Methods for Investigating Gene-Environment Interactions in Candidate Pathway and Genome-Wide Association Studies

Department of Preventive Medicine, University of Southern California, Los Angeles, California, 90089-9011, USA.
Annual Review of Public Health (Impact Factor: 6.47). 04/2010; 31(1):21-36. DOI: 10.1146/annurev.publhealth.012809.103619
Source: PubMed


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.

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Available from: Duncan C Thomas, Oct 04, 2015
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    • "There are a growing number of literature works on methods for pathway modeling, motivated in large part by an interest in mining GWAS data for commonalities across related genes that individually may not achieve genomewide significance but in the aggregate may point to novel pathways (see [18] for a review of gene set enrichment analysis and alternatives). Our goal here is more modest, guided by an a priori selection of strong candidate genes [19]. Like other methods of pathway analysis, however, we aim to exploit external knowledge about the biological function of each gene and the relationships between them [20]. "
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    International Journal of Genomics 12/2013; 2013:406217. DOI:10.1155/2013/406217 · 0.95 Impact Factor
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    • "In a simple case-control study when the outcome is a binary variable, exploration of genotype, and gender interaction by logistic regression is a mostly neglected, but rather straightforward approach. For more complicated designs and for quantitative outcomes, methods developed to analyze gene and environment interactions can be used as if gender was a binary environmental exposure variable (Thomas, 2010). "
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    Frontiers in Genetics 11/2012; 3:268. DOI:10.3389/fgene.2012.00268
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    • "Observed human variation in response to environmental stressors suggests the possibility of gene by environment (G × E) interactions (Gottlieb, 2003; Johnston and Edwards, 2002). Identifying these G × E interactions through empirical research, however, has been difficult (Burmeister et al., 2008; Thomas, 2010b). "
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