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EB Research - Methodological Research in Epidemiology
Biomarker/Analytical Development: Gene-Environment Interactions
In studying complex diseases such as gestational diabetes, birth defects, miscarriage, etc., gene-environment interactions are critical hypotheses. One important implication of unmasking gene-environment interactions is to identify highly susceptible populations such that modifiable exposures that cause disease can be prevented. However, genetically susceptible individuals cannot be identified unless there exist a better understanding of how genes interact with individuals' environment to cause disease.
One complication in studying gene-environment interactions is the high cost due to genotyping the large number of people necessary to have sufficient power to detect an interaction. Additionally, samples may physically lack the volume of biospecimen necessary to perform a genotype, or due to confidentiality of the individuals in the study, individual genotyping might be prohibited. Researchers here are examining a new study design to increase power by strategically pooling biospecimens. Pooling can reduce the cost due to genotyping, it requires less biospecimens from each individual, and it protects the confidentiality of the individuals in the study. Therefore, by using a pooling strategy, previously underpowered or abandoned gene-environment hypotheses can be explored.
These issues have been the motivation for numerous papers as well as a collaborative effort funded by the American Chemistry Council with the goal of providing the methodological tools necessary to assess and address issues related to gene environment interactions.
Enrique F. Schisterman, Ph.D.
- Roy A, Danaher MR, Mumford SL, Chen Z. (2012). A Bayesian order restricted model for hormonal dynamics during menstrual cycles of healthy women. Statistics in Medicine, 31(22):2428-2440. DOI: 10.1002/sim.4419. PMID: 22147446
- Danaher MR, Schisterman EF, Roy A, Albert PS. Estimation of gene-environment interaction by pooling biospecimens. Statistics in Medicine; in press. doi: 10.1002/sim.5357. PMID: 22859290
- Danaher MR, Roy A, Chen Z, Mumford SL, Schisterman EF. Minkowski-Weyl priors for models with parameter constraints: an analysis of the BioCycle Study. Journal of the American Statistical Association; in press.