Discovering genetic interactions bridging pathways in genome-wide association studies

Gang Fang (), Wen Wang, Vanja Paunic, Hamed Heydari, Michael Costanzo, Xiaoye Liu, Xiaotong Liu, Benjamin VanderSluis, Benjamin Oately, Michael Steinbach, Brian Van Ness, Eric E. Schadt, Nathan D. Pankratz, Charles Boone, Vipin Kumar () and Chad L. Myers ()
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Gang Fang: Icahn School of Medicine at Mount Sinai
Wen Wang: University of Minnesota
Vanja Paunic: University of Minnesota
Hamed Heydari: University of Toronto
Michael Costanzo: University of Toronto
Xiaoye Liu: University of Minnesota
Xiaotong Liu: University of Minnesota
Benjamin VanderSluis: University of Minnesota
Benjamin Oately: University of Minnesota
Michael Steinbach: University of Minnesota
Brian Van Ness: University of Minnesota
Eric E. Schadt: Icahn School of Medicine at Mount Sinai
Nathan D. Pankratz: University of Minnesota
Charles Boone: University of Toronto
Vipin Kumar: University of Minnesota
Chad L. Myers: University of Minnesota

Nature Communications, 2019, vol. 10, issue 1, 1-18

Abstract: Abstract 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, a global genetic network 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 discover 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.

Date: 2019
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DOI: 10.1038/s41467-019-12131-7

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