Genomewide Selection in Apple: Prediction and Postdiction in the University of Minnesota Apple Breeding Program
2019-10
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Genomewide Selection in Apple: Prediction and Postdiction in the University of Minnesota Apple Breeding Program
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2019-10
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Although marker assisted breeding is now considered routine in apple breeding programs, the adoption of genomewide selection is still in its infancy. Genomewide selection offers the potential to be a valuable tool to apple breeders. The first aim of this research was to assess the predictive ability of genomewide selection for fruit traits by testing an additive prediction model, a model fitting heterozygote effects, and a model fitting fixed effects for major QTL. The second aim of this research was to assess the utility of genomewide selection for fruit traits in the University of Minnesota apple breeding program. This comprised two main objectives, a comparison of selections based on genomewide predictions to selections made based on phenotypic selection and an analysis of the impact on predictive ability when full-sibs are included in the training data. This research finds that in general, a simple linear model is the most efficient choice for genomewide selection in apple unless major effect QTL are known, in which case including them as fixed effects may improve predictive abilities. We also confirmed that predictions made based on genomewide selection to be consistent with selections based on traditional phenotypic selection and that including five to 15 full-sibs from the test population in the training population data can improve predictive ability.
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University of Minnesota Ph.D. dissertation. October 2019. Major: Applied Plant Sciences. Advisors: Rex Bernardo, James Luby. 1 computer file (PDF); v, 99 pages.
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Blissett, Elizabeth. (2019). Genomewide Selection in Apple: Prediction and Postdiction in the University of Minnesota Apple Breeding Program. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/209201.
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