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

Bottolo, Leonardo, Chadeau-Hyam, Marc, Hastie, David I., Zeller, Tanja, Liquet, Benoit, Newcombe, Paul, Yengo, Loic, Wild, Philipp S., Schillert, Arne, Ziegler, Andreas, Nielsen, Sune F., Butterworth, Adam S., Ho, Weang Kee, Castagne, Raphaele, Munzel, Thomas, Tregouet, David, Falchi, Mario, Cambien, Francois, Nordestgaard, Borge G., Fumeron, Frederic, Tybjaerg-Hansen, Anne, Froguel, Philippe, Danesh, John, Petretto, Enrico, Blankenberg, Stefan, Tiret, Laurence and Richardson, Sylvia (2013) GUESS-ing Polygenic Associations with Multiple Phenotypes Using a GPU-Based Evolutionary Stochastic Search Algorithm. PLoS Genetics, 9 8: . doi:10.1371/journal.pgen.1003657

Author Bottolo, Leonardo
Chadeau-Hyam, Marc
Hastie, David I.
Zeller, Tanja
Liquet, Benoit
Newcombe, Paul
Yengo, Loic
Wild, Philipp S.
Schillert, Arne
Ziegler, Andreas
Nielsen, Sune F.
Butterworth, Adam S.
Ho, Weang Kee
Castagne, Raphaele
Munzel, Thomas
Tregouet, David
Falchi, Mario
Cambien, Francois
Nordestgaard, Borge G.
Fumeron, Frederic
Tybjaerg-Hansen, Anne
Froguel, Philippe
Danesh, John
Petretto, Enrico
Blankenberg, Stefan
Tiret, Laurence
Richardson, Sylvia
Title GUESS-ing Polygenic Associations with Multiple Phenotypes Using a GPU-Based Evolutionary Stochastic Search Algorithm
Journal name PLoS Genetics   Check publisher's open access policy
ISSN 1553-7390
Publication date 2013-01-01
Year available 2013
Sub-type Critical review of research, literature review, critical commentary
DOI 10.1371/journal.pgen.1003657
Open Access Status DOI
Volume 9
Issue 8
Total pages 17
Place of publication San Francisco, CA United States
Publisher Public Library of Science
Language eng
Subject 1311 Genetics
1312 Molecular Biology
1105 Dentistry
1306 Cancer Research
2716 Genetics (clinical)
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.
Keyword Bayesian analysis
Genome wide association studies
Population cohort
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID G1002319
AZ 961-386261/733
A3 01GS0833
ANR 09 GENO 106 01
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Critical review of research, literature review, critical commentary
Collection: School of Mathematics and Physics
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 22 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 24 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 23 Sep 2014, 21:57:07 EST by Kay Mackie on behalf of School of Mathematics & Physics