Methods for investigating gene-environment interactions in candidate pathway and genome-wide association studies

Abstract

Despite the considerable enthusiasm about the yield of novel and replicated discoveries of genetic associations from the new generation of genome-wide association studies (GWAS), the proportion of the heritability of most complex diseases that have been studied to date remains small. Some of this "dark matter" could be due to gene-environment (G x E) interactions or more complex pathways involving multiple genes and exposures. We review the basic epidemiologic study design and statistical analysis approaches to studying G x E interactions individually and then consider more comprehensive approaches to studying entire pathways or GWAS data. In addition to the usual issues in genetic association studies, particular care is needed in exposure assessment, and very large sample sizes are required. Although hypothesis-driven, pathway-based and agnostic GWA study approaches are generally viewed as opposite poles, we suggest that the two can be usefully married using hierarchical modeling strategies that exploit external pathway knowledge in mining genome-wide data.

Figure 1

Conceptual basis for G×E interactions in bladder and colon cancer (33, 65, 71)

Figure 2

Example of a four-way interaction among two environmental factors, well done red meat (R/M = rare-medium, WD = well done) and smoking, and two genes, CYP1A2 phenotype (L = ≤ median; H = > median) and NAT2 (S/I = slow-intermediate, R = rapid). Data from Table 6 of (65), risks scaled separately for ever and never smokers (baseline RR = 1.29 (95%CI 0.7–2.3)).