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Bagging E-Bayes for Estimated Breeding Value Prediction Open Access


Other title
Genomic Selection
QTL Mapping
Type of item
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 of Computing Science

Date accepted
Graduation date
Master of Science
Degree level
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.
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