Integrative Statistical Methods To Understand The Genetic Basis Of Complex Trait
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Integrative Statistical Methods to Understand the Genetic Basis of Complex Trait
Author | : Gleb Kichaev |
Publisher | : |
Total Pages | : 166 |
Release | : 2018 |
Genre | : |
ISBN | : |
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The Genome-wide Association study (GWAS) is one of the primary tools for understanding the genetic basis of complex traits. In this dissertation I introduce enhanced statistical methods to do integrative GWAS analysis with functional genomic data. First, I describe an integrative fine-mapping framework to prioritize causal variants at known GWAS risk loci. Next, I expand upon this framework to exploit genetic heterogeniety across human populations to improve statistical efficiency. I then consider a new inference strategy to reduce the computational burden of the methodology. Finally, I propose a new approach for GWAS discovery that leverages functional genomic data through polygenic modeling.
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