Dissertation/Thesis Abstract

Models and forward simulations of selection, human demography, and complex traits
by Uricchio, Lawrence Hart, Ph.D., University of California, San Francisco, 2014, 112; 3681226
Abstract (Summary)

Evolutionary forces such as recombination, demography, and selection can shape patterns of genetic diversity within populations and contribute to phenotypic variation. While theoretical models exist for each of these forces independently, mathematically modeling their joint impact on patterns of genetic diversity remains very challenging. Fortunately, it is possible to perform forward-in-time computer simulations of DNA sequences that incorporate all of these forces simultaneously. Here, I show that there are trade-offs between computational efficiency and accuracy for simulations of a widely investigated model of recurrent positive selection. I develop a theoretical model to explain this trade-off, and a simple algorithm that obtains the best possible computational performance for a given error tolerance. I then pivot to develop a framework for simulations of human DNA sequences and genetically complex phenotypes, incorporating recently inferred demographic models of human continental groups and selection on genes and non-coding elements. I use these simulations to investigate the power of rare variant association tests in the context of rampant selection and non-equilibrium demography. I show that the power of rare variant association tests is in some cases quite sensitive to underlying assumptions about the relationship between selection and effect sizes. This work highlights both the challenge and the promise of applying forward simulations in genetic studies that seek to infer the parameters of evolutionary models and detect statistical associations.

Indexing (document details)
Advisor: Hernandez, Ryan D.
Commitee: Wall, Jeff D., Witte, John S.
School: University of California, San Francisco
Department: Biological and Medical Informatics
School Location: United States -- California
Source: DAI-B 76/06(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Genetics, Bioinformatics
Keywords: Complex phenotypes, Genetic diversity, Natural selection, Rare variants, Simulation
Publication Number: 3681226
ISBN: 978-1-321-53868-7
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