@article{Batty2020,
title = {Comparison of risk factor associations in UK Biobank against representative, general population based studies with conventional response rates: prospective cohort study and individual participant meta-analysis},
author = {G Batty and et al },
url = {https://www.ncbi.nlm.nih.gov/pubmed/32051121
},
year = {2020},
date = {2020-02-12},
journal = {BMJ },
keywords = {10279, generalisation, risk factors},
pubstate = {published},
tppubtype = {article}
}

@article{Buchanan2020,
title = {The effect of network thresholding and weighting on structural brain networks in the UK Biobank},
author = {C Buchanan and et al},
url = {https://www.ncbi.nlm.nih.gov/pubmed/31927129
},
year = {2020},
date = {2020-01-10},
journal = {Neuroimage},
keywords = {10279, brain, imaging},
pubstate = {published},
tppubtype = {article}
}

@article{Hill2019b,
title = {Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income},
author = {W Hill and et al},
url = {https://www.ncbi.nlm.nih.gov/pubmed/31844048
},
year = {2019},
date = {2019-12-16},
journal = {Nature Communications},
keywords = {10279},
pubstate = {published},
tppubtype = {article}
}

@article{Cox2019b,
title = {Structural brain imaging correlates of general intelligence in UK Biobank},
author = {S. R Cox and S.J Ritchie and C Fawns-Ritchie and E.M Tucker-Drob and IJ Deary},
url = {https://www.sciencedirect.com/science/article/pii/S0160289619300789},
year = {2019},
date = {2019-10-01},
journal = {Science Direct},
abstract = {The associations between indices of brain structure and measured intelligence are unclear. This is partly because the evidence to-date comes from mostly small and heterogeneous studies. Here, we report brain structure-intelligence associations on a large sample from the UK Biobank study. The overall N = 29,004, with N = 18,426 participants providing both brain MRI and at least one cognitive test, and a complete four-test battery with MRI data available in a minimum N = 7201, depending upon the MRI measure. Participants' age range was 44–81 years (M = 63.13, SD = 7.48). A general factor of intelligence (g) was derived from four varied cognitive tests, accounting for one third of the variance in the cognitive test scores. The association between (age- and sex- corrected) total brain volume and a latent factor of general intelligence is r = 0.276, 95% C.I. = [0.252, 0.300]. A model that incorporated multiple global measures of grey and white matter macro- and microstructure accounted for more than double the g variance in older participants compared to those in middle-age (13.6% and 5. 4%, respectively). There were no sex differences in the magnitude of associations between g and total brain volume or other global aspects of brain structure. The largest brain regional correlates of g were volumes of the insula, frontal, anterior/superior and medial temporal, posterior and paracingulate, lateral occipital cortices, thalamic volume, and the white matter microstructure of thalamic and association fibres, and of the forceps minor. Many of these regions exhibited unique contributions to intelligence, and showed highly stable out of sample prediction.},
keywords = {10279, brain, general intelligence, imaging},
pubstate = {published},
tppubtype = {article}
}

@article{Cox2019c,
title = {Structural brain imaging correlates of general intelligence in UK Biobank},
author = {S Cox and et al},
url = {https://www.ncbi.nlm.nih.gov/pubmed/31787788
},
year = {2019},
date = {2019-10-01},
journal = {Intelligence},
keywords = {10279},
pubstate = {published},
tppubtype = {article}
}

@article{Batty2019,
title = {Assessment of Relative Utility of Underlying vs Contributory Causes of Death},
author = {G Batty and et al},
url = {https://www.ncbi.nlm.nih.gov/pubmed/31365105},
year = {2019},
date = {2019-07-03},
journal = {JAMA Open Network},
abstract = {Importance:
In etiological research, investigators using death certificate data have traditionally extracted underlying cause of mortality alone. With multimorbidity being increasingly common, more than one condition is often compatible with the manner of death. Using contributory cause plus underlying cause would also have some analytical advantages, but their combined utility is largely untested.

Objective:
To compare the relative utility of cause of death data extracted from the underlying cause field vs any location on the death certificate (underlying and contributing combined).

Design, Setting, and Participants:
This study compares the association of 3 known risk factors (cigarette smoking, low educational attainment, and hypertension) with health outcomes based on where cause of death data appears on the death certificate in 2 prospective cohort study collaborations (UK Biobank [N = 502 655] and the Health Survey for England [15 studies] and the Scottish Health Surveys [3 studies] [HSE-SHS; N = 193 873]). Data were collected in UK Biobank from March 2006 to October 2010 and in HSE-SHS from January 1994 to December 2008. Data analysis began in June 2018 and concluded in June 2019.

Main Outcomes and Measures:
Death from cardiovascular disease, cancer, dementia, and injury. For each risk factor-mortality end point combination, a ratio of hazard ratios (RHR) was computed by dividing the effect estimate for the underlying cause by the effect estimate for any mention.

Results:
In UK Biobank, there were 14 421 deaths (2.9%) during a mean (SD) of 6.99 (1.03) years of follow up; in HSE-SHS, there were 21 314 deaths (11.0%) during a mean (SD) of 9.61 (4.44) years of mortality surveillance. Established associations of risk factors with death outcomes were essentially the same irrespective of placement of cause on the death certificate. Results from each study were mutually supportive. For having ever smoked cigarettes (vs never having smoked) in the UK Biobank, the RHR for cardiovascular disease was 0.98 (95% CI, 0.87-1.10; P value for difference = .69); for cancer, the RHR was 0.99 (95% CI, 0.93-1.05; P value for difference = .69). In the HSE-SHS, the RHR for cardiovascular disease was 0.94 (95% CI, 0.87-1.01; P value for difference = .09); for cancer, it was 1.01 (95% CI, 0.94-1.10; P value for difference = .75).

Conclusions and Relevance:
Risk factor-end point associations were not sensitive to the placement of data on the death certificate. This has implications for the examination of the association of risk factors with causes of death where there may be too few events to compute reliable effect estimates based on the underlying field alone.},
keywords = {10279, cause of death},
pubstate = {published},
tppubtype = {article}
}

@article{Cox2019,
title = {Associations between vascular risk factors and brain MRI indices in UK Biobank },
author = {Simon R Cox and Donald M Lyall and Ian J Deary et al.},
url = {https://academic.oup.com/eurheartj/advance-article-abstract/doi/10.1093/eurheartj/ehz100/5371095?redirectedFrom=fulltext},
year = {2019},
date = {2019-03-11},
journal = {European Heart Journal},
abstract = {Aims

Several factors are known to increase risk for cerebrovascular disease and dementia, but there is limited evidence on associations between multiple vascular risk factors (VRFs) and detailed aspects of brain macrostructure and microstructure in large community-dwelling populations across middle and older age.

Methods and results

Associations between VRFs (smoking, hypertension, pulse pressure, diabetes, hypercholesterolaemia, body mass index, and waist–hip ratio) and brain structural and diffusion MRI markers were examined in UK Biobank (N = 9722, age range 44–79 years). A larger number of VRFs was associated with greater brain atrophy, lower grey matter volume, and poorer white matter health. Effect sizes were small (brain structural R2 ≤1.8%). Higher aggregate vascular risk was related to multiple regional MRI hallmarks associated with dementia risk: lower frontal and temporal cortical volumes, lower subcortical volumes, higher white matter hyperintensity volumes, and poorer white matter microstructure in association and thalamic pathways. Smoking pack years, hypertension and diabetes showed the most consistent associations across all brain measures. Hypercholesterolaemia was not uniquely associated with any MRI marker.

Conclusion

Higher levels of VRFs were associated with poorer brain health across grey and white matter macrostructure and microstructure. Effects are mainly additive, converging upon frontal and temporal cortex, subcortical structures, and specific classes of white matter fibres. Though effect sizes were small, these results emphasize the vulnerability of brain health to vascular factors even in relatively healthy middle and older age, and the potential to partly ameliorate cognitive decline by addressing these malleable risk factors.},
note = {Simon R Cox and Donald M Lyall and Stuart J Ritchie and Mark E Bastin and Mathew A Harris and Colin R Buchanan and Chloe Fawns-Ritchie and Miruna C Barbu and Laura de Nooij and Lianne M Reus and Clara Alloza and Xueyi Shen and Emma Neilson and Helen L Alderson and Stuart Hunter and David C Liewald and Heather C Whalley and Andrew M McIntosh and Stephen J Lawrie and Jill P Pell and Elliot M Tucker-Drob and Joanna M Wardlaw and Catharine R Gale and Ian J Deary },
keywords = {10279, featured, imaging, MRI, vascular risk factors},
pubstate = {published},
tppubtype = {article}
}

@article{Luciano2019,
title = {The influence of X chromosome variants on trait neuroticism},
author = {Michelle Luciano and Gail Davies and Kim M. Summers and W. David Hill and Caroline Hayward and David C. Liewald and David J. Porteous and Catharine R. Gale and Andrew M. McIntosh and Ian J. Deary },
url = {https://www.nature.com/articles/s41380-019-0388-2},
year = {2019},
date = {2019-03-06},
journal = {Molecular Psychiatry},
abstract = {Autosomal variants have successfully been associated with trait neuroticism in genome-wide analysis of adequately powered samples. But such studies have so far excluded the X chromosome from analysis. Here, we report genetic association analyses of X chromosome and XY pseudoautosomal single nucleotide polymorphisms (SNPs) and trait neuroticism using UK Biobank samples (N = 405,274). Significant association was found with neuroticism on the X chromosome for 204 markers found within three independent loci (a further 783 were suggestive). Most of the lead neuroticism-related X chromosome variants were located in intergenic regions (n = 397). Involvement of HS6ST2, which has been previously associated with sociability behaviour in the dog, was supported by single SNP and gene-based tests. We found that the amino acid and nucleotide sequences are highly conserved between dogs and humans. From the suggestive X chromosome variants, there were 19 nearby genes which could be linked to gene ontology information. Molecular function was primarily related to binding and catalytic activity; notable biological processes were cellular and metabolic, and nucleic acid binding and transcription factor protein classes were most commonly involved. X-variant heritability of neuroticism was estimated at 0.22% (SE = 0.05) from a full dosage compensation model. A polygenic X-variant score created in an independent sample (maximum N ≈ 7,300) did not predict significant variance in neuroticism, psychological distress, or depressive disorder. We conclude that the X chromosome harbours significant variants influencing neuroticism, and might prove important for other quantitative traits and complex disorders.},
keywords = {10279, 4844, featured, genetics, neuroticism},
pubstate = {published},
tppubtype = {article}
}

@article{Gale2019,
title = {Cognitive ability and risk of death from lower respiratory tract infection: findings from UK Biobank},
author = {CR Gale and IJ Deary and GD Batty},
url = {https://www.ncbi.nlm.nih.gov/pubmed/30718728},
year = {2019},
date = {2019-02-04},
journal = {Scientific Reports},
abstract = {Dementia increases the risk of lower respiratory tract infection, but it is unclear whether risk varies across the normal range of cognitive ability. People with higher cognitive ability tend to behave in a healthier fashion as regards risk factors for lower respiratory tract infection and there is evidence that they have a lower risk of dying from respiratory disease as a whole. We therefore investigated the relationship between cognitive ability and mortality from lower respiratory tract infection. Participants were 434,413 people from UK Biobank (54% female). Cognitive ability was measured using tests of reaction time and reasoning. Data on deaths from lower respiratory infection were obtained from death certificates. Over a mean follow-up period of 6.99 years, 1,282 people died of lower respiratory infection. Mortality from lower respiratory tract infection fell as cognitive ability increased. For a standard deviation faster reaction time, the age- and sex-adjusted hazard ratio (95% confidence interval) was 0.80 (0.76, 0.83) and the multivariable-adjusted hazard ratio was 0.87 (0.83, 0.91). There were similar though weaker associations when cognitive ability was assessed using a reasoning test. These findings suggest that variation across the normal range of cognitive ability increase risk of dying from lower respiratory tract infection.},
keywords = {10279, cognitive ability, respiratory tract infection},
pubstate = {published},
tppubtype = {article}
}

@article{Calvin2019,
title = {Sex-specific moderation by lifestyle and psychosocial factors on the genetic contributions to adiposity in 112,151 individuals from UK Biobank},
author = {CM Calvin and SP Hagenaars and J Gallacher and SE Harris and G Davies and DC Liewald and CR Gale and IJ Deary },
url = {https://www.ncbi.nlm.nih.gov/pubmed/30675005},
year = {2019},
date = {2019-01-23},
journal = {Scientific Reports},
abstract = {Evidence suggests that lifestyle factors, e.g. physical activity, moderate the manifestation of genetic susceptibility to obesity. The present study uses UK Biobank data to investigate interaction between polygenic scores (PGS) for two obesity indicators, and lifestyle and psychosocial factors in the prediction of the two indicators, with attention to sex-specific effects. Analyses were of 112 151 participants (58 914 females; 40 to 73 years) whose genetic data passed quality control. Moderation effects were analysed in linear regression models predicting body mass index (BMI) and waist-to-hip ratio (WHR), including interaction terms for PGS and each exposure. Greater physical activity, more education, higher income, moderate vs low alcohol consumption, and low material deprivation were each associated with a relatively lower risk for manifestation of genetic susceptibility to obesity (p < 0.001); the moderating effects of physical activity and alcohol consumption were greater in women than men (three-way interaction: p = 0.009 and p = 0.008, respectively). More income and less neuroticism were related to reduced manifestation of genetic susceptibility to high WHR (p = 0.007; p = 0.003); the effect of income was greater in women (three-way interaction: p = 0.001). Lifestyle and psychosocial factors appear to offset genetic risk for adiposity in mid to late adulthood, with some sex-specific associations},
keywords = {10279, Genetic, psychosocial, sex},
pubstate = {published},
tppubtype = {article}
}

@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 = {10279, cognitive ability, featured, genetics},
pubstate = {published},
tppubtype = {article}
}

@article{Davies2018b,
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 = {10279, 4844, Cognitive Function, genetics},
pubstate = {published},
tppubtype = {article}
}

@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 David W 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 = {10279, alzheimers, featured, genetics, GWAS},
pubstate = {published},
tppubtype = {article}
}

@article{Ritchie2018,
title = {Sex Differences in the Adult Human Brain: Evidence from 5216 UK Biobank Participants},
author = {Stuart J Ritchie and Simon R Cox and Xueyi Shen and Michael V Lombardo and Lianne M Reus and Clara Alloza and Mathew A Harris and Helen L Alderson and Stuart Hunter and Emma Neilson},
url = {https://academic.oup.com/cercor/advance-article/doi/10.1093/cercor/bhy109/4996558},
year = {2018},
date = {2018-05-16},
journal = {Cerebral Cortex},
abstract = {Sex differences in the human brain are of interest for many reasons: for example, there are sex differences in the observed prevalence of psychiatric disorders and in some psychological traits that brain differences might help to explain. We report the largest single-sample study of structural and functional sex differences in the human brain (2750 female, 2466 male participants; mean age 61.7 years, range 44–77 years). Males had higher raw volumes, raw surface areas, and white matter fractional anisotropy; females had higher raw cortical thickness and higher white matter tract complexity. There was considerable distributional overlap between the sexes. Subregional differences were not fully attributable to differences in total volume, total surface area, mean cortical thickness, or height. There was generally greater male variance across the raw structural measures. Functional connectome organization showed stronger connectivity for males in unimodal sensorimotor cortices, and stronger connectivity for females in the default mode network. This large-scale study provides a foundation for attempts to understand the causes and consequences of sex differences in adult brain structure and function.},
keywords = {10279, brain, sex differences},
pubstate = {published},
tppubtype = {article}
}

@article{Batty2018,
title = {Psychosocial characteristics as potential predictors of suicide in adults: an overview of the evidence with new results from prospective cohort studies},
author = {G D Batty and M Kivimäki and S Bell and C Gale and M Shipley and E Whitley and D Gunnell},
url = {http://eprints.gla.ac.uk/156613/},
year = {2018},
date = {2018-01-12},
journal = {Translational Psychiatry},
abstract = {In this narrative overview of the evidence linking psychosocial factors with future suicide risk, we collected results from published reports of prospective studies with verified suicide events (mortality or, less commonly, hospitalisation) alongside analyses of new data. There is abundant evidence indicating that low socioeconomic position, irrespective of the economic status of the country in question, is associated with an increased risk of suicide, including the suggestion that the recent global economic recession has been responsible for an increase in suicide deaths and, by proxy, attempts. Social isolation, low scores on tests of intelligence, serious mental illness (both particularly strongly), chronic psychological distress, and lower physical stature (a marker of childhood exposures) were also consistently related to elevated suicide rates. Although there is some circumstantial evidence for psychosocial stress, personality disposition, and early-life characteristics such as bullying being risk indices for suicide, the general paucity of studies means it is not currently possible to draw clear conclusions about their role. Most suicide intervention strategies have traditionally not explored the modification of psychosocial factors, partly because evidence linking psychosocial factors with suicide risk is, as shown herein, largely in its infancy, or, where is does exist, for instance for intelligence and personality disposition, the characteristics in question do not appear to be easily malleable.},
keywords = {10279},
pubstate = {published},
tppubtype = {article}
}

@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 McIntosh Gale Davies & Deary A M C R G I J S. J. Ritchie S. P. Hagenaars},
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 = {10279, genetics, Intelligence},
pubstate = {published},
tppubtype = {article}
}

@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 = {10279, 4844, featured, genetics, neuroticism},
pubstate = {published},
tppubtype = {article}
}

@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 = {10279, Cognitive Function, genetics, Trail making test},
pubstate = {published},
tppubtype = {article}
}

@article{Luciano2017b,
title = {Single Nucleotide Polymorphisms Associated with Reading Ability Show Connection to Socio-Economic Outcomes},
author = {Michelle Luciano and Saskia P Hagenaars and Simon R Cox and William David Hill and Gail Davies and Sarah E Harris and Ian J Deary and David M Evans and Nicholas G Martin and Margaret J Wright and Timothy C Bates},
url = {https://link.springer.com/article/10.1007/s10519-017-9859-x},
year = {2017},
date = {2017-07-15},
journal = {Bahaviour Genetics},
abstract = {Impairments in reading and in language have negative consequences on life outcomes, but it is not known to what extent genetic effects influence this association. We constructed polygenic scores for difficulties with language and learning to read from genome-wide data in ~6,600 children, adolescents and young adults, and tested their association with health, socioeconomic outcomes and brain structure measures collected in adults (maximal N = 111,749). Polygenic risk of reading difficulties was associated with reduced income, educational attainment, self-rated health and verbal-numerical reasoning (p < 0.00055). Polygenic risk of language difficulties predicted income (p = 0.0005). The small effect sizes ranged 0.01–0.03 of a standard deviation, but these will increase as genetic studies for reading ability get larger. Polygenic scores for childhood cognitive ability and educational attainment were correlated with polygenic scores of reading and language (up to 0.09 and 0.05, respectively). But when they were included in the prediction models, the observed associations between polygenic reading and adult outcomes mostly remained. This suggests that the pathway from reading ability to social outcomes is not only via associated polygenic loads for general cognitive function and educational attainment. The presence of non-overlapping genetic effect is indicated by the genetic correlations of around 0.40 (childhood intelligence) and 0.70 (educational attainment) with reading ability. Mendelian randomization approaches will be important to dissociate any causal and moderating effects of reading and related traits on social outcomes.},
keywords = {10279, genetics, reading ability, socio-economic},
pubstate = {published},
tppubtype = {article}
}

@article{Gale2017,
title = {When Is Higher Neuroticism Protective Against Death? Findings From UK Biobank},
author = {Catharine R Gale and Iva Čukić and David G Batty and Andrew M McIntosh and Alexander Weiss and Ian J Deary},
url = {http://journals.sagepub.com/doi/abs/10.1177/0956797617709813},
year = {2017},
date = {2017-07-13},
journal = {Psychological Science},
abstract = {We examined the association between neuroticism and mortality in a sample of 321,456 people from UK Biobank and explored the influence of self-rated health on this relationship. After adjustment for age and sex, a 1-SD increment in neuroticism was associated with a 6% increase in all-cause mortality (hazard ratio = 1.06, 95% confidence interval = [1.03, 1.09]). After adjustment for other covariates, and, in particular, self-rated health, higher neuroticism was associated with an 8% reduction in all-cause mortality (hazard ratio = 0.92, 95% confidence interval = [0.89, 0.95]), as well as with reductions in mortality from cancer, cardiovascular disease, and respiratory disease, but not external causes. Further analyses revealed that higher neuroticism was associated with lower mortality only in those people with fair or poor self-rated health, and that higher scores on a facet of neuroticism related to worry and vulnerability were associated with lower mortality. Research into associations between personality facets and mortality may elucidate mechanisms underlying neuroticism’s covert protection against death.},
keywords = {10279, featured, mortality, neuroticism},
pubstate = {published},
tppubtype = {article}
}

@article{Hagenaars2017bb,
title = {Cognitive ability and physical health: a Mendelian randomization study},
author = {Ian Deary & Sarah Harris J E Saskia P. Hagenaars Catharine R. Gale},
url = {https://www.nature.com/articles/s41598-017-02837-3},
year = {2017},
date = {2017-06-01},
journal = {Scientific Reports},
abstract = {Causes of the association between cognitive ability and health remain unknown, but may reflect a shared genetic aetiology. This study examines the causal genetic associations between cognitive ability and physical health. We carried out two-sample Mendelian randomization analyses using the inverse-variance weighted method to test for causality between later life cognitive ability, educational attainment (as a proxy for cognitive ability in youth), BMI, height, systolic blood pressure, coronary artery disease, and type 2 diabetes using data from six independent GWAS consortia and the UK Biobank sample (N = 112 151). BMI, systolic blood pressure, coronary artery disease and type 2 diabetes showed negative associations with cognitive ability; height was positively associated with cognitive ability. The analyses provided no evidence for casual associations from health to cognitive ability. In the other direction, higher educational attainment predicted lower BMI, systolic blood pressure, coronary artery disease, type 2 diabetes, and taller stature. The analyses indicated no causal association from educational attainment to physical health. The lack of evidence for causal associations between cognitive ability, educational attainment, and physical health could be explained by weak instrumental variables, poorly measured outcomes, or the small number of disease cases.},
keywords = {10279, cognition, genetics, physical activity},
pubstate = {published},
tppubtype = {article}
}

@article{Deary2017,
title = {Genetic contributions to self-reported tiredness},
author = {Deary I J V Deary},
url = {http://www.nature.com/mp/journal/vaop/ncurrent/full/mp20175a.html},
year = {2017},
date = {2017-02-14},
journal = {Molecular Psychiatry},
abstract = {Self-reported tiredness and low energy, often called fatigue, are associated with poorer physical and mental health. Twin studies have indicated that this has a heritability between 6 and 50%. In the UK Biobank sample (N=108 976), we carried out a genome-wide association study (GWAS) of responses to the question, ‘Over the last two weeks, how often have you felt tired or had little energy?’ Univariate GCTA-GREML found that the proportion of variance explained by all common single-nucleotide polymorphisms for this tiredness question was 8.4% (s.e.=0.6%). GWAS identified one genome-wide significant hit (Affymetrix id 1:64178756_C_T; P=1.36 × 10−11). Linkage disequilibrium score regression and polygenic profile score analyses were used to test for shared genetic aetiology between tiredness and up to 29 physical and mental health traits from GWAS consortia. Significant genetic correlations were identified between tiredness and body mass index (BMI), C-reactive protein, high-density lipoprotein (HDL) cholesterol, forced expiratory volume, grip strength, HbA1c, longevity, obesity, self-rated health, smoking status, triglycerides, type 2 diabetes, waist–hip ratio, attention deficit hyperactivity disorder, bipolar disorder, major depressive disorder, neuroticism, schizophrenia and verbal-numerical reasoning (absolute rg effect sizes between 0.02 and 0.78). Significant associations were identified between tiredness phenotypic scores and polygenic profile scores for BMI, HDL cholesterol, low-density lipoprotein cholesterol, coronary artery disease, C-reactive protein, HbA1c, height, obesity, smoking status, triglycerides, type 2 diabetes, waist–hip ratio, childhood cognitive ability, neuroticism, bipolar disorder, major depressive disorder and schizophrenia (standardised β’s had absolute values<0.03). These results suggest that tiredness is a partly heritable, heterogeneous and complex phenomenon that is phenotypically and genetically associated with affective, cognitive, personality and physiological processes.},
note = {V Deary, S P Hagenaars, S E Harris, W D Hill, G Davies, D C M Liewald, International Consortium for Blood Pressure GWAS, CHARGE Consortium Aging and Longevity Group, CHARGE Consortium Inflammation Group, A M McIntosh, C R Gale, I J Deary},
keywords = {10279, fatigue, featured, GWAS, tiredness},
pubstate = {published},
tppubtype = {article}
}

@article{Hagenaars2017c,
title = {Genetic prediction of male pattern baldness},
author = {Sarah Harris Stuart Ritchie Gail Davies David Liewald Catharine Gale David Porteous Ian Deary Riccardo Marioni E J C R J J E Saskia P. Hagenaars W. David Hill},
url = {http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1006594},
year = {2017},
date = {2017-02-14},
journal = {PLOS Genetics},
abstract = {Male pattern baldness can have substantial psychosocial effects, and it has been phenotypically linked to adverse health outcomes such as prostate cancer and cardiovascular disease. We explored the genetic architecture of the trait using data from over 52,000 male participants of UK Biobank, aged 40–69 years. We identified over 250 independent genetic loci associated with severe hair loss (P<5x10-8). By splitting the cohort into a discovery sample of 40,000 and target sample of 12,000, we developed a prediction algorithm based entirely on common genetic variants that discriminated (AUC = 0.78},
keywords = {10279, baldness, genetics},
pubstate = {published},
tppubtype = {article}
}

@article{HillWD2016b,
title = {Molecular genetic aetiology of general cognitive function is enriched in evolutionarily conserved regions},
author = {Harris Hagenaars Liewald Penke Gale Deary G S E S P D C L C R I J Hill W. D. Davies},
url = {http://www.nature.com/tp/journal/v6/n12/full/tp2016246a.html},
year = {2016},
date = {2016-12-14},
journal = {Transl Psychiatry},
abstract = {Differences in general cognitive function have been shown to be partly heritable and to show genetic correlations with several psychiatric and physical disease states. However, to date, few single-nucleotide polymorphisms (SNPs) have demonstrated genome-wide significance, hampering efforts aimed at determining which genetic variants are most important for cognitive function and which regions drive the genetic associations between cognitive function and disease states. Here, we combine multiple large genome-wide association study (GWAS) data sets, from the CHARGE cognitive consortium (n=53 949) and UK Biobank (n=36 035), to partition the genome into 52 functional annotations and an additional 10 annotations describing tissue-specific histone marks. Using stratified linkage disequilibrium score regression we show that, in two measures of cognitive function, SNPs associated with cognitive function cluster in regions of the genome that are under evolutionary negative selective pressure. These conserved regions contained ~2.6% of the SNPs from each GWAS but accounted for ~40% of the SNP-based heritability. The results suggest that the search for causal variants associated with cognitive function, and those variants that exert a pleiotropic effect between cognitive function and health, will be facilitated by examining these enriched regions.},
keywords = {10279, Cognitive Function, genetics},
pubstate = {published},
tppubtype = {article}
}

@article{HarrisSE2016,
title = {Molecular genetic contributions to self-rated health},
author = {Saskia Davies Gail David Hill Liewald David Ritchie Stuart Marioni Riccardo Sudlow Cathie Wardlaw Joanna McIntosh Andrew Gale Catharine Deary Ian Metastroke Consortium International Consortium Blood Pressure Genome-Wide Association Studies Charge Consortium Aging Longevity Group Charge Consortium Cognitive Group P W C M J E L M M M R J for Harris Sarah E. Hagenaars},
url = {http://ije.oxfordjournals.org/content/early/2016/11/10/ije.dyw219.full},
year = {2016},
date = {2016-11-17},
journal = {International Journal of Epidemiology},
abstract = {BACKGROUND: Poorer self-rated health (SRH) predicts worse health outcomes, even when adjusted for objective measures of disease at time of rating. Twin studies indicate SRH has a heritability of up to 60% and that its genetic architecture may overlap with that of personality and cognition.; METHODS: We carried out a genome-wide association study (GWAS) of SRH on 111 749 members of the UK Biobank sample. Univariate genome-wide complex trait analysis (GCTA)-GREML analyses were used to estimate the proportion of variance explained by all common autosomal single nucleotide polymorphisms (SNPs) for SRH. Linkage disequilibrium (LD) score regression and polygenic risk scoring, two complementary methods, were used to investigate pleiotropy between SRH in the UK Biobank and up to 21 health-related and personality and cognitive traits from published GWAS consortia.; RESULTS: The GWAS identified 13 independent signals associated with SRH, including several in regions previously associated with diseases or disease-related traits. The strongest signal was on chromosome 2 (rs2360675},
keywords = {10279, Genetic, heritability, pleiotrophy, polygenic score, self-rated health},
pubstate = {published},
tppubtype = {article}
}

@article{HillWD2016b,
title = {Molecular Genetic Contributions to Social Deprivation and Household Income in UK Biobank},
author = {Marioni Harris Liewald Davies Okbay McIntosh Gale Deary S P R E S E D C G A A M C R I J Hill W. D. Hagenaars},
url = {https://www.ncbi.nlm.nih.gov/pubmed/27818178},
year = {2016},
date = {2016-11-03},
journal = {Curr Biology},
abstract = {Individuals with lower socio-economic status (SES) are at increased risk of physical and mental illnesses and tend to die at an earlier age [1-3]. Explanations for the association between SES and health typically focus on factors that are environmental in origin [4]. However, common SNPs have been found collectively to explain around 18% of the phenotypic variance of an area-based social deprivation measure of SES [5]. Molecular genetic studies have also shown that common physical and psychiatric diseases are partly heritable [6]. It is possible that phenotypic associations between SES and health arise partly due to a shared genetic etiology. We conducted a genome-wide association study (GWAS) on social deprivation and on household income using 112,151 participants of UK Biobank. We find that common SNPs explain 21% of the variation in social deprivation and 11% of household income. Two independent loci attained genome-wide significance for household income, with the most significant SNP in each of these loci being rs187848990 on chromosome 2 and rs8100891 on chromosome 19. Genes in the regions of these SNPs have been associated with intellectual disabilities, schizophrenia, and synaptic plasticity. Extensive genetic correlations were found between both measures of SES and illnesses, anthropometric variables, psychiatric disorders, and cognitive ability. These findings suggest that some SNPs associated with SES are involved in the brain and central nervous system. The genetic associations with SES obviously do not reflect direct causal effects and are probably mediated via other partly heritable variables, including cognitive ability, personality, and health.},
keywords = {10279, genetics, GWAS, income, social deprivation, socioeconomic status},
pubstate = {published},
tppubtype = {article}
}

@article{WeissA2016,
title = {Personality Polygenes, Positive Affect, and Life Satisfaction},
author = {Hofer Yang Okbay Lind Miller Nolte Zhao Hagenaars Hottenga Matteson Snieder Faul Hartman Boyle Tiemeier Mosing Pattie Davies Liewald Schmidt De Jager Heath Jokela Starr Oldehinkel Johannesson Cesarini Hofman Harris Smith Keltikangas-Jarvinen Pulkki-Raback Schmidt Smith Iacono McGue Bennett Pedersen Magnusson Deary Martin Boomsma Bartels Luciano B M E J A P A M B I M W S P J J L K H J D C A P A H M A A G D C R P L A C M J M A J M D A S E J A L L H J W G M D A N L P K I J N G D I M M Weiss A. Baselmans},
url = {https://www.ncbi.nlm.nih.gov/pubmed/27546527},
year = {2016},
date = {2016-08-23},
journal = {Twin Res Hum Genet},
abstract = {Approximately half of the variation in wellbeing measures overlaps with variation in personality traits. Studies of non-human primate pedigrees and human twins suggest that this is due to common genetic influences. We tested whether personality polygenic scores for the NEO Five-Factor Inventory (NEO-FFI) domains and for item response theory (IRT) derived extraversion and neuroticism scores predict variance in wellbeing measures. Polygenic scores were based on published genome-wide association (GWA) results in over 17,000 individuals for the NEO-FFI and in over 63,000 for the IRT extraversion and neuroticism traits. The NEO-FFI polygenic scores were used to predict life satisfaction in 7 cohorts, positive affect in 12 cohorts, and general wellbeing in 1 cohort (maximal N = 46,508). Meta-analysis of these results showed no significant association between NEO-FFI personality polygenic scores and the wellbeing measures. IRT extraversion and neuroticism polygenic scores were used to predict life satisfaction and positive affect in almost 37,000 individuals from UK Biobank. Significant positive associations (effect sizes <0.05%) were observed between the extraversion polygenic score and wellbeing measures, and a negative association was observed between the polygenic neuroticism score and life satisfaction. Furthermore, using GWA data, genetic correlations of -0.49 and -0.55 were estimated between neuroticism with life satisfaction and positive affect, respectively. The moderate genetic correlation between neuroticism and wellbeing is in line with twin research showing that genetic influences on wellbeing are also shared with other independent personality domains.},
keywords = {10279, genetic correlation, genetics, happiness, polygenic, prediction, wellbeing},
pubstate = {published},
tppubtype = {article}
}

@article{Batty2016,
title = {Psychological distress, neuroticism, and cause-specific mortality: early prospective evidence from UK Biobank},
author = {Tom Russ Ian Deary Catharine Gale C J R G David Batty Andrew M McIntosh},
url = {http://jech.bmj.com/content/early/2016/08/12/jech-2016-207267.abstract},
year = {2016},
date = {2016-08-12},
abstract = {Background It is well established that psychological distress (depression and anxiety) is related to an increased risk of mortality. The personality trait of neuroticism, reflecting a relatively stable tendency towards negative emotions, has also been associated with elevated rates of death in some studies. Accordingly, we tested the possibility that it is the neuroticism trait itself, rather than the distress state, that is generating an increased risk of mortality.

Methods We used data from the UK Biobank study, a UK-wide prospective cohort study (2006–2010) in which distress was ascertained using the Patient Health Questionnaire and neuroticism using the Eysenck Personality Questionnaire-Revised Short Form.
Results A mean of 6.2 years of follow-up of 308 721 study members gave rise to 4334 deaths. Higher neuroticism was weakly associated with total mortality (age-adjusted and sex-adjusted HR per SD increase; 95% CI 1.05; 1.02 to 1.09), and moderately strongly correlated with distress symptoms (r=0.55, p<0.0001). Distress symptoms were positively related to risk of total mortality (age-adjusted and sex-adjusted HR per SD increase in distress; 95% CI 1.23; 1.20 to 1.26). This gradient was, in fact, slightly strengthened after adding neuroticism to the multivariable model (1.30; 1.26 to 1.34) but markedly attenuated after taking into account other covariates which included health behaviours and somatic disease (1.16; 1.12 to 1.20). Similar results were apparent when cardiovascular disease, cancer and external cause of death were the end points of interest.

Conclusions While there was good a priori reasons to anticipate the neuroticism would at least partially explain the relation between distress symptoms and cause-specific mortality, we found no such evidence in the present study.},
keywords = {10279, mortality, neuroticism},
pubstate = {published},
tppubtype = {article}
}

@article{Davies2016,
title = {Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N=112 151)},
author = {Liewald Hill Hagenaars Harris Ritchie Luciano Fawns-Ritchie Lyall Cullen Cox Hayward Porteous Evans McIntosh Gallacher Craddock Pell Smith Gale D C W D S P S E S J M C D B S R C D J J A M J N J P D J C R G Davies R E Marioni and I J Deary},
url = {http://www.nature.com/mp/journal/vaop/ncurrent/full/mp201645a.html},
year = {2016},
date = {2016-04-05},
journal = {Molecular Psychiatry},
abstract = {People’s differences in cognitive functions are partly heritable and are associated with important life outcomes. Previous genome-wide association (GWA) studies of cognitive functions have found evidence for polygenic effects yet, to date, there are few replicated genetic associations. Here we use data from the UK Biobank sample to investigate the genetic contributions to variation in tests of three cognitive functions and in educational attainment. GWA analyses were performed for verbal–numerical reasoning (N=36 035), memory (N=112 067), reaction time (N=111 483) and for the attainment of a college or a university degree (N=111 114). We report genome-wide significant single-nucleotide polymorphism (SNP)-based associations in 20 genomic regions, and significant gene-based findings in 46 regions. These include findings in the ATXN2, CYP2DG, APBA1 and CADM2 genes. We report replication of these hits in published GWA studies of cognitive function, educational attainment and childhood intelligence. There is also replication, in UK Biobank, of SNP hits reported previously in GWA studies of educational attainment and cognitive function. GCTA-GREML analyses, using common SNPs (minor allele frequency>0.01), indicated significant SNP-based heritabilities of 31% (s.e.m.=1.8%) for verbal–numerical reasoning, 5% (s.e.m.=0.6%) for memory, 11% (s.e.m.=0.6%) for reaction time and 21% (s.e.m.=0.6%) for educational attainment. Polygenic score analyses indicate that up to 5% of the variance in cognitive test scores can be predicted in an independent cohort. The genomic regions identified include several novel loci, some of which have been associated with intracranial volume, neurodegeneration, Alzheimer’s disease and schizophrenia.},
keywords = {10279, cognition, education},
pubstate = {published},
tppubtype = {article}
}

@article{Hagenaars2016b,
title = {Shared genetic aetiology between cognitive functions and physical and mental health in UK Biobank (N=112 151) and 24 GWAS consortia},
author = {Davies Hill Liewald Ritchie Marioni Fawns-Ritchie Cullen Malik METASTROKE Consortium International Worrall Sudlow Wardlaw Gallacher Pell McIntosh Smith Gale Deary G W D D C M S J R E C B R B B C L M J M J J A M D J C R J S P Hagenaars S E Harris},
url = {http://www.nature.com/mp/journal/vaop/ncurrent/full/mp2015225a.html},
year = {2016},
date = {2016-01-26},
journal = {Molecular Psychiatry - Nature},
abstract = {Causes of the well-documented association between low levels of cognitive functioning and many adverse neuropsychiatric outcomes, poorer physical health and earlier death remain unknown. We used linkage disequilibrium regression and polygenic profile scoring to test for shared genetic aetiology between cognitive functions and neuropsychiatric disorders and physical health. Using information provided by many published genome-wide association study consortia, we created polygenic profile scores for 24 vascular–metabolic, neuropsychiatric, physiological–anthropometric and cognitive traits in the participants of UK Biobank, a very large population-based sample (N=112 151). Pleiotropy between cognitive and health traits was quantified by deriving genetic correlations using summary genome-wide association study statistics and to the method of linkage disequilibrium score regression. Substantial and significant genetic correlations were observed between cognitive test scores in the UK Biobank sample and many of the mental and physical health-related traits and disorders assessed here. In addition, highly significant associations were observed between the cognitive test scores in the UK Biobank sample and many polygenic profile scores, including coronary artery disease, stroke, Alzheimer’s disease, schizophrenia, autism, major depressive disorder, body mass index, intracranial volume, infant head circumference and childhood cognitive ability. Where disease diagnosis was available for UK Biobank participants, we were able to show that these results were not confounded by those who had the relevant disease. These findings indicate that a substantial level of pleiotropy exists between cognitive abilities and many human mental and physical health disorders and traits and that it can be used to predict phenotypic variance across samples.},
keywords = {10279, cognition, disease, genetics},
pubstate = {published},
tppubtype = {article}
}