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

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 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 (GE), genetic composition, size of the training and validation set, relatedness, and the number of SNPs on the 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 more than 30% using one site as training for GS prediction to the second site, indicating that GE 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. Conclusions:The 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  E effect when a strong GE is detected. BackgroundNorway spruce (Picea abies (L.) Karst.) is one of the most important conifer species for commercial wood production and ecological integrity in Europe [1]. A conventional breeding program for Norway spruce based on pedigree-based phenotypic selection usually takes between 20-30 years in Scandinavian countries [2]. To shorten the breeding cycle, genomic selection (GS) has recently been proposed as an alternative in many tree species such as eucalypts (Eucalyptus) [3][4][5], maritime pine (Pinus pinaster Aiton) [6,7], loblolly pine (Pinus taeda L.) [8,9], white spruce and its hybrids (Picea glauca [Moench] Voss) [10-13] , and black spruce (Picea Mariana [Mill] B.S.P.) [14]. Hayes et al. [15] considered four major factors affecting the accuracy of GS: 1) heritability of the target trait; 2) the extent of linkage disequilibrium (LD) between the marker a...

Genomic selection for resistance to spruce budworm in white spruce and relationships with growth and wood quality traits

“…In this study, estimates of predictive accuracies based on “true breeding values” (TBV) obtained from the models with 100% of phenotypes were also estimated (Table 6) because this approach is very common in the literature (e.g., Beaulieu, Doerksen, Clément, et al, 2014; Chen et al., 2018; Lenz et al., 2017). We found higher predictive accuracy values using this approach, by about 1%–124% depending on the trait, especially when the same model was used to estimate reference true breeding values (i.e., ABLUP predicted versus ABLUP 100% phenotypes, or GBLUP predicted versus GBLUP 100% phenotypes).…”

Leveraging breeding programs and genomic data in Norway spruce (Picea abies L. Karst) for GWAS analysis

“…After these filtering steps, a total of ~300K SNPs with MAF > 0.005 were left for population structure analysis. In our previous study [ 63 ], with filtering criteria of GQ < 6 and DP < 2 as missing, the averaged discordance estimated from 148 technical replicates was less than 1%. Thus, we considered that such filtering criteria were sufficient for downstream analysis.…”

“…A second group with 1370 full-sib progenies was planted in two northern Swedish field plantations. This group was sequenced previously [ 63 ] and was used here to validate the association signals detected from the first group. The third group with 914 full-sib progenies was planted in three southern Swedish field plantations.…”

“…The 1370 full-sib progenies from 55 parents/elite trees and established in two field plantations (Vindeln and Hädanberg) in northern Sweden that were genotyped in a previous study [ 63 ] were used for validation. Here, 51 parents (95%) belonged to the 1041 northern Swedish population (NFE) (Table 1 ).…”

Development of a highly efficient 50K single nucleotide polymorphism genotyping array for the large and complex genome of Norway spruce (Picea abies L. Karst) by whole genome resequencing and its transferability to other spruce species

“…Recently, an Axiom SNP genotyping array with 55K SNPs was developed for Douglas‐fir ( Pseudotsuga menziesii ) from transcriptome sequencing (Perry et al., 2020). For Norway spruce, high‐quality SNPs have been developed based on large‐scale sequence capture and have been employed for both GWAS and GS (Azaiez et al 2018; Baison et al., 2019; Chen et al., 2018; Vidalis et al 2018). Various SNP arrays have also been available for poplar and other broadleaved tree species that have been used in association genetics and GS studies (Geraldes et al., 2013).…”