Genomic prediction of superior heartwood traits in pedunculate oak (Quercus robur L.) using GWAS-informed Bayesian models

Heartwood traits in trees are critical for timber quality but are notoriously difficult to phenotype due to their late expression and the need for destructive sampling. In this proof-of-concept study, we demonstrate that combining genome-wide association studies (GWAS) with Bayesian genomic prediction models provides an effective strategy to overcome these challenges. By using GWAS to preselect trait associated single nucleotide polymorphisms (SNPs) and integrating them into predictive models, we substantially improve the accuracy of genomic predictions for heartwood related traits in oaks. Our approach facilitates reliable selection of superior genotypes within a breeding population, long before heartwood traits can be directly measured, thus enabling early and cost-effective breeding decisions. We also identify the number of rings in sapwood as a genetically controlled, easily measured proxy trait that enhances selection strategies for heartwood content. Together, these findings provide a scalable framework for integrating genomics into operational tree breeding programs and demonstrate how combining GWAS and genomic prediction can accelerate the improvement of complex wood traits in long lived forest tree species. • Heartwood traits are difficult to phenotype as they require destructive sampling. • Genomic prediction enables early selection of superior heartwood genotypes. • Sapwood ring number provides an easy proxy for heartwood content. • Genomics-based breeding framework in oaks transferable to other tree species.