Institute of Plant Breeding, Genetics and Genomics, and Department of Crop and Soil Sciences, University of Georgia, Athens, Georgia, USA.
USDA-ARS, Beltsville, Maryland, USA.
Plant Genome. 2023 Dec;16(4):e20384. doi: 10.1002/tpg2.20384. Epub 2023 Sep 25.
Genomic selection has been utilized for genetic improvement in both plant and animal breeding and is a favorable technique for quantitative trait development. Within this study, genomic selection was evaluated within a breeding program, using novel validation methods in addition to plant materials and data from a commercial soybean (Glycine max) breeding program. A total of 1501 inbred lines were used to test multiple genomic selection models for multiple traits. Validation included cross-validation, inter-environment, and empirical validation. The results indicated that the extended genomic best linear unbiased prediction (EGBLUP) model was the most effective model tested for yield, protein, and oil in cross-validation with accuracies of 0.50, 0.68, and 0.64, respectively. Increasing marker number from 1000 to 3000 to 6000 single nucleotide polymorphism markers leads to statistically significant increases in accuracy. Cross-environment predictions were statistically lower than cross-validation with accuracies of 0.24, 0.54, and 0.42 for yield, protein, and oil, respectively, using the extended genomic BLUP model. Empirical validation, predicting the yield of 510 soybean lines, had a prediction accuracy of 0.34, with the inclusion of a maturity covariate leading to a notable increase in accuracy. Genomic selection identified high-performance lines in inter-environment predictions: 34% of lines within the upper quartile of yield, and 51% and 48% of the highest quartile protein and oil lines, respectively. Statistically similar results occurred comparing rankings in empirical validation and selection for advancements in yield trials. These results indicate that genomic selection is a useful tool for selection decisions.
基因组选择已被用于动植物的遗传改良，是一种开发数量性状的有利技术。在这项研究中，除了使用来自商业大豆（Glycine max）育种计划的植物材料和数据外，还使用了新的验证方法来评估基因组选择在育种计划中的应用。共使用了 1501 个自交系来测试多个基因组选择模型的多个性状。验证包括交叉验证、环境间验证和经验验证。结果表明，扩展基因组最佳线性无偏预测（EGBLUP）模型是在交叉验证中测试产量、蛋白质和油分的最有效模型，其准确性分别为 0.50、0.68 和 0.64。将标记数量从 1000 增加到 3000 再增加到 6000 个单核苷酸多态性标记，准确性会显著提高。使用扩展基因组 BLUP 模型进行环境间预测的准确性显著低于交叉验证，其产量、蛋白质和油分的准确性分别为 0.24、0.54 和 0.42。经验验证，预测 510 个大豆品系的产量，预测准确性为 0.34，包含成熟度协变量后，准确性显著提高。基因组选择在环境间预测中鉴定出高表现品系：产量的前四分之一中有 34%的品系，蛋白质和油分的前四分之一中分别有 51%和 48%的品系。在经验验证和产量试验选择中的排名比较中，出现了统计学上相似的结果。这些结果表明，基因组选择是一种有用的选择决策工具。
