title: FLAGS: A Flexible and Adaptive Association Test for Gene Sets Using Summary Statistics.
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.
authors.affiliation: Department of Psychiatry, University of Iowa, Iowa City, Iowa 52242.
authors.forename: Jianfei
authors.initials: J
authors.lastname: Huang
authors.affiliation: Department of Biostatistics, University of Iowa, Iowa City, Iowa 52242.
authors.forename: Kai
authors.initials: K
authors.lastname: Wang
authors.affiliation: Department of Biostatistics, University of Texas School of Public Health, Houston, Texas 77225.
authors.forename: Peng
authors.initials: P
authors.lastname: Wei
authors.affiliation: Department of Psychiatry, University of Iowa, Iowa City, Iowa 52242.
authors.forename: Xiangtao
authors.initials: X
authors.lastname: Liu
authors.affiliation: Human Genetics Center, University of Texas Health Science Center, Houston, Texas 77030.
authors.forename: Xiaoming
authors.initials: X
authors.lastname: Liu
authors.affiliation: Department of Internal Medicine, University of Iowa, Iowa City, Iowa 52242 Interdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, Iowa 52242.
authors.forename: Kai
authors.initials: K
authors.lastname: Tan
authors.affiliation: Human Genetics Center, University of Texas Health Science Center, Houston, Texas 77030 Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030.
authors.forename: Eric
authors.initials: E
authors.lastname: Boerwinkle
authors.affiliation: Department of Psychiatry, University of Iowa, Iowa City, Iowa 52242.
authors.forename: James B
authors.initials: JB
authors.lastname: Potash
authors.affiliation: Department of Psychiatry, University of Iowa, Iowa City, Iowa 52242 Interdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, Iowa 52242 shizhong-han@uiowa.edu.
authors.forename: Shizhong
authors.initials: S
authors.lastname: Han
mesh_terms: D003198:Computer Simulation; D003424:Crohn Disease / Q000235:genetics*; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide
keywords: GWAS; association; complex disease; gene set; summary statistics