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Permanent link (DOI): https://doi.org/10.7939/R3D36W

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Missing SNP Genotype Imputation Open Access

Descriptions

Other title
Subject/Keyword
Genetics--Technique
Nucleotide sequence
Missing observations (Statistics)
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Wang, Yining
Supervisor and department
Lin, Guohui (Computing Science)
Examining committee member and department
Li, Changxi (Agricultural, Food and Nutritional Science)
Greiner, Russ (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2011-06-02T19:07:51Z
Graduation date
2011-11
Degree
Master of Science
Degree level
Master's
Abstract
High-throughput single nucleotide polymorphism (SNP) genotyping technologies conveniently produce large SNP genotype datasets for genome-wide linkage and association studies. Various factors, from array design and hybridization, can give rise to a certain percentage of missing calls, and the problem becomes severe when the target organisms such as cattle do not have a high resolution genomic sequence available. Missing calls in SNP genotype datasets would undermine downstream data analysis. Therefore, effective methodologies for dealing with missing genotypes are in urgent need. In this dissertation, we start with a brief introduction to the concepts in genetics, then present a collection of imputation methods, with focus on machine learning algorithms, to tackle the missing SNP genotype problem. We demonstrate that these imputation approaches can achieve satisfactory accuracies, tested on the real population SNP genotype datasets, and highlight the places where our new methods find useful. We conclude with some possible future directions for the genome-wide SNP genotype imputation problem.
Language
English
DOI
doi:10.7939/R3D36W
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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