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Published on Apr 25, 2014
Wen-Yun Yang Thesis Defense December 5th, 2013.12:30pm to 2:30pm Boelter 4750
"Efficient Probabilistic Model Based Approaches for Analysis of Human Genomic Data"
Abstract: The advent of genotyping and sequencing technologies has enabled human genetics to discover numerous genetic variants and perform analysis in the level of populations. Understanding the genetic diversity of populations has broad applications in studies of human disease, history, and the relationships within and among populations. First, I propose a new approach, spatial ancestry analysis, for the modeling of genotypes in two and three dimensional space. I show that the explicit modeling of the allele frequency allows us to localize individuals on the geographical map based on their genetic information alone. Second, I therefore generalize the spatial ancestry analysis based on hidden Markov models of admixture to infer the location of the ancestors. This generalized approach is able to localize their recent ancestors with an average of 470Km of the reported locations of their grandparents, for mixed European ancestries. I also introduce a novel spatial-aware genotype imputation method, which achieves superior accuracy over the standard spatial-unaware method. Finally,I propose a novel framework for haplotype inference from short read sequencing that leverages reads that span multiple single nucleotide polymorphism. We devised an efficient sampling method within a probabilistic model to achieve superior performance than existing methods.
Committee: Eleazar Eskin (chair) Bogdan Pasaniuc Wei Wang Jason Ernst.