Semiparametric and Nonparametric Gene Mapping
نویسندگان
چکیده
We review gene mapping, or inference for quantitative trait loci, in the context of recent research in semi-parametric and non-parametric inference for mixture models. Gene mapping studies the relationship between a phenotypic trait and inherited genotype. Semi-parametric gene mapping using the exponential tilt covers most standard exponential families and improves estimation of genetic effects. Non-parametric gene mapping, including a generalized Hodges-Lehmann shift estimator and Kaplan-Meier survival curve, provide a general framework for model selection for the influence of genotype on phenotype. Examples and summaries of reported simulations show the power of these methods when data are far from normal.
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تاریخ انتشار 2006