Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance
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 and recover higher-performi...
| Autores principales: | , , , , |
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| Formato: | Preprint |
| Lenguaje: | Inglés |
| Publicado: |
Research Square Platform LLC
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/163949 |
| _version_ | 1855527538065932288 |
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| author | Fritsche-Neto, Roberto Ali, Jauhar De Asis, Erik Jon Allahgholipour, Mehrzad Labroo, Marlee Rose |
| author_browse | Ali, Jauhar Allahgholipour, Mehrzad De Asis, Erik Jon Fritsche-Neto, Roberto Labroo, Marlee Rose |
| author_facet | Fritsche-Neto, Roberto Ali, Jauhar De Asis, Erik Jon Allahgholipour, Mehrzad Labroo, Marlee Rose |
| author_sort | Fritsche-Neto, Roberto |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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 and recover higher-performing hybrids. The impact of the latter method on genetic gain has not been previously reported. Therefore, our study compared 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 phenotypic schemes. We used stochastic simulation to compare compared five RRS breeding schemes in terms of genetic gain and best hybrid performance: Traditional (TRAD_RRS), drift (DRIFT_RRS), Traditional but updating testers every cycle (TRAD_RRS_ UP), Genomic Additive (GS_A_RRS), and Genomic Additive+Dominace (GS_AD_RRS). We also compared three breeding sizes which varied the number of genotypes crossed within heterotic pools, the number of genotypes crossed between heterotic pools, the number of the number of phenotyped hybrids, and the number of genomic predicted hybrids. Schemes which used genomic prediction of hybrid performance outperformed the others for both the average interpopulation hybrid population performance and the best hybrid performance. Furthermore, updating the testers increased hybrid genetic gain with phenotypic RRS. Overall, the largest breeding size tested had the highest rates of genetic gain and in the lowest decrease in additive genetic variance due to drift, although cost was not considered. This study demonstrates the usefulness of single-cross prediction, which initially may be easier to implement than rapid-cycling RRS, and cyclical updating of testers. We also demonstrate that larger population sizes tend to have higher genetic gain and less depletion of genetic variance, disregarding cost. |
| format | Preprint |
| id | CGSpace163949 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Research Square Platform LLC |
| publisherStr | Research Square Platform LLC |
| record_format | dspace |
| spelling | CGSpace1639492024-12-22T05:44:55Z 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 breeding progammes additives genetic gain genetic variance genotypes heterotic groups 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 and recover higher-performing hybrids. The impact of the latter method on genetic gain has not been previously reported. Therefore, our study compared 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 phenotypic schemes. We used stochastic simulation to compare compared five RRS breeding schemes in terms of genetic gain and best hybrid performance: Traditional (TRAD_RRS), drift (DRIFT_RRS), Traditional but updating testers every cycle (TRAD_RRS_ UP), Genomic Additive (GS_A_RRS), and Genomic Additive+Dominace (GS_AD_RRS). We also compared three breeding sizes which varied the number of genotypes crossed within heterotic pools, the number of genotypes crossed between heterotic pools, the number of the number of phenotyped hybrids, and the number of genomic predicted hybrids. Schemes which used genomic prediction of hybrid performance outperformed the others for both the average interpopulation hybrid population performance and the best hybrid performance. Furthermore, updating the testers increased hybrid genetic gain with phenotypic RRS. Overall, the largest breeding size tested had the highest rates of genetic gain and in the lowest decrease in additive genetic variance due to drift, although cost was not considered. This study demonstrates the usefulness of single-cross prediction, which initially may be easier to implement than rapid-cycling RRS, and cyclical updating of testers. We also demonstrate that larger population sizes tend to have higher genetic gain and less depletion of genetic variance, disregarding cost. 2023-05-18 2024-12-19T12:53:13Z 2024-12-19T12:53:13Z Preprint https://hdl.handle.net/10568/163949 en Open Access Research Square Platform LLC Fritsche-Neto, Roberto; Ali, Jauhar; De Asis, Erik Jon; Allahgholipour, Mehrzad and Labroo, Marlee Rose. 2023. Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance. Research Square, [pre-prints]; 16 p. |
| spellingShingle | breeding progammes additives genetic gain genetic variance genotypes heterotic groups Fritsche-Neto, Roberto Ali, Jauhar De Asis, Erik Jon Allahgholipour, Mehrzad Labroo, Marlee Rose 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 |
| title_full | Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance |
| title_fullStr | Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance |
| title_full_unstemmed | Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance |
| title_short | Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance |
| title_sort | improving hybrid rice breeding programs via stochastic simulations number of parents number of hybrids tester update and genomic prediction of hybrid performance |
| topic | breeding progammes additives genetic gain genetic variance genotypes heterotic groups |
| url | https://hdl.handle.net/10568/163949 |
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