Uncovering the genetic basis of agronomic traits in over 1,000 grapevine genotypes derived from a disease resistance breeding program

Breeding disease-resistant grapevines that retain agronomic performance under variable climates requires loci and predictions that transfer across related hybrid families. We analyzed 1,081 genotypes from 95 crosses within the French INRAE-ResDur breeding program for 13 phenology, yield and berry-composition traits evaluated at five sites from 2006 to 2024. We integrated within-family QTL mapping, kinship- and population-adjusted multiple-population QTL mapping in 772 progeny, and structure-aware GWAS in 899-968 individuals, depending on the trait, together with cross-environment, cross-trait and local genomic estimated breeding-value analyses. Genetic and phenotypic differentiation among families accompanied extensive family specificity of detected loci. Of 79 family-QTL intervals, seven, representing six trait-region hypotheses, were locally concordant across all three mapping frameworks. The strongest recurrent evidence involved a chromosome-16 region for veraison and harvest date, where a localGEBV block at 14.69 Mb ranked first for veraison and second for harvest; chromosome-14 cluster traits and chromosome-1 compactness emerged as additional validation priorities. Cross-environment meta-analysis detected no common fixed-effect association at 5% FDR but revealed extensive heterogeneous evidence. Cross-trait analysis grouped 270 significant marker tests into 54 candidate multi-trait regions, 53 of which were retained when the derived trait mean cluster weight was excluded, without establishing biological pleiotropy. Population-adjusted localGEBV yielded a mean leave-one-population-out correlation of 0.470 between phenotypic BLUPs and genomic scores across traits. These results distinguish compact haplotype-validation targets from background- and environment-dependent signals, supporting a staged strategy that combines marker-assisted selection for validated recurrent regions with externally validated multi-trait, multi-environment genomic prediction for polygenic traits.