Genomic prediction of arsenic tolerance and grain yield in rice: Contribution of trait-specific markers and multi-environment models

Ahmadi Nourollah, Cao Tuong-Vi, Frouin Julien, Norton Gareth J., Price Adam H.. 2021. Genomic prediction of arsenic tolerance and grain yield in rice: Contribution of trait-specific markers and multi-environment models. Rice Science, 28 (3) : 268-278.

Quartile : Q1, Sujet : AGRONOMY / Quartile : Q1, Sujet : PLANT SCIENCES

Résumé : Many rice-growing areas are affected by high concentrations of arsenic (As). Rice varieties that prevent As uptake and/or accumulation can mitigate As threats to human health. Genomic selection is known to facilitate rapid selection of superior genotypes for complex traits. We explored the predictive ability (PA) of genomic prediction with single-environment models, accounting or not for trait-specific markers, multi-environment models, and multi-trait and multi-environment models, using the genotypic (1600K SNPs) and phenotypic (grain As content, grain yield and days to flowering) data of the Bengal and Assam Aus Panel. Under the base-line single-environment model, PA of up to 0.707 and 0.654 was obtained for grain yield and grain As content, respectively; the three prediction methods (Bayesian Lasso, genomic best linear unbiased prediction and reproducing kernel Hilbert spaces) were considered to perform similarly, and marker selection based on linkage disequilibrium allowed to reduce the number of SNP to 17K, without negative effect on PA of genomic predictions. Single-environment models giving distinct weight to trait-specific markers in the genomic relationship matrix outperformed the base-line models up to 32%. Multi-environment models, accounting for genotype × environment interactions, and multi-trait and multi-environment models outperformed the base-line models by up to 47% and 61%, respectively. Among the multi-trait and multi-environment models, the Bayesian multi-output regressor stacking function obtained the highest predictive ability (0.831 for grain As) with much higher efficiency for computing time. These findings pave the way for breeding for As-tolerance in the progenies of biparental crosses involving members of the Bengal and Assam Aus Panel. Genomic prediction can also be applied to breeding for other complex traits under multiple environments.

Mots-clés Agrovoc : génome, marqueur génétique, prévision de rendement, modèle de simulation, génotype, génomique, phénotype, arsenic, modèle mathématique, technique de prévision, Oryza sativa, riz, sélection, tolérance

Mots-clés géographiques Agrovoc : Assam

Mots-clés libres : Genomic prediction model, Genomic selection, Complex trait, Arsenic tolerance, Rice, Predictive ability

Classification Agris : F30 - Génétique et amélioration des plantes
H50 - Troubles divers des plantes
F60 - Physiologie et biochimie végétale

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

Auteurs et affiliations

• Ahmadi Nourollah, Université de Montpellier (FRA) ORCID: 0000-0003-0072-6285 - auteur correspondant
• Cao Tuong-Vi, CIRAD-BIOS-UMR AGAP (BEN) ORCID: 0000-0002-7011-2003
• Frouin Julien, CIRAD-BIOS-UMR AGAP (FRA) ORCID: 0000-0003-1591-0755
• Norton Gareth J., University of Aberdeen (GBR)
• Price Adam H., University of Aberdeen (GBR)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/605812/)

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