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Genomewide Selection in Apple: Prediction and Postdiction in the University of Minnesota Apple Breeding Program

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서명/저자사항Genomewide Selection in Apple: Prediction and Postdiction in the University of Minnesota Apple Breeding Program.
개인저자Blissett, Elizabeth Nicole.
단체저자명University of Minnesota. Applied Plant Sciences.
발행사항[S.l.]: University of Minnesota., 2019.
발행사항Ann Arbor: ProQuest Dissertations & Theses, 2019.
형태사항103 p.
기본자료 저록Dissertations Abstracts International 81-06B.
Dissertation Abstract International
ISBN9781392771518
학위논문주기Thesis (Ph.D.)--University of Minnesota, 2019.
일반주기 Source: Dissertations Abstracts International, Volume: 81-06, Section: B.
Advisor: Bernardo, Rex
이용제한사항This item must not be sold to any third party vendors.This item must not be added to any third party search indexes.
요약Although 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.
일반주제명Genetics.
Agronomy.
Horticulture.
언어영어
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