Adaptive and neutral evolutionary insights from statistical analyses of population genetic data [electronic resource]
- Julie Marie Granka.
- Physical description
- 1 online resource.
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|3781 2013 G||In-library use|
- Organismic evolution involves both selective and neutral forces, although their relative contributions are often unknown. This thesis proposes novel statistical methods for analyzing genetic data from a variety of organisms, including yeast, Mycobacterium tuberculosis, and humans. The chapters of this thesis provide complimentary perspectives on the relative roles of selection and neutrality, from the molecular to the population level, and present various statistical tools for genetic data analysis. Chapter 2 proposes a maximum-likelihood based method with which to classify and identify interactions, or epistasis, between pairs of genes. Chapter 3 details a study of genetic data from Mycobacterium tuberculosis isolated from human Aboriginal Canadian communities; our analyses suggest that the bacterium spread to these communities via the Canadian fur trade in the 18th and 19th centuries. Chapter 4 discusses the detection of signatures of natural selection in the genomes of 12 diverse African human populations, and proposes novel considerations for identifying biological functions under selection and for comparing signals of selection between populations. Finally, Chapter 5 details the inference of the genetic basis and evolutionary history of light skin pigmentation and short stature in the genetically diverse ≠Khomani Bushmen of the Kalahari Desert of South Africa, believed to be one of the world's most ancient human populations. These chapters emphasize that a more complete understanding of the evolutionary history of humans and other organisms requires not only the consideration of neutral and selective processes, but also both phenotypic and genetic information. The statistical methods and approaches presented in the following chapters have the potential to improve inferences of natural selection and demography from genetic data, as well as provide insight into the relative roles of both.
- Publication date
- Submitted to the Department of Biology.
- Ph.D. Stanford University 2013
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