Assessing the Role of Marker Density and Minor Allele Frequency on Machine Learning–Driven Genomic Selection Accuracy in Grapevine
The results suggest that compared with traditional GS models that rely on a genomic kinship matrix, ML-based approaches offer greater flexibility in feature reduction, and that GS integration is promising for enhancing breeding efficiency in grapevines.