Genomic Prediction of Tree Height, Wood Stiffness, and Male Flower Quantity Traits across Two Generations in Selected Individuals of Cryptomeria japonica D. Don (Japanese Cedar)

Breeding long-lived trees is challenging, but it has been shown that genomic information can be used to improve efficiency. In this study, genomic prediction (GP) was tested on selected individuals of a two-generation breeding population of Cryptomeria japonica, the most common plantation tree in Japan. In the 1980s, the second-generation plus trees (101 clones) were selected from about 8500 individuals obtained by cross-mating the first-generation plus trees (47 clones). RAD-seq based on 8664 SNPs was used to perform GP for three important traits in this population: tree height, wood stiffness, and male flower quantity. The association between traits and genotypes was modeled using five Bayesian models whose predictive accuracy was evaluated by cross-validation, revealing that the best model differed for each trait (BRR for tree height, BayesA for wood stiffness, and BayesB for male flower quantity). GP was 1.2-16.0 times more accurate than traditional pedigree-based methods, attributed to its ability to model Mendelian sampling. However, an analysis of the effects of intergenerational kinship showed that parent-offspring relationships reduce the predictive accuracy of GP for traits strongly affected by selection pressure. Overall, these results show that GP could significantly expedite tree breeding when supported by a deep understanding of the targeted population's genetic background.