Genetic merit, or breeding values as referred to in livestock and crop breeding programs, is one of the keys to the successful selection of animals in commercial farming systems. The developments in statistical methods during the twentieth century and single nucleotide polymorphism (SNP) chip technologies in the twenty-first century have revolutionized agricultural production, by allowing highly accurate predictions of breeding values for selection candidates at a very early age. Nonetheless, for many breeding populations, realized accuracies of predicted breeding values (PBV) remain below the theoretical maximum, even when the reference population is sufficiently large, and SNPs included in the model are in sufficient linkage disequilibrium (LD) with the quantitative trait locus (QTL). This is particularly noticeable over generations, as we observe the so-called erosion of the effects of SNPs due to recombinations, accompanied by the erosion of the accuracy of prediction. While accurately quantifying the erosion at the individual SNP level is a difficult and unresolved task, quantifying the erosion of the accuracy of prediction is a more tractable problem. In this paper, we describe a method that uses the relationship between reference and target populations to calculate expected values for the accuracies of predicted breeding values for non-phenotyped individuals accounting for erosion. The accuracy of the expected values was evaluated through simulations, and a further evaluation was performed on real data. Using simulations, we empirically confirmed that our expected values for the accuracy of PBV accounting for erosion were able to correctly determine the prediction accuracy of breeding values for non-phenotyped individuals. When comparing the expected to the realized accuracies of PBV with real data, only one out of the four traits evaluated presented accuracies that were significantly higher than the expected, approaching $$\sqrt{{{\text{h}}}^{2}}$$ . We defined an index of genetic correlation between reference and target populations, which summarizes the expected overall erosion due to differences in allele frequencies and LD patterns between populations. We used this correlation along with a trait’s heritability to derive expected values for the accuracy ( $${\text{R}}$$ ) of PBV accounting for the erosion, and demonstrated that our derived $${\text{E}}\left[{\text{R}}|{\text{erosion}}\right]$$ is a reliable metric.

中文翻译：

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考虑参考种群和目标种群之间遗传差异的预测育种值准确性的预期值


遗传价值，或牲畜和农作物育种计划中提到的育种价值，是商业农业系统中成功选择动物的关键之一。二十世纪统计方法的发展和二十一世纪单核苷酸多态性 (SNP) 芯片技术的发展彻底改变了农业生产，允许在很小的时候就对选育候选者的育种值进行高度准确的预测。尽管如此，对于许多育种群体来说，即使参考群体足够大，并且模型中包含的 SNP 与数量性状基因座存在足够的连锁不平衡 (LD)，预测育种值 (PBV) 的实现精度仍然低于理论最大值。 （QTL）。这在几代人中尤其明显，因为我们观察到由于重组而导致的所谓 SNP 效应的侵蚀，同时伴随着预测准确性的侵蚀。虽然准确量化单个 SNP 水平的侵蚀是一项困难且尚未解决的任务，但量化预测准确性的侵蚀是一个更容易处理的问题。在本文中，我们描述了一种方法，该方法使用参考种群和目标种群之间的关系来计算考虑侵蚀的非表型个体的预测育种值的准确性的预期值。通过模拟评估期望值的准确性，并根据真实数据进行进一步评估。通过模拟，我们凭经验证实，我们对 PBV 侵蚀的准确性的预期值能够正确确定非表型个体育种值的预测准确性。 当将 PBV 的预期准确度与实际数据进行比较时，评估的四个特征中只有一个的准确度显着高于预期，接近 $$\sqrt{{{\text{h}}}^{2 }}$$ 。我们定义了参考人群和目标人群之间的遗传相关性指数，该指数总结了由于人群之间等位基因频率和 LD 模式差异而导致的预期总体侵蚀。我们使用这种相关性以及性状的遗传力来推导 PBV 解释侵蚀的准确性 ( $${\text{R}}$$ ) 的预期值，并证明我们推导的 $${\text{E}} \left[{\text{R}}|{\text{erosion}}\right]$$ 是一个可靠的指标。