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dc.contributor.authorIsidro Sanchez, Julioen_US
dc.identifier.otherbibid: 9154442
dc.description.abstractThe optimization of the training set (TRS) in genomic selection has received much interest in both animal and plant breeding, because it is critical to the accuracy of the prediction models. In this study, five different TRS sampling algorithms, stratified sampling, mean of the coefficient of determination (CDmean), mean of predictor error variance (PEVmean), stratified CDmean (StratCDmean) and random sampling, were evaluated for prediction accuracy in the presence of different levels of population structure. In the presence of population structure, the most phenotypic variation captured by a sampling method in the TRS is desirable. The wheat dataset showed mild population structure, and CDmean and stratified CDmean methods showed the highest accuracies for all the traits except for test weight and heading date. The rice dataset had strong population structure and the approach based on stratified sampling showed the highest accuracies for all traits. In general, CDmean minimized the relationship between genotypes in the TRS, maximizing the relationship between TRS and the test set. This makes it suitable as an optimization criterion for long-term selection. Our results indicated that the best selection criterion used to optimize the TRS seems to depend on the interaction of trait architecture and population structure.en_US
dc.subjectGenomic Selectionen_US
dc.subjectpopulation structureen_US
dc.titleTraining Set Optimization Under Population Structure In Genomic Selectionen_US
dc.typedissertation or thesisen_US Breeding Universityen_US of Science, Plant Breeding
dc.contributor.chairSorrells, Mark Earlen_US
dc.contributor.committeeMemberJannink, Jean-Lucen_US

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