VitisGP: Multi-Task Benchmark for Genotype to Phenotype Grapevine Prediction

As climate change reshapes grape-growing conditions, predicting grapevine phenotypes from genomic variation is increasingly important for identifying and breeding cultivars suited to emerging environmental pressures. Existing research on the grapevine genotype–phenotype relationship using machine learning has often focused on individual traits, isolated regions, or a limited set of predictive models. We introduce VitisGP, a multi-phenotype curated dataset, integrating genome-wide SNP data with 26 heterogeneous phenotype prediction tasks spanning sommelier-curated aroma traits (N=11), botanical traits (N=14) and wine color---across 1,197 Vitis vinifera cultivars. We employ VitisGP to benchmark phenotype prediction, comparing machine-learning models with differing inductive biases---L2-regularized logistic regression, Random Forest, XGBoost, CatBoost, and Transformer-based models, providing the first systematic comparison of this breadth on harmonized grapevine data. Across the benchmark, CatBoost achieves the strongest overall performance (macro-average AUROC=$0.696$ across 26 tasks), although model performance varies substantially across phenotype classes, with sensory and color traits showing stronger predictability than agronomic characteristics. VitisGP provides the community with a resource for investigating the genomic basis of phenotypic traits and screening grapevine diversity for climate-relevant characteristics.