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

N. Bonifaci, E. Colas, J. Serra-Musach, N. Karbalai, J. Brunet, A. Gomez, M. Esteller, E. Fernandez-Taboada, A. Berenguer, J. Reventos, B. Muller-Myhsok, L. Amundadottir (+2 others)
2013 Carcinogenesis  
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 × G). Systematic screens for G × G in model organisms have revealed their fundamental influence in complex phenotypes. In this scenario, G × G overlap
more » ... with other types of gene and/or protein relationships. Here, by integrating predicted G × G from GWAS data and complex-and context-defined gene coexpression profiles, we provide evidence for G × G associated with cancer risk. G × 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 × G, experimental assays demonstrated functional interplay between lipoma-preferred partner and transforming growth factor-β signaling in the MCF10A non-tumorigenic mammary epithelial cell model. Next, integration of pancreatic tumor gene expression profiles with pancreatic cancer G × 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.
doi:10.1093/carcin/bgt403 pmid:24296589 fatcat:4u43t55xbjdebhmoyfej7ca4om