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

Núria Bonifaci, Eva Colas, Jordi Serra-Musach, Nazanin Karbalai, Joan Brunet, Antonio Gómez, Manel Esteller, Enrique Fernández-Taboada, Antoni Berenguer, Jaume Reventós, Bertram Müller-Myhsok, Laufey Amundadottir, Eric J. Duell, Miquel Ángel Pujana

    Research output: Contribution to journalArticleResearchpeer-review

    1 Citation (Scopus)


    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 significantly 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. © The Author 2013. Published by Oxford University Press. All rights reserved.
    Original languageEnglish
    Article numberbgt403
    Pages (from-to)578-585
    Issue number3
    Publication statusPublished - 1 Jan 2014


    Dive into the research topics of 'Integrating gene expression and epidemiological data for the discovery of genetic interactions associated with cancer risk'. Together they form a unique fingerprint.

    Cite this