Performance of phenomic selection in rice: Effects of population size and genotype-environment interactions on predictive ability

De Verdal Hugues, Segura Vincent, Pot David, Salas Nicolas, Garin Vincent, Rakotoson Tatiana, Raboin Louis-Marie, VomBrocke Kirsten, Dusserre Julie, Castro Pacheco Sergio Antonio, Grenier Cécile. 2024. Performance of phenomic selection in rice: Effects of population size and genotype-environment interactions on predictive ability. PloS One, 19 (12):e0309502, 21 p.

Url - jeu de données - Entrepôt autre : https://doi.org/10.6084/m9.figshare.27282459.v1 / Url - jeu de données - Dataverse Cirad : https://doi.org/10.18167/DVN1/JN5I8K

Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Psychologie-éthologie-ergonomie; Staps

Résumé : Phenomic prediction (PP), a novel approach utilizing Near Infrared Spectroscopy (NIRS) data, offers an alternative to genomic prediction (GP) for breeding applications. In PP, a hyperspectral relationship matrix replaces the genomic relationship matrix, potentially capturing both additive and non-additive genetic effects. While PP boasts advantages in cost and throughput compared to GP, the factors influencing its accuracy remain unclear and need to be defined. This study investigated the impact of various factors, namely the training population size, the multi-environment information integration, and the incorporations of genotype x environment (GxE) effects, on PP compared to GP. We evaluated the prediction accuracies for several agronomically important traits (days to flowering, plant height, yield, harvest index, thousand-grain weight, and grain nitrogen content) in a rice diversity panel grown in four distinct environments. Training population size and GxE effects inclusion had minimal influence on PP accuracy. The key factor impacting the accuracy of PP was the number of environments included. Using data from a single environment, GP generally outperformed PP. However, with data from multiple environments, using genotypic random effect and relationship matrix per environment, PP achieved comparable accuracies to GP. Combining PP and GP information did not significantly improve predictions compared to the best model using a single source of information (e.g., average predictive ability of GP, PP, and combined GP and PP for grain yield were of 0.44, 0.42, and 0.44, respectively). Our findings suggest that PP can be as accurate as GP when all genotypes have at least one NIRS measurement, potentially offering significant advantages for rice breeding programs, reducing the breeding cycles and lowering program costs.

Mots-clés Agrovoc : Oryza sativa, intéraction génotype environnement, spectroscopie infrarouge, génotype, amélioration des plantes, riz pluvial, génome, modèle mathématique, caractère agronomique, spectroscopie, technique de prévision, engrais azoté

Mots-clés géographiques Agrovoc : France, Madagascar

Mots-clés libres : Rice, Genomic prediction, Genomic selection, Phenomic prediction, Genotype by environment interaction;, Training sample designs, Population size, Predictive ability

Auteurs et affiliations

• De Verdal Hugues, CIRAD-BIOS-UMR AGAP (SEN) ORCID: 0000-0002-1923-8575 - auteur correspondant
• Segura Vincent, INRAE (FRA)
• Pot David, CIRAD-BIOS-UMR AGAP (FRA) ORCID: 0000-0001-6144-8448
• Salas Nicolas, Université de Montpellier (FRA)
• Garin Vincent, CIRAD-BIOS-UMR AGAP (FRA)
• Rakotoson Tatiana, Université d'Antananarivo (MDG)
• Raboin Louis-Marie, CIRAD-PERSYST-UPR AIDA (FRA) ORCID: 0000-0002-8749-0406
• VomBrocke Kirsten, Université de Montpellier (FRA)
• Dusserre Julie, CIRAD-PERSYST-UPR AIDA (FRA) ORCID: 0000-0002-4971-8799
• Castro Pacheco Sergio Antonio, CIRAD-BIOS-UMR AGAP (MDG) ORCID: 0000-0001-5946-289X
• Grenier Cécile, CIRAD-BIOS-UMR AGAP (FRA) ORCID: 0000-0001-5390-8344

Source : Cirad-Agritrop (https://agritrop.cirad.fr/612053/)

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