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 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.
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dig-cirad-fr-6000982024-08-04T16:02:20Z http://agritrop.cirad.fr/600098/ http://agritrop.cirad.fr/600098/ Impact of early genomic prediction for recurrent selection in an upland rice synthetic population. Baertschi Cédric, Cao Tuong-Vi, Bartholome Jérôme, Ospina Yolima, Quintero Constanza, Frouin Julien, Bouvet Jean-Marc, Grenier Cécile. 2021. G3 - Genes Genomes Genetics, 11 (12):jkab320, 16 p.https://doi.org/10.1093/g3journal/jkab320 <https://doi.org/10.1093/g3journal/jkab320> Impact of early genomic prediction for recurrent selection in an upland rice synthetic population Baertschi, Cédric Cao, Tuong-Vi Bartholome, Jérôme Ospina, Yolima Quintero, Constanza Frouin, Julien Bouvet, Jean-Marc Grenier, Cécile eng 2021 G3 - Genes Genomes Genetics F30 - Génétique et amélioration des plantes Oryza sativa sélection artificielle sélection récurrente technique de prévision génotype feuille essai de variété expérimentation au champ méthode statistique performance de culture phénotype http://aims.fao.org/aos/agrovoc/c_5438 http://aims.fao.org/aos/agrovoc/c_37565 http://aims.fao.org/aos/agrovoc/c_27595 http://aims.fao.org/aos/agrovoc/c_3041 http://aims.fao.org/aos/agrovoc/c_3225 http://aims.fao.org/aos/agrovoc/c_4243 http://aims.fao.org/aos/agrovoc/c_26833 http://aims.fao.org/aos/agrovoc/c_33990 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_35199 http://aims.fao.org/aos/agrovoc/c_5776 Colombie http://aims.fao.org/aos/agrovoc/c_1767 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. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/600098/1/Baertschi%20et%20al.%2C%202022.pdf text cc_by info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1093/g3journal/jkab320 10.1093/g3journal/jkab320 info:eu-repo/semantics/altIdentifier/doi/10.1093/g3journal/jkab320 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1093/g3journal/jkab320 info:eu-repo/semantics/dataset/purl/https://doi.org/10.25387/g3.14139806 |
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F30 - Génétique et amélioration des plantes Oryza sativa sélection artificielle sélection récurrente technique de prévision génotype feuille essai de variété expérimentation au champ méthode statistique performance de culture phénotype http://aims.fao.org/aos/agrovoc/c_5438 http://aims.fao.org/aos/agrovoc/c_37565 http://aims.fao.org/aos/agrovoc/c_27595 http://aims.fao.org/aos/agrovoc/c_3041 http://aims.fao.org/aos/agrovoc/c_3225 http://aims.fao.org/aos/agrovoc/c_4243 http://aims.fao.org/aos/agrovoc/c_26833 http://aims.fao.org/aos/agrovoc/c_33990 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_35199 http://aims.fao.org/aos/agrovoc/c_5776 http://aims.fao.org/aos/agrovoc/c_1767 F30 - Génétique et amélioration des plantes Oryza sativa sélection artificielle sélection récurrente technique de prévision génotype feuille essai de variété expérimentation au champ méthode statistique performance de culture phénotype http://aims.fao.org/aos/agrovoc/c_5438 http://aims.fao.org/aos/agrovoc/c_37565 http://aims.fao.org/aos/agrovoc/c_27595 http://aims.fao.org/aos/agrovoc/c_3041 http://aims.fao.org/aos/agrovoc/c_3225 http://aims.fao.org/aos/agrovoc/c_4243 http://aims.fao.org/aos/agrovoc/c_26833 http://aims.fao.org/aos/agrovoc/c_33990 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_35199 http://aims.fao.org/aos/agrovoc/c_5776 http://aims.fao.org/aos/agrovoc/c_1767 |
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F30 - Génétique et amélioration des plantes Oryza sativa sélection artificielle sélection récurrente technique de prévision génotype feuille essai de variété expérimentation au champ méthode statistique performance de culture phénotype http://aims.fao.org/aos/agrovoc/c_5438 http://aims.fao.org/aos/agrovoc/c_37565 http://aims.fao.org/aos/agrovoc/c_27595 http://aims.fao.org/aos/agrovoc/c_3041 http://aims.fao.org/aos/agrovoc/c_3225 http://aims.fao.org/aos/agrovoc/c_4243 http://aims.fao.org/aos/agrovoc/c_26833 http://aims.fao.org/aos/agrovoc/c_33990 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_35199 http://aims.fao.org/aos/agrovoc/c_5776 http://aims.fao.org/aos/agrovoc/c_1767 F30 - Génétique et amélioration des plantes Oryza sativa sélection artificielle sélection récurrente technique de prévision génotype feuille essai de variété expérimentation au champ méthode statistique performance de culture phénotype http://aims.fao.org/aos/agrovoc/c_5438 http://aims.fao.org/aos/agrovoc/c_37565 http://aims.fao.org/aos/agrovoc/c_27595 http://aims.fao.org/aos/agrovoc/c_3041 http://aims.fao.org/aos/agrovoc/c_3225 http://aims.fao.org/aos/agrovoc/c_4243 http://aims.fao.org/aos/agrovoc/c_26833 http://aims.fao.org/aos/agrovoc/c_33990 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_35199 http://aims.fao.org/aos/agrovoc/c_5776 http://aims.fao.org/aos/agrovoc/c_1767 Baertschi, Cédric Cao, Tuong-Vi Bartholome, Jérôme Ospina, Yolima Quintero, Constanza Frouin, Julien Bouvet, Jean-Marc Grenier, Cécile Impact of early genomic prediction for recurrent selection in an upland rice synthetic population |
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 |
article |
topic_facet |
F30 - Génétique et amélioration des plantes Oryza sativa sélection artificielle sélection récurrente technique de prévision génotype feuille essai de variété expérimentation au champ méthode statistique performance de culture phénotype http://aims.fao.org/aos/agrovoc/c_5438 http://aims.fao.org/aos/agrovoc/c_37565 http://aims.fao.org/aos/agrovoc/c_27595 http://aims.fao.org/aos/agrovoc/c_3041 http://aims.fao.org/aos/agrovoc/c_3225 http://aims.fao.org/aos/agrovoc/c_4243 http://aims.fao.org/aos/agrovoc/c_26833 http://aims.fao.org/aos/agrovoc/c_33990 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_35199 http://aims.fao.org/aos/agrovoc/c_5776 http://aims.fao.org/aos/agrovoc/c_1767 |
author |
Baertschi, Cédric Cao, Tuong-Vi Bartholome, Jérôme Ospina, Yolima Quintero, Constanza Frouin, Julien Bouvet, Jean-Marc Grenier, Cécile |
author_facet |
Baertschi, Cédric Cao, Tuong-Vi Bartholome, Jérôme Ospina, Yolima Quintero, Constanza Frouin, Julien Bouvet, Jean-Marc Grenier, Cécile |
author_sort |
Baertschi, Cédric |
title |
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_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_sort |
impact of early genomic prediction for recurrent selection in an upland rice synthetic population |
url |
http://agritrop.cirad.fr/600098/ http://agritrop.cirad.fr/600098/1/Baertschi%20et%20al.%2C%202022.pdf |
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1807170486460743680 |