| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000433502 |
| 005 | | 20200225142705 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781088379059 |
| 035 | |
▼a (MiAaPQ)AAI22615153 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 630 |
| 100 | 1 |
▼a Neyhart, Jeffrey L. |
| 245 | 10 |
▼a Applications of Genomewide Selection in a New Plant Breeding Program. |
| 260 | |
▼a [S.l.]:
▼b University of Minnesota.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 132 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-05, Section: B. |
| 500 | |
▼a Advisor: Smith, Kevin P. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Minnesota, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0130. |
| 650 | 4 |
▼a Plant sciences. |
| 650 | 4 |
▼a Genetics. |
| 650 | 4 |
▼a Agriculture. |
| 690 | |
▼a 0479 |
| 690 | |
▼a 0369 |
| 690 | |
▼a 0473 |
| 710 | 20 |
▼a University of Minnesota.
▼b Applied Plant Sciences. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-05B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0130 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15493277
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |