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Statistical Methods for Mapping Quantitative Trait Loci in Experimental Crosses

 Ani Manichaikul, PhD Candidate, Johns Hopkins Department of Biostatistics

Identifying and mapping genetic loci underlying variation in a quantitative phenotype is a key step toward dissecting the molecular bases of disease. Using data from experimental crosses, it is possible to pick out such genomic regions of interest, known as Quantitative Trait Loci (QTL), by applying models relating observed phenotype values with genotypes. This thesis presents three problems in QTL mapping along with approaches we have developed to address these problems. First, we look at methods of constructing confidence intervals for QTL location. Such intervals are an important tool in guiding follow-up of experimental crosses to target confirmatory experiments in genomic regions of interest from an initial cross. Next, we consider the challenge of identifying QTL involved in gene-gene interaction. Such epistatic QTL can be particularly difficult to find by traditional methods of analysis which consider genetic loci one at a time. We use multiple QTL models to allow for pairwise interaction and present a model selection approach to identify QTL. Finally, we consider the problem of binary trait mapping in the presence of selective genotyping, with individuals genotyped disproportionately according to affectation status. We explain why typical methods of analysis do not apply in this context and present more appropriate methods that account for selective genotyping. The problems presented in this thesis span a range of issues encountered in analysis of experimental cross data. Our approaches demonstrate how classical methods for QTL mapping can be refined to provide more precise results, and adapted to deal with new experimental approaches that are emerging as modifications of more traditional designs.

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