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
Geneticsjournal_title
Geneticsauthors
Huang J,Wang K,Wei P,Liu X,Liu X,Tan K,Boerwinkle E,Potash JB,Han Sdoi
10.1534/genetics.115.185009subject
Has Abstractpub_date
2016-03-01 00:00:00pages
919-29issue
3eissn
0016-6731issn
1943-2631pii
genetics.115.185009journal_volume
202pub_type
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