Empirically assessing the impact of preselection on genetic evaluation of preselected animals requires comparing scenarios that take different approaches into account, including scenarios without preselection. However, preselection is almost always performed in animal breeding programs, so it is difficult to have a dataset without preselection. Hence, most studies on preselection have used simulated datasets, and have concluded that genomic estimated breeding values (GEBV) from subsequent single-step genomic best linear unbiased prediction (ssGBLUP) evaluations are unbiased. The aim of this study was to investigate the impact of genomic preselection (GPS) on accuracy and bias in subsequent ssGBLUP evaluations, using data from a commercial pig breeding program. We used data on average daily gain during performance testing, average daily gain throughout life, backfat thickness, and loin depth from one sire line and one dam line of pigs. As these traits have different weights in the breeding goals of the two lines, we analyzed the lines separately. For each line, we implemented a reference GPS scenario that kept all available data, against which the next two scenarios were compared. We then implemented two other scenarios with additional layers of GPS by removing all animals without progeny either (i) only in the validation generation, or (ii) in all generations. We conducted subsequent ssGBLUP evaluations for each GPS scenario, using all the data remaining after implementing the GPS scenario. Accuracy and bias were computed by comparing GEBV against progeny yield deviations of validation animals. Results for all traits and in both lines showed a marginal loss in accuracy due to the additional layers of GPS. Average accuracies across all GPS scenarios in the two lines were 0.39, 0.47, 0.56, and 0.60, for average daily gain during performance testing and throughout life, backfat thickness, and loin depth, respectively. Biases were largely absent, and when present, did not differ greatly between the GPS scenarios. We conclude that the impact of preselection on accuracy and bias in subsequent ssGBLUP evaluations of selection candidates in pigs is generally minimal. We expect this conclusion to apply for other animal breeding programs as well, since preselection of any type or intensity generally has the same effect in animal breeding programs.

中文翻译：

---

基因组预选对使用猪的真实数据使用 ssGBLUP 进行后续遗传评估的影响

凭经验评估预选对预选动物遗传评估的影响需要比较考虑不同方法的情景，包括没有预选的情景。然而，预选几乎总是在动物育种计划中进行，因此很难有一个没有预选的数据集。因此，大多数关于预选的研究都使用了模拟数据集，并得出结论，来自后续单步基因组最佳线性无偏预测 (ssGBLUP) 评估的基因组估计育种值 (GEBV) 是无偏的。本研究的目的是利用来自商业猪育种计划的数据，研究基因组预选 (GPS) 对后续 ssGBLUP 评估的准确性和偏差的影响。我们在性能测试期间使用了平均每日收益数据，一生中的平均日增重、背膘厚度和腰部深度来自一个父系和一个母系的猪。由于这些性状在两个品系的育种目标中具有不同的权重，我们分别对这些品系进行了分析。对于每条线路，我们实施了一个参考 GPS 场景，该场景保留了所有可用数据，并与接下来的两个场景进行了比较。然后，我们通过（i）仅在验证代中或（ii）在所有代中删除所有没有后代的动物，实现了另外两个具有额外 GPS 层的场景。我们使用实施 GPS 场景后剩余的所有数据，对每个 GPS 场景进行了后续的 ssGBLUP 评估。通过将 GEBV 与验证动物的后代产量偏差进行比较来计算准确性和偏差。由于 GPS 的附加层，所有特征和两条线的结果都显示出准确性的边际损失。两条生产线中所有 GPS 场景的平均准确度分别为 0.39、0.47、0.56 和 0.60，用于性能测试期间和整个生命周期的平均日增重、背膘厚度和腰部深度。偏差基本上不存在，并且当存在时，GPS场景之间没有太大差异。我们得出结论，预选对随后的猪选择候选者的 ssGBLUP 评估中的准确性和偏差的影响通常是最小的。我们希望这一结论也适用于其他动物育种计划，因为任何类型或强度的预选通常在动物育种计划中具有相同的效果。两条生产线中所有 GPS 场景的平均准确度分别为 0.39、0.47、0.56 和 0.60，用于性能测试期间和整个生命周期的平均日增重、背膘厚度和腰部深度。偏差基本上不存在，并且当存在时，GPS场景之间没有太大差异。我们得出结论，预选对随后的猪选择候选者的 ssGBLUP 评估中的准确性和偏差的影响通常是最小的。我们希望这一结论也适用于其他动物育种计划，因为任何类型或强度的预选通常在动物育种计划中具有相同的效果。两条生产线中所有 GPS 场景的平均准确度分别为 0.39、0.47、0.56 和 0.60，用于性能测试期间和整个生命周期的平均日增重、背膘厚度和腰部深度。偏差基本上不存在，并且当存在时，GPS场景之间没有太大差异。我们得出结论，预选对随后的猪选择候选者的 ssGBLUP 评估中的准确性和偏差的影响通常是最小的。我们希望这一结论也适用于其他动物育种计划，因为任何类型或强度的预选通常在动物育种计划中具有相同的效果。GPS场景之间没有太大差异。我们得出结论，预选对随后的猪选择候选者的 ssGBLUP 评估中的准确性和偏差的影响通常是最小的。我们希望这一结论也适用于其他动物育种计划，因为任何类型或强度的预选通常在动物育种计划中具有相同的效果。GPS场景之间没有太大差异。我们得出结论，预选对随后的猪选择候选者的 ssGBLUP 评估中的准确性和偏差的影响通常是最小的。我们希望这一结论也适用于其他动物育种计划，因为任何类型或强度的预选通常在动物育种计划中具有相同的效果。