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Population based genotype imputation Open Access


Other title
genotype imputation
genomic prediction
Type of item
Degree grantor
University of Alberta
Author or creator
Wang, Yining
Supervisor and department
Lin, Guohui (Computing Science)
Stothard, Paul (Agricultural, Food and Nutritional Science)
Examining committee member and department
Greiner, Russ (Computing Science)
Lin, Guohui (Computing Science)
Goebel, Randy (Computing Science)
Li, Changxi (Agriculture and Agri-Food Canada)
Ma, Bin (Computer Science)
Stothard, Paul (Agricultural, Food and Nutritional Science)
Department of Computing Science

Date accepted
Graduation date
2017-11:Fall 2017
Doctor of Philosophy
Degree level
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.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. 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.
Citation for previous publication
Wang, Y., Wylie, T., Stothard, P. and Lin, G., 2015. Whole genome SNP genotype piecemeal imputation. BMC bioinformatics, 16(1), p.340.Wang, Y., Lin, G., Li, C. and Stothard, P., 2016. Genotype Imputation Methods and Their Effects on Genomic Predictions in Cattle. Springer Science Reviews, 4(2), pp.79-98.

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