• Genomic selection is increasingly considered vital to accelerate genetic improvement. However, it is unknown how accurate genomic selection prediction models remain when used across environments and ages. This knowledge is critical for breeders to apply this strategy in genetic improvement. • Here, we evaluated the utility of genomic selection in a Pinus taeda population of c. 800 individuals clonally replicated and grown on four sites, and genotyped for 4825 single‐nucleotide polymorphism (SNP) markers. Prediction models were estimated for diameter and height at multiple ages using genomic random regression best linear unbiased predictor (BLUP). • Accuracies of prediction models ranged from 0.65 to 0.75 for diameter, and 0.63 to 0.74 for height. The selection efficiency per unit time was estimated as 53–112% higher using genomic selection compared with phenotypic selection, assuming a reduction of 50% in the breeding cycle. Accuracies remained high across environments as long as they were used within the same breeding zone. However, models generated at early ages did not perform well to predict phenotypes at age 6 yr. • These results demonstrate the feasibility and remarkable gain that can be achieved by incorporating genomic selection in breeding programs, as long as models are used at the relevant selection age and within the breeding zone in which they were estimated.

Understanding the consequences of local adaptation at the genomic diversity is a central goal in evolutionary genetics of natural populations. In species with large continuous geographical distributions the phenotypic signal of local adaptation is frequently clear, but the genetic basis often remains elusive. We examined the patterns of genetic diversity in Pinus sylvestris, a keystone species in many Eurasian ecosystems with a huge distribution range and decades of forestry research showing that it is locally adapted to the vast range of environmental conditions. Making P. sylvestris an even more attractive subject of local adaptation study, population structure has been shown to be weak previously and in this study. However, little is known about the molecular genetic basis of adaptation, as the massive size of gymnosperm genomes has prevented large scale genomic surveys. We generated a both geographically and genomically extensive dataset using a targeted sequencing approach. By applying divergence-based and landscape genomics methods we identified several loci contributing to local adaptation, but only few with large allele frequency changes across latitude. We also discovered a very large (ca. 300 Mbp) putative inversion potentially under selection, which to our knowledge is the first such discovery in conifers. Our results call for more detailed analysis of structural variation in relation to genomic basis of local adaptation, emphasize the lack of large effect loci contributing to local adaptation in the coding regions and thus point out the need for more attention towards multi-locus analysis of polygenic adaptation.

Premise: An informatics approach was used for the construction of an Axiom genotyping array from heterogeneous, high‐throughput sequence data to assess the complex genome of loblolly pine (Pinus taeda).Methods: High‐throughput sequence data, sourced from exome capture and whole genome reduced‐representation approaches from 2698 trees across five sequence populations, were analyzed with the improved genome assembly and annotation for the loblolly pine. A variant detection, filtering, and probe design pipeline was developed to detect true variants across and within populations. From 8.27 million variants, a total of 642,275 were evaluated and 423,695 of those were screened across a range‐wide population.Results: The final informatics and screening approach delivered an Axiom array representing 46,439 high‐confidence variants to the forest tree breeding and genetics community. Based on the annotated reference genome, 34% were located in or directly upstream or downstream of genic regions.Discussion: The Pita50K array represents a genome‐wide resource developed from sequence data for an economically important conifer, loblolly pine. It uniquely integrates independent projects that assessed trees sampled across the native range. The challenges associated with the large and repetitive genome are addressed in the development of this resource.

Loblolly pine (Pinus taeda) and slash pine (Pinus elliottii) are ecologically and economically important pine species that dominate many forest ecosystems in the southern United States, but like all conifers, the study of their genetic diversity and demographic history has been hampered by their large genome size. A small number of studies mainly based on candidate-gene sequencing have been reported for P. taeda to date, whereas none are available for P. elliottii. Targeted exome resequencing has recently enabled population genomics studies for conifers, approach used here to assess genomic diversity, signatures of selection, population structure, and demographic history of P. elliottii and P. taeda. Extensive similarities were revealed between these species: both species feature rapid linkage disequilibrium decay and high levels of genetic diversity. Moreover, genome-wide positive correlations for measures of genetic diversity between the species were also observed, likely due to shared structural genomic constraints. Also, positive selection appears to be targeting a common set of genes in both pines. Demographic history differs between both species, with only P. taeda being affected by a dramatic bottleneck during the last glacial period. The ability of P. taeda to recover from a dramatic reduction in population size while still retaining high levels of genetic diversity shows promise for other pines facing environmental stressors associated with climate change, indicating that these too may be able to adapt successfully to new future conditions even after a drastic population size contraction.