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    • 2019 Dissertations (MU)
    • 2019 MU dissertations - Freely available online
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    Applications of ancestry estimation in cattle population genetics

    Crum, Tamar
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    [PDF] CrumTamar.pdf (13.33Mb)
    Date
    2019
    Format
    Thesis
    Metadata
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    Abstract
    Accounting for ancestry and cryptic population structure is important in population genetic data analysis. The estimation of ancestry can help elucidate the effects of admixture or stratification on allele and genotype frequencies caused by population substructure. Human population genetics has examined these issues in detail and has developed models which incorporate ancestry effects in the estimation of population susceptibilities to disease risk in association analyses, as well as for the interpretation of genotype disease risks in admixed individuals for use in personalized medicine. In livestock, allele frequencies frequently differ between breeds and these breeds frequently also differ for phenotypes of interest. If these population differences are not accounted for in the analysis, the differences in allele frequencies due to population structure can lead to erroneous associations of loci with phenotypes. The effects of domestication and breed formation in shaping the cattle genome along with the rampant use of crossbreeding to form admixed individuals in the modern beef cattle industry provides an opportunity to evaluate the impacts of breed differences on association analyses of economically relevant traits such as the conversion of feed to weight gain and disease susceptibility. Here, we review the methods used for ancestry estimation and the impacts of ancestry prediction in human population genetic analyses and opportunities for use of these techniques in cattle, and more generally in livestock populations. The incorporation of accurate genomic-based predictions of breed composition could fine-tune the resolution of mapping studies targeting the identification of causal loci for livestock species.
    URI
    https://hdl.handle.net/10355/75002
    https://doi.org/10.32469/10355/75002
    Degree
    Ph. D.
    Thesis Department
    Animal sciences (MU)
    Rights
    OpenAccess.
    This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
    Collections
    • Animal Sciences electronic theses and dissertations (MU)
    • 2019 MU dissertations - Freely available online

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