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||Integrating gene expression and epidemiological data for the discovery of genetic interactions associated with cancer risk.
||Bonifaci N, Colas E, Serra-Musach J, Karbalai N, Brunet J, GÃ³mez A, Esteller M, FernÃ¡ndez-Taboada E, Berenguer A, ReventÃ³s J, MÃ¼ller-Myhsok B, Amundadottir L, Duell EJ, Pujana MÃ
||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 (G Ã G). Systematic screens for G Ã G in model organisms have revealed their fundamental influence in complex phenotypes. In this scenario, G Ã G overlap significantly with other types of gene and/or protein relationships. Here, by integrating predicted G Ã G from GWAS data and complex- and context-defined gene coexpression profiles, we provide evidence for G Ã G associated with cancer risk. G Ã G predicted from a breast cancer GWAS dataset identified significant overlaps [relative enrichments (REs) of 8-36%, empirical P values < 0.05 to 10(-4)] with complex (non-linear) gene coexpression in breast tumors. The use of gene or protein data not specific for breast cancer did not reveal overlaps. According to the predicted G Ã G, experimental assays demonstrated functional interplay between lipoma-preferred partner and transforming growth factor-Î² signaling in the MCF10A non-tumorigenic mammary epithelial cell model. Next, integration of pancreatic tumor gene expression profiles with pancreatic cancer G Ã G predicted from a GWAS corroborated the observations made for breast cancer risk (REs 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.