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Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance.
Fritsche-Neto, Roberto; Ali, Jauhar; De Asis, Erik Jon; Allahgholipour, Mehrzad; Labroo, Marlee Rose.
Afiliación
  • Fritsche-Neto R; International Rice Research Institute (IRRI), Los Banos, Philippines. rfneto@agcenter.lsu.edu.
  • Ali J; H. Rouse Caffey Rice Research Station, LSU AgCenter, Rayne, USA. rfneto@agcenter.lsu.edu.
  • De Asis EJ; International Rice Research Institute (IRRI), Los Banos, Philippines. J.Ali@irri.org.
  • Allahgholipour M; International Rice Research Institute (IRRI), Los Banos, Philippines.
  • Labroo MR; International Rice Research Institute (IRRI), Los Banos, Philippines.
Theor Appl Genet ; 137(1): 3, 2023 Dec 12.
Article en En | MEDLINE | ID: mdl-38085288
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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Oryza / Hibridación Genética Límite: Humans Idioma: En Revista: Theor Appl Genet Año: 2023 Tipo del documento: Article País de afiliación: Filipinas

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Oryza / Hibridación Genética Límite: Humans Idioma: En Revista: Theor Appl Genet Año: 2023 Tipo del documento: Article País de afiliación: Filipinas