Genotype imputation improves SNP density and genetic analysis accuracy in slash pine

Slash pine (Pinus elliottii) possesses an exceptionally large, repeat-rich genome, and existing low-density SNP arrays provide limited marker coverage and resolution of linkage patterns. To increase marker density for population-based genetic analyses and improve the accuracy of genomic relationship matrix (GRM) estimation, we constructed a reference panel from about 10× whole-genome resequencing of 50 maternal parents and performed genome-wide imputation for 51K SNP-array genotypes of 715 half-sib progeny. Imputation accuracy at array loci was quantified using chromosome-local masking experiments, whereas reference-panel-expanded loci not represented on the array were evaluated for concordance and filtered via external validation using progeny resequencing data. Masking-based concordance remained stable at 95.5%, and after threshold-based filtering of expanded loci, we generated a high-density genotype matrix for all 715 individuals comprising 120,650,180 SNPs. Comparative local linkage disequilibrium (LD) heatmaps indicated more continuous LD signals and clearer block structures after densification; for the Chr4 10.22-10.33 Mb interval, the proportion of high-LD SNP pairs increased from 14.5% to 27.6%. The GRM derived from the densified dataset was highly consistent with the array-based GRM in off-diagonal elements (Pearson's r≈0.984). Distance-stratified analyses further showed higher concordance for GRMs constructed from imputed loci within 500 kb of array markers, with concordance decreasing progressively in more distant windows, suggesting limited incremental benefit from long-range imputed loci. Collectively, the reference panel-driven imputation, validation, and integration framework established here provides a high-density genotypic resource for genome-wide association studies and genomic selection in slash pine and other conifer species.