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.
Original language | English (US) |
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Pages (from-to) | 762-770 |
Number of pages | 9 |
Journal | American journal of epidemiology |
Volume | 186 |
Issue number | 7 |
DOIs | |
State | Published - Oct 1 2017 |
Keywords
- Exposure
- GWAS
- Gene-environment interaction
- Power
- Software
- Statistical models
ASJC Scopus subject areas
- General Medicine