Semiparametric analysis of case-control genetic data in the presence of environmental factors
In the past decade, many statistical methods have been proposed for the analysis of caseâ€“control genetic data with an emphasis on haplotype-based disease association studies. Most of the methodology has concentrated on the estimation of genetic (haplotype) main effects. Most methods accounted for environmental and gene-environment interaction effects by utilizing prospective-type analyses that may lead to biased estimates when used with caseâ€“control data. Several recent publications addressed the issue of retrospective sampling in the analysis of caseâ€“control genetic data in the presence of environmental factors by developing new efficient semiparametric statistical methods. I present the new Stata command, haplologit, that implements efficient profile-likelihood semiparametric methods for fitting geneâ€“environment models in the very important special cases of a) a rare disease, b) a single candidate gene in Hardy-Weinberg equilibrium, and c) independence of genetic and environmental factors.
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