Accuracy of genomic selection for growth and wood quality traits in two control-pollinated progeny trials using exome capture as genotyping platform in Norway spruce
BackgroundGenomic selection (GS) can increase genetic gain by reducing the length of breeding cycle in forest trees. Here we genotyped 1370 control-pollinated progeny trees from 128 full-sib families in Norway spruce (Picea abies (L.) Karst.), using exome capture as a genotyping platform. We used 116,765 high quality SNPs to develop genomic prediction models for tree height and wood quality traits. We assessed the impact of different genomic prediction methods, genotype-by-environment interaction (GxE), genetic composition, size of the training and validation set, relatedness, and the number of SNPs on the accuracy and predictive ability (PA) of GS.\n\nResultsUsing G matrix slightly altered heritability estimates relative to pedigree-based method. GS accuracies were about 11-14% lower than those based on pedigree-based selection. The efficiency of GS per year varied from 1.71 to 1.78, compared to that of the pedigree-based model if breeding cycle length was halved using GS. Height GS accuracy decreased more than 30% using one site as training for GS prediction to the second site, indicating that GxE for tree height should be accommodated in model fitting. Using half-sib family structure instead of full-sib led a significant reduction in GS accuracy and PA. The full-sib family structure only needed 750 makers to reach similar accuracy and PA as 100,000 markers required for half-sib family, indicating that maintaining the high relatedness in the model improves accuracy and PA. Using 4000-8000 markers in full-sib family structure was sufficient to obtain GS model accuracy and PA for tree height and wood quality traits, almost equivalent to that obtained with all makers.\n\nConclusionsThe study indicates GS would be efficient in reducing generation time of a breeding cycle in conifer tree breeding program that requires a long-term progeny testing. Sufficient number of trees within-family (16 for growth and 12 for wood quality traits) and number of SNPs (8000) are required for GS with full-sib family relationship. GS methods had little impact on GS efficiency for growth and wood quality traits. GS model should incorporate G x E effect when a strong GxE is detected.