자료유형 | 학위논문 |
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서명/저자사항 | Applications of Genomewide Selection in a New Plant Breeding Program. |
개인저자 | Neyhart, Jeffrey L. |
단체저자명 | University of Minnesota. Applied Plant Sciences. |
발행사항 | [S.l.]: University of Minnesota., 2019. |
발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
형태사항 | 132 p. |
기본자료 저록 | Dissertations Abstracts International 81-05B. Dissertation Abstract International |
ISBN | 9781088379059 |
학위논문주기 | Thesis (Ph.D.)--University of Minnesota, 2019. |
일반주기 |
Source: Dissertations Abstracts International, Volume: 81-05, Section: B.
Advisor: Smith, Kevin P. |
이용제한사항 | This item must not be sold to any third party vendors. |
요약 | Newly established breeding programs must undergo population improvement and determine superior germplasm for deployment in diverse growing environments. More rapid progress towards these goals may be made by incorporating genomewide selection, or the use of genomewide molecular markers to predict the merit of unphenotyped individuals. Within the context of a new two-row barley (Hordeum vulgare L.) breeding program, my objectives were to i) investigate various methods of updating training population data and their impact on long-term genomewide recurrent selection, ii) assess genomewide prediction accuracy with informed subsetting of data across diverse environments, and iii) validate genomewide predictions of the mean, genetic variance, and superior progeny mean of potential breeding crossses. My first study relied on simulations to examine the impact on prediction accuracy and response to selection when updating the training population each cycle with lines selected based on predictions (best, worst, both best and worst), model criteria (PEVmean and CDmean), random sampling, or no selections. In the short-term, we found that updating with the best or both best and worst predicted lines resulted in high prediction accuracy and genetic gain |
일반주제명 | Plant sciences. Genetics. Agriculture. |
언어 | 영어 |
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