While it is interesting to examine the evolutionary history and phylogenetic relationship between species, for example, in a sort of tree of life, there is also a great deal to be learned from examining population structure and relationships within species. A careful description of phylogenetic relationships within species provides insights into causes of phenotypic variation, including disease susceptibility. The better we are able to understand the patterns of genotypic variation within species, the better these populations may be used as models to identify causative variants and possible therapies, for example through targeted genome-wide association studies (GWAS). My thesis describes a model of local phylogenetic structure, how it can be effectively derived under various circumstances, and useful applications and visualizations of this model to aid genetic studies. I introduce a method for discovering phylogenetic structure among individuals of a population by partitioning the genome into a minimal set of intervals within which there is no evidence of recombination. I describe two extensions of this basic method. The first allows it to be applied to heterozygous, in addition to homozygous, genotypes and the second makes it more robust to errors in the source genotypes. I demonstrate the predictive power of my local phylogeny model using a novel method for genome-wide genotype imputation. This imputation method achieves very high accuracy - on the order of the accuracy rate in the sequencing technology - by imputing genotypes in regions of shared inheritance based on my local phylogenies. Comparative genomic analysis within species can be greatly aided by appropriate visualization and analysis tools. I developed a framework for web-based visualization and analysis of multiple individuals within a species, with my model of local phylogeny providing the underlying structure. I will describe the utility of these tools and the applications for which they have found widespread use.Doctor of Philosoph
