Munich Cluster for Systems Neurology

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Integrating gene expression and epidemiological data for the discovery of genetic interactions associated with cancer risk

Carcinogenesis. 2013 Dec 2. [Epub ahead of print]

Authors/Editors: Bonifaci N, Colas E, Serra Musach J, Karbalai N, Brunet J, Gómez A, Esteller M, Fernández-Taboada E, Berenguer A, Reventós J, Müller-Myhsok B, Amundadottir L, Duell EJ, Angel Pujana M.
Publication Date: 2013


Dozens of common genetic variants associated with cancer risk have been identified through genome-wide association studies (GWASs). However, these variants only explain a modest fraction of the heritability of disease. The missing heritability has been attributed to several factors, among them the existence of genetic interactions (GxG). Systematic screens for GxG in model organisms have revealed their fundamental influence in complex phenotypes. In this scenario, GxG overlap significantly with other types of gene and/or protein relationships. Here, by integrating predicted GxG from GWAS data and complex- and context-defined gene co-expression profiles, we provide evidence for GxG associated with cancer risk. GxG predicted from a breast cancer GWAS dataset identified significant overlaps (relative enrichments of 8-36%, empirical P values < 0.05 - 10-4) with complex (non-linear) gene co-expression in breast tumors. The use of gene or protein data not specific for breast cancer did not reveal overlaps. According to the predicted GxG, experimental assays demonstrated functional interplay between LPP and TGFβ signaling in the MCF10A non-tumorigenic mammary epithelial cell model. Next, integration of pancreatic tumor gene expression profiles with pancreatic cancer GxG predicted from a GWAS corroborated the observations made for breast cancer risk (relative enrichments of 25-59%). The method presented here can potentially support the identification of genetic interactions associated with cancer risk, providing novel mechanistic hypotheses for carcinogenesis.

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