Population Genetics has led to countless numbers of fruitful studies of evolution, due to its abilities for prediction and description of the most important evolutionary processes such as mutation, genetic drift and selection. The field is still growing today, with new methods and models being developed to answer questions of evolutionary relevance and to lift the veil on the past of all life forms. In this thesis, I present a modest contribution to the growth of population genetics. I investigate different questions related to the dynamics of populations, with particular focus on studying human evolution. I derive an upper bound and a lower bound for FST, a classical measure of population differentiation, as functions of the homozygosity in each of the two studied populations, and apply the result to discuss observed differentiation levels between human populations. I introduce a new criterion, the Gain of Informativeness for Assignment, to help us decide whether two genetic markers should be combined into a haplotype marker and improve the assignment of individuals to a panel of reference populations. Applying the method on SNP data for French, German and Swiss individuals, I show how haplotypes can lead to better assignment results when they are supervised by GIA. I also derive the population size over time as a function of the densities of cumulative coalescent times, show the robustness of this result to the number of loci as well as the sample size, and together with a simple algorithm of gene-genealogy inference, apply the method on low recombining regions of the human genome for four worldwide populations. I recover previously observed population size shapes, as well as uncover an early divergence of the Yoruba population from the non-African populations, suggesting ancient population structure on the African continent prior to the Out-of-Africa event. Finally, I present a case study of human adaptation to an arsenic-rich environment.
|Publisher||Uppsala universitet, Evolutionsbiologi, Uppsala|
|Source Sets||DiVA Archive at Upsalla University|
|Type||Doctoral thesis, comprehensive summary, info:eu-repo/semantics/doctoralThesis, text|
|Relation||Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, 1651-6214 ; 1280|
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