@article{f16a27dce7d444d1ac28fb08bef08369,
title = "Update on the State of the Science for Analytical Methods for Gene-Environment Interactions",
abstract = "The analysis of gene-environment interaction (G×E) may hold the key for further understanding the etiology of many complex traits. The current availability of high-volume genetic data, the wide range in types of environmental data that can be measured, and the formation of consortiums of multiple studies provide new opportunities to identify G×E but also new analytical challenges. In this article, we summarize several statistical approaches that can be used to test for G×E in a genome-wide association study. These include traditional models of G×E in a casecontrol or quantitative trait study as well as alternative approaches that can provide substantially greater power. The latest methods for analyzing G×E with gene sets and with data in a consortium setting are summarized, as are issues that arise due to the complexity of environmental data. We provide some speculation on why detecting G×E in a genome-wide association study has thus far been difficult. We conclude with a description of software programs that can be used to implement most of the methods described in the paper.",
keywords = "Exposure, GWAS, Gene-environment interaction, Power, Software, Statistical models",
author = "Gauderman, {W. James} and Bhramar Mukherjee and Hugues Aschard and Li Hsu and Lewinger, {Juan Pablo} and Patel, {Chirag J.} and Witte, {John S.} and Christopher Amos and Tai, {Caroline G.} and David Conti and Torgerson, {Dara G.} and Seunggeun Lee and Nilanjan Chatterjee",
note = "Funding Information: Massachusetts (Hugues Aschard); Centre de Bioinformatique, Biostatistique et Biologie Int{\'e}grative (C3BI), Institut Pasteur, Paris, France (Hugues Aschard); Biostatistics Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington (Li Hsu); Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts (Chirag J. Patel); Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California (John S. Witte, Caroline G. Tai); Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, New Hampshire (Christopher Amos); Department of Medicine, University of California San Francisco, San Francisco, California (Dara G. Torgerson); Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland (Nilanjan Chatterjee); and Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, Maryland (Nilanjan Chatterjee). Research reported in this publication was supported by National Institute of Environmental Health Sciences, National Cancer Institute, and National Human Genome Research Institute of the National Institutes of Health under (grants P01CA196569, R01CA201407, P30ES07048, and R21ES024844 to W.J.G.; R21HG007687 to H.A.; R01CA140561, R01CA201407, and P01CA196569 to D.C.; R01CA189532, R01CA195789, and P01CA53996 to L.H.; P01CA196569 and R01CA201407 to J.P.L.; R21ES020811 and NSF DMS 1406712 to B.M.; UL1TR001086, R01LM012012, U19CA203654, and P30CA023108 to C.A.; R00ES023504 and R21ES025052 to C.J.P.; R01CA201358 and CA088164 to J.S.W.; and F31CA200139 to C.G.T.).",
year = "2017",
doi = "10.1093/aje/kwx228",
language = "English (US)",
volume = "186",
pages = "762--770",
journal = "American Journal of Epidemiology",
issn = "0002-9262",
publisher = "Oxford University Press",
number = "7",
}