Genomic selection is a powerful tool to reduce the cycle length and enhance the genetic gain of complex traits in plant breeding. However, questions remain about the optimum design and composition of the training set. In this study, we used 944 soybean [Glycine max (L.) Merr.] recombinant inbred lines from eight families derived through a partial–diallel mating design among five parental lines. The cross-validated prediction accuracies for the six traits seed yield, 1,000-seed weight, protein yield, plant height, protein content, and oil content were high, ranging from 0.79 to 0.87. We investigated among-family predictions, making use of the special mating design with different degrees of relatedness among families. Generally, the prediction accuracy decreased from full-sibs to half-sib families to unrelated families. However, half-sib and unrelated families also showed substantial variation in their prediction accuracy for a given family, which appeared to be caused at least in part by the shared segregation of quantitative trait loci in both the training and prediction sets. Combining several half-sib families in composite training sets generally led to an increase in the prediction accuracy compared with the best family alone. The prediction accuracy increased with the size of the training set, but for comparable prediction accuracy, substantially more half-sibs were required than full-sibs. Collectively, our results highlight the potential of genomic selection for soybean breeding and, in a broader context, illustrate the importance of the targeted design of the training set.

中文翻译：

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具有多个双亲家庭的基因组预测训练集设计

基因组选择是植物育种中减少循环长度和提高复杂性状遗传增益的有力工具。然而，关于训练集的最佳设计和组成的问题仍然存在。在本研究中，我们使用了 944 大豆 [ Glycine max(L.) Merr.] 来自 8 个家族的重组近交系，通过 5 个亲本系之间的部分双等位交配设计获得。6个性状种子产量、千粒重、蛋白质产量、株高、蛋白质含量和含油量的交叉验证预测准确度较高，范围从0.79到0.87。我们调查了家庭间的预测，利用了家庭间不同程度相关性的特殊交配设计。一般来说，预测准确度从全同胞到半同胞到不相关的家庭降低。然而，半同胞和无关家族对给定家族的预测准确性也表现出很大差异，这似乎至少部分是由于训练和预测集中数量性状基因座的共享分离造成的。与单独的最佳家庭相比，在复合训练集中结合几个半同胞家庭通常会提高预测准确性。预测准确度随着训练集的大小而增加，但要获得相当的预测准确度，需要的半同胞比全同胞要多得多。总的来说，我们的结果突出了基因组选择在大豆育种中的潜力，并在更广泛的背景下说明了有针对性的训练集设计的重要性。