Impact of early genomic prediction for recurrent selection in an upland rice synthetic population
Baertschi, Cédric | Cao, Tuong-Vi | Bartholomé, Jérôme | Ospina, Yolima | Quintero, Constanza | Frouin, Julien | Bouvet, Jean-Marc | Grenier, Cécile | Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro ; Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) | Département Systèmes Biologiques (Cirad-BIOS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad) | International Rice Research Institute [Philippines] (IRRI) ; Consultative Group on International Agricultural Research [CGIAR] (CGIAR) | Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) [Rome] (Alliance) ; Consultative Group on International Agricultural Research [CGIAR] (CGIAR) | Direction Générale Déléguée à la Recherche et à la Stratégie (Cirad-Dgdrs) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad) | CIRAD-UMR AGAP HPC Data Center of the South Green Bioinformatics platform | HarvestPlus, part of the CGIAR Research Program Agriculture for Nutrition and Health (A4NH)
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Показать больше [+] Меньше [-]Английский. 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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