Department of Plant Sciences, University of Saskatchewan, Saskatoon, SK, Canada.
Canadian Grain Commission, Grain Research Laboratory, Winnipeg, MB, Canada.
Theor Appl Genet. 2021 Jan;134(1):381-398. doi: 10.1007/s00122-020-03703-z. Epub 2020 Nov 1.
Genomic predictions across environments and within populations resulted in moderate to high accuracies but across-population genomic prediction should not be considered in wheat for small population size. Genomic selection (GS) is a marker-based selection suggested to improve the genetic gain of quantitative traits in plant breeding programs. We evaluated the effects of training population (TP) composition, cross-validation design, and genetic relationship between the training and breeding populations on the accuracy of GS in spring wheat (Triticum aestivum L.). Two populations of 231 and 304 spring hexaploid wheat lines that were phenotyped for six agronomic traits and genotyped with the wheat 90 K array were used to assess the accuracy of seven GS models (RR-BLUP, G-BLUP, BayesB, BL, RKHS, GS + de novo GWAS, and reaction norm) using different cross-validation designs. BayesB outperformed the other models for within-population genomic predictions in the presence of few quantitative trait loci (QTL) with large effects. However, including fixed-effect marker covariates gave better performance for an across-population prediction when the same QTL underlie traits in both populations. The accuracy of prediction was highly variable based on the cross-validation design, which suggests the importance to use a design that resembles the variation within a breeding program. Moderate to high accuracies were obtained when predictions were made within populations. In contrast, across-population genomic prediction accuracies were very low, suggesting that the evaluated models are not suitable for prediction across independent populations. On the other hand, across-environment prediction and forward prediction designs using the reaction norm model resulted in moderate to high accuracies, suggesting that GS can be applied in wheat to predict the performance of newly developed lines and lines in incomplete field trials.
跨环境和群体内的基因组预测产生了中等至高度的准确性，但由于群体规模较小，不应该考虑跨群体基因组预测。基因组选择（GS）是一种基于标记的选择方法，被建议用于提高植物育种计划中数量性状的遗传增益。我们评估了训练群体（TP）组成、交叉验证设计以及训练和育种群体之间的遗传关系对春小麦（Triticum aestivum L.）GS 准确性的影响。使用六个农艺性状的表型和小麦 90K 阵列的基因型对 231 个和 304 个春六倍体小麦群体进行了评估，以评估七种 GS 模型（RR-BLUP、G-BLUP、BayesB、BL、RKHS、GS+从头 GWAS 和反应规范）在不同交叉验证设计下的准确性。当存在少数具有大效应的数量性状基因座（QTL）时，BayesB 优于其他模型的群体内基因组预测。然而，当相同的 QTL 是两个群体中性状的基础时，包括固定效应标记协变量可以提高群体间预测的性能。基于交叉验证设计，预测的准确性高度可变，这表明使用类似于育种计划内变化的设计的重要性。当在群体内进行预测时，可以获得中等至高度的准确性。相比之下，跨群体基因组预测的准确性非常低，表明评估的模型不适合预测独立群体。另一方面，使用反应规范模型进行跨环境预测和正向预测设计导致了中等至高度的准确性，这表明 GS 可以应用于小麦，以预测新开发的品系和不完全田间试验中的品系的表现。
