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学術論文

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

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Müller-Myhsok,  Bertram
Max Planck Institute of Psychiatry, Max Planck Society;

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引用

Bonifaci, N., Colas, E., Serra-Musach, J., Karbalai, N., Brunet, J., Gomez, A., Esteller, M., Fernandez-Taboada, E., Berenguer, A., Reventos, J., Müller-Myhsok, B., Amundadottir, L., Duell, E. J., & Angel Pujana, M. (2014). Integrating gene expression and epidemiological data for the discovery of genetic interactions associated with cancer risk. CARCINOGENESIS, 35(3), 578-585. doi:10.1093/carcin/bgt403.


引用: http://hdl.handle.net/11858/00-001M-0000-0026-A948-4
要旨
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 836%, 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- 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 2559%). The method presented here can potentially support the identification of genetic interactions associated with cancer risk, providing novel mechanistic hypotheses for carcinogenesis.