Conifers dominate the world's forest ecosystems and are the most widely planted tree species. Their giant and complex genomes present great challenges for assembling a complete reference genome for evolutionary and genomic studies. We present a 25.4-Gb chromosome-level assembly of Chinese pine (Pinus tabuliformis) and revealed that its genome size is mostly attributable to huge intergenic regions and long introns with high transposable element (TE) content. Large genes with long introns exhibited higher expressions levels. Despite a lack of recent whole-genome duplication, 91.2% of genes were duplicated through dispersed duplication, and expanded gene families are mainly related to stress responses, which may underpin conifers' adaptation, particularly in cold and/or arid conditions. The reproductive regulation network is distinct compared with angiosperms. Slow removal of TEs with high-level methylation may have contributed to genomic expansion. This study provides insights into conifer evolution and resources for advancing research on conifer adaptation and development.

The biogeographical relationships between far-separated populations, in particular, those in the mainland and islands, remain unclear for widespread species in eastern Asia where the current distribution of plants was greatly influenced by the Quaternary climate. Deciduous Oriental oak (Quercus variabilis) is one of the most widely distributed species in eastern Asia. In this study, leaf material of 528 Q. variabilis trees from 50 populations across the whole distribution (Mainland China, Korea Peninsular as well as Japan, Zhoushan and Taiwan Islands) was collected, and three cpDNA intergenic spacer fragments were sequenced using universal primers. A total of 26 haplotypes were detected, and it showed a weak phylogeographical structure in eastern Asia populations at species level, however, in the central-eastern region of Mainland China, the populations had more haplotypes than those in other regions, with a significant phylogeographical structure (N_ST =0.751> G_ST =0.690, P<0.05). Q. variabilis displayed high interpopulation and low intrapopulation genetic diversity across the distribution range. Both unimodal mismatch distribution and significant negative Fu’s F_S indicated a demographic expansion of Q. variabilis populations in East Asia. A fossil calibrated phylogenetic tree showed a rapid speciation during Pleistocene, with a population augment occurred in Middle Pleistocene. Both diversity patterns and ecological niche modelling indicated there could be multiple glacial refugia and possible bottleneck or founder effects occurred in the southern Japan. We dated major spatial expansion of Q. variabilis population in eastern Asia to the last glacial cycle(s), a period with sea-level fluctuations and land bridges in East China Sea as possible dispersal corridors. This study showed that geographical heterogeneity combined with climate and sea-level changes have shaped the genetic structure of this wide-ranging tree species in East Asia.

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.

The use of vegetative propagation in forestry has a long history. In this chapter of special issue, the genetic gain from clonal forestry relative to family forestry is reviewed. Both theoretical studies and experimental data from progeny and clonal trials indicate that extra genetic gain (5–25%) is possible in conifer from clone testing and deployment relative to deployment of family forestry, effectively doubling that achievable from family forestry within the same generation. There are three perceived risks from using clones in forestry: (1) risk of plantation failure, (2) risk of diversity loss at the forest and landscape levels, and (3) risk associated with success rate of vegetative (or SE) propagation. Three theoretical models are reviewed and described to assess risk and to determine the number of clones required to mitigate these risks. All studies support that a “safe” number of clones is between 5 and 30. Genetic gains and experiences are reported for individual species, particularly in conifers, as well as in Eucalypts. The combination of genomic selection with somatic embryogenesis has the potential to accelerate the development of clonal forestry by shortening clonal testing or omitting long-term clonal testing completely.

Mapping the genetic architecture of forest tree traits is important in order to understand the evolutionary forces that have shaped these traits and to facilitate the development of genomic-based breeding strategies. We examined the number, size, and distribution of allelic effects influencing eight types of traits using 30 published mapping studies (linkage and association mapping) in forest trees. The sizes of allelic effects, measured as the phenotypic variance explained, generally showed a severely right-skewed distribution. We estimated the numbers of underlying causal effects (n _qtl ) for different trait categories by improving a method previously developed by Otto and Jones (Genetics 156:2093–2107, 2000). Estimates of n _qtl based on association mapping studies were generally higher (median at 643) than those based on linkage mapping (median at 33). Comparisons with simulated linkage and association mapping data suggested that the lower n _qtl estimates for the linkage mapping studies could partly be explained by fewer causal loci segregating within the full-sib family populations normally used, but also by the cosegregation of causal loci due to limited recombination. Disease resistance estimates based on linkage mapping studies had the lowest median of four underlying effects, while growth traits based on association mapping had about 580 effects. Theoretically, the capture of 50% of the genetic variation would thus require a population size of about 200 for disease resistance in linkage mapping, while growth traits in association mapping would require about 25,000. The adequacy and reliability of the improved method was successfully verified by applying it to the simulated data.