Impact of early genomic prediction for recurrent selection in an upland rice synthetic population
Population breeding through recurrent selection is based on the repetition of evaluation and recombination among best-selected individuals. In this type of breeding strategy, early evaluation of selection candidates combined with genomic prediction could substantially shorten the breeding cycle leng...
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Oxford University Press
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/121096 |
| _version_ | 1855542926542635008 |
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| author | Baertschi, Cédric Cao, Tuong-Vi Bartholomé, Jérôme Ospina Rey, Yolima Quintero, Constanza Frouin, Julien Bouvet, Jean-M. Grenier, Cécile |
| author_browse | Baertschi, Cédric Bartholomé, Jérôme Bouvet, Jean-M. Cao, Tuong-Vi Frouin, Julien Grenier, Cécile Ospina Rey, Yolima Quintero, Constanza |
| author_facet | Baertschi, Cédric Cao, Tuong-Vi Bartholomé, Jérôme Ospina Rey, Yolima Quintero, Constanza Frouin, Julien Bouvet, Jean-M. Grenier, Cécile |
| author_sort | Baertschi, Cédric |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Population breeding through recurrent selection is based on the repetition of evaluation and recombination among best-selected individuals. In this type of breeding strategy, early evaluation of selection candidates combined with genomic prediction could substantially shorten the breeding cycle length, thus increasing the rate of genetic gain. The objective of this study was to optimize early genomic prediction in an upland rice (Oryza sativa L.) synthetic population improved through recurrent selection via shuttle breeding in two sites. To this end, we used genomic prediction on 334 S0 genotypes evaluated with early generation progeny testing (S0:2 and S0:3) across two sites. Four traits were measured (plant height, days to flowering, grain yield, and grain zinc concentration) and the predictive ability was assessed for the target site. For days to flowering and plant height, which correlate well among sites (0.51–0.62), an increase of up to 0.4 in predictive ability was observed when the model was trained using the two sites. For grain zinc concentration, adding the phenotype of the predicted lines in the nontarget site to the model improved the predictive ability (0.51 with two-site and 0.31 with single-site model), whereas for grain yield the gain was less (0.42 with two-site and 0.35 with single-site calibration). Through these results, we found a good opportunity to optimize the genomic recurrent selection scheme and maximize the use of resources by performing early progeny testing in two sites for traits with best expression and/or relevance in each specific environment. |
| format | Journal Article |
| id | CGSpace121096 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Oxford University Press |
| publisherStr | Oxford University Press |
| record_format | dspace |
| spelling | CGSpace1210962025-11-12T05:00:18Z Impact of early genomic prediction for recurrent selection in an upland rice synthetic population Baertschi, Cédric Cao, Tuong-Vi Bartholomé, Jérôme Ospina Rey, Yolima Quintero, Constanza Frouin, Julien Bouvet, Jean-M. Grenier, Cécile rice recurrent selection genomics progeny testing plant breeding calibration arroz selección recurrente genómica Population breeding through recurrent selection is based on the repetition of evaluation and recombination among best-selected individuals. In this type of breeding strategy, early evaluation of selection candidates combined with genomic prediction could substantially shorten the breeding cycle length, thus increasing the rate of genetic gain. The objective of this study was to optimize early genomic prediction in an upland rice (Oryza sativa L.) synthetic population improved through recurrent selection via shuttle breeding in two sites. To this end, we used genomic prediction on 334 S0 genotypes evaluated with early generation progeny testing (S0:2 and S0:3) across two sites. Four traits were measured (plant height, days to flowering, grain yield, and grain zinc concentration) and the predictive ability was assessed for the target site. For days to flowering and plant height, which correlate well among sites (0.51–0.62), an increase of up to 0.4 in predictive ability was observed when the model was trained using the two sites. For grain zinc concentration, adding the phenotype of the predicted lines in the nontarget site to the model improved the predictive ability (0.51 with two-site and 0.31 with single-site model), whereas for grain yield the gain was less (0.42 with two-site and 0.35 with single-site calibration). Through these results, we found a good opportunity to optimize the genomic recurrent selection scheme and maximize the use of resources by performing early progeny testing in two sites for traits with best expression and/or relevance in each specific environment. 2021-12-08 2022-09-05T12:26:01Z 2022-09-05T12:26:01Z Journal Article https://hdl.handle.net/10568/121096 en Open Access application/pdf Oxford University Press Baertschi, C.; Cao, T.V.; Bartholomé, J., Ospina R.Y.; Quintero, C.; Frouin, J.; Bouvet J.M.; Grenier, C. (2021) Impact of early genomic prediction for recurrent selection in an upland rice synthetic population. G3 Genes Genomes Genetics 11(12): jkab320. ISSN 2160-1836 |
| spellingShingle | rice recurrent selection genomics progeny testing plant breeding calibration arroz selección recurrente genómica Baertschi, Cédric Cao, Tuong-Vi Bartholomé, Jérôme Ospina Rey, Yolima Quintero, Constanza Frouin, Julien Bouvet, Jean-M. Grenier, Cécile Impact of early genomic prediction for recurrent selection in an upland rice synthetic population |
| title | Impact of early genomic prediction for recurrent selection in an upland rice synthetic population |
| title_full | Impact of early genomic prediction for recurrent selection in an upland rice synthetic population |
| title_fullStr | Impact of early genomic prediction for recurrent selection in an upland rice synthetic population |
| title_full_unstemmed | Impact of early genomic prediction for recurrent selection in an upland rice synthetic population |
| title_short | Impact of early genomic prediction for recurrent selection in an upland rice synthetic population |
| title_sort | impact of early genomic prediction for recurrent selection in an upland rice synthetic population |
| topic | rice recurrent selection genomics progeny testing plant breeding calibration arroz selección recurrente genómica |
| url | https://hdl.handle.net/10568/121096 |
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