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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.935885/full |
| _version_ | 1797997148831219712 |
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| author | Felipe Sabadin Julio César DoVale John Damien Platten Roberto Fritsche-Neto Roberto Fritsche-Neto |
| author_facet | Felipe Sabadin Julio César DoVale John Damien Platten Roberto Fritsche-Neto Roberto Fritsche-Neto |
| author_sort | Felipe Sabadin |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T10:28:20Z |
| format | Article |
| id | doaj.art-bdcc4debcf774743ae87141117e639fb |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-11T10:28:20Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-bdcc4debcf774743ae87141117e639fb2022-12-22T04:29:30ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-10-011310.3389/fpls.2022.935885935885Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training setsFelipe Sabadin0Julio César DoVale1John Damien Platten2Roberto Fritsche-Neto3Roberto Fritsche-Neto4School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United StatesDepartment of Crop Science, Federal University of Ceará, Fortaleza, Ceará, BrazilInternational Rice Research Institute (IRRI), Los Baños, PhilippinesInternational Rice Research Institute (IRRI), Los Baños, PhilippinesH. Rouse Caffey Rice Research Station, Louisiana State University (LSU) AgCenter, Rayne, LA, United StatesLong-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.https://www.frontiersin.org/articles/10.3389/fpls.2022.935885/fullrecurrent genomic selectiontraining set designstochastic simulationself-pollinated cropsGS-based methods |
| spellingShingle | Felipe Sabadin Julio César DoVale John Damien Platten Roberto Fritsche-Neto Roberto Fritsche-Neto Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets Frontiers in Plant Science recurrent genomic selection training set design stochastic simulation self-pollinated crops GS-based methods |
| 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 | recurrent genomic selection training set design stochastic simulation self-pollinated crops GS-based methods |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2022.935885/full |
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