Comparative assessment of genomic, phenomic, and metabolomic prediction models in biparental grapevine breeding populations

Abstract Accelerating grapevine breeding for disease resistance and climate adaptation remains constrained by long generation cycles. We benchmarked genomic (SNP), phenomic (NIRS), and metabolomic (untargeted LC-MS) prediction for 24 agronomic traits in a biparental population phenotyped over 3 years. Seven statistical frameworks and four tissue × timepoint combinations (wood; vineyard leaves at budbreak and flowering; greenhouse leaves at flowering) were evaluated, together with feature-wise BLUPs across samples. Cross-year and cross-population analyses with two additional populations assessed temporal robustness and transferability. Genomic prediction was most accurate (up to r = 0.83), metabolomic prediction was intermediate (up to r = 0.59), and phenomic prediction was lowest (up to r = 0.39) despite its lower acquisition cost. Metabolite features were more heritable than NIR wavelengths, for which most unexplained variation remained residual under the fitted model. Multi-omics integration produced limited overall gains. These results support genomic selection as the primary approach, with metabolomic or phenomic screening considered only for traits and sampling designs that show a reproducible predictive signal.

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Publication Details

Journal
npj Science of Plants
Published
2026-11-03
DOI
https://doi.org/10.1038/s44383-026-00042-8
Primary Topic
Genetic and phenotypic traits in livestock
Type
article
Field-Weighted Citation Impact
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article

Comparative assessment of genomic, phenomic, and metabolomic prediction models in biparental grapevine breeding populations

Éric Duchêne, Alessandra Maia‐Grondard, Komlan Avia, Elsa Chedid et al.
1 links
npj Science of Plants
Genetic and phenotypic traits in livestock
article

Comparative assessment of genomic, phenomic, and metabolomic prediction models in biparental grapevine breeding populations

Éric Duchêne, Alessandra Maia‐Grondard, Komlan Avia, Elsa Chedid, Philippe Hugueney, A. Guillaume, Didier Merdinoglu, Hadrien Chepca, Vincent Dumas, Raymonde Baltenweck, Louise Delannoy, Marcos Calcaterra, Clémentine Borrelli
article en

Abstract

Abstract Accelerating grapevine breeding for disease resistance and climate adaptation remains constrained by long generation cycles. We benchmarked genomic (SNP), phenomic (NIRS), and metabolomic (untargeted LC-MS) prediction for 24 agronomic traits in a biparental population phenotyped over 3 years. Seven statistical frameworks and four tissue × timepoint combinations (wood; vineyard leaves at budbreak and flowering; greenhouse leaves at flowering) were evaluated, together with feature-wise BLUPs across samples. Cross-year and cross-population analyses with two additional populations assessed temporal robustness and transferability. Genomic prediction was most accurate (up to r = 0.83), metabolomic prediction was intermediate (up to r = 0.59), and phenomic prediction was lowest (up to r = 0.39) despite its lower acquisition cost. Metabolite features were more heritable than NIR wavelengths, for which most unexplained variation remained residual under the fitted model. Multi-omics integration produced limited overall gains. These results support genomic selection as the primary approach, with metabolomic or phenomic screening considered only for traits and sampling designs that show a reproducible predictive signal.

npj Science of PlantsVol. 2(1)
Université de Bordeaux (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), École Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (FR), Santé de la Vigne et Qualité du Vin (FR), Unité mixte de recherche Œnologie (FR), Institut de Génétique, Environnement et Protection des Plantes (FR), Ministère de l'Agriculture, des Pêcheries et de l'Alimentation (CA), Université Laval (CA), L'Institut Agro (FR), Université de Rennes (FR), Université de Strasbourg (FR)
Zero hunger
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Genetic and phenotypic traits in livestock
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