Environment-aware genomic prediction enhances the transferability of polygenic resistance to ash dieback in Fraxinus excelsior

Ash dieback caused by Hymenoscyphus fraxineus threatens European ash ( Fraxinus excelsior L.) across its range, yet natural populations retain heritable, polygenic variation in disease response. A major challenge for genomic prediction in long-lived trees is reduced transferability across heterogeneous environments, where genotype-by-environment ( G×E ) interactions may influence phenotypic expression. Here, we combined nationwide sampling across Poland (320 trees from 107 populations), whole-genome SNP data, and climate-derived predictors to test whether modelling environmental similarity and G×E can improve the prediction of ash dieback severity, quantified using a synthetic tree damage index ( Syn ). Environmental ordination identified a primary hydroclimatic gradient as a key driver of Syn ( PC1 _env : β = 0.45 ± 0.14, p = 0.0016), although broad-scale environmental predictors explained only a modest proportion of phenotypic variance. Genome-wide association analyses revealed substantial additive genetic signal (SNP-based heritability h ² _SNP = 0.63; extreme-phenotype h ² _SNP = 0.81) and identified 414 suggestive loci (p < 1 × 10⁻⁵), consistent with a broadly polygenic architecture of resistance, but with pronounced local enrichment of association signals in two candidate regions on chromosomes 2 and 4. In genomic prediction, trait-enriched SNP panels consistently outperformed random panels across marker densities. Predictive ability reached r ≈ 0.89 in internal validation for a 500-SNP panel and remained robust ( r ≈ 0.80) in an independent external validation set (n = 64). Incorporating G×E in a multi-kernel framework yielded modest but consistent gains over main-effect models, particularly under environmental extrapolation, with REML variance partitioning supported a non-zero interaction component (V _G×E ≈ 14.9% and 20.9%). Our results demonstrate that ash dieback resistance is predictably polygenic and that accounting for environmental heterogeneity enhances the robustness and transferability of genomic prediction, supporting environment-aware selection and assisted migration strategies for European ash restoration.