Methods for investigating gene-environment interactions in candidate pathway and genome-wide association studies.
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.
Thomas
Duncan
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Department of Preventive Medicine, University of Southern California, Los Angeles, California, 90089-9011, USA. dthomas@usc.edu
D001499:Bayes Theorem; D004781:Environmental Exposure / Q000009:adverse effects*; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D011634:Public Health; D012107:Research Design