Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Recursos Biológicos, Centro de Investigación en Recursos Naturales, De Los Reseros y Dr. Nicolás Repetto s/n, 1686, Hurlingham, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina.
FIBRIA S.A. Technology Center, Jacareí, SP, 12340-010, Brazil.
Plant Sci. 2019 Jul;284:9-15. doi: 10.1016/j.plantsci.2019.03.017. Epub 2019 Mar 28.
Genomic Best Linear Unbiased Prediction (GBLUP) in tree breeding typically only uses information from genotyped trees. However, information from phenotyped but non-genotyped trees can also be highly valuable. The single-step GBLUP approach (ssGBLUP) allows genomic prediction to take into account both genotyped and non-genotyped trees simultaneously in a single evaluation. In this study, we investigated the advantage, in terms of breeding value accuracy and bias, of including phenotypic observation from non-genotyped trees in a standard tree GBLUP evaluation. We compared the efficiency of the conventional pedigree-based (ABLUP), GBLUP and ssGBLUP approaches to evaluate eight growth and wood quality traits in a Eucalyptus hybrid population, genotyped with 33,398 single nucleotide polymorphisms (SNPs) using the EucHIP60k. Theoretical accuracies, predictive ability and bias were calculated by ten-fold cross validation on all traits. The use of additional phenotypic information from non-genotyped trees by means of ssGBLUP provided higher predictive ability (from 37% to 75%) and lower prediction bias (from 21% to 73%) for the genetic component of non-phenotyped but genotyped trees when compared to GBLUP. The increase (decrease) in the prediction accuracy (bias) became stronger as trait heritability decreased. We concluded that ssGBLUP is a promising breeding tool to improve accuracies and bias over classical GBLUP for genomic evaluation in Eucalyptus breeding practice.
基因组最佳线性无偏预测（GBLUP）在树木育种中通常只使用已基因型树木的信息。然而，表型但未基因型树木的信息也可能非常有价值。单步 GBLUP 方法（ssGBLUP）允许基因组预测在单次评估中同时考虑基因型和非基因型树木。在这项研究中，我们研究了在标准树木 GBLUP 评估中包含非基因型树木表型观测值的优势，从育种值准确性和偏差两个方面进行评估。我们比较了传统基于系谱的（ABLUP）、GBLUP 和 ssGBLUP 方法在评估 33,398 个单核苷酸多态性（SNP）基因型的杂种桉树群体的 8 个生长和木材质量性状方面的效率，使用 EucHIP60k 进行基因型分析。在所有性状上通过 10 倍交叉验证计算理论准确性、预测能力和偏差。与 GBLUP 相比，ssGBLUP 利用非基因型树木的额外表型信息，为非表型但基因型树木的遗传成分提供了更高的预测能力（从 37%到 75%）和更低的预测偏差（从 21%到 73%）。随着性状遗传力的降低，预测准确性（偏差）的增加（减少）变得更强。我们得出结论，ssGBLUP 是一种很有前途的育种工具，可以提高桉树育种实践中基因组评估的准确性和偏差，超过经典的 GBLUP。
