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 three years. Seven statistical frameworks and four tissue x 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 reproducible predictive signal.

Plant phenotyping relevance

ブドウ育種集団の複数形質について、NIRSによるフェノミック測定を含む予測モデルを比較・検証し、交差年・集団で頑健性と転移性も評価しているため、形質推定法の技術的ベンチマークが中心である。

Code and data availability

The supplied blocks contain only results, figures, and tables describing genomic/phenomic/metabolomic prediction in grapevine population 50025. No public phenotype/NIR/metabolomic datasets, author analysis code, trained models, or data availability statements with URLs are present, so no paper-specific reproducibility-

No evidence-backed public reproduction asset is currently recorded.