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Pré-Publication, Document De Travail Année : 2023

Portability of genomic predictions trained on sparse factorial designs across two maize silage breeding cycles

Alizarine Lorenzi
  • Fonction : Auteur
Cyril Bauland
  • Fonction : Auteur
Sophie Pin
  • Fonction : Auteur
Delphine Madur
  • Fonction : Auteur
Valérie Combes
  • Fonction : Auteur
Carine Palaffre
  • Fonction : Auteur
Colin Guillaume
  • Fonction : Auteur
Gaëtan Touzy
  • Fonction : Auteur
Tristan Mary-Huard
  • Fonction : Auteur
Alain Charcosset
  • Fonction : Auteur
Laurence Moreau
Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale)
CV

Résumé

Abstract Genomic selection offers new prospects for revisiting hybrid breeding schemes by replacing extensive phenotyping of individuals with genomic predictions. Finding the ideal design for training genomic prediction models is still an open question. Previous studies have shown promising predictive abilities using sparse factorial instead of tester-based training sets to predict single-cross hybrids from the same generation. This study aims to further investigate the use of factorials and their optimization to predict line general combining abilities (GCAs) and hybrid values across breeding cycles. It relies on two breeding cycles of a maize reciprocal genomic selection scheme involving multiparental connected reciprocal populations from flint and dent complementary heterotic groups selected for silage performances. Selection based on genomic predictions trained on a factorial design resulted in a significant genetic gain for dry matter yield in the new generation. Results confirmed the efficiency of sparse factorial training sets to predict candidate line GCAs and hybrid values across breeding cycles. Compared to a previous study based on the first generation, the advantage of factorial over tester training sets appeared lower across generations. Updating factorial training sets by adding single-cross hybrids between selected lines from the previous generation or a random subset of hybrids from the new generation both improved predictive abilities. The CDmean criterion helped determine the set of single-crosses to phenotype to update the training set efficiently. Our results validated the efficiency of sparse factorial designs for calibrating hybrid genomic prediction experimentally and showed the benefit of updating it along generations.

Domaines

Sciences du Vivant [q-bio]

laurence moreau  : Connectez-vous pour contacter le contributeur

https://hal.inrae.fr/hal-04351595

Soumis le : lundi 18 décembre 2023-18:07:45

Dernière modification le : mercredi 20 décembre 2023-03:26:41

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Dates et versions

hal-04351595 , version 1 (18-12-2023)

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Alizarine Lorenzi, Cyril Bauland, Sophie Pin, Delphine Madur, Valérie Combes, et al.. Portability of genomic predictions trained on sparse factorial designs across two maize silage breeding cycles. 2023. ⟨hal-04351595⟩

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AGROPARISTECH CNRS GQE UNIV-PARIS-SACLAY INRAE GS-BIOSPHERA GS-LIFE-SCIENCES-HEALTH BIOLOGIE_ET_AMELIORATION_DES_PLANTES
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