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Genomic prediction accounting for genotype by environment interaction offers an effective framework for breeding simultaneously for adaptation to an abiotic stress and performance under normal cropping conditions in rice

Ben Hassen Manel, Bartholome Jérôme, Valè Giampiero, Cao Tuong-Vi, Ahmadi Nourollah. 2018. Genomic prediction accounting for genotype by environment interaction offers an effective framework for breeding simultaneously for adaptation to an abiotic stress and performance under normal cropping conditions in rice. G3 - Genes Genomes Genetics, 8 (7) : pp. 2319-2332.

Journal article ; Article de recherche ; Article de revue à facteur d'impact Revue en libre accès total
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Url - jeu de données : https://doi.org/10.25387/g3.6170999

Quartile : Q2, Sujet : GENETICS & HEREDITY

Abstract : Developing rice varieties adapted to alternate wetting and drying water management is crucial for the sustainability of irrigated rice cropping systems. Here we report the first study exploring the feasibility of breeding rice for adaptation to alternate wetting and drying using genomic prediction methods that account for genotype by environment interactions. Two breeding populations (a reference panel of 284 accessions and a progeny population of 97 advanced lines) were evaluated under alternate wetting and drying and continuous flooding management systems. The predictive ability of genomic prediction for response variables (index of relative performance and the slope of the joint regression) and for multi-environment genomic prediction models were compared. For the three traits considered (days to flowering, panicle weight and nitrogen-balance index), significant genotype by environment interactions were observed in both populations. In cross validation, predictive ability for the index was on average lower (0.31) than that of the slope of the joint regression (0.64) whatever the trait considered. Similar results were found for progeny validation. Both cross-validation and progeny validation experiments showed that the performance of multi-environment models predicting unobserved phenotypes of untested entrees was similar to the performance of single environment models with differences in predictive ability ranging from -6–4% depending on the trait and on the statistical model concerned. The predictive ability of multi-environment models predicting unobserved phenotypes of entrees evaluated under both water management systems outperformed single environment models by an average of 30%. Practical implications for breeding rice for adaptation to alternate wetting and drying system are discussed.

Mots-clés Agrovoc : Oryza sativa, Riz irrigué, amélioration génétique, Sélection précoce, Génotype, Stress abiotique, Technique de culture

Mots-clés géographiques Agrovoc : Italie

Mots-clés libres : Rice, Genomic selection, Progeny prediction, GxE interaction, Alternate wetting and drying (AWD)

Classification Agris : F30 - Plant genetics and breeding
F06 - Irrigation

Champ stratégique Cirad : Axe 1 (2014-2018) - Agriculture écologiquement intensive

Auteurs et affiliations

  • Ben Hassen Manel, CIRAD-BIOS-UMR AGAP (FRA)
  • Bartholome Jérôme, CIRAD-BIOS-UMR AGAP (FRA) ORCID: 0000-0002-0855-3828
  • Valè Giampiero, CREA (ITA)
  • Cao Tuong-Vi, CIRAD-BIOS-UMR AGAP (FRA)
  • Ahmadi Nourollah, CIRAD-BIOS-UMR AGAP (FRA) ORCID: 0000-0003-0072-6285 - auteur correspondant

Source : Cirad-Agritrop (https://agritrop.cirad.fr/589134/)

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