Ignatieva, Anastasia (2021) Population genetics models for viral data with recombination. PhD thesis, University of Warwick.

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Abstract

Utilising genetic sequencing data to infer the biological parameters that govern the evolution of a population is an important goal of population genetics. Common features of viral evolution mean that widely used modelling assumptions do not hold, such as that the population size is deterministic, that each site of the genome undergoes at most one mutation, or that recombination (individuals inheriting genetic material from two different parent genomes) is absent. In this thesis, models and methods are developed that relax these assumptions, and are thus particularly suited for the analysis of viral sequencing data.

Birth-death process models naturally capture the stochastic variation and exponential growth in population size that is commonly seen, for instance, with intra-host viral populations. I investigate the properties of sample genealogies when the population evolves according to a birth-death process, and focus in particular on the setting of the population size growing to infinity. Through utilising a time rescaling formalism, distributions characterising the process are derived explicitly, and the results show that the genealogy has an interesting structure in this setting.

The reconstruction of possible histories given a sample of genetic data in the presence of recombination is a challenging problem, and existing methods commonly assume the absence of recurrent mutation. I present KwARG, which implements a heuristic-based algorithm for finding plausible genealogical histories that are minimal or near-minimal in the number of posited recombination and recurrent mutation events. Through applying KwARG to reconstruct possible histories for samples of SARS-CoV-2 data, and combining the results with a principled statistical framework for recombination detection, I present evidence of ongoing recombination of SARS-CoV-2 within human hosts.

Item Type:	Thesis (PhD)
Subjects:	Q Science > QA Mathematics Q Science > QH Natural history > QH426 Genetics
Library of Congress Subject Headings (LCSH):	Population genetics -- Mathematical models, Human gene mapping -- Data processing, Birth and death processes (Stochastic processes), Genealogy -- Statistical methods
Official Date:	October 2021
Dates:	Date Event October 2021 UNSPECIFIED
Institution:	University of Warwick
Theses Department:	Department of Statistics
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Jenkins, Paul ; Hein, Jotun
Sponsors:	Engineering and Physical Sciences Research Council ; Medical Research Council (Great Britain)
Format of File:	pdf
Extent:	x, 145 leaves : illustrations
Language:	eng

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