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. 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. 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.

Summary: Norway spruce is a boreal forest tree species of significant ecological and economic importance. Hence there is a strong imperative to dissect the genetics underlying important wood quality traits in the species. We performed a functional genome‐wide association study (GWAS) of 17 wood traits in Norway spruce using 178 101 single nucleotide polymorphisms (SNPs) generated from exome genotyping of 517 mother trees. The wood traits were defined using functional modelling of wood properties across annual growth rings. We applied aLeast Absolute Shrinkage and Selection Operator (LASSO‐based) association mapping method using a functional multilocus mapping approach that utilizes latent traits, with a stability selection probability method as the hypothesis testing approach to determine a significant quantitative trait locus. The analysis provided 52 significantSNPs from 39 candidate genes, including genes previously implicated in wood formation and tree growth in spruce and other species. Our study represents a multilocusGWASfor complex wood traits in Norway spruce. The results advance our understanding of the genetics influencing wood traits and identifies candidate genes for future functional studies.

Norway spruce (Picea abies (L.) Karst.) is a conifer species of substanital economic and ecological importance. In common with most conifers, the P. abies genome is very large (∼20 Gbp) and contains a high fraction of repetitive DNA. The current P. abies genome assembly (v1.0) covers approximately 60% of the total genome size but is highly fragmented, consisting of >10 million scaffolds. The genome annotation contains 66,632 gene models that are at least partially validated (www.congenie.org), however, the fragmented nature of the assembly means that there is currently little information available on how these genes are physically distributed over the 12 P. abies chromosomes. By creating an ultra-dense genetic linkage map, we anchored and ordered scaffolds into linkage groups, which complements the fine-scale information available in assembly contigs. Our ultra-dense haploid consensus genetic map consists of 21,056 markers derived from 14,336 scaffolds that contain 17,079 gene models (25.6% of the validated gene models) that we have anchored to the 12 linkage groups. We used data from three independent component maps, as well as comparisons with previously published Picea maps to evaluate the accuracy and marker ordering of the linkage groups. We demonstrate that approximately 3.8% of the anchored scaffolds and 1.6% of the gene models covered by the consensus map have likely assembly errors as they contain genetic markers that map to different regions within or between linkage groups. We further evaluate the utility of the genetic map for the conifer research community by using an independent data set of unrelated individuals to assess genome-wide variation in genetic diversity using the genomic regions anchored to linkage groups. The results show that our map is sufficiently dense to enable detailed evolutionary analyses across the P. abies genome.

Gymnosperms respond differently to light intensity and R:FR; although some aspects of shade response appear conserved, yet underlying mechanisms seem to be diverse in gymnosperms as compared to angiosperms.

Quantitative trait loci (QTL) mapping of wood properties in conifer species has focused on single time point measurements or on trait means based on heterogeneous wood samples (e.g. increment cores) thus ignoring systematic within-tree trends. In this study, functional QTL mapping was performed for a set of important wood properties in increment cores from a 17-year-old Scots pine (Pinus sylvestris L.) full-sib family with the aim of detecting wood trait QTLs for general intercepts (means) and for linear slopes by increasing cambial age. Two multi-locus functional QTL analysis approaches were proposed and their performances were compared on trait datasets comprising 2 - 9 timepoints, 91 - 455 individual tree measurements and genotype datasets of amplified length polymorphisms (AFLP) and single nucleotide polymorphism (SNP) markers. The first method was a multilevel LASSO analysis where trend parameter estimation and QTL mapping were conducted consecutively while the second method was our Bayesian linear mixed model where trends and underlying genetic effects were estimated simultaneously. We also compared several different hypothesis testing methods under either the LASSO or Bayesian framework to perform QTL inference. In total, five and four significant QTLs were observed for the intercepts and slopes, respectively, across wood traits such as earlywood percentage, wood density, radial fiberwidth and spiral grain angle. Four of these QTLs were represented by candidate gene SNPs thus providing promising targets for future research in QTL-mapping and molecular function. Bayesian and LASSO methods both detected similar sets of QTLs given datasets that comprised large numbers of individuals.

Simple Sequence Repeats (SSRs) derived from Expressed Sequence Tags (ESTs) belong to the expressed fraction of the genome and are important for gene regulation, recombination, DNA replication, cell cycle and mismatch repair. Here, we present a comparative analysis of the SSR motif distribution in the 5′UTR, ORF and 3′UTR fractions of ESTs across selected genera of woody trees representing gymnosperms (17 species from seven genera) and angiosperms (40 species from eight genera). Our analysis supports a modest contribution of EST-SSR length to genome size in gymnosperms, while EST-SSR density was not associated with genome size in neither angiosperms nor gymnosperms. Multiple factors seem to have contributed to the lower abundance of EST-SSRs in gymnosperms that has resulted in a non-linear relationship with genome size diversity. The AG/CT motif was found to be the most abundant in SSRs of both angiosperms and gymnosperms, with a relative increase in AT/AT in the latter. Our data also reveals a higher abundance of hexamers across the gymnosperm genera. Our analysis provides the foundation for future comparative studies at the species level to unravel the evolutionary processes that control the SSR genesis and divergence between angiosperm and gymnosperm tree species.