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SIMULATION-DRIVEN ANALYSES IN POPULATION GENETICS: FROM GENETIC MAPPING TO THE EVOLUTION OF GENES AND POPULATIONS

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Advancements in genome sequencing technology and the development of powerful evolutionary simulations have brought exciting new ideas to the forefront of population genetic research, spanning both theoretical models and applications. Population genetics investigates how the patterns of genetic variation in biological populations are shaped by various evolutionary processes, such as natural selection, random genetic drift, and demographic events. One key goal in population genetics is to develop and apply mathematical models that allow us to interpret the distribution and temporal dynamics of genetic variants in a population and to test evolutionary hypotheses. As population genomic data becomes more widely available and detailed, the complexity of modeling has also increased. In particular, there is a need to include much more realistic evolutionary processes in our analyses. Since such processes are often difficult to model mathematically, simulations have become an increasingly critical tool to help us better understand the complicated interactions between such processes and the signatures they are expected to produce in empirical genetic data.In this thesis, I use extensive evolutionary simulations in concert with mathematical approaches to investigate the connection between population genetic signatures and their underlying evolutionary processes. In Chapter 2, I first investigate a novel genetic mapping design called Bulk Segregant Analysis (BSA). I derive an analytical solution to predict the genomic resolution at which the causal variants associated with a selected trait in such experiments can be identified, and validate our results with simulations. In Chapter 3, I use simulations of evolutionary scenarios with different models of natural selection to explore the potential driving forces behind the observed signatures of positive selection on the bam protein in Drosophila. These simulation results suggest a new model to characterize the genetic interaction between bam and the endosymbiont bacterium Wolbachia pipientis, which could explain the signals of positive selection observed at bam. In Chapter 4, I conduct a comprehensive demographic inference on the Drosophila melanogaster Global Diversity Lines (GDL) data set to test different hypotheses about the demographic history of this species across five continents. The result of this demographic inference can serve as a new null model for the detection of positive selection in Drosophila melanogaster.

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142 pages

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2022-05

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coalescence theory; demographic inference; genetic simulations; natural selection; Population genetics; QTL mapping

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Messer, Philipp

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Aquadro, Charles F.
Williams, Amy L

Degree Discipline

Computational Biology

Degree Name

Ph. D., Computational Biology

Degree Level

Doctor of Philosophy

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Government Document

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dissertation or thesis

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