@article{OriolCanela-Xandri2016,
title = {Improved Genetic Profiling of Anthropometric Traits Using a Big Data Approach},
author = {Oriol Canela-Xandri , Konrad Rawlik , John A. Woolliams, Albert Tenesa },
url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0166755},
year = {2016},
date = {2016-12-15},
journal = {Plos One},
abstract = {Genome-wide association studies (GWAS) promised to translate their findings into clinically beneficial improvements of patient management by tailoring disease management to the individual through the prediction of disease risk. However, the ability to translate genetic findings from GWAS into predictive tools that are of clinical utility and which may inform clinical practice has, so far, been encouraging but limited. Here we propose to use a more powerful statistical approach, the use of which has traditionally been limited due to computational requirements and lack of sufficiently large individual level genotyped cohorts, but which improve the prediction of multiple medically relevant phenotypes using the same panel of SNPs. As a proof of principle, we used a shared panel of 319,038 common SNPs with MAF > 0.05 to train the prediction models in 114,264 unrelated White-British individuals for height and four obesity related traits (body mass index, basal metabolic rate, body fat percentage, and waist-to-hip ratio). We obtained prediction accuracies that ranged between 46% and 75% of the maximum achievable given the captured heritable component. For height, this represents an improvement in prediction accuracy of up to 68% (184% more phenotypic variance explained) over SNPs reported to be robustly associated with height in a previous GWAS meta-analysis of similar size. Across-population predictions in White non-British individuals were similar to those in White-British whilst those in Asian and Black individuals were informative but less accurate. We estimate that the genotyping of circa 500,000 unrelated individuals will yield predictions between 66% and 82% of the SNP-heritability captured by common variants in our array. Prediction accuracies did not improve when including rarer SNPs or when fitting multiple traits jointly in multivariate models.},
keywords = {8447, anthropometric traits, Genetic profiling},
pubstate = {published},
tppubtype = {article}
}

@article{Canela-XandriO2016,
title = {Improved Genetic Profiling of Anthropometric Traits using a Big Data Approach},
author = {Canela-Xandri, O.
Rawlik, K.
Woolliams, John A.
Tenesa, A.},
url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0166755},
year = {2016},
date = {2016-12-15},
journal = {PLoS One},
keywords = {anthropometric traits, Big data},
pubstate = {published},
tppubtype = {article}
}

@misc{Loos2016,
title = {First genome-wide CNV association meta-analysis on anthropometric traits in 71,288 adults},
author = {R Loos, Beckmann JS, Jacquemont S, Metspalu A, Franke L, Frayling TM, Reymond A, Kutalik Z, Macé A, GIANT Consortium},
year = {2016},
date = {2016-04-01},
abstract = {Many common SNPs influence disease susceptibility, but explain only part of the heritability. On the other hand, we have shown that the rare 16p11.2 copy number variant (CNV) has substantial effect on body-mass index (BMI). It is likely that additional CNVs could further contribute.
As SNP-array based CNV calling has high false positive rate, we first developed a novel quality score (QS) to estimate the probability of a CNV call being true. The QS optimally combines available CNV- and sample-related quality metrics. Simulation results showed that our QS-based association could yield up to 5–fold increase in power compared to classical filtering approaches, in particular for CNVs up to 10% frequency and low quality.
Using the inferred probabilistic CNV calls we performed a genome-wide CNV association meta-analysis on anthropometric traits using 34 cohorts of the GIANT consortium, representing 71,288 unrelated adults genotyped on various Illumina platforms. Results from this large meta-analysis showed 4.4-fold enrichment of low P-values (P<0.05) for known obesity CNVs and gave new insights on the impact of the 16p11.2 region: beyond replicating the known effects of the 220kb deletion (P=2E-6) and 600kb rearrangements (P=3E-5), we found that the 220kb deletion (frq=0.03%) increases BMI mainly through increasing weight (by 1.26 SD, P=5E-8) and the 600kb deletion (frq=0.02%) does so by principally decreasing height (by 0.85 SD, P=1E-6) along with a small increase in weight (P=1E-3). A further highlight – at the limit of genome-wide significance level for CNV associations (P<2E-7) – includes a link between the rare (frq=0.01%) duplication of a region encompassing ENOX1 and higher weight (P=5E-7). We are currently extending the meta-analysis to ~190K individuals including samples from the UK BioBank. This large CNV-association study is best positioned to shed light on the impact of CNVs on anthropometric traits in the adult general population.
},
keywords = {9072, anthropometric traits},
pubstate = {published},
tppubtype = {presentation}
}