Dissertation/Thesis Abstract

Inferring Interpretable Representations of Population Structure
by Marcus, Joseph Henry, Ph.D., The University of Chicago, 2020, 201; 28089505
Abstract (Summary)

Inferring population structure is important for several applications in medical and population genetic studies. However, the output of population structure inference methods can often be challenging to interpret. The goal of this dissertation is to apply population structure inference tools to learn and visualize demographic history and develop statistical methods for interpretable population structure inference. In Chapter 2, I apply population structure inference tools to learn about the genetic history of the Mediterranean island of Sardinia using a new ancient DNA dataset. In Chapter 3, I develop a fast and flexible statistical method and optimization algorithm for inferring and visualizing non-homogeneous patterns of migration using spatially indexed population genetic data. Finally, in Chapter 4, I develop a new Bayesian matrix factorization method and variational inference algorithm for emphasizing shared evolutionary histories when representing population structure. Overall, the work presented in this dissertation aims to provide interpretable representations of population structure which, in turn, give understanding into the underlying demographic factors that shape patterns of genetic variation.

Indexing (document details)
Advisor: Novembre, John
Commitee: Foygel Barber, Rina, Di Rienzo, Anna, Stephens, Matthew
School: The University of Chicago
Department: Human Genetics
School Location: United States -- Illinois
Source: DAI-A 82/6(E), Dissertation Abstracts International
Subjects: Genetics, Statistics, Bioinformatics, Computational chemistry, Demography
Keywords: Ancient DNA, Computational genomics, Data visualization, Population genetics, Statistical genetics, Mediterranean island of Sardinia
Publication Number: 28089505
ISBN: 9798557020855
Copyright © 2021 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy