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, Chad L. Myers

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


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.

Original languageEnglish (US)
Article number4274
JournalNature communications
Issue number1
StatePublished - Dec 1 2019

Bibliographical note

Funding Information:
We thank Dr. Frank Albert and Dr. Jing Hou for constructive comments on the manuscript. This work was partially supported by NSF grants DBI 0953881 (C.L.M.) and IIS 0916439 (V.K.), NIH grants R01HG005084 (C.L.M.) and R01HG005853 (C.L.M., C.B.), R01MH097276 (G.F., E.E.S.) and R01GM114472 (G.F.), a University of Minnesota Rochester Biomedical Informatics and Computational Biology Program Traineeship Award (G.F.) and a Walter Barnes Lang Fellowship (G.F.). C.L.M. and C.B. are supported by the CIFAR Genetic Networks program. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. Computing resources and data storage services were partially provided by the Minnesota Supercomputing Institute and the UMN Office of Information Technology, respectively. Additional acknowledgements of data sources are included in Supplementary Information.

Publisher Copyright:
© 2019, The Author(s).


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