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

TY - JOUR

T1 - GUESS-ing polygenic associations with multiple phenotypes using a GPU-based evolutionary stochastic search algorithm

AU - Bottolo, Leonardo

AU - Chadeau-Hyam, Marc

AU - Hastie, David I

AU - Zeller, Tanja

AU - Liquet, Benoit

AU - Newcombe, Paul

AU - Yengo, Loic

AU - Wild, Philipp S

AU - Schillert, Arne

AU - Ziegler, Andreas

AU - Nielsen, Sune F

AU - Butterworth, Adam S

AU - Ho, Weang Kee

AU - Castagné, Raphaële

AU - Munzel, Thomas

AU - Tregouet, David

AU - Falchi, Mario

AU - Cambien, François

AU - Nordestgaard, Børge G

AU - Fumeron, Fredéric

AU - Tybjærg-Hansen, Anne

AU - Froguel, Philippe

AU - Danesh, John

AU - Petretto, Enrico

AU - Blankenberg, Stefan

AU - Tiret, Laurence

AU - Richardson, Sylvia

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Algorithms

KW - Bayes Theorem

KW - Biological Evolution

KW - Exome/genetics

KW - Gene Expression

KW - Genome-Wide Association Study

KW - Humans

KW - Linkage Disequilibrium

KW - Phenotype

KW - Polymorphism, Single Nucleotide/genetics

KW - Quantitative Trait Loci/genetics

U2 - 10.1371/journal.pgen.1003657

DO - 10.1371/journal.pgen.1003657

M3 - SCORING: Journal article

C2 - 23950726

VL - 9

SP - e1003657

JO - PLOS GENET

JF - PLOS GENET

SN - 1553-7404

IS - 8

ER -