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Abstract:
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Recent advances in association mapping methods along with improvements in sequencing technology have made it possible to link locations along the genome (single nucleotide polymorphisms, or SNPs) with quantitative traits. Although classical methods have identified some links, progress has been stinted by an inability of these techniques to consider biologically realistic scenarios such as traits affected by multiple SNPs. Methods such as regression-based techniques search for these relationships, but often ignore information about the uneven evolutionary relatedness among individuals. Moreover, most phylogenetic models, or models that account for evolutionary relatedness, are limited by an inability to consider multiple genetic influences on a trait. Here, a phylogenetic model that includes multiple genetic and external influences on a trait is used to analyze a real mouse data set. In this context, a Gibbs sampler is proposed to estimate parameters, and estimates are used to make inference during analysis. This phylogenetic method aims to gain power by accounting for evolutionary relatedness while considering multiple genetic and external influences on quantitative traits.
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