자료유형 | 학위논문 |
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서명/저자사항 | Genomewide Selection in Apple: Prediction and Postdiction in the University of Minnesota Apple Breeding Program. |
개인저자 | Blissett, Elizabeth Nicole. |
단체저자명 | University of Minnesota. Applied Plant Sciences. |
발행사항 | [S.l.]: University of Minnesota., 2019. |
발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
형태사항 | 103 p. |
기본자료 저록 | Dissertations Abstracts International 81-06B. Dissertation Abstract International |
ISBN | 9781392771518 |
학위논문주기 | Thesis (Ph.D.)--University of Minnesota, 2019. |
일반주기 |
Source: Dissertations Abstracts International, Volume: 81-06, Section: B.
Advisor: Bernardo, Rex |
이용제한사항 | This item must not be sold to any third party vendors.This item must not be added to any third party search indexes. |
요약 | 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. |
일반주제명 | Genetics. Agronomy. Horticulture. |
언어 | 영어 |
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