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

With climate change, the pressure on tree breeding to provide varieties with improved resilience to biotic and abiotic stress is increasing. As such, pest resistance is of high priority but has been neglected in most tree breeding programs, given the complexity of phenotyping for these traits and delays to assess mature trees. In addition, the existing genetic variation of resistance and its relationship with productivity should be better understood for their consideration in multitrait breeding. In this study, we evaluated the prospects for genetic improvement of the levels of acetophenone aglycones (AAs) in white spruce needles, which have been shown to be tightly linked to resistance to spruce budworm. Furthermore, we estimated the accuracy of genomic selection (GS) for these traits, allowing selection at a very early stage to accelerate breeding. A total of 1,516 progeny trees established on five sites and belonging to 136 full-sib families from a mature breeding population in New Brunswick were measured for height growth and genotyped for 4,148 high-quality SNPs belonging to as many genes along the white spruce genome. In addition, 598 trees were assessed for levels of AAs piceol and pungenol in needles, and 578 for wood stiffness. GS models were developed with the phenotyped trees and then applied to predict the trait values of unphenotyped trees. AAs were under moderate-to-high genetic control (h 2: 0.43-0.57) with null or marginally negative genetic correlations with other traits. The prediction accuracy of GS models (GBLUP) for AAs was high (PAAC: 0.63-0.67) and comparable or slightly higher than pedigree-based (ABLUP) or BayesCπ models. We show that AA traits can be improved and that GS speeds up the selection of improved trees for insect resistance and for growth and wood quality traits. Various selection strategies were tested to optimize multitrait gains.

Genomic prediction for hastening and improving efficiency of forward selection in conifer polycross mating designs: an example from white spruce.

Genomic selection (GS) has a large potential for improving the prediction accuracy of breeding values and significantly reducing the length of breeding cycles. In this context, the choice of mating designs becomes critical to improve the efficiency of breeding operations and to obtain the largest genetic gains per time unit. Polycross mating designs have been traditionally used in tree and plant breeding to perform backward selection of the female parents. The possibility to use genetic markers for paternity identification and for building genomic prediction models should allow for a broader use of polycross tests in forward selection schemes. We compared the accuracies of genomic predictions of offspring's breeding values from a polycross and a full-sib (partial diallel) mating design with similar genetic background in white spruce (Picea glauca). Trees were phenotyped for growth and wood quality traits, and genotyped for 4092 SNPs representing as many gene loci distributed across the 12 spruce chromosomes. For the polycross progeny test, heritability estimates were smaller, but more precise using the genomic BLUP (GBLUP) model as compared with pedigree-based models accounting for the maternal pedigree or for the reconstructed full pedigree. Cross-validations showed that GBLUP predictions were 22-52% more accurate than predictions based on the maternal pedigree, and 5-7% more accurate than predictions using the reconstructed full pedigree. The accuracies of GBLUP predictions were high and in the same range for most traits between the polycross (0.61-0.70) and full-sib progeny tests (0.61-0.74). However, higher genetic gains per time unit were expected from the polycross mating design given the shorter time needed to conduct crosses. Considering the operational advantages of the polycross design in terms of easier handling of crosses and lower associated costs for test establishment, we believe that this mating scheme offers great opportunities for the development and operational application of forward GS.

Development of high-density SNP genotyping arrays for white spruce (Picea glauca) and transferability to subtropical and nordic congeners.

High-density SNP genotyping arrays can be designed for any species given sufficient sequence information of high quality. Two high-density SNP arrays relying on the Infinium iSelect technology (Illumina) were designed for use in the conifer white spruce (Picea glauca). One array contained 7338 segregating SNPs representative of 2814 genes of various molecular functional classes for main uses in genetic association and population genetics studies. The other one contained 9559 segregating SNPs representative of 9543 genes for main uses in population genetics, linkage mapping of the genome and genomic prediction. The SNPs assayed were discovered from various sources of gene resequencing data. SNPs predicted from high-quality sequences derived from genomic DNA reached a genotyping success rate of 64.7%. Nonsingleton in silico SNPs (i.e. a sequence polymorphism present in at least two reads) predicted from expressed sequenced tags obtained with the Roche 454 technology and Illumina GAII analyser resulted in a similar genotyping success rate of 71.6% when the deepest alignment was used and the most favourable SNP probe per gene was selected. A variable proportion of these SNPs was shared by other nordic and subtropical spruce species from North America and Europe. The number of shared SNPs was inversely proportional to phylogenetic divergence and standing genetic variation in the recipient species, but positively related to allele frequency in P. glauca natural populations. These validated SNP resources should open up new avenues for population genetics and comparative genetic mapping at a genomic scale in spruce species.

Association genetics of wood physical traits in the conifer white spruce and relationships with gene expression.

Marker-assisted selection holds promise for highly influencing tree breeding, especially for wood traits, by considerably reducing breeding cycles and increasing selection accuracy. In this study, we used a candidate gene approach to test for associations between 944 single-nucleotide polymorphism markers from 549 candidate genes and 25 wood quality traits in white spruce. A mixed-linear model approach, including a weak but nonsignificant population structure, was implemented for each marker-trait combination. Relatedness among individuals was controlled using a kinship matrix estimated either from the known half-sib structure or from the markers. Both additive and dominance effect models were tested. Between 8 and 21 single-nucleotide polymorphisms (SNPs) were found to be significantly associated (P ≤ 0.01) with each of earlywood, latewood, or total wood traits. After controlling for multiple testing (Q ≤ 0.10), 13 SNPs were still significant across as many genes belonging to different families, each accounting for between 3 and 5% of the phenotypic variance in 10 wood characters. Transcript accumulation was determined for genes containing SNPs associated with these traits. Significantly different transcript levels (P ≤ 0.05) were found among the SNP genotypes of a 1-aminocyclopropane-1-carboxylate oxidase, a ß-tonoplast intrinsic protein, and a long-chain acyl-CoA synthetase 9. These results should contribute toward the development of efficient marker-assisted selection in an economically important tree species.