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

  • Genotype imputation
  • Genomic prediction

• Graduation date

  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.