Most genomic predictions use a unique population that is split into a training and a validation set. However, genomic prediction using genetically heterogeneous training sets could provide more flexibility when constructing the training sets in small populations. The aim of our study was to investigate the potential of genomic prediction of feed efficiency related traits using training sets that combine animals from two different, but genetically-related lines. We compared realized prediction accuracy and prediction bias for different training set compositions for five production traits. Genomic breeding values (GEBV) were predicted using the single-step genomic best linear unbiased prediction method in six scenarios applied iteratively to two genetically-related lines (i.e. 12 scenarios). The objective for all scenarios was to predict GEBV of pigs in the last three generations (~ 400 pigs, G7 to G9) of a given line. For each line, a control scenario was set up with a training set that included only animals from that line (target line). For all traits, adding more animals from the other line to the training set did not increase prediction accuracy compared to the control scenario. A small decrease in prediction accuracies was found for average daily gain, backfat thickness, and daily feed intake as the number of animals from the target line decreased in the training set. Including more animals from the other line did not decrease prediction accuracy for feed conversion ratio and residual feed intake, which were both highly affected by selection within lines. However, prediction biases were systematic for these cases and might be reduced with bivariate analyses. Our results show that genomic prediction using a training set that includes animals from genetically-related lines can be as accurate as genomic prediction using a training set from the target population. With combined reference sets, accuracy increased for traits that were highly affected by selection. Our results provide insights into the design of reference populations, especially to initiate genomic selection in small-sized lines, for which the number of historical samples is small and that are developed simultaneously. This applies especially to poultry and pig breeding and to other crossbreeding schemes.

中文翻译：

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培训对来自遗传相关品系的数据的影响对猪饲料效率性状的基因组预测准确性的影响

大多数基因组预测使用唯一的种群，将其分为训练和验证集。但是，使用遗传异质训练集进行基因组预测可以在小人群中构建训练集时提供更大的灵活性。我们研究的目的是使用结合了来自两个不同但与遗传相关的品系的动物的训练集，研究与饲料效率相关性状的基因组预测的潜力。我们比较了五个生产性状的不同训练集组成的已实现预测准确性和预测偏差。使用单步基因组最佳线性无偏预测方法预测了六种情况下的基因组育种值（GEBV），这些情况被迭代应用于两个与遗传相关的品系（即12种情况）。所有方案的目的都是预测给定品系的最后三代猪（〜400头猪，G7至G9）的GEBV。对于每条线，都建立了一个控制场景，并带有一个训练集，该训练集仅包含该线（目标线）中的动物。对于所有特征，与控制情景相比，从另一行添加更多动物到训练集中不会增加预测准确性。在训练集中，随着目标线的动物数量减少，平均日增重，背脂厚度和日采食量的预测准确性略有下降。包括来自其他品系的更多动物不会降低饲料转化率和剩余饲料采食量的预测准确性，这两者都受品系选择的影响。然而，对于这些情况，预测偏差是系统性的，可以通过双变量分析来减少。我们的结果表明，使用包括来自遗传相关品系的动物的训练集进行的基因组预测可以与使用目标人群的训练集进行的基因组预测一样准确。通过组合参考集，可以提高受选择高度影响的性状的准确性。我们的结果为参考种群的设计提供了见识，尤其是在小样本中启动基因组选择，因为小样本中的历史样本数量很少并且是同时开发的。这尤其适用于家禽和猪的育种以及其他杂交方案。我们的结果表明，使用包括来自遗传相关品系的动物的训练集进行的基因组预测可以与使用目标人群的训练集进行的基因组预测一样准确。通过组合参考集，可以提高受选择高度影响的性状的准确性。我们的结果为参考种群的设计提供了见识，尤其是在小样本中启动基因组选择，因为小样本中的历史样本数量很少并且是同时开发的。这尤其适用于家禽和猪的育种以及其他杂交方案。我们的结果表明，使用包括来自遗传相关品系的动物的训练集进行的基因组预测可以与使用目标人群的训练集进行的基因组预测一样准确。通过组合参考集，可以提高受选择高度影响的性状的准确性。我们的结果为参考种群的设计提供了见识，尤其是在小样本中启动基因组选择，因为小样本中的历史样本数量很少并且是同时开发的。这尤其适用于家禽和猪的育种以及其他杂交方案。我们的结果为参考种群的设计提供了见识，尤其是在小样本中启动基因组选择，因为小样本中的历史样本数量很少并且是同时开发的。这尤其适用于家禽和猪的育种以及其他杂交方案。我们的结果为参考种群的设计提供了见识，尤其是在小样本中启动基因组选择，因为小样本中的历史样本数量很少并且是同时开发的。这尤其适用于家禽和猪的育种以及其他杂交方案。