FLAGS: A Flexible and Adaptive Association Test for Gene Sets Using Summary Statistics.

TitleFLAGS: A Flexible and Adaptive Association Test for Gene Sets Using Summary Statistics.
Publication TypeJournal Article
Year of Publication2016
AuthorsHuang, J, Wang, K, Wei, P, Liu, X, Liu, X, Tan, K, Boerwinkle, E, Potash, JB, Han, S
JournalGenetics
Volume202
Issue3
Pagination919-29
Date Published2016 Mar
ISSN1943-2631
KeywordsComputer Simulation, Crohn Disease, Genome-Wide Association Study, Genotype, Humans, Models, Genetic, Models, Statistical, Phenotype, Polymorphism, Single Nucleotide
Abstract

Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Despite remarkable success in uncovering many risk variants and providing novel insights into disease biology, genetic variants identified to date fail to explain the vast majority of the heritability for most complex diseases. One explanation is that there are still a large number of common variants that remain to be discovered, but their effect sizes are generally too small to be detected individually. Accordingly, gene set analysis of GWAS, which examines a group of functionally related genes, has been proposed as a complementary approach to single-marker analysis. Here, we propose a FL: exible and A: daptive test for G: ene S: ets (FLAGS), using summary statistics. Extensive simulations showed that this method has an appropriate type I error rate and outperforms existing methods with increased power. As a proof of principle, through real data analyses of Crohn's disease GWAS data and bipolar disorder GWAS meta-analysis results, we demonstrated the superior performance of FLAGS over several state-of-the-art association tests for gene sets. Our method allows for the more powerful application of gene set analysis to complex diseases, which will have broad use given that GWAS summary results are increasingly publicly available.

DOI10.1534/genetics.115.185009
Alternate JournalGenetics
PubMed ID26773050
PubMed Central IDPMC4788129
Grant List076113 / WT_ / Wellcome Trust / United Kingdom
R01 HG006130 / HG / NHGRI NIH HHS / United States
/ WT_ / Wellcome Trust / United Kingdom
R01 GM108716 / GM / NIGMS NIH HHS / United States
R01 AA022994 / AA / NIAAA NIH HHS / United States
R01 GM104369 / GM / NIGMS NIH HHS / United States
R01 AA024486 / AA / NIAAA NIH HHS / United States
R01AA022994 / AA / NIAAA NIH HHS / United States
P30 ES005605 / ES / NIEHS NIH HHS / United States

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