Gene-Environment Independence in Case–Control Studies: Issues of Parameterization and Bayesian Inference
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We consider the problem of exploiting the gene–environment independence (GEI) assumption in a case–control study inferring the joint effect of genotype and environmental exposure on disease risk. Specifically, we focus on the special case that both genotype and environmental exposure are binary. We note that the prospective intercept can sometimes be identified as a pair of “twin” values. Also, the GEI and general maximum-likelihood estimators of the gene–environment interaction coincide if the data cell proportions are directly compatible with the GEI assumption. Further, we approach the problem in a Bayesian framework by reweighing the general posterior subject to the prior specified over the subset of parameter space that is consistent with the GEI assumption. Some simulation studies have been conducted to compare the proposed method to its general counterpart. Finally, we have also extended the proposed method to address the concern that the GEI assumption may sometimes be violated.
KeywordsBayesian inference Binary covariates Case–control study Constrained posterior Gene–environment independence Identifiability
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