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. Impact of early genomic prediction for recurrent selection in an upland rice synthetic population. G3 - Genes Genomes Genetics, 11 (12):jkab320, 16 p.

Url - jeu de données - Entrepôt autre : https://doi.org/10.25387/g3.14139806

Quartile : Q2, Sujet : GENETICS & HEREDITY

Résumé : 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.

Mots-clés Agrovoc : Oryza sativa, sélection artificielle, sélection récurrente, technique de prévision, génotype, feuille, essai de variété, expérimentation au champ, méthode statistique, performance de culture, phénotype

Mots-clés géographiques Agrovoc : Colombie

Mots-clés complémentaires : crop sequence experiments (en)

Mots-clés libres : Rice, Recurrent selection, Genomic prediction, GxE, Grain zinc concentration

Classification Agris : F30 - Génétique et amélioration des plantes

Champ stratégique Cirad : CTS 2 (2019-) - Transitions agroécologiques

Auteurs et affiliations

• Baertschi Cédric, Université de Montpellier (FRA)
• Cao Tuong-Vi, CIRAD-BIOS-UMR AGAP (FRA)
• Bartholome Jérôme, CIRAD-BIOS-UMR AGAP (FRA) ORCID: 0000-0002-0855-3828
• Ospina Yolima, CIAT (COL)
• Quintero Constanza, CIAT (COL)
• Frouin Julien, CIRAD-BIOS-UMR AGAP (FRA) ORCID: 0000-0003-1591-0755
• Bouvet Jean-Marc, CIRAD-BIOS-UMR AGAP (MDG)
• Grenier Cécile, CIRAD-BIOS-UMR AGAP (FRA) ORCID: 0000-0001-5390-8344 - auteur correspondant

Source : Cirad-Agritrop (https://agritrop.cirad.fr/600098/)

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