The design of the training set is a key factor in the success of the genomic selection approach. The nature of line inclusion in soybean [Sorghum bicolor (L.) Moench.] breeding programs is highly dynamic, so generating a training set that endures across the years and regions is challenging. Therefore, we aimed to define the best strategies for building training sets to apply genomic selection in segregating soybean populations for traits with different genetic architectures. We used two datasets for grain yield (GY) and maturity group (MG) from two different soybean breeding regions in Brazil. Five training set schemes were tested. In addition, we included a training set formed by an optimization algorithm based on the predicted error variance. The predictions achieved good values for both traits, reaching 0.5 in some scenarios. The best scenario changed according to the trait. Although the best performance was achieved with the use of full‐sibs in the MG dataset, for GY, full‐sibs and a set of advanced lines were equivalent. For both traits, no improvement in predictive ability resulted from training set optimization. Furthermore, the use of advanced lines from the same breeding program is recommended as a training set for GY, so the training set is continually renewed and closely related to the breeding populations, and no additional phenotyping is needed. On the other hand, to improve prediction accuracies for MG, it is necessary to use training sets with less genetic variability but with more segregation resolution.

中文翻译：

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构建用于隔离大豆种群的基因组预测的训练集的不同策略的准确性

训练集的设计是基因组选择方法成功的关键因素。大豆中夹杂物的性质[高粱（L.）Moench。]的育种计划具有很高的动态性，因此，要建立一个可以持续多年和持续存在的训练集是一项挑战。因此，我们旨在定义构建训练集的最佳策略，以便将基因组选择应用于分离具有不同遗传结构特征的大豆种群。我们使用了两个数据集，分别来自巴西两个不同大豆育种地区的谷物产量（GY）和成熟度组（MG）。测试了五个训练集方案。另外，我们包括了一个基于预测误差方差的优化算法形成的训练集。预测对这两个性状均取得了良好的价值，在某些情况下达到了0.5。最佳方案根据特征而变化。尽管在MG数据集中使用全同胞可达到最佳性能，但对于GY，完全同胞和一组高级线是等效的。对于这两个特质，训练集的优化都无法提高预测能力。此外，建议使用来自同一育种程序的高级品系作为GY的训练集，因此该训练集将不断更新并与育种种群密切相关，并且不需要其他表型。另一方面，为了提高MG的预测准确性，有必要使用遗传变异性较小但分离分辨率更高的训练集。因此，训练集会不断更新，并且与育种种群紧密相关，不需要其他表型。另一方面，为了提高MG的预测准确性，有必要使用遗传变异性较小但分离分辨率更高的训练集。因此，训练集会不断更新，并且与育种种群紧密相关，不需要其他表型。另一方面，为了提高MG的预测准确性，有必要使用遗传变异性较小但分离分辨率更高的训练集。