Quercus petraea Raw sequence reads
Climate change is reshaping the adaptive landscape of forest ecosystems, demanding more efficient strategies to identify and deploy resilient tree genotypes. GWAS and genomic prediction offers a powerful framework to accelerate selection for complex physiological traits underlying climate adaptability in long-lived species such as sessile oak (Quercus petraea (Matt.) Liebl.).The project focuses identifying candidate genes and genotypes for drought adaptation using three key physiological traits carbon isotope composition, nitrogen isotope composition, and the carbon-to-nitrogen content ratio (C/N ratio) measured in 768 trees genotyped with dense genome-wide markers (approximately 580,000 SNPs). Foliar physiological traits have been widely reported as proxies for intrinsic water-use efficiency (iWUE), nutrient acquisition efficiency, and drought- and nutrient-stress responses in forest trees.These samples were collected from eight provenances across four European countries (France, Denmark, United Kingdom, and Germany) and were tested at two experimental sites in Germany: Harz and Unterluss. The provenances and sites used here are part of the international Madsen Collection (Madsen, 1990).Genotyping was performed using Single Primer Enrichment Technology (SPET), a targeted genotyping-by-sequencing (GBS) approach for non-model species (Scaglione et al., 2019). SPET targets single-nucleotide polymorphisms (SNPs) located within gene coding regions and 500 bp downstream flanking sequences, based on prior whole-genome resequencing data from a subset of 96 individuals, generated by IGATech (IGA Technology Services Srl, Udine, Italy) (Zormpa et al., 2025).High genomic heritabilities were estimated across traits, with within-year prediction accuracies reaching 0.77. Notably, across-year and across-provenance predictions remained substantial (0.41 - 0.82), with predictability declining with increasing genetic distance (FST) between training and test provenances for nitrogen isotope composition and C/N ratio. In addition, GWAS-guided SNP preselection increased heritability capture by approximately 15% relative to random SNP subsets. And, the pronounced provenance-by-environment interactions observed indicated substantial phenotypic plasticity in these traits.These results demonstrate the strong potential of applying genomic prediction to foliar physiological traits as polygenic predictors for climate adaptation in plants, support provenance-aware breeding to improve forest establishment, and provide practical strategies for deploying genomic prediction in long-lived species.