Interest of phenomic prediction as an alternative to genomic prediction in grapevine

Brault Charlotte, Lazerges Juliette, Doligez Agnès, Thomas Miguel, Ecarnot Martin, Roumet Pierre, Bertrand Yves, Berger Gilles, Pons Thierry, François Pierre, Le Cunff Loïc, This Patrice, Segura Vincent. 2022. Interest of phenomic prediction as an alternative to genomic prediction in grapevine. Plant Methods, 18 (1):108, 13 p.

Url - jeu de données - Entrepôt autre : https://doi.org/10.15454/PNQQUQ / Url - jeu de données - Entrepôt autre : https://doi.org/10.15454/BICRFX

Résumé : Background: Phenomic prediction has been defined as an alternative to genomic prediction by using spectra instead of molecular markers. A reflectance spectrum provides information on the biochemical composition within a tissue, itself being under genetic determinism. Thus, a relationship matrix built from spectra could potentially capture genetic signal. This new methodology has been mainly applied in several annual crop species but little is known so far about its interest in perennial species. Besides, phenomic prediction has only been tested for a restricted set of traits, mainly related to yield or phenology. This study aims at applying phenomic prediction for the first time in grapevine, using spectra collected on two tissues and over two consecutive years, on two populations and for 15 traits, related to berry composition, phenology, morphological and vigour. A major novelty of this study was to collect spectra and phenotypes several years apart from each other. First, we characterized the genetic signal in spectra and under which condition it could be maximized, then phenomic predictive ability was compared to genomic predictive ability. Results: For the first time, we showed that the similarity between spectra and genomic relationship matrices was stable across tissues or years, but variable across populations, with co-inertia around 0.3 and 0.6 for diversity panel and half-diallel populations, respectively. Applying a mixed model on spectra data increased phenomic predictive ability, while using spectra collected on wood or leaves from one year or another had less impact. Differences between populations were also observed for predictive ability of phenomic prediction, with an average of 0.27 for the diversity panel and 0.35 for the half-diallel. For both populations, a significant positive correlation was found across traits between predictive ability of genomic and phenomic predictions. Conclusion: NIRS is a new low-cost alternative to genotyping for predicting complex traits in perennial species such as grapevine. Having spectra and phenotypes from different years allowed us to exclude genotype-by-environment interactions and confirms that phenomic prediction can rely only on genetics.

Mots-clés Agrovoc : génotype, amélioration des plantes, vigne, bois, marqueur génétique, variation génétique, génomique, phénotype, héritabilité, génome, modèle mathématique, essai de variété, phénologie, spectroscopie infrarouge, technique de prévision

Mots-clés libres : Phenomic prediction, Genomic prediction, Plant Breeding, Spectroscopy, Phenomics, Grapevine

Agences de financement hors UE : Agence Nationale de la Recherche

Projets sur financement : (FRA) Sélection génomique au service de l'amélioration de la vigne pour la diversification et le déploiement de variétés résistantes à forts potentiel œnologique

Auteurs et affiliations

• Brault Charlotte, CIRAD-BIOS-UMR AGAP (FRA)
• Lazerges Juliette, Université de Montpellier (FRA)
• Doligez Agnès, INRAE (FRA)
• Thomas Miguel, CIRAD-BIOS-UMR AGAP (FRA)
• Ecarnot Martin, INRAE (FRA)
• Roumet Pierre, Montpellier SupAgro (FRA)
• Bertrand Yves, Université de Montpellier (FRA)
• Berger Gilles, Université de Montpellier (FRA)
• Pons Thierry, INRAE (FRA)
• François Pierre, INRAE (FRA)
• Le Cunff Loïc, INRAE (FRA)
• This Patrice, INRAE (FRA)
• Segura Vincent, INRAE (FRA) - auteur correspondant

Source : Cirad-Agritrop (https://agritrop.cirad.fr/611022/)

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