Data from: A comparison of genomic selection models across time in interior spruce (Picea engelmannii × glauca) using unordered SNP imputation methods

Genomic selection (GS) potentially offers an unparalleled advantage over traditional pedigree-based selection (TS) methods by reducing the time commitment required to carry out a single cycle of tree improvement. This quality is particularly appealing to tree breeders, where lengthy improvement cycles are the norm. We explored the prospect of implementing GS for interior spruce (Picea engelmannii × glauca) utilizing a genotyped population of 769 trees belonging to 25 open-pollinated families. A series of repeated tree height measurements through ages 3–40 years permitted the testing of GS methods temporally. The genotyping-by-sequencing (GBS) platform was used for single nucleotide polymorphism (SNP) discovery in conjunction with three unordered imputation methods applied to a data set with 60% missing information. Further, three diverse GS models were evaluated based on predictive accuracy (PA), and their marker effects. Moderate levels of PA (0.31–0.55) were observed and were of sufficient capacity to deliver improved selection response over TS. Additionally, PA varied substantially through time accordingly with spatial competition among trees. As expected, temporal PA was well correlated with age-age genetic correlation (r=0.99), and decreased substantially with increasing difference in age between the training and validation populations (0.04–0.47). Moreover, our imputation comparisons indicate that k-nearest neighbor and singular value decomposition yielded a greater number of SNPs and gave higher predictive accuracies than imputing with the mean. Furthermore, the ridge regression (rrBLUP) and BayesCπ (BCπ) models both yielded equal, and better PA than the generalized ridge regression heteroscedastic effect model for the traits evaluated.

基因组选择（Genomic selection, GS）相较于传统系谱选择（pedigree-based selection, TS）方法，有望通过缩短林木改良单周期所需的时间成本，展现出无可比拟的优势。这一特性对于改良周期普遍漫长的林木育种工作者而言极具吸引力。本研究针对内陆云杉（Picea engelmannii × glauca）开展GS应用可行性探索，所用的分型群体包含源自25个自由授粉家系的769株已完成基因分型的林木。通过对3至40年生林木的树高进行多轮重复测量，得以在时间维度上对多种GS方法进行测试。本研究采用测序分型（genotyping-by-sequencing, GBS）技术开展单核苷酸多态性（single nucleotide polymorphism, SNP）发掘，并针对存在60%缺失信息的数据集，结合三种非定向填充方法进行处理。进一步基于预测准确性（predictive accuracy, PA）及其标记效应，对三种不同的GS模型进行评估。研究结果显示，PA值处于0.31~0.55的中等水平，足以实现优于TS的选择响应。此外，PA随时间发生显著变化，这与林木间的空间竞争情况密切相关。正如预期，随时间变化的PA与年龄-年龄遗传相关系数（r=0.99）呈现高度相关，且随着训练群体与验证群体的年龄差增大，PA值大幅下降（0.04~0.47）。此外，填充方法对比结果表明，相较于均值填充法，k近邻法与奇异值分解法可获得更多的SNP位点，并能获得更高的预测准确性。针对本研究评估的性状而言，岭回归（ridge regression, rrBLUP）与BayesCπ（BCπ）模型均能获得相当且更优的预测准确性，其效果优于广义岭回归异方差效应模型。