Journal
ANNUAL REVIEW OF PUBLIC HEALTH, VOL 31
Volume 31, Issue -, Pages 21-36Publisher
ANNUAL REVIEWS
DOI: 10.1146/annurev.publhealth.012809.103619
Keywords
complex diseases; study design; hierarchical models; mechanistic models; synergism; Bayesian methods; exploratory methods for high-dimensional data
Categories
Funding
- NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES [U01ES015090, R01ES019876] Funding Source: NIH RePORTER
- NIEHS NIH HHS [R01 ES019876, U01 ES015090-01, U01 ES015090] Funding Source: Medline
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available