Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance.
| Title: | Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance. |
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| Authors: | Fritsche-Neto R; International Rice Research Institute (IRRI), Los Banos, Philippines. rfneto@agcenter.lsu.edu.; H. Rouse Caffey Rice Research Station, LSU AgCenter, Rayne, USA. rfneto@agcenter.lsu.edu.; Ali J; International Rice Research Institute (IRRI), Los Banos, Philippines. J.Ali@irri.org.; De Asis EJ; International Rice Research Institute (IRRI), Los Banos, Philippines.; Allahgholipour M; International Rice Research Institute (IRRI), Los Banos, Philippines.; Labroo MR; Excellence in Breeding Platform, Consultative Group of International Agricultural Research, Lisbon, Mexico.; International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico. |
| Source: | TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik [Theor Appl Genet] 2023 Dec 12; Vol. 137 (1), pp. 3. Date of Electronic Publication: 2023 Dec 12. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Springer Country of Publication: Germany NLM ID: 0145600 Publication Model: Electronic Cited Medium: Internet ISSN: 1432-2242 (Electronic) Linking ISSN: 00405752 NLM ISO Abbreviation: Theor Appl Genet Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Berlin, New York, Springer |
| MeSH Terms: | Oryza*/genetics ; Hybridization, Genetic*; Genomics/methods ; Humans ; Models, Genetic ; Plant Breeding ; Parents |
| Abstract: | Key Message: Schemes that use genomic prediction outperform others, updating testers increases hybrid genetic gain, and larger population sizes tend to have higher genetic gain and less depletion of genetic variance One of the most common methods to improve hybrid performance is reciprocal recurrent selection (RRS). Genomic prediction (GP) can be used to increase genetic gain in RRS by reducing cycle length, but it is also possible to use GP to predict single-cross hybrid performance. The impact of the latter method on genetic gain has yet to be previously reported. Therefore, we compared via stochastic simulations various phenotypic and genomics-assisted RRS breeding schemes which used GP to predict hybrid performance rather than reducing cycle length, which allows minimal changes to traditional breeding schemes. We also compared three breeding sizes scenarios that varied the number of genotypes crossed within heterotic pools, the number of genotypes crossed between heterotic pools, the number of hybrids evaluated, and the number of genomic predicted hybrids. Our results demonstrated that schemes that used genomic prediction of hybrid performance outperformed the others for the average interpopulation hybrid population and the best hybrid performance. Furthermore, updating the testers increased hybrid genetic gain with phenotypic RRS. As expected, the largest breeding size tested had the highest rates of genetic improvement and the lowest decrease in additive genetic variance due to the drift. Therefore, this study demonstrates the usefulness of single-cross prediction, which may be easier to implement than rapid-cycling RRS and cyclical updating of testers. We also reiterate that larger population sizes tend to have higher genetic gain and less depletion of genetic variance.; (© 2023. The Author(s).) |
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| Grant Information: | OPP1194889 Bill and Melinda Gates Foundation |
| Entry Date(s): | Date Created: 20231212 Date Completed: 20231216 Latest Revision: 20241023 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC10716074 |
| DOI: | 10.1007/s00122-023-04508-6 |
| PMID: | 38085288 |
| Database: | MEDLINE |
Journal Article