PT  - JOURNAL ARTICLE
AU  - Gang Fang
AU  - Wen Wang
AU  - Vanja Paunic
AU  - Hamed Heydari
AU  - Michael Costanzo
AU  - Xiaoye Liu
AU  - Xiaotong Liu
AU  - Benjamin Oately
AU  - Michael Steinbach
AU  - Brian Van Ness
AU  - Eric E. Schadt
AU  - Nathan D. Pankratz
AU  - Charles Boone
AU  - Vipin Kumar
AU  - Chad L. Myers
TI  - Discovering genetic interactions bridging pathways in genome-wide association studies
AID  - 10.1101/182741
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 182741
4099  - http://biorxiv.org/content/early/2017/08/30/182741.short
4100  - http://biorxiv.org/content/early/2017/08/30/182741.full
AB  - Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, the global genetic networks mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. Applying BridGE broadly, we discovered significant interactions in Parkinson’s disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data.