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Permanent link (DOI): https://doi.org/10.7939/R3MW4X

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Bagging E-Bayes for Estimated Breeding Value Prediction Open Access

Descriptions

Other title
Subject/Keyword
Genomic Selection
QTL Mapping
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Xu, Jiaofen
Supervisor and department
Lin, Guohui (Computing Science)
Stothard, Paul (Agricultural, Food and Nutritional Science)
Examining committee member and department
Moore, Stephen (Agricultural, Food and Nutritional Science)
Goebel, Randy (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2009-09-16T18:20:13Z
Graduation date
2009-11
Degree
Master of Science
Degree level
Master's
Abstract
This work focuses on the evaluation of a bagging EB method in terms of its ability to select a subset of QTL-related markers for accurate EBV prediction. Experiments were performed on several simulated and real datasets consisting of SNP genotypes and phenotypes. The simulated datasets modeled different dominance levels and different levels of background noises. Our results show that the bagging EB method is able to detect most of the simulated QTL, even with large background noises. The average recall of QTL detection was $0.71$. When using the markers detected by the bagging EB method to predict EBVs, the prediction accuracy improved dramatically on the simulation datasets compared to using the entire set of markers. However, the prediction accuracy did not improve much when doing the same experiments on the two real datasets. The best accuracy of EBV prediction we achieved for the dairy dataset is 0.57 and the best accuracy for the beef dataset is 0.73.
Language
English
DOI
doi:10.7939/R3MW4X
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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