Inter- and Intra-Generation Genomic Predictions for Douglas-fir Growth in Unobserved Environments

Abstract

Conifers are prime candidates for genomic selection (GS) due to their long breeding cycles. Previous studies have shown much reduced prediction accuracies (PA) of breeding values in unobserved environments, which may impede its adoption. The impact of explicit environmental heterogeneity modeling including genotype-by-environment (GxE) interaction effects using environmental covariates (EC) in a reaction-norm genomic prediction model was tested using single-step GBLUP (ssGBLUP). A three-generation coastal Douglas-fir experimental population with 14 genetic trials (n = 13,615) permitted estimation of intra- and inter-generation PA in unobserved environments using 66,969 SNPs derived from exome capture. Intra- and inter-generation PAs ranged from 0.447-0.640 and 0.317-0.538, respectively. The inclusion of ECs in the prediction models explained up to 23% of the phenotypic variation for the fully specified model and resulted in the best model fit. Modeling GxE effects in the training population increased PA up to 6% and 13% over the base model for inter- and intra-generations, respectively. GS-PA can be substantially improved using ECs to explain environmental heterogeneity and GxE effects. The ssGBLUP methodology allows historical genetic trials containing non-genotyped samples to contribute in genomic prediction, and, thus, effectively boosting training population size which is a critical step. Further pheno- and enviro-typing developments may improve GS-PA.