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Population based genotype imputation

  • Author / Creator
    Wang, Yining
  • In this dissertation, I focus on the study of genotype imputation in population data. Genotype imputation is a process of inferring missing values for genotype data and has been extended to predicting “untyped” genotypes for samples in low-density chips with a reference population assayed using dense marker chips. It has been successfully and routinely applied to merge genotype datasets of different densities that arise from various genotyping and sequencing platforms. First, I examine and compare several influential imputation models that incorporate biological concepts, mine for associations among genetic markers and explore genetic relatedness. I further evaluate the effect of imputation on genomic prediction, which combines dense marker data with phenotypic data for improving quantitative traits. Additionally we propose a multi-step strategy that can work with any existing genotype imputation methods to boost the accuracy of imputation from low-density chips to high-density chips. Finally we describe a new hidden Markov model for genotype imputation based on an existing framework.

  • Subjects / Keywords
  • Graduation date
    2017-11:Fall 2017
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/R3RB6WG3W
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Doctoral
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Stothard, Paul (Agricultural, Food and Nutritional Science)
    • Lin, Guohui (Computing Science)
  • Examining committee members and their departments
    • Ma, Bin (Computer Science)
    • Greiner, Russ (Computing Science)
    • Stothard, Paul (Agricultural, Food and Nutritional Science)
    • Li, Changxi (Agriculture and Agri-Food Canada)
    • Goebel, Randy (Computing Science)
    • Lin, Guohui (Computing Science)