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

Am J Epidemiol. 2017 Oct 1;186(7):762-770. doi: 10.1093/aje/kwx228.


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.

Keywords: GWAS; exposure; gene-environment interaction; power; software; statistical models.

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*