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Update on the State of the Science for Analytical Methods for Gene-Environment Interactions

TitleUpdate on the State of the Science for Analytical Methods for Gene-Environment Interactions
Publication TypeJournal Article
Year of Publication2017
AuthorsGauderman, WJ, Mukherjee, B, Aschard, H, Hsu, L, Lewinger, JP, Patel, CJ, Witte, JS, Amos, C, Tai, CG, Conti, D, Torgerson, DG, Lee, S, Chatterjee, N
JournalAm J Epidemiol
Date PublishedOct 01
ISBN Number0002-9262
Accession Number28978192
Keywords*Gene-Environment Interaction, *Models, Genetic, *Models, Statistical, *Software, Bayes Theorem, Disease/*etiology/genetics, exposure, gene-environment interaction, Genetic Predisposition to Disease, Genome-Wide Association Study/*methods, Gwas, Humans, Logistic Models, power, Sequence Analysis, DNA, Software, statistical models

The analysis of gene-environment interaction (GxE) 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 GxE but also new analytical challenges. In this article, we summarize several statistical approaches that can be used to test for GxE in a genome-wide association study. These include traditional models of GxE in a case-control or quantitative trait study as well as alternative approaches that can provide substantially greater power. The latest methods for analyzing GxE 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 GxE 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.