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.

journal_name

Genetics

journal_title

Genetics

authors

Huang J,Wang K,Wei P,Liu X,Liu X,Tan K,Boerwinkle E,Potash JB,Han S

doi

10.1534/genetics.115.185009

subject

Has Abstract

pub_date

2016-03-01 00:00:00

pages

919-29

issue

3

eissn

0016-6731

issn

1943-2631

pii

genetics.115.185009

journal_volume

202

pub_type

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