Genealogical-based inference in statistical genetics

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The variation observed today in the human genome is a result of genetic processes, such as mutation, acting over time on the DNA of our ancestors. The 'gene genealogy' for a sample of genes from unrelated individuals is a tree describing the historical ancestral genetic events and relationships giving rise to this variation. However, since the time scale is on the order of tens of thousands of years, the true gene genealogy cannot be known. I am interested in using information from genetic markers - variable DNA sites - to infer genealogical relationships between unrelated individuals. My research, which draws on statistics and population genetics, involves the development and dissemination of statistical approaches incorporating these ancestral trees. ****I have recently developed an approach to probabilistically sample genealogical trees that are likely to have given rise to a sample of genetic data. The central goal of my research program is to increase the utility and application of the genealogy sampling approach that I developed. This is accomplished with two long-term objectives. The first objective of my research program is to make computational improvements to the sampling approach. The first improvement will use statistical techniques to speed up the computational time of the sampler. The second improvement will extend the sampler to handle missing genotype data. ****Additional power to discover genetic risk factors is of great interest to human geneticists. The second objective of my research program is to develop a new genealogy-based method for finding genetic risk factors, and in particular rare variants. I will develop and evaluate statistics that relate similarity in disease state to genetic relatedness, as measured by the genealogical tree. The sampling approach that I have developed will be used to handle the uncertainty of the genealogy and I will compare my approach to existing approaches that ignore the uncertainty. ********************************************