood instability, including the DCC netrin 1 receptor (DCC) gene, eukaryotic translation initiation factor 2B subunit beta (eIF2B2), placental growth factor (PGF) and protein tyrosine phosphatase, receptor type D (PTPRD). Strengths of this study include the very large sample size, but our measure of mood instability may be limited by the use of a single question. Overall, this work suggests a polygenic basis for mood instability. This simple measure can be obtained in very large samples; our findings suggest that doing so may offer the opportunity to illuminate the fundamental biology of mood regulation. |
David M. Howard; Lynsey S. Hall; Jonathan D. Hafferty; Yanni Zeng; Mark J. Adams; Toni-Kim Clarke; David J. Porteous; Reka Nagy; Caroline Hayward; Blair H. Smith; Alison D. Murray; Niamh M. Ryan; Kathryn L. Evans; Chris S. Haley; Ian J. Deary; Pippa A. Thomson; Andrew M. McIntosh Genome-wide haplotype-based association analysis of major depressive disorder in Generation Scotland and UK Biobank Journal Article Nature Translational Psychiatry, 2017. @article{Howard2017b, title = {Genome-wide haplotype-based association analysis of major depressive disorder in Generation Scotland and UK Biobank}, author = {David M. Howard and Lynsey S. Hall and Jonathan D. Hafferty and Yanni Zeng and Mark J. Adams and Toni-Kim Clarke and David J. Porteous and Reka Nagy and Caroline Hayward and Blair H. Smith and Alison D. Murray and Niamh M. Ryan and Kathryn L. Evans and Chris S. Haley and Ian J. Deary and Pippa A. Thomson and Andrew M. McIntosh}, url = {https://www.nature.com/articles/s41398-017-0010-9}, year = {2017}, date = {2017-11-30}, journal = {Nature Translational Psychiatry}, abstract = {Genome-wide association studies using genotype data have had limited success in the identification of variants associated with major depressive disorder (MDD). Haplotype data provide an alternative method for detecting associations between variants in weak linkage disequilibrium with genotyped variants and a given trait of interest. A genome-wide haplotype association study for MDD was undertaken utilising a family-based population cohort, Generation Scotland: Scottish Family Health Study (n = 18,773), as a discovery cohort with UK Biobank used as a population-based replication cohort (n = 25,035). Fine mapping of haplotype boundaries was used to account for overlapping haplotypes potentially tagging the same causal variant. Within the discovery cohort, two haplotypes exceeded genome-wide significance (P < 5 × 10−8) for an association with MDD. One of these haplotypes was nominally significant in the replication cohort (P < 0.05) and was located in 6q21, a region which has been previously associated with bipolar disorder, a psychiatric disorder that is phenotypically and genetically correlated with MDD. Several haplotypes with P < 10−7 in the discovery cohort were located within gene coding regions associated with diseases that are comorbid with MDD. Using such haplotypes to highlight regions for sequencing may lead to the identification of the underlying causal variants.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Genome-wide association studies using genotype data have had limited success in the identification of variants associated with major depressive disorder (MDD). Haplotype data provide an alternative method for detecting associations between variants in weak linkage disequilibrium with genotyped variants and a given trait of interest. A genome-wide haplotype association study for MDD was undertaken utilising a family-based population cohort, Generation Scotland: Scottish Family Health Study (n = 18,773), as a discovery cohort with UK Biobank used as a population-based replication cohort (n = 25,035). Fine mapping of haplotype boundaries was used to account for overlapping haplotypes potentially tagging the same causal variant. Within the discovery cohort, two haplotypes exceeded genome-wide significance (P < 5 × 10−8) for an association with MDD. One of these haplotypes was nominally significant in the replication cohort (P < 0.05) and was located in 6q21, a region which has been previously associated with bipolar disorder, a psychiatric disorder that is phenotypically and genetically correlated with MDD. Several haplotypes with P < 10−7 in the discovery cohort were located within gene coding regions associated with diseases that are comorbid with MDD. Using such haplotypes to highlight regions for sequencing may lead to the identification of the underlying causal variants. |
Nicola Pirastu; Peter K. Joshi; Paul S. de Vries; Marilyn C. Cornelis; Paul M. McKeigue; NaNa Keum; Nora Franceschini; Marco Colombo; Edward L. Giovannucci; Athina Spiliopoulou; Lude Franke; Kari E. North; Peter Kraft; Alanna C. Morrison; Tõnu Esko; James F. Wilson GWAS for male-pattern baldness identifies 71 susceptibility loci explaining 38% of the risk Journal Article Nature Communications, 2017. @article{Pirastu2017, title = {GWAS for male-pattern baldness identifies 71 susceptibility loci explaining 38% of the risk}, author = {Nicola Pirastu and Peter K. Joshi and Paul S. de Vries and Marilyn C. Cornelis and Paul M. McKeigue and NaNa Keum and Nora Franceschini and Marco Colombo and Edward L. Giovannucci and Athina Spiliopoulou and Lude Franke and Kari E. North and Peter Kraft and Alanna C. Morrison and Tõnu Esko and James F. Wilson}, url = {https://www.nature.com/articles/s41467-017-01490-8}, year = {2017}, date = {2017-11-17}, journal = {Nature Communications}, abstract = {Male pattern baldness (MPB) or androgenetic alopecia is one of the most common conditions affecting men, reaching a prevalence of ~50% by the age of 50; however, the known genes explain little of the heritability. Here, we present the results of a genome-wide association study including more than 70,000 men, identifying 71 independently replicated loci, of which 30 are novel. These loci explain 38% of the risk, suggesting that MPB is less genetically complex than other complex traits. We show that many of these loci contain genes that are relevant to the pathology and highlight pathways and functions underlying baldness. Finally, despite only showing genome-wide genetic correlation with height, pathway-specific genetic correlations are significant for traits including lifespan and cancer. Our study not only greatly increases the number of MPB loci, illuminating the genetic architecture, but also provides a new approach to disentangling the shared biological pathways underlying complex diseases.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Male pattern baldness (MPB) or androgenetic alopecia is one of the most common conditions affecting men, reaching a prevalence of ~50% by the age of 50; however, the known genes explain little of the heritability. Here, we present the results of a genome-wide association study including more than 70,000 men, identifying 71 independently replicated loci, of which 30 are novel. These loci explain 38% of the risk, suggesting that MPB is less genetically complex than other complex traits. We show that many of these loci contain genes that are relevant to the pathology and highlight pathways and functions underlying baldness. Finally, despite only showing genome-wide genetic correlation with height, pathway-specific genetic correlations are significant for traits including lifespan and cancer. Our study not only greatly increases the number of MPB loci, illuminating the genetic architecture, but also provides a new approach to disentangling the shared biological pathways underlying complex diseases. |
LC Weng; SR Preis; OL Hulme; MG Larson; SH Choi; B Wang; L Trinquart; DD McManus; L Staerk; H Lin; KL Lunetta; PT Ellinor; EJ Benjamin; SA Lubitz Genetic Predisposition, Clinical Risk Factor Burden, and Lifetime Risk of Atrial Fibrillation. Journal Article Circulation, 2017. @article{Weng2017, title = {Genetic Predisposition, Clinical Risk Factor Burden, and Lifetime Risk of Atrial Fibrillation.}, author = {LC Weng and SR Preis and OL Hulme and MG Larson and SH Choi and B Wang and L Trinquart and DD McManus and L Staerk and H Lin and KL Lunetta and PT Ellinor and EJ Benjamin and SA Lubitz }, url = {https://www.ncbi.nlm.nih.gov/pubmed/29129827}, year = {2017}, date = {2017-11-12}, journal = {Circulation}, abstract = {Background -The long-term probability of developing atrial fibrillation (AF) considering genetic predisposition and clinical risk factor burden is unknown. Methods -We estimated lifetime risk of AF in individuals from the community-based Framingham Heart Study. Polygenic risk for AF was derived using a score of approximately 1,000 AF-associated single nucleotide polymorphisms. Clinical risk factor burden was calculated for each individual using a validated risk score for incident AF comprised of height, weight, systolic and diastolic blood pressure, current smoking status, antihypertensive medication use, diabetes, history of myocardial infarction, and history of heart failure. We estimated the lifetime risk of AF within tertiles of polygenic and clinical risk. Results -Among 4,606 participants without AF at age 55 years, 580 developed incident AF (median follow-up, 9.4 years; 25th-75th percentile, 4.4-14.3 years). The lifetime risk of AF after age 55 years was 37.1%, and was substantially influenced by both polygenic and clinical risk factor burden. Among individuals free of AF at age 55 years, those in low polygenic and clinical risk tertiles had a lifetime risk of AF of 22.3% (95% confidence interval [CI], 15.4%-29.1%), whereas those in high risk tertiles had a risk of 48.2% (95% CI, 41.3%-55.1%). A lower clinical risk factor burden was associated with later AF onset after adjusting for genetic predisposition (P value <0.001). Conclusions -In our community-based cohort, the lifetime risk of AF was 37%. Estimation of polygenic AF risk is feasible, and together with clinical risk factor burden explains a substantial gradient in long-term AF risk.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background -The long-term probability of developing atrial fibrillation (AF) considering genetic predisposition and clinical risk factor burden is unknown. Methods -We estimated lifetime risk of AF in individuals from the community-based Framingham Heart Study. Polygenic risk for AF was derived using a score of approximately 1,000 AF-associated single nucleotide polymorphisms. Clinical risk factor burden was calculated for each individual using a validated risk score for incident AF comprised of height, weight, systolic and diastolic blood pressure, current smoking status, antihypertensive medication use, diabetes, history of myocardial infarction, and history of heart failure. We estimated the lifetime risk of AF within tertiles of polygenic and clinical risk. Results -Among 4,606 participants without AF at age 55 years, 580 developed incident AF (median follow-up, 9.4 years; 25th-75th percentile, 4.4-14.3 years). The lifetime risk of AF after age 55 years was 37.1%, and was substantially influenced by both polygenic and clinical risk factor burden. Among individuals free of AF at age 55 years, those in low polygenic and clinical risk tertiles had a lifetime risk of AF of 22.3% (95% confidence interval [CI], 15.4%-29.1%), whereas those in high risk tertiles had a risk of 48.2% (95% CI, 41.3%-55.1%). A lower clinical risk factor burden was associated with later AF onset after adjusting for genetic predisposition (P value <0.001). Conclusions -In our community-based cohort, the lifetime risk of AF was 37%. Estimation of polygenic AF risk is feasible, and together with clinical risk factor burden explains a substantial gradient in long-term AF risk. |
C Marquez-Luna; P Loh; A L Price Multiethnic polygenic risk scores improve risk prediction in diverse populations Journal Article Genetic Epidemiology, 2017. @article{Marquez-Luna2017, title = {Multiethnic polygenic risk scores improve risk prediction in diverse populations}, author = {C Marquez-Luna and P Loh and A L Price}, url = {http://onlinelibrary.wiley.com/doi/10.1002/gepi.22083/abstract}, year = {2017}, date = {2017-11-07}, journal = {Genetic Epidemiology}, abstract = {Methods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multiethnic polygenic risk score that combines training data from European samples and training data from the target population. We applied this approach to predict type 2 diabetes (T2D) in a Latino cohort using both publicly available European summary statistics in large sample size (Neff = 40k) and Latino training data in small sample size (Neff = 8k). Here, we attained a >70% relative improvement in prediction accuracy (from R2 = 0.027 to 0.047) compared to methods that use only one source of training data, consistent with large relative improvements in simulations. We observed a systematically lower load of T2D risk alleles in Latino individuals with more European ancestry, which could be explained by polygenic selection in ancestral European and/or Native American populations. We predict T2D in a South Asian UK Biobank cohort using European (Neff = 40k) and South Asian (Neff = 16k) training data and attained a >70% relative improvement in prediction accuracy, and application to predict height in an African UK Biobank cohort using European (N = 113k) and African (N = 2k) training data attained a 30% relative improvement. Our work reduces the gap in polygenic risk prediction accuracy between European and non-European target populations.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Methods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multiethnic polygenic risk score that combines training data from European samples and training data from the target population. We applied this approach to predict type 2 diabetes (T2D) in a Latino cohort using both publicly available European summary statistics in large sample size (Neff = 40k) and Latino training data in small sample size (Neff = 8k). Here, we attained a >70% relative improvement in prediction accuracy (from R2 = 0.027 to 0.047) compared to methods that use only one source of training data, consistent with large relative improvements in simulations. We observed a systematically lower load of T2D risk alleles in Latino individuals with more European ancestry, which could be explained by polygenic selection in ancestral European and/or Native American populations. We predict T2D in a South Asian UK Biobank cohort using European (Neff = 40k) and South Asian (Neff = 16k) training data and attained a >70% relative improvement in prediction accuracy, and application to predict height in an African UK Biobank cohort using European (N = 113k) and African (N = 2k) training data attained a 30% relative improvement. Our work reduces the gap in polygenic risk prediction accuracy between European and non-European target populations. |
MA Ferreira; colleagues. Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology. Journal Article Nature Genetics, 2017, (MA Ferreira and JM Vonk and H Baurecht and I Marenholz and C Tian and JD Hoffman and Q Helmer and A Tillander and V Ullemar and J van Dongen and Y Lu and F Rüschendorf and J Esparza-Gordillo and CW Medway and E Mountjoy and K Burrows and O Hummel and S Grosche and BM Brumpton and JS Witte and JJ Hottenga and G Willemsen and J Zheng and E Rodríguez and M Hotze and A Franke and JA Revez and J Beesley and MC Matheson and SC Dharmage and LM Bain and LG Fritsche and ME Gabrielsen and B Balliu and 23andMe Research Team and AAGC collaborators and BIOS consortium and LifeLines Cohort Study and JB Nielsen and W Zhou and K Hveem and A Langhammer and OL Holmen and M Løset and GR Abecasis and CJ Willer and A Arnold and G Homuth and CO Schmidt and PJ Thompson and NG Martin and DL Duffy and N Novak and H Schulz and S Karrasch and C Gieger and and K Strauch and RB Melles DA Hinds and N Hübner and S Weidinger and PKE Magnusson and R Jansen and E Jorgenson and YA Lee and D Boomsma and C Almqvist and R Karlsson and GH Koppelman and L Paternoster.). @article{Ferreira2017, title = {Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology.}, author = {MA Ferreira and colleagues.}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29083406}, year = {2017}, date = {2017-10-30}, journal = {Nature Genetics}, abstract = {Asthma, hay fever (or allergic rhinitis) and eczema (or atopic dermatitis) often coexist in the same individuals, partly because of a shared genetic origin. To identify shared risk variants, we performed a genome-wide association study (GWAS; n = 360,838) of a broad allergic disease phenotype that considers the presence of any one of these three diseases. We identified 136 independent risk variants (P < 3 × 10-8), including 73 not previously reported, which implicate 132 nearby genes in allergic disease pathophysiology. Disease-specific effects were detected for only six variants, confirming that most represent shared risk factors. Tissue-specific heritability and biological process enrichment analyses suggest that shared risk variants influence lymphocyte-mediated immunity. Six target genes provide an opportunity for drug repositioning, while for 36 genes CpG methylation was found to influence transcription independently of genetic effects. Asthma, hay fever and eczema partly coexist because they share many genetic risk variants that dysregulate the expression of immune-related genes.}, note = {MA Ferreira and JM Vonk and H Baurecht and I Marenholz and C Tian and JD Hoffman and Q Helmer and A Tillander and V Ullemar and J van Dongen and Y Lu and F Rüschendorf and J Esparza-Gordillo and CW Medway and E Mountjoy and K Burrows and O Hummel and S Grosche and BM Brumpton and JS Witte and JJ Hottenga and G Willemsen and J Zheng and E Rodríguez and M Hotze and A Franke and JA Revez and J Beesley and MC Matheson and SC Dharmage and LM Bain and LG Fritsche and ME Gabrielsen and B Balliu and 23andMe Research Team and AAGC collaborators and BIOS consortium and LifeLines Cohort Study and JB Nielsen and W Zhou and K Hveem and A Langhammer and OL Holmen and M Løset and GR Abecasis and CJ Willer and A Arnold and G Homuth and CO Schmidt and PJ Thompson and NG Martin and DL Duffy and N Novak and H Schulz and S Karrasch and C Gieger and and K Strauch and RB Melles DA Hinds and N Hübner and S Weidinger and PKE Magnusson and R Jansen and E Jorgenson and YA Lee and D Boomsma and C Almqvist and R Karlsson and GH Koppelman and L Paternoster.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Asthma, hay fever (or allergic rhinitis) and eczema (or atopic dermatitis) often coexist in the same individuals, partly because of a shared genetic origin. To identify shared risk variants, we performed a genome-wide association study (GWAS; n = 360,838) of a broad allergic disease phenotype that considers the presence of any one of these three diseases. We identified 136 independent risk variants (P < 3 × 10-8), including 73 not previously reported, which implicate 132 nearby genes in allergic disease pathophysiology. Disease-specific effects were detected for only six variants, confirming that most represent shared risk factors. Tissue-specific heritability and biological process enrichment analyses suggest that shared risk variants influence lymphocyte-mediated immunity. Six target genes provide an opportunity for drug repositioning, while for 36 genes CpG methylation was found to influence transcription independently of genetic effects. Asthma, hay fever and eczema partly coexist because they share many genetic risk variants that dysregulate the expression of immune-related genes. |
Dajiang J Liu; Gina M Peloso; Sekar Kathiresan Exome-wide association study of plasma lipids in >300,000 individuals Journal Article Nature Genetics , 2017. @article{Liu2017, title = {Exome-wide association study of plasma lipids in >300,000 individuals}, author = {Dajiang J Liu and Gina M Peloso and Sekar Kathiresan}, url = {https://www.nature.com/articles/ng.3977}, year = {2017}, date = {2017-10-30}, journal = {Nature Genetics }, abstract = {We screened variants on an exome-focused genotyping array in >300,000 participants (replication in >280,000 participants) and identified 444 independent variants in 250 loci significantly associated with total cholesterol (TC), high-density-lipoprotein cholesterol (HDL-C), low-density-lipoprotein cholesterol (LDL-C), and/or triglycerides (TG). At two loci (JAK2 and A1CF), experimental analysis in mice showed lipid changes consistent with the human data. We also found that: (i) beta-thalassemia trait carriers displayed lower TC and were protected from coronary artery disease (CAD); (ii) excluding the CETP locus, there was not a predictable relationship between plasma HDL-C and risk for age-related macular degeneration; (iii) only some mechanisms of lowering LDL-C appeared to increase risk for type 2 diabetes (T2D); and (iv) TG-lowering alleles involved in hepatic production of TG-rich lipoproteins (TM6SF2 and PNPLA3) tracked with higher liver fat, higher risk for T2D, and lower risk for CAD, whereas TG-lowering alleles involved in peripheral lipolysis (LPL and ANGPTL4) had no effect on liver fat but decreased risks for both T2D and CAD.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We screened variants on an exome-focused genotyping array in >300,000 participants (replication in >280,000 participants) and identified 444 independent variants in 250 loci significantly associated with total cholesterol (TC), high-density-lipoprotein cholesterol (HDL-C), low-density-lipoprotein cholesterol (LDL-C), and/or triglycerides (TG). At two loci (JAK2 and A1CF), experimental analysis in mice showed lipid changes consistent with the human data. We also found that: (i) beta-thalassemia trait carriers displayed lower TC and were protected from coronary artery disease (CAD); (ii) excluding the CETP locus, there was not a predictable relationship between plasma HDL-C and risk for age-related macular degeneration; (iii) only some mechanisms of lowering LDL-C appeared to increase risk for type 2 diabetes (T2D); and (iv) TG-lowering alleles involved in hepatic production of TG-rich lipoproteins (TM6SF2 and PNPLA3) tracked with higher liver fat, higher risk for T2D, and lower risk for CAD, whereas TG-lowering alleles involved in peripheral lipolysis (LPL and ANGPTL4) had no effect on liver fat but decreased risks for both T2D and CAD. |
Richard J Allen; Joanne Porte MSc; Rebecca Braybrooke RGN; Carlos Flores PhD; Tasha E Fingerlin PhD; Justin M Oldham MD; Beatriz Guillen-Guio MSC; Shwu-Fan Ma PhD; Tsukasa Okamoto MD Alison E John PhD; Ma'en Obeidat PhD; Ivana V Yang PhD; Amanda Henry PhD; Prof Richard B Hubbard MD; Vidya Navaratnam PhD; Gauri Saini MD; Norma Thompson RGN; Helen L Booth MD; Simon P Hart MD; Mike R Hill DPhil; Nik Hirani MD; Prof Toby M Maher PhD; Prof Robin J McAnulty PhD; Prof Ann B Millar; Philip L Molyneaux; Helen Parfrey MD; Doris M Rassl MD; Prof Moira K B Whyte PhD; William A Fahy; Richard P Marshall; Eunice Oballa MSc; Yohan Bossé, PhD; David C Nickle PhD; Prof Don D Sin MD; Prof Wim Timens MD; Nick Shrine; Prof Ian Sayers PhD; Prof Ian P Hall DM; Prof Imre Noth, MD; Prof David A Schwartz, MD; Prof Martin D Tobin, PhD; Prof Louise V Wain Genetic variants associated with susceptibility to idiopathic pulmonary fibrosis in people of European ancestry: a genome-wide association study Journal Article The Lancet Respiratory Medicine, 2017. @article{Allen2017, title = {Genetic variants associated with susceptibility to idiopathic pulmonary fibrosis in people of European ancestry: a genome-wide association study}, author = { Richard J Allen and Joanne Porte MSc and Rebecca Braybrooke RGN and Carlos Flores PhD and Tasha E Fingerlin PhD and Justin M Oldham MD and Beatriz Guillen-Guio MSC and Shwu-Fan Ma PhD and Tsukasa Okamoto MD Alison E John PhD and Ma'en Obeidat PhD and Ivana V Yang PhD and Amanda Henry PhD and Prof Richard B Hubbard MD and Vidya Navaratnam PhD and Gauri Saini MD and Norma Thompson RGN and Helen L Booth MD and Simon P Hart MD and Mike R Hill DPhil and Nik Hirani MD and Prof Toby M Maher PhD and Prof Robin J McAnulty PhD and Prof Ann B Millar and Philip L Molyneaux and Helen Parfrey MD and Doris M Rassl MD and Prof Moira K B Whyte PhD and William A Fahy and Richard P Marshall and Eunice Oballa MSc and Yohan Bossé, PhD and David C Nickle PhD and Prof Don D Sin MD and Prof Wim Timens MD and Nick Shrine and Prof Ian Sayers PhD and Prof Ian P Hall DM and Prof Imre Noth, MD and Prof David A Schwartz, MD and Prof Martin D Tobin, PhD and Prof Louise V Wain}, url = {http://www.thelancet.com/journals/lanres/article/PIIS2213-2600(17)30387-9/fulltext}, year = {2017}, date = {2017-10-20}, journal = {The Lancet Respiratory Medicine}, abstract = {Background Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with high mortality, uncertain cause, and few treatment options. Studies have identified a significant genetic risk associated with the development of IPF; however, mechanisms by which genetic risk factors promote IPF remain unclear. We aimed to identify genetic variants associated with IPF susceptibility and provide mechanistic insight using gene and protein expression analyses. Methods We used a two-stage approach: a genome-wide association study in patients with IPF of European ancestry recruited from nine different centres in the UK and controls selected from UK Biobank (stage 1) matched for age, sex, and smoking status; and a follow-up of associated genetic variants in independent datasets of patients with IPF and controls from two independent US samples from the Chicago consortium and the Colorado consortium (stage 2). We investigated the effect of novel signals on gene expression in large transcriptomic and genomic data resources, and examined expression using lung tissue samples from patients with IPF and controls. Findings 602 patients with IPF and 3366 controls were selected for stage 1. For stage 2, 2158 patients with IPF and 5195 controls were selected. We identified a novel genome-wide significant signal of association with IPF susceptibility near A-kinase anchoring protein 13 (AKAP13; rs62025270, odds ratio [OR] 1·27 [95% CI 1·18–1·37], p=1·32 × 10−9) and confirmed previously reported signals, including in mucin 5B (MUC5B; rs35705950, OR 2·89 [2·56–3·26], p=1·12 × 10−66) and desmoplakin (DSP; rs2076295, OR 1·44 [1·35–1·54], p=7·81 × 10−28). For rs62025270, the allele A associated with increased susceptibility to IPF was also associated with increased expression of AKAP13 mRNA in lung tissue from patients who had lung resection procedures (n=1111). We showed that AKAP13 is expressed in the alveolar epithelium and lymphoid follicles from patients with IPF, and AKAP13 mRNA expression was 1·42-times higher in lung tissue from patients with IPF (n=46) than that in lung tissue from controls (n=51). Interpretation AKAP13 is a Rho guanine nucleotide exchange factor regulating activation of RhoA, which is known to be involved in profibrotic signalling pathways. The identification of AKAP13 as a susceptibility gene for IPF increases the prospect of successfully targeting RhoA pathway inhibitors in patients with IPF. Funding UK Medical Research Council, National Heart, Lung, and Blood Institute of the US National Institutes of Health, Agencia Canaria de Investigación, Innovación y Sociedad de la Información, Spain, UK National Institute for Health Research, and the British Lung Foundation}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with high mortality, uncertain cause, and few treatment options. Studies have identified a significant genetic risk associated with the development of IPF; however, mechanisms by which genetic risk factors promote IPF remain unclear. We aimed to identify genetic variants associated with IPF susceptibility and provide mechanistic insight using gene and protein expression analyses. Methods We used a two-stage approach: a genome-wide association study in patients with IPF of European ancestry recruited from nine different centres in the UK and controls selected from UK Biobank (stage 1) matched for age, sex, and smoking status; and a follow-up of associated genetic variants in independent datasets of patients with IPF and controls from two independent US samples from the Chicago consortium and the Colorado consortium (stage 2). We investigated the effect of novel signals on gene expression in large transcriptomic and genomic data resources, and examined expression using lung tissue samples from patients with IPF and controls. Findings 602 patients with IPF and 3366 controls were selected for stage 1. For stage 2, 2158 patients with IPF and 5195 controls were selected. We identified a novel genome-wide significant signal of association with IPF susceptibility near A-kinase anchoring protein 13 (AKAP13; rs62025270, odds ratio [OR] 1·27 [95% CI 1·18–1·37], p=1·32 × 10−9) and confirmed previously reported signals, including in mucin 5B (MUC5B; rs35705950, OR 2·89 [2·56–3·26], p=1·12 × 10−66) and desmoplakin (DSP; rs2076295, OR 1·44 [1·35–1·54], p=7·81 × 10−28). For rs62025270, the allele A associated with increased susceptibility to IPF was also associated with increased expression of AKAP13 mRNA in lung tissue from patients who had lung resection procedures (n=1111). We showed that AKAP13 is expressed in the alveolar epithelium and lymphoid follicles from patients with IPF, and AKAP13 mRNA expression was 1·42-times higher in lung tissue from patients with IPF (n=46) than that in lung tissue from controls (n=51). Interpretation AKAP13 is a Rho guanine nucleotide exchange factor regulating activation of RhoA, which is known to be involved in profibrotic signalling pathways. The identification of AKAP13 as a susceptibility gene for IPF increases the prospect of successfully targeting RhoA pathway inhibitors in patients with IPF. Funding UK Medical Research Council, National Heart, Lung, and Blood Institute of the US National Institutes of Health, Agencia Canaria de Investigación, Innovación y Sociedad de la Información, Spain, UK National Institute for Health Research, and the British Lung Foundation |
Jane Maddock; Ang Zhou; Alana Cavadino; Elżbieta Kuźma; Yanchun Bao; Melissa C. Smart; Kai-Uwe Saum; Ben Schöttker; Jorgen Engmann; Marie Kjærgaard; Ville Karhunen; Yiqiang Zhan; Terho Lehtimäki; Suvi P. Rovio; Liisa Byberg; Jari Lahti; Pedro Marques-Vidal; Abhijit Sen; Laura Perna; Henrik Schirmer; Archana Singh-Manoux; Juha Auvinen; Nina Hutri-Kähönen; Mika Kähönen; Lena Kilander; Katri Räikkönen; Håkan Melhus; Erik Ingelsson; Idris Guessous; Katja E Petrovic; Helena Schmidt; Reinhold Schmidt; Peter Vollenweider; Lars Lind; Johan G. Eriksson; Karl Michaëlsson; Olli T. Raitakari; Sara Hägg; Nancy L. Pedersen; Karl-Heinz Herzig; Marjo-Riitta Järvelin; Juha Veijola; Mika Kivimaki; Rolf Jorde; Hermann Brenner; Meena Kumari; Chris Power; David J. Llewellyn; Elina Hyppönen Vitamin D and cognitive function: A Mendelian randomisation study Journal Article Scientific Reports, 2017. @article{Maddock2017, title = {Vitamin D and cognitive function: A Mendelian randomisation study}, author = {Jane Maddock and Ang Zhou and Alana Cavadino and Elżbieta Kuźma and Yanchun Bao and Melissa C. Smart and Kai-Uwe Saum and Ben Schöttker and Jorgen Engmann and Marie Kjærgaard and Ville Karhunen and Yiqiang Zhan and Terho Lehtimäki and Suvi P. Rovio and Liisa Byberg and Jari Lahti and Pedro Marques-Vidal and Abhijit Sen and Laura Perna and Henrik Schirmer and Archana Singh-Manoux and Juha Auvinen and Nina Hutri-Kähönen and Mika Kähönen and Lena Kilander and Katri Räikkönen and Håkan Melhus and Erik Ingelsson and Idris Guessous and Katja E Petrovic and Helena Schmidt and Reinhold Schmidt and Peter Vollenweider and Lars Lind and Johan G. Eriksson and Karl Michaëlsson and Olli T. Raitakari and Sara Hägg and Nancy L. Pedersen and Karl-Heinz Herzig and Marjo-Riitta Järvelin and Juha Veijola and Mika Kivimaki and Rolf Jorde and Hermann Brenner and Meena Kumari and Chris Power and David J. Llewellyn and Elina Hyppönen}, url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5643555/}, year = {2017}, date = {2017-10-16}, journal = {Scientific Reports}, abstract = {The causal nature of the association between hypovitaminosis D and poor cognitive function in mid- to later-life is uncertain. Using a Mendelian randomisation(MR) approach, we examined the causal relationship between 25(OH)D and cognitive function. Data came from 172,349 participants from 17 cohorts. DHCR7(rs12785878), CYP2R1 rs12794714) and their combined synthesis score were chosen to proxy 25(OH)D. Cognitive tests were standardised into global and memory scores. Analyses were stratified by 25(OH)D tertiles, sex and age. Random effects meta-analyses assessed associations between 25(OH)D and cognitive function. Associations of serum 25(OH)D with global and memory-related cognitive function were non-linear (lower cognitive scores for both low and high 25(OH)D, p curvature ≤ 0.006), with much of the curvature attributed to a single study. DHCR7, CYP2R1, and the synthesis score were associated with small reductions in 25(OH)D per vitamin D-decreasing allele. However, coefficients for associations with global or memory-related cognitive function were non-significant and in opposing directions for DHCR7 and CYP2R1, with no overall association observed for the synthesis score. Coefficients for the synthesis score and global and memory cognition were similar when stratified by 25(OH)D tertiles, sex and age. We found no evidence for serum 25(OH)D concentration as a causal factor for cognitive performance in mid- to later life.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The causal nature of the association between hypovitaminosis D and poor cognitive function in mid- to later-life is uncertain. Using a Mendelian randomisation(MR) approach, we examined the causal relationship between 25(OH)D and cognitive function. Data came from 172,349 participants from 17 cohorts. DHCR7(rs12785878), CYP2R1 rs12794714) and their combined synthesis score were chosen to proxy 25(OH)D. Cognitive tests were standardised into global and memory scores. Analyses were stratified by 25(OH)D tertiles, sex and age. Random effects meta-analyses assessed associations between 25(OH)D and cognitive function. Associations of serum 25(OH)D with global and memory-related cognitive function were non-linear (lower cognitive scores for both low and high 25(OH)D, p curvature ≤ 0.006), with much of the curvature attributed to a single study. DHCR7, CYP2R1, and the synthesis score were associated with small reductions in 25(OH)D per vitamin D-decreasing allele. However, coefficients for associations with global or memory-related cognitive function were non-significant and in opposing directions for DHCR7 and CYP2R1, with no overall association observed for the synthesis score. Coefficients for the synthesis score and global and memory cognition were similar when stratified by 25(OH)D tertiles, sex and age. We found no evidence for serum 25(OH)D concentration as a causal factor for cognitive performance in mid- to later life. |
Aldi T. Kraja; James P. Cook; Helen R. Warren; Praveen Surendran; Chunyu Liu; Evangelos Evangelou; Alisa K. Manning; Niels Grarup; Fotios Drenos; Xueling Sim; Albert Vernon Smith; Najaf Amin; Alexandra I.F. Blakemore; Jette Bork-Jensen; Ivan Brandslund; Aliki-Eleni Farmaki; Cristiano Fava; Teresa Ferreira; Karl-Heinz Herzig; Ayush Giri; Franco Giulianini; Megan L. Grove; Xiuqing Guo; Sarah E. Harris; Christian T. Have; Aki S. Havulinna; He Zhang; Marit E. Jørgensen; AnneMari Käräjämäki; Charles Kooperberg; Allan Linneberg; Louis Little; Yongmei Liu; Lori L. Bonnycastle; Yingchang Lu; Reedik Mägi; Anubha Mahajan; Giovanni Malerba; Riccardo E. Marioni; Hao Mei; Cristina Menni, Alanna C. Morrison, Sandosh Padmanabhan, Walter Palmas, Alaitz Poveda, Rainer Rauramaa, Nigel William Rayner; Muhammad Riaz; Ken Rice; Melissa A. Richard; Jennifer A. Smith; Lorraine Southam; Alena Stančáková; Kathleen E. Stirrups; Vinicius Tragante; Tiinamaija Tuomi; Ioanna Tzoulaki; Tibor V. Varga; Stefan Weiss; Andrianos M. Yiorkas; Robin Young; Weihua Zhang; Michael R. Barnesa; Claudia P. Cabrera; He Gao; Michael Boehnke; Eric Boerwinkle; John C. Chambers; John M. Connell, Cramer K. Christensen, Rudolf A. de Boer, Ian J. Deary, George Dedoussis, Panos Deloukas, Anna F. Dominiczak, Marcus Dörr; Roby Joehanes; Todd L. Edwards; Tõnu Esko; Myriam Fornage; Nora Franceschini; Paul W. Franks; Giovanni Gambaro; Leif Groop; Göran Hallmans; Torben Hansen; Caroline Hayward; Oksa Heikki; Erik Ingelsson; Jaakko Tuomilehto; Marjo-Riitta Jarvelin; Sharon L.R. Kardia; Fredrik Karpe; Jaspal S. Kooner; Timo A. Lakka; Claudia Langenberg; Lars Lind; Ruth J.F. Loos; Markku Laakso; Mark I. McCarthy; Olle Melander; Karen L. Mohlke; Andrew P. Morris; Colin N.A. Palmer; Oluf Pedersen, Ozren Polasek, Neil R. Poulter, Michael A. Province, Bruce M. Psaty, Paul M. Ridker, Jerome I. Rotter, Igor Rudan; Veikko Salomaa; Nilesh J. Samani; Peter J. Sever; Tea Skaaby; Jeanette M. Stafford; John M. Starr; Pim van der Harst; Peter van der Meer; The Understanding Society Scientific Group; Cornelia M. van Duijn; Anne-Claire Vergnaud; Vilmundur Gudnason; Nicholas J. Wareham; James G. Wilson; Cristen J. Willer; Daniel R. Witte; Eleftheria Zeggini; Danish Saleheen; Adam S. Butterworth; John Danesh; Folkert W. Asselbergs; Louise V. Wain; Georg B. Ehret; Daniel I. Chasman; Mark J. Caulfield; Paul Elliott; Cecilia M. Lindgren; Daniel Levy; Christopher Newton-Cheh; Patricia B. Munroe; Joanna M.M. Howson New Blood Pressure–Associated Loci Identified in Meta-Analyses of 475 000 Individuals Journal Article Cardiovascular Genetics, 2017. @article{Kraja2017, title = {New Blood Pressure–Associated Loci Identified in Meta-Analyses of 475 000 Individuals}, author = {Aldi T. Kraja and James P. Cook and Helen R. Warren and Praveen Surendran and Chunyu Liu and Evangelos Evangelou and Alisa K. Manning and Niels Grarup and Fotios Drenos and Xueling Sim and Albert Vernon Smith and Najaf Amin and Alexandra I.F. Blakemore and Jette Bork-Jensen and Ivan Brandslund and Aliki-Eleni Farmaki and Cristiano Fava and Teresa Ferreira and Karl-Heinz Herzig and Ayush Giri and Franco Giulianini and Megan L. Grove and Xiuqing Guo and Sarah E. Harris and Christian T. Have and Aki S. Havulinna and He Zhang and Marit E. Jørgensen and AnneMari Käräjämäki and Charles Kooperberg and Allan Linneberg and Louis Little and Yongmei Liu and Lori L. Bonnycastle and Yingchang Lu and Reedik Mägi and Anubha Mahajan and Giovanni Malerba and Riccardo E. Marioni and Hao Mei and Cristina Menni, Alanna C. Morrison, Sandosh Padmanabhan, Walter Palmas, Alaitz Poveda, Rainer Rauramaa, Nigel William Rayner and Muhammad Riaz and Ken Rice and Melissa A. Richard and Jennifer A. Smith and Lorraine Southam and Alena Stančáková and Kathleen E. Stirrups and Vinicius Tragante and Tiinamaija Tuomi and Ioanna Tzoulaki and Tibor V. Varga and Stefan Weiss and Andrianos M. Yiorkas and Robin Young and Weihua Zhang and Michael R. Barnesa and Claudia P. Cabrera and He Gao and Michael Boehnke and Eric Boerwinkle and John C. Chambers and John M. Connell, Cramer K. Christensen, Rudolf A. de Boer, Ian J. Deary, George Dedoussis, Panos Deloukas, Anna F. Dominiczak, Marcus Dörr and Roby Joehanes and Todd L. Edwards and Tõnu Esko and Myriam Fornage and Nora Franceschini and Paul W. Franks and Giovanni Gambaro and Leif Groop and Göran Hallmans and Torben Hansen and Caroline Hayward and Oksa Heikki and Erik Ingelsson and Jaakko Tuomilehto and Marjo-Riitta Jarvelin and Sharon L.R. Kardia and Fredrik Karpe and Jaspal S. Kooner and Timo A. Lakka and Claudia Langenberg and Lars Lind and Ruth J.F. Loos and Markku Laakso and Mark I. McCarthy and Olle Melander and Karen L. Mohlke and Andrew P. Morris and Colin N.A. Palmer and Oluf Pedersen, Ozren Polasek, Neil R. Poulter, Michael A. Province, Bruce M. Psaty, Paul M. Ridker, Jerome I. Rotter, Igor Rudan and Veikko Salomaa and Nilesh J. Samani and Peter J. Sever and Tea Skaaby and Jeanette M. Stafford and John M. Starr and Pim van der Harst and Peter van der Meer and The Understanding Society Scientific Group and Cornelia M. van Duijn and Anne-Claire Vergnaud and Vilmundur Gudnason and Nicholas J. Wareham and James G. Wilson and Cristen J. Willer and Daniel R. Witte and Eleftheria Zeggini and Danish Saleheen and Adam S. Butterworth and John Danesh and Folkert W. Asselbergs and Louise V. Wain and Georg B. Ehret and Daniel I. Chasman and Mark J. Caulfield and Paul Elliott and Cecilia M. Lindgren and Daniel Levy and Christopher Newton-Cheh and Patricia B. Munroe and Joanna M.M. Howson}, url = {http://circgenetics.ahajournals.org/content/10/5/e001778}, year = {2017}, date = {2017-10-13}, journal = {Cardiovascular Genetics}, abstract = {Background—Genome-wide association studies have recently identified >400 loci that harbor DNA sequence variants that influence blood pressure (BP). Our earlier studies identified and validated 56 single nucleotide variants (SNVs) associated with BP from meta-analyses of exome chip genotype data. An additional 100 variants yielded suggestive evidence of association. Methods and Results—Here, we augment the sample with 140 886 European individuals from the UK Biobank, in whom 77 of the 100 suggestive SNVs were available for association analysis with systolic BP or diastolic BP or pulse pressure. We performed 2 meta-analyses, one in individuals of European, South Asian, African, and Hispanic descent (pan-ancestry, ≈475 000), and the other in the subset of individuals of European descent (≈423 000). Twenty-one SNVs were genome-wide significant (P<5×10−8) for BP, of which 4 are new BP loci: rs9678851 (missense, SLC4A1AP), rs7437940 (AFAP1), rs13303 (missense, STAB1), and rs1055144 (7p15.2). In addition, we identified a potentially independent novel BP-associated SNV, rs3416322 (missense, SYNPO2L) at a known locus, uncorrelated with the previously reported SNVs. Two SNVs are associated with expression levels of nearby genes, and SNVs at 3 loci are associated with other traits. One SNV with a minor allele frequency <0.01, (rs3025380 at DBH) was genome-wide significant. Conclusions—We report 4 novel loci associated with BP regulation, and 1 independent variant at an established BP locus. This analysis highlights several candidate genes with variation that alter protein function or gene expression for potential follow-up.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background—Genome-wide association studies have recently identified >400 loci that harbor DNA sequence variants that influence blood pressure (BP). Our earlier studies identified and validated 56 single nucleotide variants (SNVs) associated with BP from meta-analyses of exome chip genotype data. An additional 100 variants yielded suggestive evidence of association. Methods and Results—Here, we augment the sample with 140 886 European individuals from the UK Biobank, in whom 77 of the 100 suggestive SNVs were available for association analysis with systolic BP or diastolic BP or pulse pressure. We performed 2 meta-analyses, one in individuals of European, South Asian, African, and Hispanic descent (pan-ancestry, ≈475 000), and the other in the subset of individuals of European descent (≈423 000). Twenty-one SNVs were genome-wide significant (P<5×10−8) for BP, of which 4 are new BP loci: rs9678851 (missense, SLC4A1AP), rs7437940 (AFAP1), rs13303 (missense, STAB1), and rs1055144 (7p15.2). In addition, we identified a potentially independent novel BP-associated SNV, rs3416322 (missense, SYNPO2L) at a known locus, uncorrelated with the previously reported SNVs. Two SNVs are associated with expression levels of nearby genes, and SNVs at 3 loci are associated with other traits. One SNV with a minor allele frequency <0.01, (rs3025380 at DBH) was genome-wide significant. Conclusions—We report 4 novel loci associated with BP regulation, and 1 independent variant at an established BP locus. This analysis highlights several candidate genes with variation that alter protein function or gene expression for potential follow-up. |
Peter K. Joshi; Nicola Pirastu; James F. Wilson Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity Journal Article Nature Communications, 2017. @article{Joshi2017, title = {Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity}, author = {Peter K. Joshi and Nicola Pirastu and James F. Wilson}, url = {https://www.nature.com/articles/s41467-017-00934-5}, year = {2017}, date = {2017-10-13}, journal = {Nature Communications}, abstract = {Genomic analysis of longevity offers the potential to illuminate the biology of human aging. Here, using genome-wide association meta-analysis of 606,059 parents’ survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA). We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity. Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated. We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD. Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Genomic analysis of longevity offers the potential to illuminate the biology of human aging. Here, using genome-wide association meta-analysis of 606,059 parents’ survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA). We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity. Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated. We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD. Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan. |
M Ren; FL Ng; H Warren; K Witkowska; M Baron; Z Jia; C Cabrera; R Zhang; B Mifsud; PB Munroe; Q Xiao; A Townsend-Nicholson; A Hobbs; S Ye; M Caulfield The biological impact of blood pressure associated genetic variants in the natriuretic peptide receptor C gene on human vascular smooth muscle. Journal Article Human molecular genetics, 2017. @article{Ren2017, title = {The biological impact of blood pressure associated genetic variants in the natriuretic peptide receptor C gene on human vascular smooth muscle.}, author = {M Ren and FL Ng and H Warren and K Witkowska and M Baron and Z Jia and C Cabrera and R Zhang and B Mifsud and PB Munroe and Q Xiao and A Townsend-Nicholson and A Hobbs and S Ye and M Caulfield}, url = {https://www.ncbi.nlm.nih.gov/pubmed/29040610}, year = {2017}, date = {2017-10-10}, journal = {Human molecular genetics}, abstract = {Elevated blood pressure (BP) is a major global risk factor for cardiovascular disease. Genome-wide association studies have identified several genetic variants at the NPR3 locus associated with BP, but the functional impact of these variants remains to be determined. Here we confirmed, by a genome-wide association study within UK Biobank, the existence of two independent BP-related signals within NPR3 locus. Using human primary vascular smooth muscle cells and endothelial cells from different individuals, we found that the BP-elevating alleles within one linkage disequilibrium block identified by the sentinel variant rs1173771 was associated with lower endogenous NPR3 mRNA and protein levels in vascular smooth muscle cells, together with reduced levels in open chromatin and nuclear protein binding. The BP-elevating alleles also increased vascular smooth muscle cell proliferation, angiotensin II-induced calcium flux, and cell contraction. However, an analogous genotype-dependent association was not observed in vascular endothelial cells. Our study identifies novel, putative mechanisms for BP-associated variants at the NPR3 locus to elevate BP, further strengthening the case for targeting NPR-C as a therapeutic approach for hypertension and cardiovascular disease prevention.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Elevated blood pressure (BP) is a major global risk factor for cardiovascular disease. Genome-wide association studies have identified several genetic variants at the NPR3 locus associated with BP, but the functional impact of these variants remains to be determined. Here we confirmed, by a genome-wide association study within UK Biobank, the existence of two independent BP-related signals within NPR3 locus. Using human primary vascular smooth muscle cells and endothelial cells from different individuals, we found that the BP-elevating alleles within one linkage disequilibrium block identified by the sentinel variant rs1173771 was associated with lower endogenous NPR3 mRNA and protein levels in vascular smooth muscle cells, together with reduced levels in open chromatin and nuclear protein binding. The BP-elevating alleles also increased vascular smooth muscle cell proliferation, angiotensin II-induced calcium flux, and cell contraction. However, an analogous genotype-dependent association was not observed in vascular endothelial cells. Our study identifies novel, putative mechanisms for BP-associated variants at the NPR3 locus to elevate BP, further strengthening the case for targeting NPR-C as a therapeutic approach for hypertension and cardiovascular disease prevention. |
Christian Benner; Aki S.Havulinna; Marjo-RiittaJärvelin; VeikkoSalomaa; Samuli Ripatti; MattiPirinen Prospects of Fine-Mapping Trait-Associated Genomic Regions by Using Summary Statistics from Genome-wide Association Studies Journal Article American Journal of Human Genetics, 2017. @article{Benner2017, title = {Prospects of Fine-Mapping Trait-Associated Genomic Regions by Using Summary Statistics from Genome-wide Association Studies}, author = {Christian Benner and Aki S.Havulinna and Marjo-RiittaJärvelin and VeikkoSalomaa and Samuli Ripatti and MattiPirinen }, url = {http://www.sciencedirect.com/science/article/pii/S0002929717303348}, year = {2017}, date = {2017-10-05}, journal = {American Journal of Human Genetics}, abstract = {During the past few years, various novel statistical methods have been developed for fine-mapping with the use of summary statistics from genome-wide association studies (GWASs). Although these approaches require information about the linkage disequilibrium (LD) between variants, there has not been a comprehensive evaluation of how estimation of the LD structure from reference genotype panels performs in comparison with that from the original individual-level GWAS data. Using population genotype data from Finland and the UK Biobank, we show here that a reference panel of 1,000 individuals from the target population is adequate for a GWAS cohort of up to 10,000 individuals, whereas smaller panels, such as those from the 1000 Genomes Project, should be avoided. We also show, both theoretically and empirically, that the size of the reference panel needs to scale with the GWAS sample size; this has important consequences for the application of these methods in ongoing GWAS meta-analyses and large biobank studies. We conclude by providing software tools and by recommending practices for sharing LD information to more efficiently exploit summary statistics in genetics research.}, keywords = {}, pubstate = {published}, tppubtype = {article} } During the past few years, various novel statistical methods have been developed for fine-mapping with the use of summary statistics from genome-wide association studies (GWASs). Although these approaches require information about the linkage disequilibrium (LD) between variants, there has not been a comprehensive evaluation of how estimation of the LD structure from reference genotype panels performs in comparison with that from the original individual-level GWAS data. Using population genotype data from Finland and the UK Biobank, we show here that a reference panel of 1,000 individuals from the target population is adequate for a GWAS cohort of up to 10,000 individuals, whereas smaller panels, such as those from the 1000 Genomes Project, should be avoided. We also show, both theoretically and empirically, that the size of the reference panel needs to scale with the GWAS sample size; this has important consequences for the application of these methods in ongoing GWAS meta-analyses and large biobank studies. We conclude by providing software tools and by recommending practices for sharing LD information to more efficiently exploit summary statistics in genetics research. |
Michael Dannemann; Janet Kelso The Contribution of Neanderthals to Phenotypic Variation in Modern Humans Journal Article American Journal of Human Genetics, 2017. @article{Dannemann2017, title = {The Contribution of Neanderthals to Phenotypic Variation in Modern Humans}, author = {Michael Dannemann and Janet Kelso}, url = {http://www.sciencedirect.com/science/article/pii/S0002929717303798}, year = {2017}, date = {2017-10-05}, journal = {American Journal of Human Genetics}, abstract = {Assessing the genetic contribution of Neanderthals to non-disease phenotypes in modern humans has been difficult because of the absence of large cohorts for which common phenotype information is available. Using baseline phenotypes collected for 112,000 individuals by the UK Biobank, we can now elaborate on previous findings that identified associations between signatures of positive selection on Neanderthal DNA and various modern human traits but not any specific phenotypic consequences. Here, we show that Neanderthal DNA affects skin tone and hair color, height, sleeping patterns, mood, and smoking status in present-day Europeans. Interestingly, multiple Neanderthal alleles at different loci contribute to skin and hair color in present-day Europeans, and these Neanderthal alleles contribute to both lighter and darker skin tones and hair color, suggesting that Neanderthals themselves were most likely variable in these traits.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Assessing the genetic contribution of Neanderthals to non-disease phenotypes in modern humans has been difficult because of the absence of large cohorts for which common phenotype information is available. Using baseline phenotypes collected for 112,000 individuals by the UK Biobank, we can now elaborate on previous findings that identified associations between signatures of positive selection on Neanderthal DNA and various modern human traits but not any specific phenotypic consequences. Here, we show that Neanderthal DNA affects skin tone and hair color, height, sleeping patterns, mood, and smoking status in present-day Europeans. Interestingly, multiple Neanderthal alleles at different loci contribute to skin and hair color in present-day Europeans, and these Neanderthal alleles contribute to both lighter and darker skin tones and hair color, suggesting that Neanderthals themselves were most likely variable in these traits. |
Connor A. Emdin; Amit V. Khera; Derek Klarin; Pradeep Natarajan; Seyedeh M. Zekavat; Akihiro Nomura; Mary E. Haas; Krishna Aragam; Diego Ardissino; James G. Wilson; Heribert Schunkert; Ruth McPherson; Hugh Watkins; Roberto Elosua; Matthew J. Bown; Nilesh J. Samani; Usman Baber; Jeanette Erdmann; Padhraig Gormley; Aarno Palotie; Nathan Stitziel; Namrata Gupta; John N. Danesh; Danish Saleheen; Stacey B. Gabriel; Sekar Kathiresan Phenotypic Consequences of a Genetic Predisposition to Enhanced Nitric Oxide Signaling Journal Article circulation, 2017. @article{Emdin2017b, title = {Phenotypic Consequences of a Genetic Predisposition to Enhanced Nitric Oxide Signaling}, author = {Connor A. Emdin and Amit V. Khera and Derek Klarin and Pradeep Natarajan and Seyedeh M. Zekavat and Akihiro Nomura and Mary E. Haas and Krishna Aragam and Diego Ardissino and James G. Wilson and Heribert Schunkert and Ruth McPherson and Hugh Watkins and Roberto Elosua and Matthew J. Bown and Nilesh J. Samani and Usman Baber and Jeanette Erdmann and Padhraig Gormley and Aarno Palotie and Nathan Stitziel and Namrata Gupta and John N. Danesh and Danish Saleheen and Stacey B. Gabriel and Sekar Kathiresan}, url = {http://circ.ahajournals.org/content/early/2017/10/04/CIRCULATIONAHA.117.028021?utm_content=buffer79393&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer}, year = {2017}, date = {2017-10-05}, journal = {circulation}, abstract = {Background—Nitric oxide signaling plays a key role in regulation of vascular tone and platelet activation. Here, we seek to understand the impact of a genetic predisposition to enhanced nitric oxide signaling on risk for cardiovascular diseases, thus informing the potential utility of pharmacologic stimulation of the nitric oxide pathway as a therapeutic strategy. Methods—We analyzed the association of common and rare genetic variants in two genes that mediate nitric oxide signaling [Nitric Oxide Synthase 3 (NOS3) and Guanylate Cyclase 1, Soluble, Alpha 3 (GUCY1A3)] with a range of human phenotypes. We selected two common variants (rs3918226 in NOS3 and rs7692387 in GUCY1A3) known to associate with increased NOS3 and GUCY1A3 expression and reduced mean arterial pressure, combined them into a genetic score, and standardized this exposure to a 5 mm Hg reduction in mean arterial pressure. Using individual-level data from 335,464 participants in the UK Biobank and summary association results from seven large-scale genome wide association studies, we examined the effect of this nitric oxide signaling score on cardiometabolic and other diseases. We also examined whether rare loss-of-function mutations in NOS3 and GUCY1A3 were associated with coronary heart disease using gene sequencing data from the Myocardial Infarction Genetics Consortium (n=27,815). Results—A genetic predisposition to enhanced nitric oxide signaling was associated with reduced risks of coronary heart disease [OR 0.37 95% CI 0.31, 0.45; p=5.5*10-26], peripheral arterial disease (OR 0.42 CI 0.26, 0.68; p=0.0005) and stroke (OR 0.53 CI 0.37, 0.76; p=0.0006). In a mediation analysis, the effect of the genetic score on decreased coronary heart disease risk extended beyond its effect on blood pressure. Conversely, rare variants that inactivate the NOS3 or GUCY1A3 genes were associated with a 23 mm Hg higher systolic blood pressure (CI 12, 34 mm Hg; p=5.6*10-5) and a three-fold higher risk of coronary heart disease (OR 3.03 CI 1.29, 7.12, p=0.01). Conclusions—A genetic predisposition to enhanced nitric oxide signaling is associated with reduced risks of coronary heart disease, peripheral arterial disease and stroke. Pharmacologic stimulation of nitric oxide signaling may prove useful in the prevention or treatment of cardiovascular disease.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background—Nitric oxide signaling plays a key role in regulation of vascular tone and platelet activation. Here, we seek to understand the impact of a genetic predisposition to enhanced nitric oxide signaling on risk for cardiovascular diseases, thus informing the potential utility of pharmacologic stimulation of the nitric oxide pathway as a therapeutic strategy. Methods—We analyzed the association of common and rare genetic variants in two genes that mediate nitric oxide signaling [Nitric Oxide Synthase 3 (NOS3) and Guanylate Cyclase 1, Soluble, Alpha 3 (GUCY1A3)] with a range of human phenotypes. We selected two common variants (rs3918226 in NOS3 and rs7692387 in GUCY1A3) known to associate with increased NOS3 and GUCY1A3 expression and reduced mean arterial pressure, combined them into a genetic score, and standardized this exposure to a 5 mm Hg reduction in mean arterial pressure. Using individual-level data from 335,464 participants in the UK Biobank and summary association results from seven large-scale genome wide association studies, we examined the effect of this nitric oxide signaling score on cardiometabolic and other diseases. We also examined whether rare loss-of-function mutations in NOS3 and GUCY1A3 were associated with coronary heart disease using gene sequencing data from the Myocardial Infarction Genetics Consortium (n=27,815). Results—A genetic predisposition to enhanced nitric oxide signaling was associated with reduced risks of coronary heart disease [OR 0.37 95% CI 0.31, 0.45; p=5.5*10-26], peripheral arterial disease (OR 0.42 CI 0.26, 0.68; p=0.0005) and stroke (OR 0.53 CI 0.37, 0.76; p=0.0006). In a mediation analysis, the effect of the genetic score on decreased coronary heart disease risk extended beyond its effect on blood pressure. Conversely, rare variants that inactivate the NOS3 or GUCY1A3 genes were associated with a 23 mm Hg higher systolic blood pressure (CI 12, 34 mm Hg; p=5.6*10-5) and a three-fold higher risk of coronary heart disease (OR 3.03 CI 1.29, 7.12, p=0.01). Conclusions—A genetic predisposition to enhanced nitric oxide signaling is associated with reduced risks of coronary heart disease, peripheral arterial disease and stroke. Pharmacologic stimulation of nitric oxide signaling may prove useful in the prevention or treatment of cardiovascular disease. |
Louise A C Millard; Neil M Davies; Tom R Gaunt; George Davey; Smith Kate Tilling Software Application Profile: PHESANT: a tool for performing automated phenome scans in UK Biobank Journal Article International Journal of Epidemiology, 2017. @article{Millard2017, title = {Software Application Profile: PHESANT: a tool for performing automated phenome scans in UK Biobank}, author = { Louise A C Millard and Neil M Davies and Tom R Gaunt and George Davey and Smith Kate Tilling}, url = {https://academic.oup.com/ije/article/4347232}, year = {2017}, date = {2017-10-05}, journal = {International Journal of Epidemiology}, abstract = {Motivation Epidemiological cohorts typically contain a diverse set of phenotypes such that automation of phenome scans is non-trivial, because they require highly heterogeneous models. For this reason, phenome scans have to date tended to use a smaller homogeneous set of phenotypes that can be analysed in a consistent fashion. We present PHESANT (PHEnome Scan ANalysis Tool), a software package for performing comprehensive phenome scans in UK Biobank. General features PHESANT tests the association of a specified trait with all continuous, integer and categorical variables in UK Biobank, or a specified subset. PHESANT uses a novel rule-based algorithm to determine how to appropriately test each trait, then performs the analyses and produces plots and summary tables. Implementation The PHESANT phenome scan is implemented in R. PHESANT includes a novel Javascript D3.js visualization and accompanying Java code that converts the phenome scan results to the required JavaScript Object Notation (JSON) format. Availability PHESANT is available on GitHub at [https://github.com/MRCIEU/PHESANT]. Git tag v0.5 corresponds to the version presented here.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Motivation Epidemiological cohorts typically contain a diverse set of phenotypes such that automation of phenome scans is non-trivial, because they require highly heterogeneous models. For this reason, phenome scans have to date tended to use a smaller homogeneous set of phenotypes that can be analysed in a consistent fashion. We present PHESANT (PHEnome Scan ANalysis Tool), a software package for performing comprehensive phenome scans in UK Biobank. General features PHESANT tests the association of a specified trait with all continuous, integer and categorical variables in UK Biobank, or a specified subset. PHESANT uses a novel rule-based algorithm to determine how to appropriately test each trait, then performs the analyses and produces plots and summary tables. Implementation The PHESANT phenome scan is implemented in R. PHESANT includes a novel Javascript D3.js visualization and accompanying Java code that converts the phenome scan results to the required JavaScript Object Notation (JSON) format. Availability PHESANT is available on GitHub at [https://github.com/MRCIEU/PHESANT]. Git tag v0.5 corresponds to the version presented here. |
G Paré; S Mao; WQ Deng A machine-learning heuristic to improve gene score prediction of polygenic traits. Journal Article Scientific Reports, 2017. @article{Paré2017, title = {A machine-learning heuristic to improve gene score prediction of polygenic traits.}, author = {G Paré and S Mao and WQ Deng}, url = {https://www.ncbi.nlm.nih.gov/pubmed/28979001}, year = {2017}, date = {2017-10-04}, journal = {Scientific Reports}, abstract = {Machine-learning techniques have helped solve a broad range of prediction problems, yet are not widely used to build polygenic risk scores for the prediction of complex traits. We propose a novel heuristic based on machine-learning techniques (GraBLD) to boost the predictive performance of polygenic risk scores. Gradient boosted regression trees were first used to optimize the weights of SNPs included in the score, followed by a novel regional adjustment for linkage disequilibrium. A calibration set with sample size of ~200 individuals was sufficient for optimal performance. GraBLD yielded prediction R 2 of 0.239 and 0.082 using GIANT summary association statistics for height and BMI in the UK Biobank study (N = 130 K; 1.98 M SNPs), explaining 46.9% and 32.7% of the overall polygenic variance, respectively. For diabetes status, the area under the receiver operating characteristic curve was 0.602 in the UK Biobank study using summary-level association statistics from the DIAGRAM consortium. GraBLD outperformed other polygenic score heuristics for the prediction of height (p < 2.2 × 10-16) and BMI (p < 1.57 × 10-4), and was equivalent to LDpred for diabetes. Results were independently validated in the Health and Retirement Study (N = 8,292; 688,398 SNPs). Our report demonstrates the use of machine-learning techniques, coupled with summary-level data from large genome-wide meta-analyses to improve the prediction of polygenic traits.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Machine-learning techniques have helped solve a broad range of prediction problems, yet are not widely used to build polygenic risk scores for the prediction of complex traits. We propose a novel heuristic based on machine-learning techniques (GraBLD) to boost the predictive performance of polygenic risk scores. Gradient boosted regression trees were first used to optimize the weights of SNPs included in the score, followed by a novel regional adjustment for linkage disequilibrium. A calibration set with sample size of ~200 individuals was sufficient for optimal performance. GraBLD yielded prediction R 2 of 0.239 and 0.082 using GIANT summary association statistics for height and BMI in the UK Biobank study (N = 130 K; 1.98 M SNPs), explaining 46.9% and 32.7% of the overall polygenic variance, respectively. For diabetes status, the area under the receiver operating characteristic curve was 0.602 in the UK Biobank study using summary-level association statistics from the DIAGRAM consortium. GraBLD outperformed other polygenic score heuristics for the prediction of height (p < 2.2 × 10-16) and BMI (p < 1.57 × 10-4), and was equivalent to LDpred for diabetes. Results were independently validated in the Health and Retirement Study (N = 8,292; 688,398 SNPs). Our report demonstrates the use of machine-learning techniques, coupled with summary-level data from large genome-wide meta-analyses to improve the prediction of polygenic traits. |
D B Hancock; Y Guo; E O Johnson Genome-wide association study across European and African American ancestries identifies a SNP in DNMT3B contributing to nicotine dependence Journal Article Nature Genetics, 2017. @article{Hancock2017, title = {Genome-wide association study across European and African American ancestries identifies a SNP in DNMT3B contributing to nicotine dependence}, author = {D B Hancock and Y Guo and E O Johnson}, url = {https://www.nature.com/articles/mp2017193}, year = {2017}, date = {2017-10-03}, journal = {Nature Genetics}, abstract = {Cigarette smoking is a leading cause of preventable mortality worldwide. Nicotine dependence, which reduces the likelihood of quitting smoking, is a heritable trait with firmly established associations with sequence variants in nicotine acetylcholine receptor genes and at other loci. To search for additional loci, we conducted a genome-wide association study (GWAS) meta-analysis of nicotine dependence, totaling 38,602 smokers (28,677 Europeans/European Americans and 9925 African Americans) across 15 studies. In this largest-ever GWAS meta-analysis for nicotine dependence and the largest-ever cross-ancestry GWAS meta-analysis for any smoking phenotype, we reconfirmed the well-known CHRNA5-CHRNA3-CHRNB4 genes and further yielded a novel association in the DNA methyltransferase gene DNMT3B. The intronic DNMT3B rs910083-C allele (frequency=44–77%) was associated with increased risk of nicotine dependence at P=3.7 × 10−8 (odds ratio (OR)=1.06 and 95% confidence interval (CI)=1.04–1.07 for severe vs mild dependence). The association was independently confirmed in the UK Biobank (N=48,931) using heavy vs never smoking as a proxy phenotype (P=3.6 × 10−4, OR=1.05, and 95% CI=1.02–1.08). Rs910083-C is also associated with increased risk of squamous cell lung carcinoma in the International Lung Cancer Consortium (N=60,586, meta-analysis P=0.0095, OR=1.05, and 95% CI=1.01–1.09). Moreover, rs910083-C was implicated as a cis-methylation quantitative trait locus (QTL) variant associated with higher DNMT3B methylation in fetal brain (N=166, P=2.3 × 10−26) and a cis-expression QTL variant associated with higher DNMT3B expression in adult cerebellum from the Genotype-Tissue Expression project (N=103, P=3.0 × 10−6) and the independent Brain eQTL Almanac (N=134, P=0.028). This novel DNMT3B cis-acting QTL variant highlights the importance of genetically influenced regulation in brain on the risks of nicotine dependence, heavy smoking and consequent lung cancer.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Cigarette smoking is a leading cause of preventable mortality worldwide. Nicotine dependence, which reduces the likelihood of quitting smoking, is a heritable trait with firmly established associations with sequence variants in nicotine acetylcholine receptor genes and at other loci. To search for additional loci, we conducted a genome-wide association study (GWAS) meta-analysis of nicotine dependence, totaling 38,602 smokers (28,677 Europeans/European Americans and 9925 African Americans) across 15 studies. In this largest-ever GWAS meta-analysis for nicotine dependence and the largest-ever cross-ancestry GWAS meta-analysis for any smoking phenotype, we reconfirmed the well-known CHRNA5-CHRNA3-CHRNB4 genes and further yielded a novel association in the DNA methyltransferase gene DNMT3B. The intronic DNMT3B rs910083-C allele (frequency=44–77%) was associated with increased risk of nicotine dependence at P=3.7 × 10−8 (odds ratio (OR)=1.06 and 95% confidence interval (CI)=1.04–1.07 for severe vs mild dependence). The association was independently confirmed in the UK Biobank (N=48,931) using heavy vs never smoking as a proxy phenotype (P=3.6 × 10−4, OR=1.05, and 95% CI=1.02–1.08). Rs910083-C is also associated with increased risk of squamous cell lung carcinoma in the International Lung Cancer Consortium (N=60,586, meta-analysis P=0.0095, OR=1.05, and 95% CI=1.01–1.09). Moreover, rs910083-C was implicated as a cis-methylation quantitative trait locus (QTL) variant associated with higher DNMT3B methylation in fetal brain (N=166, P=2.3 × 10−26) and a cis-expression QTL variant associated with higher DNMT3B expression in adult cerebellum from the Genotype-Tissue Expression project (N=103, P=3.0 × 10−6) and the independent Brain eQTL Almanac (N=134, P=0.028). This novel DNMT3B cis-acting QTL variant highlights the importance of genetically influenced regulation in brain on the risks of nicotine dependence, heavy smoking and consequent lung cancer. |
LC Pilling; JL Atkins; MO Duff; RN Beaumont; SE Jones; J Tyrrell; CL Kuo; KS Ruth; MA Tuke; H Yaghootkar; AR Wood; A Murray; MN Weedon; LW Harries; GA Kuchel; L Ferrucci; TM Frayling; D Melzer Red blood cell distribution width: Genetic evidence for aging pathways in 116,666 volunteers. Journal Article PLoS One, 2017. @article{Pilling2017, title = {Red blood cell distribution width: Genetic evidence for aging pathways in 116,666 volunteers.}, author = {LC Pilling and JL Atkins and MO Duff and RN Beaumont and SE Jones and J Tyrrell and CL Kuo and KS Ruth and MA Tuke and H Yaghootkar and AR Wood and A Murray and MN Weedon and LW Harries and GA Kuchel and L Ferrucci and TM Frayling and D Melzer}, url = {https://www.ncbi.nlm.nih.gov/pubmed/28957414}, year = {2017}, date = {2017-09-28}, journal = {PLoS One}, abstract = {Variability in red blood cell volumes (distribution width, RDW) increases with age and is strongly predictive of mortality, incident coronary heart disease and cancer. We investigated inherited genetic variation associated with RDW in 116,666 UK Biobank human volunteers. RESULTS: A large proportion RDW is explained by genetic variants (29%), especially in the older group (60+ year olds, 33.8%, <50 year olds, 28.4%). RDW was associated with 194 independent genetic signals; 71 are known for conditions including autoimmune disease, certain cancers, BMI, Alzheimer's disease, longevity, age at menopause, bone density, myositis, Parkinson's disease, and age-related macular degeneration. Exclusion of anemic participants did not affect the overall findings. Pathways analysis showed enrichment for telomere maintenance, ribosomal RNA, and apoptosis. The majority of RDW-associated signals were intronic (119 of 194), including SNP rs6602909 located in an intron of oncogene GAS6, an eQTL in whole blood. CONCLUSIONS: Although increased RDW is predictive of cardiovascular outcomes, this was not explained by known CVD or related lipid genetic risks, and a RDW genetic score was not predictive of incident disease. The predictive value of RDW for a range of negative health outcomes may in part be due to variants influencing fundamental pathways of aging.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Variability in red blood cell volumes (distribution width, RDW) increases with age and is strongly predictive of mortality, incident coronary heart disease and cancer. We investigated inherited genetic variation associated with RDW in 116,666 UK Biobank human volunteers. RESULTS: A large proportion RDW is explained by genetic variants (29%), especially in the older group (60+ year olds, 33.8%, <50 year olds, 28.4%). RDW was associated with 194 independent genetic signals; 71 are known for conditions including autoimmune disease, certain cancers, BMI, Alzheimer's disease, longevity, age at menopause, bone density, myositis, Parkinson's disease, and age-related macular degeneration. Exclusion of anemic participants did not affect the overall findings. Pathways analysis showed enrichment for telomere maintenance, ribosomal RNA, and apoptosis. The majority of RDW-associated signals were intronic (119 of 194), including SNP rs6602909 located in an intron of oncogene GAS6, an eQTL in whole blood. CONCLUSIONS: Although increased RDW is predictive of cardiovascular outcomes, this was not explained by known CVD or related lipid genetic risks, and a RDW genetic score was not predictive of incident disease. The predictive value of RDW for a range of negative health outcomes may in part be due to variants influencing fundamental pathways of aging. |
SP Hagenaars; SR Cox; WD Hill; G Davies; DCM Liewald; CHARGE consortium Cognitive Working Group,; SE Harris; AM McIntosh; CR Gale; IJ Deary. Genetic contributions to Trail Making Test performance in UK Biobank. Journal Article Molecular Psychiatry, 2017. @article{Hagenaars2017b, title = {Genetic contributions to Trail Making Test performance in UK Biobank.}, author = {SP Hagenaars and SR Cox and WD Hill and G Davies and DCM Liewald and CHARGE consortium Cognitive Working Group, and SE Harris and AM McIntosh and CR Gale and IJ Deary.}, url = {https://www.ncbi.nlm.nih.gov/pubmed/28924184}, year = {2017}, date = {2017-09-19}, journal = {Molecular Psychiatry}, abstract = {The Trail Making Test (TMT) is a widely used test of executive function and has been thought to be strongly associated with general cognitive function. We examined the genetic architecture of the TMT and its shared genetic aetiology with other tests of cognitive function in 23 821 participants from UK Biobank. The single-nucleotide polymorphism-based heritability estimates for trail-making measures were 7.9% (part A), 22.4% (part B) and 17.6% (part B-part A). Significant genetic correlations were identified between trail-making measures and verbal-numerical reasoning (rg>0.6), general cognitive function (rg>0.6), processing speed (rg>0.7) and memory (rg>0.3). Polygenic profile analysis indicated considerable shared genetic aetiology between trail making, general cognitive function, processing speed and memory (standardized β between 0.03 and 0.08). These results suggest that trail making is both phenotypically and genetically strongly associated with general cognitive function and processing speed.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The Trail Making Test (TMT) is a widely used test of executive function and has been thought to be strongly associated with general cognitive function. We examined the genetic architecture of the TMT and its shared genetic aetiology with other tests of cognitive function in 23 821 participants from UK Biobank. The single-nucleotide polymorphism-based heritability estimates for trail-making measures were 7.9% (part A), 22.4% (part B) and 17.6% (part B-part A). Significant genetic correlations were identified between trail-making measures and verbal-numerical reasoning (rg>0.6), general cognitive function (rg>0.6), processing speed (rg>0.7) and memory (rg>0.3). Polygenic profile analysis indicated considerable shared genetic aetiology between trail making, general cognitive function, processing speed and memory (standardized β between 0.03 and 0.08). These results suggest that trail making is both phenotypically and genetically strongly associated with general cognitive function and processing speed. |
Hakhamanesh Mostafavi; Tomaz Berisa; Felix R. Day; John R. B. Perry; Molly Przeworski; Joseph K. Pickrell Identifying genetic variants that affect viability in large cohorts Journal Article PLoS Biology , 2017. @article{Mostafavi2017, title = {Identifying genetic variants that affect viability in large cohorts}, author = {Hakhamanesh Mostafavi and Tomaz Berisa and Felix R. Day and John R. B. Perry and Molly Przeworski and Joseph K. Pickrell }, url = {http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2002458}, year = {2017}, date = {2017-09-05}, journal = {PLoS Biology }, abstract = {A number of open questions in human evolutionary genetics would become tractable if we were able to directly measure evolutionary fitness. As a step towards this goal, we developed a method to examine whether individual genetic variants, or sets of genetic variants, currently influence viability. The approach consists in testing whether the frequency of an allele varies across ages, accounting for variation in ancestry. We applied it to the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort and to the parents of participants in the UK Biobank. Across the genome, we found only a few common variants with large effects on age-specific mortality: tagging the APOE ε4 allele and near CHRNA3. These results suggest that when large, even late-onset effects are kept at low frequency by purifying selection. Testing viability effects of sets of genetic variants that jointly influence 1 of 42 traits, we detected a number of strong signals. In participants of the UK Biobank of British ancestry, we found that variants that delay puberty timing are associated with a longer parental life span (P~6.2 × 10−6 for fathers and P~2.0 × 10−3 for mothers), consistent with epidemiological studies. Similarly, variants associated with later age at first birth are associated with a longer maternal life span (P~1.4 × 10−3). Signals are also observed for variants influencing cholesterol levels, risk of coronary artery disease (CAD), body mass index, as well as risk of asthma. These signals exhibit consistent effects in the GERA cohort and among participants of the UK Biobank of non-British ancestry. We also found marked differences between males and females, most notably at the CHRNA3 locus, and variants associated with risk of CAD and cholesterol levels. Beyond our findings, the analysis serves as a proof of principle for how upcoming biomedical data sets can be used to learn about selection effects in contemporary humans.}, keywords = {}, pubstate = {published}, tppubtype = {article} } A number of open questions in human evolutionary genetics would become tractable if we were able to directly measure evolutionary fitness. As a step towards this goal, we developed a method to examine whether individual genetic variants, or sets of genetic variants, currently influence viability. The approach consists in testing whether the frequency of an allele varies across ages, accounting for variation in ancestry. We applied it to the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort and to the parents of participants in the UK Biobank. Across the genome, we found only a few common variants with large effects on age-specific mortality: tagging the APOE ε4 allele and near CHRNA3. These results suggest that when large, even late-onset effects are kept at low frequency by purifying selection. Testing viability effects of sets of genetic variants that jointly influence 1 of 42 traits, we detected a number of strong signals. In participants of the UK Biobank of British ancestry, we found that variants that delay puberty timing are associated with a longer parental life span (P~6.2 × 10−6 for fathers and P~2.0 × 10−3 for mothers), consistent with epidemiological studies. Similarly, variants associated with later age at first birth are associated with a longer maternal life span (P~1.4 × 10−3). Signals are also observed for variants influencing cholesterol levels, risk of coronary artery disease (CAD), body mass index, as well as risk of asthma. These signals exhibit consistent effects in the GERA cohort and among participants of the UK Biobank of non-British ancestry. We also found marked differences between males and females, most notably at the CHRNA3 locus, and variants associated with risk of CAD and cholesterol levels. Beyond our findings, the analysis serves as a proof of principle for how upcoming biomedical data sets can be used to learn about selection effects in contemporary humans. |
M Rask-Andersen; T Karlsson; WE Ek; A Johansson PLOS Genetics, 2017. @article{Rask-Andersen2017, title = {Gene-environment interaction study for BMI reveals interactions between genetic factors and physical activity, alcohol consumption and socioeconomic status.}, author = {M Rask-Andersen and T Karlsson and WE Ek and A Johansson}, url = {https://www.ncbi.nlm.nih.gov/pubmed/28873402}, year = {2017}, date = {2017-09-05}, journal = {PLOS Genetics}, abstract = {Previous genome-wide association studies (GWAS) have identified hundreds of genetic loci to be associated with body mass index (BMI) and risk of obesity. Genetic effects can differ between individuals depending on lifestyle or environmental factors due to gene-environment interactions. In this study, we examine gene-environment interactions in 362,496 unrelated participants with Caucasian ancestry from the UK Biobank resource. A total of 94 BMI-associated SNPs, selected from a previous GWAS on BMI, were used to construct weighted genetic scores for BMI (GSBMI). Linear regression modeling was used to estimate the effect of gene-environment interactions on BMI for 131 lifestyle factors related to: dietary habits, smoking and alcohol consumption, physical activity, socioeconomic status, mental health, sleeping patterns, as well as female-specific factors such as menopause and childbirth. In total, 15 lifestyle factors were observed to interact with GSBMI, of which alcohol intake frequency, usual walking pace, and Townsend deprivation index, a measure of socioeconomic status, were all highly significant (p = 1.45*10-29, p = 3.83*10-26, p = 4.66*10-11, respectively). Interestingly, the frequency of alcohol consumption, rather than the total weekly amount resulted in a significant interaction. The FTO locus was the strongest single locus interacting with any of the lifestyle factors. However, 13 significant interactions were also observed after omitting the FTO locus from the genetic score. Our analyses indicate that many lifestyle factors modify the genetic effects on BMI with some groups of individuals having more than double the effect of the genetic score. However, the underlying causal mechanisms of gene-environmental interactions are difficult to deduce from cross-sectional data alone and controlled experiments are required to fully characterise the causal factors.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Previous genome-wide association studies (GWAS) have identified hundreds of genetic loci to be associated with body mass index (BMI) and risk of obesity. Genetic effects can differ between individuals depending on lifestyle or environmental factors due to gene-environment interactions. In this study, we examine gene-environment interactions in 362,496 unrelated participants with Caucasian ancestry from the UK Biobank resource. A total of 94 BMI-associated SNPs, selected from a previous GWAS on BMI, were used to construct weighted genetic scores for BMI (GSBMI). Linear regression modeling was used to estimate the effect of gene-environment interactions on BMI for 131 lifestyle factors related to: dietary habits, smoking and alcohol consumption, physical activity, socioeconomic status, mental health, sleeping patterns, as well as female-specific factors such as menopause and childbirth. In total, 15 lifestyle factors were observed to interact with GSBMI, of which alcohol intake frequency, usual walking pace, and Townsend deprivation index, a measure of socioeconomic status, were all highly significant (p = 1.45*10-29, p = 3.83*10-26, p = 4.66*10-11, respectively). Interestingly, the frequency of alcohol consumption, rather than the total weekly amount resulted in a significant interaction. The FTO locus was the strongest single locus interacting with any of the lifestyle factors. However, 13 significant interactions were also observed after omitting the FTO locus from the genetic score. Our analyses indicate that many lifestyle factors modify the genetic effects on BMI with some groups of individuals having more than double the effect of the genetic score. However, the underlying causal mechanisms of gene-environmental interactions are difficult to deduce from cross-sectional data alone and controlled experiments are required to fully characterise the causal factors. |
John P Kemp, John A Morris, Carolina Medina-Gomez, Vincenzo Forgetta, Nicole M Warrington, Scott E Youlten, Jie Zheng, Celia L Gregson, Elin Grundberg, Katerina Trajanoska, John G Logan, Andrea S Pollard, Penny C Sparkes; Elena J Ghirardello and Rebecca Allen and Victoria D Leitch and Natalie C Butterfield and Davide Komla-Ebri and Anne-Tounsia Adoum and Katharine F Curry and Jacqueline K White and Fiona Kussy and Keelin M Greenlaw and Changjiang Xu and Nicholas C Harvey Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis Journal Article Nature Genetics, 2017. @article{Kemp2017, title = {Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis}, author = { John P Kemp, John A Morris, Carolina Medina-Gomez, Vincenzo Forgetta, Nicole M Warrington, Scott E Youlten, Jie Zheng, Celia L Gregson, Elin Grundberg, Katerina Trajanoska, John G Logan, Andrea S Pollard, Penny C Sparkes and Elena J Ghirardello and Rebecca Allen and Victoria D Leitch and Natalie C Butterfield and Davide Komla-Ebri and Anne-Tounsia Adoum and Katharine F Curry and Jacqueline K White and Fiona Kussy and Keelin M Greenlaw and Changjiang Xu and Nicholas C Harvey }, url = {https://www.nature.com/ng/journal/vaop/ncurrent/full/ng.3949.html}, year = {2017}, date = {2017-09-04}, journal = {Nature Genetics}, abstract = {Osteoporosis is a common disease diagnosed primarily by measurement of bone mineral density (BMD). We undertook a genome-wide association study (GWAS) in 142,487 individuals from the UK Biobank to identify loci associated with BMD as estimated by quantitative ultrasound of the heel. We identified 307 conditionally independent single-nucleotide polymorphisms (SNPs) that attained genome-wide significance at 203 loci, explaining approximately 12% of the phenotypic variance. These included 153 previously unreported loci, and several rare variants with large effect sizes. To investigate the underlying mechanisms, we undertook (1) bioinformatic, functional genomic annotation and human osteoblast expression studies; (2) gene-function prediction; (3) skeletal phenotyping of 120 knockout mice with deletions of genes adjacent to lead independent SNPs; and (4) analysis of gene expression in mouse osteoblasts, osteocytes and osteoclasts. The results implicate GPC6 as a novel determinant of BMD, and also identify abnormal skeletal phenotypes in knockout mice associated with a further 100 prioritized genes.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Osteoporosis is a common disease diagnosed primarily by measurement of bone mineral density (BMD). We undertook a genome-wide association study (GWAS) in 142,487 individuals from the UK Biobank to identify loci associated with BMD as estimated by quantitative ultrasound of the heel. We identified 307 conditionally independent single-nucleotide polymorphisms (SNPs) that attained genome-wide significance at 203 loci, explaining approximately 12% of the phenotypic variance. These included 153 previously unreported loci, and several rare variants with large effect sizes. To investigate the underlying mechanisms, we undertook (1) bioinformatic, functional genomic annotation and human osteoblast expression studies; (2) gene-function prediction; (3) skeletal phenotyping of 120 knockout mice with deletions of genes adjacent to lead independent SNPs; and (4) analysis of gene expression in mouse osteoblasts, osteocytes and osteoclasts. The results implicate GPC6 as a novel determinant of BMD, and also identify abnormal skeletal phenotypes in knockout mice associated with a further 100 prioritized genes. |
T Sofer; Q Wong; FP Hartwig; K Taylor; HR Warren; E Evangelou; CP Cabrera; D Levy; H Kramer; LA Lange; BL Horta; KF Kerr; AP Reiner; N Franceschini Genome-Wide Association Study of Blood Pressure Traits by Hispanic/Latino Background: the Hispanic Community Health Study/Study of Latinos. Journal Article Scientific Reports, 2017. @article{Sofer2017, title = {Genome-Wide Association Study of Blood Pressure Traits by Hispanic/Latino Background: the Hispanic Community Health Study/Study of Latinos.}, author = {T Sofer and Q Wong and FP Hartwig and K Taylor and HR Warren and E Evangelou and CP Cabrera and D Levy and H Kramer and LA Lange and BL Horta and KF Kerr and AP Reiner and N Franceschini}, url = {https://www.ncbi.nlm.nih.gov/pubmed/28871152}, year = {2017}, date = {2017-09-04}, journal = {Scientific Reports}, abstract = {Hypertension prevalence varies between ethnic groups, possibly due to differences in genetic, environmental, and cultural determinants. Hispanic/Latino Americans are a diverse and understudied population. We performed a genome-wide association study (GWAS) of blood pressure (BP) traits in 12,278 participants from the Hispanics Community Health Study/Study of Latinos (HCHS/SOL). In the discovery phase we identified eight previously unreported BP loci. In the replication stage, we tested these loci in the 1982 Pelotas Birth Cohort Study of admixed Southern Brazilians, the COGENT-BP study of African descent, women of European descent from the Women Health Initiative (WHI), and a sample of European descent from the UK Biobank. No loci met the Bonferroni-adjusted level of statistical significance (0.0024). Two loci had marginal evidence of replication: rs78701042 (NGF) with diastolic BP (P = 0.008 in the 1982 Pelotas Birth Cohort Study), and rs7315692 (SLC5A8) with systolic BP (P = 0.007 in European ancestry replication). We investigated whether previously reported loci associated with BP in studies of European, African, and Asian ancestry generalize to Hispanics/Latinos. Overall, 26% of the known associations in studies of individuals of European and Chinese ancestries generalized, while only a single association previously discovered in a people of African descent generalized.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Hypertension prevalence varies between ethnic groups, possibly due to differences in genetic, environmental, and cultural determinants. Hispanic/Latino Americans are a diverse and understudied population. We performed a genome-wide association study (GWAS) of blood pressure (BP) traits in 12,278 participants from the Hispanics Community Health Study/Study of Latinos (HCHS/SOL). In the discovery phase we identified eight previously unreported BP loci. In the replication stage, we tested these loci in the 1982 Pelotas Birth Cohort Study of admixed Southern Brazilians, the COGENT-BP study of African descent, women of European descent from the Women Health Initiative (WHI), and a sample of European descent from the UK Biobank. No loci met the Bonferroni-adjusted level of statistical significance (0.0024). Two loci had marginal evidence of replication: rs78701042 (NGF) with diastolic BP (P = 0.008 in the 1982 Pelotas Birth Cohort Study), and rs7315692 (SLC5A8) with systolic BP (P = 0.007 in European ancestry replication). We investigated whether previously reported loci associated with BP in studies of European, African, and Asian ancestry generalize to Hispanics/Latinos. Overall, 26% of the known associations in studies of individuals of European and Chinese ancestries generalized, while only a single association previously discovered in a people of African descent generalized. |
Jian Yang; Jian Zeng; Michael E Goddard and Naomi R Wray ; Peter M Visscher Concepts, estimation and interpretation of SNP-based heritability Journal Article Nature Genetics, 2017. @article{Yang2017, title = {Concepts, estimation and interpretation of SNP-based heritability}, author = { Jian Yang and Jian Zeng and Michael E Goddard and Naomi R Wray and Peter M Visscher}, url = {http://www.nature.com/ng/journal/v49/n9/full/ng.3941.html?foxtrotcallback=true}, year = {2017}, date = {2017-08-30}, journal = {Nature Genetics}, abstract = {Narrow-sense heritability (h2) is an important genetic parameter that quantifies the proportion of phenotypic variance in a trait attributable to the additive genetic variation generated by all causal variants. Estimation of h2 previously relied on closely related individuals, but recent developments allow estimation of the variance explained by all SNPs used in a genome-wide association study (GWAS) in conventionally unrelated individuals, that is, the SNP-based heritability ( ). In this Perspective, we discuss recently developed methods to estimate for a complex trait (and genetic correlation between traits) using individual-level or summary GWAS data. We discuss issues that could influence the accuracy of , definitions, assumptions and interpretations of the models, and pitfalls of misusing the methods and misinterpreting the models and results.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Narrow-sense heritability (h2) is an important genetic parameter that quantifies the proportion of phenotypic variance in a trait attributable to the additive genetic variation generated by all causal variants. Estimation of h2 previously relied on closely related individuals, but recent developments allow estimation of the variance explained by all SNPs used in a genome-wide association study (GWAS) in conventionally unrelated individuals, that is, the SNP-based heritability ( ). In this Perspective, we discuss recently developed methods to estimate for a complex trait (and genetic correlation between traits) using individual-level or summary GWAS data. We discuss issues that could influence the accuracy of , definitions, assumptions and interpretations of the models, and pitfalls of misusing the methods and misinterpreting the models and results. |
W Gan; RJ Clarke; A Mahajan; B Kulohoma; H, Kitajima; NR Robertson; NW Rayner; RG Walters; MV Holmes; Z Chen; M McCarthy Bone mineral density and risk of type 2 diabetes and coronary heart disease: A Mendelian randomization study. Journal Article Wellcome open research, 2017. @article{Gan2017, title = {Bone mineral density and risk of type 2 diabetes and coronary heart disease: A Mendelian randomization study.}, author = {W Gan and RJ Clarke and A Mahajan and B Kulohoma and H, Kitajima and NR Robertson and NW Rayner and RG Walters and MV Holmes and Z Chen and M McCarthy }, url = {https://www.ncbi.nlm.nih.gov/pubmed/28989980}, year = {2017}, date = {2017-08-22}, journal = {Wellcome open research}, abstract = {Background: Observational studies have demonstrated that increased bone mineral density is associated with a higher risk of type 2 diabetes (T2D), but the relationship with risk of coronary heart disease (CHD) is less clear. Moreover, substantial uncertainty remains about the causal relevance of increased bone mineral density for T2D and CHD, which can be assessed by Mendelian randomisation studies. Methods: We identified 235 independent single nucleotide polymorphisms (SNPs) associated at p<5×10 -8 with estimated heel bone mineral density (eBMD) in 116,501 individuals from the UK Biobank study, accounting for 13.9% of eBMD variance. For each eBMD-associated SNP, we extracted effect estimates from the largest available GWAS studies for T2D (DIAGRAM: n=26,676 T2D cases and 132,532 controls) and CHD (CARDIoGRAMplusC4D: n=60,801 CHD cases and 123,504 controls). A two-sample design using several Mendelian randomization approaches was used to investigate the causal relevance of eBMD for risk of T2D and CHD. In addition, we explored the relationship of eBMD, instrumented by the 235 SNPs, on 12 cardiovascular and metabolic risk factors. Finally, we conducted Mendelian randomization analysis in the reverse direction to investigate reverse causality. Results: Each one standard deviation increase in genetically instrumented eBMD (equivalent to 0.14 g/cm 2) was associated with an 8% higher risk of T2D (odds ratio [OR] 1.08; 95% confidence interval [CI]: 1.02 to 1.14; p=0.012) and 5% higher risk of CHD (OR 1.05; 95%CI: 1.00 to 1.10; p=0.034). Consistent results were obtained in sensitivity analyses using several different Mendelian randomization approaches. Equivalent increases in eBMD were also associated with lower plasma levels of HDL-cholesterol and increased insulin resistance. Mendelian randomization in the reverse direction using 94 T2D SNPs or 52 CHD SNPs showed no evidence of reverse causality with eBMD. Conclusions: These findings suggest a causal relationship between elevated bone mineral density with risks of both T2D and CHD.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Background: Observational studies have demonstrated that increased bone mineral density is associated with a higher risk of type 2 diabetes (T2D), but the relationship with risk of coronary heart disease (CHD) is less clear. Moreover, substantial uncertainty remains about the causal relevance of increased bone mineral density for T2D and CHD, which can be assessed by Mendelian randomisation studies. Methods: We identified 235 independent single nucleotide polymorphisms (SNPs) associated at p<5×10 -8 with estimated heel bone mineral density (eBMD) in 116,501 individuals from the UK Biobank study, accounting for 13.9% of eBMD variance. For each eBMD-associated SNP, we extracted effect estimates from the largest available GWAS studies for T2D (DIAGRAM: n=26,676 T2D cases and 132,532 controls) and CHD (CARDIoGRAMplusC4D: n=60,801 CHD cases and 123,504 controls). A two-sample design using several Mendelian randomization approaches was used to investigate the causal relevance of eBMD for risk of T2D and CHD. In addition, we explored the relationship of eBMD, instrumented by the 235 SNPs, on 12 cardiovascular and metabolic risk factors. Finally, we conducted Mendelian randomization analysis in the reverse direction to investigate reverse causality. Results: Each one standard deviation increase in genetically instrumented eBMD (equivalent to 0.14 g/cm 2) was associated with an 8% higher risk of T2D (odds ratio [OR] 1.08; 95% confidence interval [CI]: 1.02 to 1.14; p=0.012) and 5% higher risk of CHD (OR 1.05; 95%CI: 1.00 to 1.10; p=0.034). Consistent results were obtained in sensitivity analyses using several different Mendelian randomization approaches. Equivalent increases in eBMD were also associated with lower plasma levels of HDL-cholesterol and increased insulin resistance. Mendelian randomization in the reverse direction using 94 T2D SNPs or 52 CHD SNPs showed no evidence of reverse causality with eBMD. Conclusions: These findings suggest a causal relationship between elevated bone mineral density with risks of both T2D and CHD. |
EM Wigmore; TK Clarke; DM Howard; MJ Adams LS Hall Y Zeng J Gibson; G Davies; AM Fernandez-Pujals; PA Thomson; C Hayward; BH Smith; LJ Hocking; S Padmanabhan; IJ Deary; DJ Porteous; KK Nicodemus; AM McIntosh Translational Psychiatry, 2017. @article{Wigmore2017, title = {Do regional brain volumes and major depressive disorder share genetic architecture? A study of Generation Scotland (n=19 762), UK Biobank (n=24 048) and the English Longitudinal Study of Ageing (n=5766).}, author = {EM Wigmore and TK Clarke and DM Howard and MJ Adams LS Hall Y Zeng J Gibson and G Davies and AM Fernandez-Pujals and PA Thomson and C Hayward and BH Smith and LJ Hocking and S Padmanabhan and IJ Deary and DJ Porteous and KK Nicodemus and AM McIntosh}, url = {https://www.ncbi.nlm.nih.gov/pubmed/28809859}, year = {2017}, date = {2017-08-15}, journal = {Translational Psychiatry}, abstract = {Major depressive disorder (MDD) is a heritable and highly debilitating condition. It is commonly associated with subcortical volumetric abnormalities, the most replicated of these being reduced hippocampal volume. Using the most recent published data from Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) consortium's genome-wide association study of regional brain volume, we sought to test whether there is shared genetic architecture between seven subcortical brain volumes and intracranial volume (ICV) and MDD. We explored this using linkage disequilibrium score regression, polygenic risk scoring (PRS) techniques, Mendelian randomisation (MR) analysis and BUHMBOX. Utilising summary statistics from ENIGMA and Psychiatric Genomics Consortium, we demonstrated that hippocampal volume was positively genetically correlated with MDD (rG=0.46, P=0.02), although this did not survive multiple comparison testing. None of the other six brain regions studied were genetically correlated and amygdala volume heritability was too low for analysis. Using PRS analysis, no regional volumetric PRS demonstrated a significant association with MDD or recurrent MDD. MR analysis in hippocampal volume and MDD identified no causal association, however, BUHMBOX analysis identified genetic subgrouping in GS:SFHS MDD cases only (P=0.00281). In this study, we provide some evidence that hippocampal volume and MDD may share genetic architecture in a subgroup of individuals, albeit the genetic correlation did not survive multiple testing correction and genetic subgroup heterogeneity was not replicated. In contrast, we found no evidence to support a shared genetic architecture between MDD and other regional subcortical volumes or ICV.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Major depressive disorder (MDD) is a heritable and highly debilitating condition. It is commonly associated with subcortical volumetric abnormalities, the most replicated of these being reduced hippocampal volume. Using the most recent published data from Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) consortium's genome-wide association study of regional brain volume, we sought to test whether there is shared genetic architecture between seven subcortical brain volumes and intracranial volume (ICV) and MDD. We explored this using linkage disequilibrium score regression, polygenic risk scoring (PRS) techniques, Mendelian randomisation (MR) analysis and BUHMBOX. Utilising summary statistics from ENIGMA and Psychiatric Genomics Consortium, we demonstrated that hippocampal volume was positively genetically correlated with MDD (rG=0.46, P=0.02), although this did not survive multiple comparison testing. None of the other six brain regions studied were genetically correlated and amygdala volume heritability was too low for analysis. Using PRS analysis, no regional volumetric PRS demonstrated a significant association with MDD or recurrent MDD. MR analysis in hippocampal volume and MDD identified no causal association, however, BUHMBOX analysis identified genetic subgrouping in GS:SFHS MDD cases only (P=0.00281). In this study, we provide some evidence that hippocampal volume and MDD may share genetic architecture in a subgroup of individuals, albeit the genetic correlation did not survive multiple testing correction and genetic subgroup heterogeneity was not replicated. In contrast, we found no evidence to support a shared genetic architecture between MDD and other regional subcortical volumes or ICV. |
Blanka Stiburkova; Katerina Pavelcova; Jakub Zavada; Lenka Petru; Pavel Simek; Pavel Cepek; Marketa Pavlikova; Hirotaka Matsuo; Tony R. Merriman; Karel Pavelka Functional non-synonymous variants of ABCG2 and gout risk Journal Article Rheumatology , 2017. @article{Stiburkova2017, title = {Functional non-synonymous variants of ABCG2 and gout risk }, author = { Blanka Stiburkova and Katerina Pavelcova and Jakub Zavada and Lenka Petru and Pavel Simek and Pavel Cepek and Marketa Pavlikova and Hirotaka Matsuo and Tony R. Merriman and Karel Pavelka}, url = {https://academic.oup.com/rheumatology/article-abstract/doi/10.1093/rheumatology/kex295/4082557/Functional-non-synonymous-variants-of-ABCG2-and?redirectedFrom=fulltext}, year = {2017}, date = {2017-08-14}, journal = {Rheumatology }, abstract = {Objectives. Common dysfunctional variants of ATP binding cassette subfamily G member 2 (Junior blood group) (ABCG2), a high-capacity urate transporter gene, that result in decreased urate excretion are major causes of hyperuricemia and gout. In the present study, our objective was to determine the frequency and effect on gout of common and rare non-synonymous and other functional allelic variants in the ABCG2 gene. Methods. The main cohort recruited from the Czech Republic consisted of 145 gout patients; 115 normouricaemic controls were used for comparison. We amplified, directly sequenced and analysed 15 ABCG2 exons. The associations between genetic variants and clinical phenotype were analysed using the t-test, Fisher’s exact test and a logistic and linear regression approach. Data from a New Zealand Polynesian sample set and the UK Biobank were included for the p.V12M analysis. Results. In the ABCG2 gene, 18 intronic (one dysfunctional splicing) and 11 exonic variants were detected: 9 were non-synonymous (2 common, 7 rare including 1 novel), namely p.V12M, p.Q141K, p.R147W, p.T153M, p.F373C, p.T434M, p.S476P, p.D620N and p.K360del. The p.Q141K (rs2231142) variant had a significantly higher minor allele frequency (0.23) in the gout patients compared with the European-origin population (0.09) and was significantly more common among gout patients than among normouricaemic controls (odds ratio = 3.26, P < 0.0001). Patients with non-synonymous allelic variants had an earlier onset of gout (42 vs 48 years, P = 0.0143) and a greater likelihood of a familial history of gout (41% vs 27%, odds ratio = 1.96, P = 0.053). In a meta-analysis p.V12M exerted a protective effect from gout (P < 0.0001). Conclusion. Genetic variants of ABCG2, common and rare, increased the risk of gout. Non-synonymous allelic variants of ABCG2 had a significant effect on earlier onset of gout and the presence of a familial gout history. ABCG2 should thus be considered a common and significant risk factor for gout.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Objectives. Common dysfunctional variants of ATP binding cassette subfamily G member 2 (Junior blood group) (ABCG2), a high-capacity urate transporter gene, that result in decreased urate excretion are major causes of hyperuricemia and gout. In the present study, our objective was to determine the frequency and effect on gout of common and rare non-synonymous and other functional allelic variants in the ABCG2 gene. Methods. The main cohort recruited from the Czech Republic consisted of 145 gout patients; 115 normouricaemic controls were used for comparison. We amplified, directly sequenced and analysed 15 ABCG2 exons. The associations between genetic variants and clinical phenotype were analysed using the t-test, Fisher’s exact test and a logistic and linear regression approach. Data from a New Zealand Polynesian sample set and the UK Biobank were included for the p.V12M analysis. Results. In the ABCG2 gene, 18 intronic (one dysfunctional splicing) and 11 exonic variants were detected: 9 were non-synonymous (2 common, 7 rare including 1 novel), namely p.V12M, p.Q141K, p.R147W, p.T153M, p.F373C, p.T434M, p.S476P, p.D620N and p.K360del. The p.Q141K (rs2231142) variant had a significantly higher minor allele frequency (0.23) in the gout patients compared with the European-origin population (0.09) and was significantly more common among gout patients than among normouricaemic controls (odds ratio = 3.26, P < 0.0001). Patients with non-synonymous allelic variants had an earlier onset of gout (42 vs 48 years, P = 0.0143) and a greater likelihood of a familial history of gout (41% vs 27%, odds ratio = 1.96, P = 0.053). In a meta-analysis p.V12M exerted a protective effect from gout (P < 0.0001). Conclusion. Genetic variants of ABCG2, common and rare, increased the risk of gout. Non-synonymous allelic variants of ABCG2 had a significant effect on earlier onset of gout and the presence of a familial gout history. ABCG2 should thus be considered a common and significant risk factor for gout. |
AE Taylor; G Davey Smith; MR Munafò Associations of coffee genetic risk scores with consumption of coffee, tea and other beverages in the UK Biobank Journal Article Addiction, 2017. @article{Taylor2017, title = {Associations of coffee genetic risk scores with consumption of coffee, tea and other beverages in the UK Biobank}, author = {AE Taylor and G Davey Smith and MR Munafò}, url = {https://www.ncbi.nlm.nih.gov/pubmed/28793181}, year = {2017}, date = {2017-08-09}, journal = {Addiction}, abstract = {AIMS: To evaluate the utility of coffee-related genetic variants as proxies for coffee consumption in Mendelian randomisation studies, by examining their association with non-alcoholic beverage consumption (including subtypes of coffee and tea) and a range of sociodemographic and lifestyle factors. DESIGN: Observational study of the association of genetic risk scores for coffee consumption with different types of non-alcoholic beverage consumption. SETTING: UK general population PARTICIPANTS: Individuals of European ancestry aged 40-70 years from the UK Biobank between 2006 and 2010 (N = 114,316). MEASUREMENTS: Genetic risk scores were constructed using two, four and eight independent single nucleotide polymorphisms (SNPs) identified in genomewide association studies (GWAS) of coffee consumption. Drinks were self-reported in a baseline questionnaire (all participants) and in detailed 24 dietary recall questionnaires in a subset (N = 48,692). FINDINGS: Genetic risk scores explained up to 0.38%, 0.19% and 0.76% of the variance in coffee, tea and combined coffee and tea consumption respectively. Genetic risk scores demonstrated positive associations with both caffeinated and decaffeinated coffee and tea consumption, and with most subtypes of coffee consumption, but only with standard tea consumption. There was no clear evidence for positive associations with most other non-alcoholic beverages, but higher genetic risk for coffee consumption was associated with lower daily water consumption. The genetic risk scores were associated with increased alcohol consumption, but not consistently with other sociodemographic and lifestyle factors. CONCLUSIONS: Coffee-related genetic risk scores could be used as instruments for combined coffee and tea consumption in Mendelian randomisation studies. However, associations observed with alcohol consumption require further investigation to determine whether these are due to causal effects of coffee and tea consumption, or biological pleiotropy.}, keywords = {}, pubstate = {published}, tppubtype = {article} } AIMS: To evaluate the utility of coffee-related genetic variants as proxies for coffee consumption in Mendelian randomisation studies, by examining their association with non-alcoholic beverage consumption (including subtypes of coffee and tea) and a range of sociodemographic and lifestyle factors. DESIGN: Observational study of the association of genetic risk scores for coffee consumption with different types of non-alcoholic beverage consumption. SETTING: UK general population PARTICIPANTS: Individuals of European ancestry aged 40-70 years from the UK Biobank between 2006 and 2010 (N = 114,316). MEASUREMENTS: Genetic risk scores were constructed using two, four and eight independent single nucleotide polymorphisms (SNPs) identified in genomewide association studies (GWAS) of coffee consumption. Drinks were self-reported in a baseline questionnaire (all participants) and in detailed 24 dietary recall questionnaires in a subset (N = 48,692). FINDINGS: Genetic risk scores explained up to 0.38%, 0.19% and 0.76% of the variance in coffee, tea and combined coffee and tea consumption respectively. Genetic risk scores demonstrated positive associations with both caffeinated and decaffeinated coffee and tea consumption, and with most subtypes of coffee consumption, but only with standard tea consumption. There was no clear evidence for positive associations with most other non-alcoholic beverages, but higher genetic risk for coffee consumption was associated with lower daily water consumption. The genetic risk scores were associated with increased alcohol consumption, but not consistently with other sociodemographic and lifestyle factors. CONCLUSIONS: Coffee-related genetic risk scores could be used as instruments for combined coffee and tea consumption in Mendelian randomisation studies. However, associations observed with alcohol consumption require further investigation to determine whether these are due to causal effects of coffee and tea consumption, or biological pleiotropy. |
MR Robinson; G English; G Moser; LR Lloyd-Jones; MA Triplett; Z Zhu; IM Nolte; JV van Vliet-Ostaptchouk; H Snieder; LifeLines Cohort Study; T Esko; L Milani; R Mägi; A Metspalu; PKE Magnusson; NL Pedersen; E Ingelsson; M Johannesson; J Yang; D Cesarini; PM Visscher Genotype-covariate interaction effects and the heritability of adult body mass index. Journal Article Nature Genetics, 2017. @article{Robinson2017, title = {Genotype-covariate interaction effects and the heritability of adult body mass index.}, author = {MR Robinson and G English and G Moser and LR Lloyd-Jones and MA Triplett and Z Zhu and IM Nolte and JV van Vliet-Ostaptchouk and H Snieder and LifeLines Cohort Study and T Esko and L Milani and R Mägi and A Metspalu and PKE Magnusson and NL Pedersen and E Ingelsson and M Johannesson and J Yang and D Cesarini and PM Visscher }, url = {https://www.ncbi.nlm.nih.gov/pubmed/28692066}, year = {2017}, date = {2017-08-08}, journal = {Nature Genetics}, abstract = {Obesity is a worldwide epidemic, with major health and economic costs. Here we estimate heritability for body mass index (BMI) in 172,000 sibling pairs and 150,832 unrelated individuals and explore the contribution of genotype-covariate interaction effects at common SNP loci. We find evidence for genotype-age interaction (likelihood ratio test (LRT) = 73.58, degrees of freedom (df) = 1, P = 4.83 × 10-18), which contributed 8.1% (1.4% s.e.) to BMI variation. Across eight self-reported lifestyle factors, including diet and exercise, we find genotype-environment interaction only for smoking behavior (LRT = 19.70, P = 5.03 × 10-5 and LRT = 30.80, P = 1.42 × 10-8), which contributed 4.0% (0.8% s.e.) to BMI variation. Bayesian association analysis suggests that BMI is highly polygenic, with 75% of the SNP heritability attributable to loci that each explain <0.01% of the phenotypic variance. Our findings imply that substantially larger sample sizes across ages and lifestyles are required to understand the full genetic architecture of BMI.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Obesity is a worldwide epidemic, with major health and economic costs. Here we estimate heritability for body mass index (BMI) in 172,000 sibling pairs and 150,832 unrelated individuals and explore the contribution of genotype-covariate interaction effects at common SNP loci. We find evidence for genotype-age interaction (likelihood ratio test (LRT) = 73.58, degrees of freedom (df) = 1, P = 4.83 × 10-18), which contributed 8.1% (1.4% s.e.) to BMI variation. Across eight self-reported lifestyle factors, including diet and exercise, we find genotype-environment interaction only for smoking behavior (LRT = 19.70, P = 5.03 × 10-5 and LRT = 30.80, P = 1.42 × 10-8), which contributed 4.0% (0.8% s.e.) to BMI variation. Bayesian association analysis suggests that BMI is highly polygenic, with 75% of the SNP heritability attributable to loci that each explain <0.01% of the phenotypic variance. Our findings imply that substantially larger sample sizes across ages and lifestyles are required to understand the full genetic architecture of BMI. |
Mousas Abdou; Ntritsos Georgios; Chen Ming-Huei; Song Ci; Huffman Jennifer E; Tzoulaki Ioanna; Elliott Paul; Psaty Bruce M; Auer Paul L; Johnson Andrew D; Evangelou Evangelos; Lettre Guillaume; Reiner Alexander P. Rare coding variants pinpoint genes that control human hematological traits. Journal Article PLOS Genetics, 2017. @article{Abdou2017, title = {Rare coding variants pinpoint genes that control human hematological traits.}, author = {Mousas Abdou and Ntritsos Georgios and Chen Ming-Huei and Song Ci and Huffman Jennifer E and Tzoulaki Ioanna and Elliott Paul and Psaty Bruce M and Auer Paul L and Johnson Andrew D and Evangelou Evangelos and Lettre Guillaume and Reiner Alexander P.}, url = {http://web.a.ebscohost.com/abstract?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=15537390&AN=124496230&h=HVR1dor7Rv3ekbZh2AGUQSNhjILET8MTqXpAyc0uFjYhxSX3100F5eo5VapIIXx18h%2fjWVnd4HzNw2d6IIBivA%3d%3d&crl=c&resultNs=AdminWebAuth&resultLocal=ErrCrlNotAuth&crlhashurl=login.aspx%3fdirect%3dtrue%26profile%3dehost%26scope%3dsite%26authtype%3dcrawler%26jrnl%3d15537390%26AN%3d124496230}, year = {2017}, date = {2017-08-07}, journal = {PLOS Genetics}, abstract = {The identification of rare coding or splice site variants remains the most straightforward strategy to link genes with human phenotypes. Here, we analyzed the association between 137,086 rare (minor allele frequency (MAF) <1%) coding or splice site variants and 15 hematological traits in up to 308,572 participants. We found 56 such rare coding or splice site variants at P<5x10^{-8}, including 31 that are associated with a blood-cell phenotype for the first time. All but one of these 31 new independent variants map to loci previously implicated in hematopoiesis by genome-wide association studies (GWAS). This includes a rare splice acceptor variant (rs146597587, MAF = 0.5%) in interleukin 33 (IL33) associated with reduced eosinophil count (P = 2.4x10^{-23}), and lower risk of asthma (P = 2.6x10^{-7}, odds ratio [95% confidence interval] = 0.56 [0.45–0.70]) and allergic rhinitis (P = 4.2x10^{-4}, odds ratio = 0.55 [0.39–0.76]). The single new locus identified in our study is defined by a rare p.Arg172Gly missense variant (rs145535174, MAF = 0.05%) in plasminogen (PLG) associated with increased platelet count (P = 6.8x10^{-9}), and decreased D-dimer concentration (P = 0.018) and platelet reactivity (P<0.03). Finally, our results indicate that searching for rare coding or splice site variants in very large sample sizes can help prioritize causal genes at many GWAS loci associated with complex human diseases and traits.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The identification of rare coding or splice site variants remains the most straightforward strategy to link genes with human phenotypes. Here, we analyzed the association between 137,086 rare (minor allele frequency (MAF) <1%) coding or splice site variants and 15 hematological traits in up to 308,572 participants. We found 56 such rare coding or splice site variants at P<5x10-8, including 31 that are associated with a blood-cell phenotype for the first time. All but one of these 31 new independent variants map to loci previously implicated in hematopoiesis by genome-wide association studies (GWAS). This includes a rare splice acceptor variant (rs146597587, MAF = 0.5%) in interleukin 33 (IL33) associated with reduced eosinophil count (P = 2.4x10-23), and lower risk of asthma (P = 2.6x10-7, odds ratio [95% confidence interval] = 0.56 [0.45–0.70]) and allergic rhinitis (P = 4.2x10-4, odds ratio = 0.55 [0.39–0.76]). The single new locus identified in our study is defined by a rare p.Arg172Gly missense variant (rs145535174, MAF = 0.05%) in plasminogen (PLG) associated with increased platelet count (P = 6.8x10-9), and decreased D-dimer concentration (P = 0.018) and platelet reactivity (P<0.03). Finally, our results indicate that searching for rare coding or splice site variants in very large sample sizes can help prioritize causal genes at many GWAS loci associated with complex human diseases and traits. |
Adrian Cortes; Calliope A Dendrou; Allan Motyer; Luke Jostins; Damjan Vukcevic; Alexander Dilthey and Peter Donnelly; Stephen Leslie; Lars Fugger; Gil McVean Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank Journal Article Nature Genetics, 2017. @article{Cortes2017, title = {Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank}, author = {Adrian Cortes and Calliope A Dendrou and Allan Motyer and Luke Jostins and Damjan Vukcevic and Alexander Dilthey and Peter Donnelly and Stephen Leslie and Lars Fugger and Gil McVean}, url = {http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.3926.html?foxtrotcallback=true}, year = {2017}, date = {2017-07-31}, journal = {Nature Genetics}, abstract = {Genetic discovery from the multitude of phenotypes extractable from routine healthcare data can transform understanding of the human phenome and accelerate progress toward precision medicine. However, a critical question when analyzing high-dimensional and heterogeneous data is how best to interrogate increasingly specific subphenotypes while retaining statistical power to detect genetic associations. Here we develop and employ a new Bayesian analysis framework that exploits the hierarchical structure of diagnosis classifications to analyze genetic variants against UK Biobank disease phenotypes derived from self-reporting and hospital episode statistics. Our method displays a more than 20% increase in power to detect genetic effects over other approaches and identifies new associations between classical human leukocyte antigen (HLA) alleles and common immune-mediated diseases (IMDs). By applying the approach to genetic risk scores (GRSs), we show the extent of genetic sharing among IMDs and expose differences in disease perception or diagnosis with potential clinical implications.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Genetic discovery from the multitude of phenotypes extractable from routine healthcare data can transform understanding of the human phenome and accelerate progress toward precision medicine. However, a critical question when analyzing high-dimensional and heterogeneous data is how best to interrogate increasingly specific subphenotypes while retaining statistical power to detect genetic associations. Here we develop and employ a new Bayesian analysis framework that exploits the hierarchical structure of diagnosis classifications to analyze genetic variants against UK Biobank disease phenotypes derived from self-reporting and hospital episode statistics. Our method displays a more than 20% increase in power to detect genetic effects over other approaches and identifies new associations between classical human leukocyte antigen (HLA) alleles and common immune-mediated diseases (IMDs). By applying the approach to genetic risk scores (GRSs), we show the extent of genetic sharing among IMDs and expose differences in disease perception or diagnosis with potential clinical implications. |
AF McDaid; PK Joshi; E Porcu; A Komljenovic H Li; V Sorrentino; M Litovchenko RP Bevers S Rüeger; A Reymond; M Bochud; B Deplancke; RW Williams; M Robinson-Rechavi; F Paccaud V Rousson; J Auwerx; JF Wilson; Z Kutalik Bayesian association scan reveals loci associated with human lifespan and linked biomarkers. Journal Article Nature Communications, 2017. @article{McDaid2017, title = {Bayesian association scan reveals loci associated with human lifespan and linked biomarkers.}, author = {AF McDaid and PK Joshi and E Porcu and A Komljenovic H Li and V Sorrentino and M Litovchenko RP Bevers S Rüeger and A Reymond and M Bochud and B Deplancke and RW Williams and M Robinson-Rechavi and F Paccaud V Rousson and J Auwerx and JF Wilson and Z Kutalik}, url = {https://www.ncbi.nlm.nih.gov/pubmed/28748955}, year = {2017}, date = {2017-07-27}, journal = {Nature Communications}, abstract = {The enormous variation in human lifespan is in part due to a myriad of sequence variants, only a few of which have been revealed to date. Since many life-shortening events are related to diseases, we developed a Mendelian randomization-based method combining 58 disease-related GWA studies to derive longevity priors for all HapMap SNPs. A Bayesian association scan, informed by these priors, for parental age of death in the UK Biobank study (n=116,279) revealed 16 independent SNPs with significant Bayes factor at a 5% false discovery rate (FDR). Eleven of them replicate (5% FDR) in five independent longevity studies combined; all but three are depleted of the life-shortening alleles in older Biobank participants. Further analysis revealed that brain expression levels of nearby genes (RBM6, SULT1A1 and CHRNA5) might be causally implicated in longevity. Gene expression and caloric restriction experiments in model organisms confirm the conserved role for RBM6 and SULT1A1 in modulating lifespan.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The enormous variation in human lifespan is in part due to a myriad of sequence variants, only a few of which have been revealed to date. Since many life-shortening events are related to diseases, we developed a Mendelian randomization-based method combining 58 disease-related GWA studies to derive longevity priors for all HapMap SNPs. A Bayesian association scan, informed by these priors, for parental age of death in the UK Biobank study (n=116,279) revealed 16 independent SNPs with significant Bayes factor at a 5% false discovery rate (FDR). Eleven of them replicate (5% FDR) in five independent longevity studies combined; all but three are depleted of the life-shortening alleles in older Biobank participants. Further analysis revealed that brain expression levels of nearby genes (RBM6, SULT1A1 and CHRNA5) might be causally implicated in longevity. Gene expression and caloric restriction experiments in model organisms confirm the conserved role for RBM6 and SULT1A1 in modulating lifespan. |
L Yengo; Z Zhu; NR Wray; BS Weir; J Yang; MR Robinson; PM Visscher Detection and quantification of inbreeding depression for complex traits from SNP data. Journal Article Proceedings of the National Academy of Sciences of USA, 2017. @article{Yengo2017, title = {Detection and quantification of inbreeding depression for complex traits from SNP data.}, author = {L Yengo and Z Zhu and NR Wray and BS Weir and J Yang and MR Robinson and PM Visscher}, url = {https://www.ncbi.nlm.nih.gov/pubmed/28747529}, year = {2017}, date = {2017-07-26}, journal = {Proceedings of the National Academy of Sciences of USA}, abstract = {Quantifying the effects of inbreeding is critical to characterizing the genetic architecture of complex traits. This study highlights through theory and simulations the strengths and shortcomings of three SNP-based inbreeding measures commonly used to estimate inbreeding depression (ID). We demonstrate that heterogeneity in linkage disequilibrium (LD) between causal variants and SNPs biases ID estimates, and we develop an approach to correct this bias using LD and minor allele frequency stratified inference (LDMS). We quantified ID in 25 traits measured in ∼140,000 participants of the UK Biobank, using LDMS, and confirmed previously published ID for 4 traits. We find unique evidence of ID for handgrip strength, waist/hip ratio, and visual and auditory acuity (ID between -2.3 and -5.2 phenotypic SDs for complete inbreeding; [Formula: see text]). Our results illustrate that a careful choice of the measure of inbreeding combined with LDMS stratification improves both detection and quantification of ID using SNP data.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Quantifying the effects of inbreeding is critical to characterizing the genetic architecture of complex traits. This study highlights through theory and simulations the strengths and shortcomings of three SNP-based inbreeding measures commonly used to estimate inbreeding depression (ID). We demonstrate that heterogeneity in linkage disequilibrium (LD) between causal variants and SNPs biases ID estimates, and we develop an approach to correct this bias using LD and minor allele frequency stratified inference (LDMS). We quantified ID in 25 traits measured in ∼140,000 participants of the UK Biobank, using LDMS, and confirmed previously published ID for 4 traits. We find unique evidence of ID for handgrip strength, waist/hip ratio, and visual and auditory acuity (ID between -2.3 and -5.2 phenotypic SDs for complete inbreeding; [Formula: see text]). Our results illustrate that a careful choice of the measure of inbreeding combined with LDMS stratification improves both detection and quantification of ID using SNP data. |
Olav B. Smeland; Oleksandr Frei; Karolina Kauppi JAMA Psychiatry, 2017. @article{Smeland2017, title = {Identification of Genetic Loci Jointly Influencing Schizophrenia Risk and the Cognitive Traits of Verbal-Numerical Reasoning, Reaction Time, and General Cognitive Function}, author = {Olav B. Smeland and Oleksandr Frei and Karolina Kauppi}, url = {http://jamanetwork.com/journals/jamapsychiatry/article-abstract/2645497}, year = {2017}, date = {2017-07-26}, journal = {JAMA Psychiatry}, abstract = {Importance Schizophrenia is associated with widespread cognitive impairments. Although cognitive deficits are one of the factors most strongly associated with functional outcome in schizophrenia, current treatment strategies largely fail to ameliorate these impairments. To develop more efficient treatment strategies in patients with schizophrenia, a better understanding of the pathogenesis of these cognitive deficits is needed. Accumulating evidence indicates that genetic risk of schizophrenia may contribute to cognitive dysfunction. Objective To identify genomic regions jointly influencing schizophrenia and the cognitive domains of reaction time and verbal-numerical reasoning, as well as general cognitive function, a phenotype that captures the shared variation in performance across cognitive domains. Design, Setting, and Participants Combining data from genome-wide association studies from multiple phenotypes using conditional false discovery rate analysis provides increased power to discover genetic variants and could elucidate shared molecular genetic mechanisms. Data from the following genome-wide association studies, published from July 24, 2014, to January 17, 2017, were combined: schizophrenia in the Psychiatric Genomics Consortium cohort (n = 79 757 [cases, 34 486; controls, 45 271]); verbal-numerical reasoning (n = 36 035) and reaction time (n = 111 483) in the UK Biobank cohort; and general cognitive function in CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) (n = 53 949) and COGENT (Cognitive Genomics Consortium) (n = 27 888). Main Outcomes and Measures Genetic loci identified by conditional false discovery rate analysis. Brain messenger RNA expression and brain expression quantitative trait locus functionality were determined. Results Among the participants in the genome-wide association studies, 21 loci jointly influencing schizophrenia and cognitive traits were identified: 2 loci shared between schizophrenia and verbal-numerical reasoning, 6 loci shared between schizophrenia and reaction time, and 14 loci shared between schizophrenia and general cognitive function. One locus was shared between schizophrenia and 2 cognitive traits and represented the strongest shared signal detected (nearest gene TCF20; chromosome 22q13.3), and was shared between schizophrenia (z score, 5.01; P = 5.53 × 10−7), general cognitive function (z score, –4.43; P = 9.42 × 10−6), and verbal-numerical reasoning (z score, –5.43; P = 5.64 × 10−8). For 18 loci, schizophrenia risk alleles were associated with poorer cognitive performance. The implicated genes are expressed in the developmental and adult human brain. Replicable expression quantitative trait locus functionality was identified for 4 loci in the adult human brain. Conclusions and Relevance The discovered loci improve the understanding of the common genetic basis underlying schizophrenia and cognitive function, suggesting novel molecular genetic mechanisms.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Importance Schizophrenia is associated with widespread cognitive impairments. Although cognitive deficits are one of the factors most strongly associated with functional outcome in schizophrenia, current treatment strategies largely fail to ameliorate these impairments. To develop more efficient treatment strategies in patients with schizophrenia, a better understanding of the pathogenesis of these cognitive deficits is needed. Accumulating evidence indicates that genetic risk of schizophrenia may contribute to cognitive dysfunction. Objective To identify genomic regions jointly influencing schizophrenia and the cognitive domains of reaction time and verbal-numerical reasoning, as well as general cognitive function, a phenotype that captures the shared variation in performance across cognitive domains. Design, Setting, and Participants Combining data from genome-wide association studies from multiple phenotypes using conditional false discovery rate analysis provides increased power to discover genetic variants and could elucidate shared molecular genetic mechanisms. Data from the following genome-wide association studies, published from July 24, 2014, to January 17, 2017, were combined: schizophrenia in the Psychiatric Genomics Consortium cohort (n = 79 757 [cases, 34 486; controls, 45 271]); verbal-numerical reasoning (n = 36 035) and reaction time (n = 111 483) in the UK Biobank cohort; and general cognitive function in CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) (n = 53 949) and COGENT (Cognitive Genomics Consortium) (n = 27 888). Main Outcomes and Measures Genetic loci identified by conditional false discovery rate analysis. Brain messenger RNA expression and brain expression quantitative trait locus functionality were determined. Results Among the participants in the genome-wide association studies, 21 loci jointly influencing schizophrenia and cognitive traits were identified: 2 loci shared between schizophrenia and verbal-numerical reasoning, 6 loci shared between schizophrenia and reaction time, and 14 loci shared between schizophrenia and general cognitive function. One locus was shared between schizophrenia and 2 cognitive traits and represented the strongest shared signal detected (nearest gene TCF20; chromosome 22q13.3), and was shared between schizophrenia (z score, 5.01; P = 5.53 × 10−7), general cognitive function (z score, –4.43; P = 9.42 × 10−6), and verbal-numerical reasoning (z score, –5.43; P = 5.64 × 10−8). For 18 loci, schizophrenia risk alleles were associated with poorer cognitive performance. The implicated genes are expressed in the developmental and adult human brain. Replicable expression quantitative trait locus functionality was identified for 4 loci in the adult human brain. Conclusions and Relevance The discovered loci improve the understanding of the common genetic basis underlying schizophrenia and cognitive function, suggesting novel molecular genetic mechanisms. |