Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets
Long-term breeding schemes using genomic selection (GS) can boost the response to selection per year. Although several studies have shown that GS delivers a higher response to selection, only a few analyze which stage GS produces better results and how to update the training population to maintain p...
| Autores principales: | , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Frontiers Media
2022
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/126257 |
| _version_ | 1855515092390510592 |
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| author | Sabadin, Felipe César DoVale, Julio Platten, John Damien Fritsche-Neto, Roberto |
| author_browse | César DoVale, Julio Fritsche-Neto, Roberto Platten, John Damien Sabadin, Felipe |
| author_facet | Sabadin, Felipe César DoVale, Julio Platten, John Damien Fritsche-Neto, Roberto |
| author_sort | Sabadin, Felipe |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Long-term breeding schemes using genomic selection (GS) can boost the response to selection per year. Although several studies have shown that GS delivers a higher response to selection, only a few analyze which stage GS produces better results and how to update the training population to maintain prediction accuracy. We used stochastic simulation to compare five GS breeding schemes in a self-pollinated long-term breeding program. Also, we evaluated four strategies, using distinct methods and sizes, to update the training set. Finally, regarding breeding schemes, we proposed a new approach using GS to select the best individuals in each F2 progeny, based on genomic estimated breeding values and genetic divergence, to cross them and generate a new recombination event. Our results showed that the best scenario was using GS in F2, followed by the phenotypic selection of new parents in F4. For TS updating, adding new data every cycle (over 768) to update the TS maintains the prediction accuracy at satisfactory levels for more breeding cycles. However, only the last three generations can be kept in the TS, optimizing the genetic relationship between TS and the targeted population and reducing the computing demand and risks. Hence, we believe that our results may help breeders optimize GS in their programs and improve genetic gain in long-term schemes. |
| format | Journal Article |
| id | CGSpace126257 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Frontiers Media |
| publisherStr | Frontiers Media |
| record_format | dspace |
| spelling | CGSpace1262572025-12-08T10:29:22Z Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets Sabadin, Felipe César DoVale, Julio Platten, John Damien Fritsche-Neto, Roberto genomics selection criteria pollination Long-term breeding schemes using genomic selection (GS) can boost the response to selection per year. Although several studies have shown that GS delivers a higher response to selection, only a few analyze which stage GS produces better results and how to update the training population to maintain prediction accuracy. We used stochastic simulation to compare five GS breeding schemes in a self-pollinated long-term breeding program. Also, we evaluated four strategies, using distinct methods and sizes, to update the training set. Finally, regarding breeding schemes, we proposed a new approach using GS to select the best individuals in each F2 progeny, based on genomic estimated breeding values and genetic divergence, to cross them and generate a new recombination event. Our results showed that the best scenario was using GS in F2, followed by the phenotypic selection of new parents in F4. For TS updating, adding new data every cycle (over 768) to update the TS maintains the prediction accuracy at satisfactory levels for more breeding cycles. However, only the last three generations can be kept in the TS, optimizing the genetic relationship between TS and the targeted population and reducing the computing demand and risks. Hence, we believe that our results may help breeders optimize GS in their programs and improve genetic gain in long-term schemes. 2022-10-06 2022-12-22T13:59:49Z 2022-12-22T13:59:49Z Journal Article https://hdl.handle.net/10568/126257 en Open Access application/pdf Frontiers Media Sabadin F, DoVale JC, Platten JD and Fritsche-Neto R (2022) Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets. Front. Plant Sci. 13:935885. doi: 10.3389/fpls.2022.935885 |
| spellingShingle | genomics selection criteria pollination Sabadin, Felipe César DoVale, Julio Platten, John Damien Fritsche-Neto, Roberto Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets |
| title | Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets |
| title_full | Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets |
| title_fullStr | Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets |
| title_full_unstemmed | Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets |
| title_short | Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets |
| title_sort | optimizing self pollinated crop breeding employing genomic selection from schemes to updating training sets |
| topic | genomics selection criteria pollination |
| url | https://hdl.handle.net/10568/126257 |
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