Revisiting superiority and stability metrics of cultivar performances using genomic data: Derivations of new estimators

Carvalho Fanelli Humberto, Rio Simon, García-Abadillo Julian, Isidro y Sánchez Julio. 2024. Revisiting superiority and stability metrics of cultivar performances using genomic data: Derivations of new estimators. Plant Methods, 20:85, 16 p.

https://doi.org/10.1186/s13007-024-01207-1

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Url - jeu de données - Entrepôt autre : https://github.com/TheRocinante-lab/Publications/tree/main/GEmetrics

Résumé : The selection of highly productive genotypes with stable performance across environments is a major challenge of plant breeding programs due to genotype-by-environment (GE) interactions. Over the years, different metrics have been proposed that aim at characterizing the superiority and/or stability of genotype performance across environments. However, these metrics are traditionally estimated using phenotypic values only and are not well suited to an unbalanced design in which genotypes are not observed in all environments. The objective of this research was to propose and evaluate new estimators of the following GE metrics: Ecovalence, Environmental Variance, Finlay–Wilkinson regression coefficient, and Lin–Binns superiority measure. Drawing from a multi-environment genomic prediction model, we derived the best linear unbiased prediction for each GE metric. These derivations included both a squared expectation and a variance term. To assess the effectiveness of our new estimators, we conducted simulations that varied in traits and environment parameters. In our results, new estimators consistently outperformed traditional phenotype-based estimators in terms of accuracy. By incorporating a variance term into our new estimators, in addition to the squared expectation term, we were able to improve the precision of our estimates, particularly for Ecovalence in situations where heritability was low and/or sparseness was high. All methods are implemented in a new R-package: GEmetrics. These genomic-based estimators enable estimating GE metrics in unbalanced designs and predicting GE metrics for new genotypes, which should help improve the selection efficiency of high-performance and stable genotypes across environments.

Mots-clés Agrovoc : amélioration des plantes, génotype, modèle mathématique, modèle de simulation, héritabilité, génomique, variété, performance de culture

Mots-clés libres : Genotype-by-environment interaction, Genomic prediction, Environmental variance, Ecovalence, Finlay–Wilkinson regression coefficient, Lin–Binns superiority measure

Classification Agris : F30 - Génétique et amélioration des plantes
U10 - Informatique, mathématiques et statistiques

Champ stratégique Cirad : CTS 7 (2019-) - Hors champs stratégiques

Agences de financement européennes : European Commission

Agences de financement hors UE : Universidad Politécnica de Madrid, Ministerio de Educación, Ministerio de Educación y Formación Profesional

Programme de financement européen : H2020

Projets sur financement : (EU) Next generation variety testing for improved cropping on European farmland

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