Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada.
International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico, DF, Mexico.
Theor Appl Genet. 2022 Feb;135(2):537-552. doi: 10.1007/s00122-021-03982-0. Epub 2021 Nov 1.
Using phenotype data of three spring wheat populations evaluated at 6-15 environments under two management systems, we found moderate to very high prediction accuracies across seven traits. The phenotype data collected under an organic management system effectively predicted the performance of lines in the conventional management and vice versa. There is growing interest in developing wheat cultivars specifically for organic agriculture, but we are not aware of the effect of organic management on the predictive ability of genomic selection (GS). Here, we evaluated within populations prediction accuracies of four GS models, four combinations of training and testing sets, three reaction norm models, and three random cross-validations (CV) schemes in three populations phenotyped under organic and conventional management systems. Our study was based on a total of 578 recombinant inbred lines and varieties from three spring wheat populations, which were evaluated for seven traits at 3-9 conventionally and 3-6 organically managed field environments and genotyped either with the wheat 90 K SNP array or DArTseq. We predicted the management systems (CV0) or environments (CV0), a subset of lines that have been evaluated in either management (CV2) or some environments (CV2), and the performance of newly developed lines in either management (CV1) or environments (CV1). The average prediction accuracies of the model that incorporated genotype × environment interactions with CV0 and CV2 schemes varied from 0.69 to 0.97. In the CV1 and CV1M schemes, prediction accuracies ranged from - 0.12 to 0.77 depending on the reaction norm models, the traits, and populations. In most cases, grain protein showed the highest prediction accuracies. The phenotype data collected under the organic management effectively predicted the performance of lines under conventional management and vice versa. This is the first comprehensive GS study that investigated the effect of the organic management system in wheat.
利用在两种管理系统下 6-15 个环境中评估的三个春小麦群体的表型数据，我们发现七个性状的预测准确率在中等至非常高之间。在有机管理系统下收集的表型数据有效地预测了常规管理下的系表现，反之亦然。人们对专门为有机农业开发小麦品种越来越感兴趣，但我们不知道有机管理对基因组选择 (GS) 的预测能力的影响。在这里，我们在有机和常规管理系统下评估了四个 GS 模型、四个训练和测试集组合、三个反应规范模型和三个随机交叉验证 (CV) 方案在三个群体中的内群体预测准确性。我们的研究基于三个春小麦群体的总共 578 个重组自交系和品种，这些系在 3-9 个常规管理和 3-6 个有机管理田间环境中对七个性状进行了评估，并使用小麦 90K SNP 阵列或 DArTseq 进行了基因型分析。我们预测了管理系统 (CV0) 或环境 (CV0)、在任一种管理 (CV2) 或某些环境 (CV2) 下进行评估的部分系的表现，以及在任一种管理 (CV1) 或环境 (CV1) 下开发的新系的表现。纳入基因型×环境互作与 CV0 和 CV2 方案的模型的平均预测准确率从 0.69 到 0.97 不等。在 CV1 和 CV1M 方案中，预测准确率取决于反应规范模型、性状和群体，范围从-0.12 到 0.77 不等。在大多数情况下，蛋白质含量表现出最高的预测准确率。在有机管理下收集的表型数据有效地预测了常规管理下的系表现，反之亦然。这是第一个全面的 GS 研究，调查了有机管理系统对小麦的影响。
