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

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

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)762-770
Number of pages9
JournalAmerican journal of epidemiology
Issue number7
StatePublished - Oct 1 2017


  • Exposure
  • GWAS
  • Gene-environment interaction
  • Power
  • Software
  • Statistical models

ASJC Scopus subject areas

  • Epidemiology


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