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Publication Detail

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

Authors: Gauderman, W James; Mukherjee, Bhramar; Aschard, Hugues; Hsu, Li; Lewinger, Juan Pablo; Patel, Chirag J; Witte, John S; Amos, Christopher; Tai, Caroline G; Conti, David; Torgerson, Dara G; Lee, Seunggeun; Chatterjee, Nilanjan

Published In Am J Epidemiol, (2017 Oct 01)

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 case-control 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.

PubMed ID: 28978192 Exiting the NIEHS site

MeSH Terms: Bayes Theorem; Disease/etiology*; Disease/genetics; Gene-Environment Interaction*; Genetic Predisposition to Disease; Genome-Wide Association Study/methods*; Humans; Logistic Models; Models, Genetic*; Models, Statistical*; Sequence Analysis, DNA; Software*

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