GUESS-ing Polygenic Associations with Multiple Phenotypes Using a GPU-Based Evolutionary Stochastic Search Algorithm

Leonardo Bottolo*, Marc Chadeau-Hyam, David I. Hastie, Tanja Zeller, Benoit Liquet, Paul Newcombe, Loic Yengo, Philipp S. Wild, Arne Schillert, Andreas Ziegler, Sune F. Nielsen, Adam S. Butterworth, Weang Kee Ho, Raphaële Castagné, Thomas Munzel, David Tregouet, Mario Falchi, François Cambien, Børge G. Nordestgaard, Fredéric FumeronAnne Tybjærg-Hansen, Philippe Froguel, John Danesh, Enrico Petretto, Stefan Blankenberg, Laurence Tiret, Sylvia Richardson

*Corresponding author for this work
43 Citations (Scopus)


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.

Original languageEnglish
Article numbere1003657
JournalPLoS Genetics
Issue number8
Publication statusPublished - 08.2013


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