A set-based association test identifies sex-specific gene sets associated with type 2 diabetes.

Abstract:

:Single variant analysis in genome-wide association studies (GWAS) has been proven to be successful in identifying thousands of genetic variants associated with hundreds of complex diseases. However, these identified variants only explain a small fraction of inheritable variability in many diseases, suggesting that other resources, such as multilevel genetic variations, may contribute to disease susceptibility. In this work, we proposed to combine genetic variants that belong to a gene set, such as at gene- and pathway-level to form an integrated signal aimed to identify major players that function in a coordinated manner conferring disease risk. The integrated analysis provides novel insight into disease etiology while individual signals could be easily missed by single variant analysis. We applied our approach to a genome-wide association study of type 2 diabetes (T2D) with male and female data analyzed separately. Novel sex-specific genes and pathways were identified to increase the risk of T2D. We also demonstrated the performance of signal integration through simulation studies.

journal_name

Front Genet

journal_title

Frontiers in genetics

authors

He T,Zhong PS,Cui Y

doi

10.3389/fgene.2014.00395

subject

Has Abstract

pub_date

2014-11-12 00:00:00

pages

395

issn

1664-8021

journal_volume

5

pub_type

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