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

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.

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

Figure 1.

Comparison of additive and multiplicative models for the joint effects of gene (G) and environment (E). Assuming binary G and binary E, the joint-effect relative risk RR_GE = RR_G × RR_E × RR_G×E—relative risk for G = 1 and E = 1 compared with G = 0 and E = 0—is shown under the additive and multiplicative models. Under the 2 different models, the RR_GE is determined by underlying main-effect relative risks (RR_G and RR_E), that is, relative-risk associated with one factor (e.g., G = 1 vs G = 0) while the other factor is fixed at baseline (e.g., E = 0) in 2 different functional forms. Throughout, it is assumed that G = 1 and E = 1 correspond to higher-risk categories than G = 0 and E = 0 (RR_G > 1 and RR_E > 1).

Figure 2.

Required sample size (n) versus gene-environment interaction (G×E) effect size (OR_G×E) to achieve 80% power using 4 different analysis methods, assuming an equal number of controls (except for the case-only analysis). The underlying model assumes that G has minor allele frequency 30% and additive (0–2) genotype coding, E is binary with prevalence 40%, and neither G nor E has a main effect on disease risk (β_G = β_E = 0.0, model 1). The calculations also assume a scan of 1 million single nucleotide polymorphisms and an overall type I error rate of 0.05, yielding significance threshold for a single interaction of single nucleotide polymorphism × E of 5 × 10⁻⁸. For example, when OR_G×E = 1.5, a case-control study would require n = 4,557 cases using a standard test of β_G×E = 0 from model 1. The same power can be achieved with n = 2,716 cases by using a 2-df joint test of G and G×E, n = 2,160 by using a case-only analysis, or with only n = 1,654 by using the 2-step EDGE (31) approach. QUANTO (83), modified to accommodate the EDGE method, was used for computations.