Single-step best linear unbiased prediction (HBLUP) is a method used to predict breeding values by combining pairwise relatedness information derived from a pedigree with the realized relationships estimated from DNA markers. It is an ideal approach for the lodgepole pine (Pinus contorta Dougl. ex. Loud.) breeding program which has an extensive progeny testing program but a small proportion of trees that are genotyped. However, it is unclear what level of genotyping is required to affect prediction accuracy and genetic parameters, the performance across test sites and test cycles of different ages, and the ability to accurately rank trees within half-sib and full-sib families. Lodgepole pine trees were sampled from four progeny test sites in British Columbia, Canada. A SNP array was used to genotype 1569 trees which resulted in 19,584 high-quality SNPs. The prediction accuracy of HBLUP was compared to (1) an uncorrected relationship matrix (ABLUP) and (2) BLUP using a realized relationship matrix based on SNP markers (GBLUP) using various cross-validation scenarios for height growth at age 10 and 5 wood quality traits. Combining average and realized pairwise relationship information through the H-matrix resulted in heritability and Type B genetic correlation estimates that were generally a compromise between estimates for ABLUP (0% genotyping) and GBLUP (100% genotyping). The highest heritability was for average wood density (0.57 for ABLUP; 0.51 for HBLUP; 0.47 for GBLUP) and the lowest was for height (0.24 for ABLUP; 0.27 for HBLUP; 0.25 for GBLUP). GBLUP always had the lowest Type B genetic correlations (except for earlywood density) of the three models (0.46 to 1.0) assessed. The prediction accuracy for HBLUP increased slightly for genotyped trees (0.77 to 0.80), but not for non-genotyped trees as genotyping effort increased. Furthermore, prediction accuracy was high when predicting between environments (0.46 to 0.85) and test cycles (0.33 to 0.76) when connected through pedigree, and prediction was more accurate when using older first-cycle tests to predict breeding values for younger second-cycle tests for all traits, except microfibril angle. Rank correlations for trees within half-sib and full-sib families when predicting values across test cycles (the training population is phenotyped and the validation population is genotyped) were very low using HBLUP (0.08) compared to GBLUP (0.38) but increased to 0.25 when 40% of the trees in the training population were genotyped (HBLUP40). HBLUP should be regarded as an effective way to combine average and realized relationship information in a breeding program for more precise estimates of genetic parameters and breeding values and can be used for predicting and ranking trees within families without phenotypic data when genotyped trees from the same families are included in the training population (genotyped and phenotyped).

单步最佳线性无偏预测（HBLUP）是一种通过将系谱来源的成对相关性信息与DNA标记估计的已实现关系相结合来预测育种值的方法。这是黑松的理想方法（松扭叶松道格。例如 大声的育种程序，具有广泛的后代测试程序，但只有一小部分经过基因分型的树木。但是，尚不清楚需要何种基因型分型来影响预测准确性和遗传参数，不同年龄的测试位点和测试周期的性能以及对半同胞和全同胞家族的树木进行准确排名的能力。小枝松树是从加拿大不列颠哥伦比亚省的四个后代测试地点取样的。使用SNP阵列对1569棵树进行基因分型，产生了19,584个高质量的SNP。将HBLUP的预测准确性与（1）未校正的关系矩阵（ABLUP）和（2）使用基于SNP标记的已实现的关系矩阵（GBLUP）进行了比较，并使用了各种交叉验证方案对10和5岁木材的身高增长进行了比较。质量特质。通过H-矩阵结合平均和已实现的成对关系信息会导致遗传力和B型遗传相关性估计，这通常是ABLUP（0％基因型）和GBLUP（100％基因型）的估计之间的折衷。最高的遗传力是平均木材密度（ABLUP为0.57； HBLUP为0.51； GBLUP为0.47），最低为高度（ABLUP为0.24； HBLUP为0.27； GBLUP 0.25）。在评估的三个模型（0.46至1.0）中，GBLUP始终具有最低的B型遗传相关性（早材密度除外）。对于基因型树，HBLUP的预测准确性略有提高（0.77至0.80），但随着基因分型工作量的增加，对于非基因型树，HBLUP的预测准确性没有提高。此外，在环境（0.46到0.85）和测试周期（0.33到0）之间进行预测时，预测准确性很高。76）通过谱系进行连接，并且使用较早的第一轮试验来预测除微纤维角之外的所有性状的较年轻的第二轮试验的育种值时，预测更为准确。使用HBLUP（0.08）进行测试周期预测值时（训练种群是表型，验证种群是基因型），半同胞和全同胞族的树木的等级相关性与GBLUP（0.38）相比非常低，但增加到0.25当训练种群中有40％的树木具有基因型（HBLUP40）时。