Whole-genome resequencing facilitates the development of a 50K single nucleotide polymorphism genotyping array for Scots pine (Pinus sylvestris L.) and its transferability to other pine species.

Scots pine (Pinus sylvestris L.) is one of the most widespread and economically important conifer species in the world. Applications like genomic selection and association studies, which could help accelerate breeding cycles, are challenging in Scots pine because of its large and repetitive genome. For this reason, genotyping tools for conifer species, and in particular for Scots pine, are commonly based on transcribed regions of the genome. In this article, we present the Axiom Psyl50K array, the first single nucleotide polymorphism (SNP) genotyping array for Scots pine based on whole-genome resequencing, that represents both genic and intergenic regions. This array was designed following a two-step procedure: first, 192 trees were sequenced, and a 430K SNP screening array was constructed. Then, 480 samples, including haploid megagametophytes, full-sib family trios, breeding population, and range-wide individuals from across Eurasia were genotyped with the screening array. The best 50K SNPs were selected based on quality, replicability, distribution across the draft genome assembly, balance between genic and intergenic regions, and genotype-environment and genotype-phenotype associations. Of the final 49 877 probes tiled in the array, 20 372 (40.84%) occur inside gene models, while the rest lie in intergenic regions. We also show that the Psyl50K array can yield enough high-confidence SNPs for genetic studies in pine species from North America and Eurasia. This new genotyping tool will be a valuable resource for high-throughput fundamental and applied research of Scots pine and other pine species.

Preselection of QTL markers enhances accuracy of genomic selection in Norway spruce.

Genomic prediction (GP) or genomic selection is a method to predict the accumulative effect of all quantitative trait loci (QTLs) in a population by estimating the realized genomic relationships between the individuals and by capturing the linkage disequilibrium between markers and QTLs. Thus, marker preselection is considered a promising method to capture Mendelian segregation effects. Using QTLs detected in a genome-wide association study (GWAS) may improve GP. Here, we performed GWAS and GP in a population with 904 clones from 32 full-sib families using a newly developed 50 k SNP Norway spruce array. Through GWAS we identified 41 SNPs associated with budburst stage (BB) and the largest effect association explained 5.1% of the phenotypic variation (PVE). For the other five traits such as growth and wood quality traits, only 2 - 13 associations were observed and the PVE of the strongest effects ranged from 1.2% to 2.0%. GP using approximately 100 preselected SNPs, based on the smallest p-values from GWAS showed the greatest predictive ability (PA) for the trait BB. For the other traits, a preselection of 2000-4000 SNPs, was found to offer the best model fit according to the Akaike information criterion being minimized. But PA-magnitudes from GP using such selections were still similar to that of GP using all markers. Analyses on both real-life and simulated data also showed that the inclusion of a large QTL SNP in the model as a fixed effect could improve PA and accuracy of GP provided that the PVE of the QTL was ≥ 2.5%.