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Abstract:
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The dataset used for QTL (quantitative trait loci) mapping contains not only genotypic values like markers but also phenotypic values measured from some traits. In practice, missing data situations are usually faced due to many reasons. A popular way of handling missing genotypic data is to simply discard subjects with missing markers, which directly results in loss of power. Here we propose to use full information from all subjects, whether having complete marker information or not, to gain as much power as possible. Nonparametric functional mapping approach is used to find evidence of existing QTL, which uses nonparametric function estimation to characterize phenotypic trajectories, and then construct a likelihood ratio test throughout the linkage map. Simulation studies and application to a real dataset are provided to illustrate the power-boosting advantage of our proposed algorithms
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