GUESS-ing polygenic associations with multiple phenotypes using a GPU-based evolutionary stochastic search algorithm

Title: GUESS-ing polygenic associations with multiple phenotypes using a GPU-based evolutionary stochastic search algorithm
Authors: Bottolo, L
Chadeau-Hyam, M
Hastie, DI
Zeller, T
Liquet, B
Newcombe, P
Yengo, L
Wild, PS
Schillert, A
Ziegler, A
Nielsen, SF
Butterworth, AS
Ho, WK
Castagne, R
Munzel, T
Tregouet, D
Falchi, M
Cambien, F
Nordestgaard, BG
Fumeron, F
Tybjaerg-Hansen, A
Froguel, P
Danesh, J
Petretto, E
Blankenberg, S
Tiret, L
Richardson, S
Item Type: Journal Article
Abstract: Genome-wide association studies (GWAS) yielded significant advances in defining the genetic architecture of complex traits and disease. Still, a major hurdle of GWAS is narrowing down multiple genetic associations to a few causal variants for functional studies. This becomes critical in multi-phenotype GWAS where detection and interpretability of complex SNP(s)-trait(s) associations are complicated by complex Linkage Disequilibrium patterns between SNPs and correlation between traits. Here we propose a computationally efficient algorithm (GUESS) to explore complex genetic-association models and maximize genetic variant detection. We integrated our algorithm with a new Bayesian strategy for multi-phenotype analysis to identify the specific contribution of each SNP to different trait combinations and study genetic regulation of lipid metabolism in the Gutenberg Health Study (GHS). Despite the relatively small size of GHS (n = 3,175), when compared with the largest published meta-GWAS (n>100,000), GUESS recovered most of the major associations and was better at refining multi-trait associations than alternative methods. Amongst the new findings provided by GUESS, we revealed a strong association of SORT1 with TG-APOB and LIPC with TG-HDL phenotypic groups, which were overlooked in the larger meta-GWAS and not revealed by competing approaches, associations that we replicated in two independent cohorts. Moreover, we demonstrated the increased power of GUESS over alternative multi-phenotype approaches, both Bayesian and non-Bayesian, in a simulation study that mimics real-case scenarios. We showed that our parallel implementation based on Graphics Processing Units outperforms alternative multi-phenotype methods. Beyond multivariate modelling of multi-phenotypes, our Bayesian model employs a flexible hierarchical prior structure for genetic effects that adapts to any correlation structure of the predictors and increases the power to identify associated variants. This provides a powerful tool for the analysis of diverse genomic features, for instance including gene expression and exome sequencing data, where complex dependencies are present in the predictor space.
Issue Date: 8-Aug-2013
Date of Acceptance: 30-May-2013
ISSN: 1553-7390
Publisher: Public Library of Science (PLoS)
Journal / Book Title: PLoS Genetics
Volume: 9
Issue: 8
Copyright Statement: © 2013 Bottolo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Sponsor/Funder: Medical Research Council (MRC)
Medical Research Council (MRC)
Medical Research Council (MRC)
Funder's Grant Number: G0801056B
Keywords: Science & Technology
Life Sciences & Biomedicine
Genetics & Heredity
Bayes Theorem
Biological Evolution
Gene Expression
Genome-Wide Association Study
Linkage Disequilibrium
Polymorphism, Single Nucleotide
Quantitative Trait Loci
0604 Genetics
Developmental Biology
Publication Status: Published
Article Number: ARTN e1003657
Appears in Collections:Clinical Sciences
Molecular Sciences
Department of Medicine
Faculty of Medicine
Faculty of Natural Sciences
Epidemiology, Public Health and Primary Care

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