Accuracy of genomic selection for growth and wood quality traits in two control-pollinated progeny trials using exome capture as the genotyping platform in Norway spruce

Abstract

Background: Genomic 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 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 (G × E), genetic composition, size of the training and validation set, relatedness, and number of SNPs on accuracy and predictive ability (PA) of GS.

Results: Using 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 to more than 30% while using one site as training for GS prediction and using this model to predict the second site, indicating that G × E for tree height should be accommodated in model fitting. Using a half-sib family structure instead of full-sib structure led to a significant reduction in GS accuracy and PA. The full-sib family structure needed only 750 markers to reach similar accuracy and PA, as compared to 100,000 markers required for the 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 markers.

Conclusions: The study indicates that GS would be efficient in reducing generation time of breeding cycle in conifer tree breeding program that requires long-term progeny testing. The 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 × E effect when a strong G × E is detected.

Keywords: Bayesian LASSO; Bayesian ridge regression; Exome capture; GBLUP; Genotype-by-environment interaction; Norway spruce; RKHS.

Fig. 1

Accuracy of different methods and increasing ratios of training set (TS) and validation set (VS)

Fig. 2

Accuracy and predictive ability (PA) of genomic selection with different number of families based on two statistical methods: 1) ABLUP and GBLUP with 9:1 for training set and validation set

Fig. 3

Accuracy and predictive ability (PA) of genomic selection with different subsets of trees per family based on two statistical methods: 1) ABLUP with randomly selecting subset from one to 20 trees per family as training set (TS); 2) GBLUP with randomly selecting subset from one to 20 trees per family as TS

Fig. 4

Accuracy and predictive ability (PA) of genomic selection with subset SNPs based on 2 scenarios: 1) randomly selecting the SNPs subset (10, 25, 50, 100, 250, 500, 750, 1000, 2000, 4000, 6000, 8000, 10,000, and 100,000 SNPs); 2) selecting the SNPs subset with the largest positive effects

Fig. 5

Accuracy and predictive ability (PA) of genomic selection with subset SNPs based on 2 scenarios: 1) randomly selecting subset of SNPs (10, 25, 50, 100, 250, 500, 750, 1000, 2000, 4000, 6000, 8000, 10,000, and 100,000 SNPs) with full-sib family structure; 2) selecting the subset of SNPs with half-sib family structure

Fig. 6

Within contigs LD decay estimated from 517 related individuals in Baison et al. [54]