Investigations into the causes and consequences of mutation using experimental, genomic, and mathematical modeling approaches [electronic resource]

Mutations provide the raw material of evolution, and thus fundamental to our ability to study genome evolution is the need to have precise measurements of mutational rates and patterns, as well as a quantitative understanding of the population dynamics of new adaptive and deleterious mutations. To this end, this dissertation explores the causes and consequences of new mutations. In the first chapter, along with coauthors Susanne Tilk, Jane Park, and Dmitri A. Petrov, I explore the rates and patterns of de novo mutations which were generated in laboratory strains of Drosophila melanogaster. These are mutation accumulation (MA) lines, which are the product of maintaining the flies in tiny populations for many generations, therefore rendering natural selection ineffective and thus allowing new mutations to accrue in the genome. In addition to generating a novel dataset of sequenced MA lines, I perform a meta-analysis of all published MA studies, which allows more precise estimates of mutational patterns across the genome. In the second chapter, along with coauthor Dmitri A. Petrov, I explore patterns of mutation using polymorphisms segregating at extremely low frequencies, which I identify by leveraging the availability of population genomic data from natural populations of Drosophila melanogaster. Extremely rare polymorphisms are difficult to detect with high confidence due to the problem of distinguishing them from sequencing error, however a dataset of true rare polymorphisms would allow the quantification of mutational patterns. This is due to the fact that rare polymorphisms share two important characteristics with truly de novo mutations - rare polymorphisms are on average younger, and, because the frequency dynamics of rare polymorphisms are dominated by stochastic forces, rare polymorphisms will have a spectrum of genetic variants that is relatively unbiased by selective forces (e.g. natural selection). In this second chapter I identify a high quality set of rare polymorphisms in populations of Drosophila melanogaster, and then use this dataset to measure mutational patterns, including the variation in mutational spectrum across different base pair contexts. In the third chapter, along with coauthors Jamie Blundell and Dmitri A. Petrov, I investigate the effect of recessive deleterious mutations on rates of adaptation in sexually reproducing diploids using a mathematical modeling approach. There has been much work modeling the dynamics of genetic hitchhiking, which is the study of how the fate of a mutation is altered when it is genetically linked on the chromosome to other mutations. Of particular interest is the question of how adaptive mutations are affected by linked deleterious neighbors, and to that end the vast majority of published work has focused on deleterious mutations with codominant effects. Codominance is when having one copy of a mutation has half the fitness effect as having two copies, however it is known that many deleterious mutations have recessive effects, where for example recessive lethal mutations have close to zero effect in just one copy. In this third chapter I model how new adaptive mutations are impacted when they land on a genetic background containing a recessive deleterious mutation, and show that their dynamics are drastically altered, resulting in a phenomenon we name a 'staggered sweep'. In the fourth chapter, along with coauthors Benjamin A. Wilson, Nandita R. Garud, Alison F. Feder, and Pleuni S. Pennings, I perform a literature review of the population genetics of rapid adaptation in human pathogens, in particular the evolution of drug resistance. Human pathogens can have very interesting population dynamics, for example large population sizes that are undergoing extreme selective pressures and evolving adaptive responses on observable time scales. In this chapter we review the use of sequence data and population genetic theory in studying the evolution of drug resistance in five organisms: HIV, influenza, tuberculosis, Staphylococcus aureus, and the malaria parasite Plasmodium falciparum.

Keywords: Genetics.
Advisor: Petrov Dmitri Alex
Bustamante Carlos (Carlos D.)
Sherlock Gavin
Tang Hua (Geneticist)