Background: Genomic selection (GS) or genomic prediction is a promising approach for tree breeding to obtain higher genetic gains by shortening time of progeny testing in breeding programs. As proof-of-concept for Scots pine (Pinus sylvestrisL.), a genomic prediction study was conducted with 694 individuals representing 183 full-sib families that were genotyped with genotyping-by-sequencing (GBS) and phenotyped for growth and wood quality traits. 8719 SNPs were used to compare different genomic with pedigree prediction models. Additionally, four prediction efficiency methods were used to evaluate the impact of genomic breeding value estimations by assigning diverse ratios of training and validation sets, as well as several subsets of SNP markers.Results: Genomic Best Linear Unbiased Prediction (GBLUP) and Bayesian Ridge Regression (BRR) combined with expectation maximization (EM) imputation algorithm showed slightly higher prediction efficiencies than Pedigree Best Linear Unbiased Prediction (PBLUP) and Bayesian LASSO, with some exceptions. A subset of approximately 6000 SNP markers, was enough to provide similar prediction efficiencies as the full set of 8719 markers. Additionally, prediction efficiencies of genomic models were enough to achieve a higher selection response, that varied between 50-143% higher than the traditional pedigree-based selection.Conclusions: Although prediction efficiencies were similar for genomic and pedigree models, the relative selection response was doubled for genomic models by assuming that earlier selections can be done at the seedling stage, reducing the progeny testing time, thus shortening the breeding cycle length roughly by 50%.

The chemical composition of wood is one of the key features that determine wood quality. The focus of this study was on identifying differences between juvenile and mature woods in Scots pine (Pinus sylvestrisL.) and developing models for predicting the chemical composition of these two wood types. Chemical traits, determined by traditional wet chemistry techniques, included the proportion of lignin, polysaccharides and extractives. Partial least squares regression of Fourier transform infrared (FTIR) spectra was used for model building. The model performance was primarily evaluated by root mean squared error of predictions (RMSEP). High predictive power was attained for the content of lignin (RMSEP of 0.476 and 0.495 for juvenile and mature woods, respectively) and extractives (0.302 and 0.471), good predictive power for cellulose (0.715 and 0.696) and hemicelluloses in juvenile wood (0.719) and low predictive power for hemicelluloses in mature wood (0.823). A distinct band was observed at 1693 cm⁻¹, and its intensity was strongly associated with the content of extractives (r = 0.968 and 0.861 in juvenile and mature woods, respectively). FTIR has proved suitable for the rapid, non-destructive, cost-efficient assessment of the chemical composition of juvenile and mature woods in Scots pine. The band at 1693 cm⁻¹is to be further investigated to unravel its link with individual extractive components.

Wood stiffness is an important wood mechanical property that predetermines the suitability of sawn timber for construction purposes. Negative genetic correlations between wood stiffness and growth traits have, however, been reported for many conifer species including Scots pine. It is, therefore, important that breeding programs consider wood stiffness and growth traits simultaneously. The study aims to (1) evaluate different approaches of calculating the dynamic modulus of elasticity (MOE, non-destructively assessed stiffness) using data from X-ray analysis (SilviScan) as a benchmark, (2) estimate genetic parameters, and (3) apply index selection. In total, we non-destructively measured 622 standing trees from 175 full-sib families for acoustic velocity (VEL) using Hitman and for wood density (DEN) using Resistograph and Pilodyn. We combined VEL with different wood densities, raw (DEN_RES) and adjusted (DEN_RES.TB) Resistograph density, Pilodyn density measured with (DEN_PIL) and without bark (DEN_PIL.B), constant of 1000 kg·m⁻³ (DEN_CONST), and SilviScan density (DEN_SILV), to calculate MOEs and compare them with the benchmark SilviScan MOE (MOE_SILV). We also derived Smith–Hazel indices for simultaneous improvement of stem diameter (DBH) and wood stiffness. The highest additive genetic and phenotypic correlations of the benchmark MOE_SILV with the alternative MOE measures (tested) were attained by MOEDEN_SILV (0.95 and 0.75, respectively) and were closely followed by MOEDEN_RES.TB (0.91 and 0.70, respectively) and MOEDEN_CONST and VEL (0.91 and 0.65, respectively for both). Correlations with MOEDEN_PIL, MOEDEN_PIL.B, and MOEDEN_RES were lower. Narrow-sense heritabilities were moderate, ranging from 0.39 (MOE_SILV) to 0.46 (MOEDEN_SILV). All indices revealed an opportunity for joint improvement of DBH and MOE. Conclusions: MOEDEN_RES.TB appears to be the most efficient approach for indirect selection for wood stiffness in Scots pine, although VEL alone and MOEDEN_CONST have provided very good results too. An index combining DBH and MOEDEN_RES.TB seems to offer the best compromise for simultaneous improvement of growth, fiber, and wood quality traits.

We investigated mating structure and gene flow in a clonal seed orchard of Scots pine (Pinus sylvestris L.) over three consecutive pollination seasons (2010–2012) with nine nuclear microsatellite markers. The paternity of 1991 offspring from four maternal parents was assigned to 28 candidate fathers using an exclusion procedure and a likelihood-based method implemented in the program CERVUS. Relative reproductive success was highly variable among pollen parents but consistent across years and ranged from 0.1% to 18.3%. Consequently, the seed crops’ effective number of fathers was reduced to 52.9%, 48.8%, and 45.7% of the census in the three seasons, respectively. Self-fertilization fluctuated around the orchard's expected value of 5.1%, reaching 4.05%, 7.71%, and 6.61%, respectively. Pollen contamination was estimated to be 5.64%, 7.29%, and 4.89%, respectively, after correction for cryptic gene flow. CERVUS provided similar results as the exclusion method, but estimates greatly varied depending on the input parameters, mainly the proportion of fathers sampled. These results indicate the studied seed orchard is a well-functioning production population with only minor negative effects of self-fertilization and pollen contamination on the quality of seed crops. Genotyping issues associated with microsatellites as a potential source of false paternity assignment and exclusion are discussed.

Chemical composition is one of the key characteristics that determines wood quality and in turn its suitability for different end products and applications. The inclusion of chemical compositional traits in forest tree improvement requires high‐throughput techniques capable of rapid, non‐destructive and cost‐efficient assessment of large‐scale breeding experiments. We tested whether Fourier‐transform infrared (FTIR) spectroscopy, coupled with partial least squares regression, could serve as an alternative to traditional wet chemistry protocols for the determination of the chemical composition of juvenile wood in Scots pine for tree improvement purposes. FTIR spectra were acquired for 1,245 trees selected in two Scots pine (Pinus sylvestris L.) full‐sib progeny tests located in northern Sweden. Predictive models were developed using 70 reference samples with known chemical composition (the proportion of lignin, carbohydrates [cellulose, hemicelluloses and their structural monosaccharides glucose, mannose, xylose, galactose, and arabinose] and extractives). Individual‐tree narrow‐sense heritabilities and additive genetic correlations were estimated for all chemical traits as well as for growth (height and stem diameter) and wood quality traits (density and stiffness). Genetic control of the chemical traits was mostly moderate. Of the major chemical components, highest heritabilities were observed for hemicelluloses (0.43–0.47), intermediate for lignin and extractives (0.30–0.39), and lowest for cellulose (0.20–0.25). Additive genetic correlations among chemical traits were, except for extractives, positive while those between chemical and wood quality traits were negative. In both groups (chemical and wood quality traits), correlations with extractives exhibited opposite signs. Correlations of chemical traits with growth traits were near zero. The best strategy for genetic improvement of Scots pine juvenile wood for bioenergy production is to decrease and stabilize the content of extractives among trees and then focus on increasing the cellulose:lignin ratio.

SUMMARY: Genetic differentiation among populations often varies significantly across the genome due to factors such as selection and recombination, resulting in a heterogeneous genomic landscape. However, variation in low‐differentiation regions—genomic valleys—remains poorly understood. Moreover, most insights into plant genomic landscapes come from flowering plants, while comparable genome‐wide studies in other taxa, such as conifers, remain limited. We analyzed whole‐genome sequencing data from 100 individuals of three pine species— Pinus banksiana , Pinus contorta , and Pinus nigra . We found substantial genome‐wide variation in recombination rates, with intergenic regions exhibiting higher recombination than genic regions, and rates decreasing with increasing distance from genes. Recombination rate was negatively correlated with gene length, driven primarily by intron length, suggesting that long introns in conifers may promote the retention of exceptionally long genes by maintaining low recombination in these regions. Genomic scans further revealed that genomic valleys are maintained through either balancing, background, or parallel selection. Additionally, multiple forms of selection were strongly associated with local recombination rate variation, highlighting the significant role of recombination in shaping patterns of genomic differentiation. Our findings provide new insight into the evolution and maintenance of extremely long genes in conifers. Moreover, the results indicate that allopatric selection in regions of low recombination is a major force structuring genomic variation in these species.