Journal article
FLAGS: A Flexible and Adaptive Association Test for Gene Sets Using Summary Statistics
Genetics (Austin), Vol.202(3), pp.919-929
03/2016
DOI: 10.1534/genetics.115.185009
PMCID: PMC4788129
PMID: 26773050
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.
Details
- Title: Subtitle
- FLAGS: A Flexible and Adaptive Association Test for Gene Sets Using Summary Statistics
- Creators
- Jianfei Huang - Department of Psychiatry, University of Iowa, Iowa City, Iowa 52242Kai Wang - Department of Biostatistics, University of Iowa, Iowa City, Iowa 52242Peng Wei - Department of Biostatistics, University of Texas School of Public Health, Houston, Texas 77225Xiangtao Liu - Department of Psychiatry, University of Iowa, Iowa City, Iowa 52242Xiaoming Liu - Human Genetics Center, University of Texas Health Science Center, Houston, Texas 77030Kai Tan - Department of Internal Medicine, University of Iowa, Iowa City, Iowa 52242 Interdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, Iowa 52242Eric Boerwinkle - Human Genetics Center, University of Texas Health Science Center, Houston, Texas 77030 Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030James B Potash - Department of Psychiatry, University of Iowa, Iowa City, Iowa 52242Shizhong Han - 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
- Resource Type
- Journal article
- Publication Details
- Genetics (Austin), Vol.202(3), pp.919-929
- Publisher
- United States
- DOI
- 10.1534/genetics.115.185009
- PMID
- 26773050
- PMCID
- PMC4788129
- ISSN
- 0016-6731
- eISSN
- 1943-2631
- Grant note
- R01 HG006130 / NHGRI NIH HHS R01 AA024486 / NIAAA NIH HHS P30 ES005605 / NIEHS NIH HHS R01AA022994 / NIAAA NIH HHS R01 GM108716 / NIGMS NIH HHS R01 GM104369 / NIGMS NIH HHS 076113 / Wellcome Trust R01 AA022994 / NIAAA NIH HHS
- Language
- English
- Date published
- 03/2016
- Academic Unit
- Psychiatry; Anatomy and Cell Biology; Biostatistics
- Record Identifier
- 9983997322702771
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