Mathematical models are needed for the design of breeding programs using genomic prediction. While deterministic models for selection on pedigree-based estimates of breeding values (PEBV) are available, these have not been fully developed for genomic selection, with a key missing component being the accuracy of genomic EBV (GEBV) of selection candidates. Here, a deterministic method was developed to predict this accuracy within a closed breeding population based on the accuracy of GEBV and PEBV in the reference population and the distance of selection candidates from their closest ancestors in the reference population. The accuracy of GEBV was modeled as a combination of the accuracy of PEBV and of EBV based on genomic relationships deviated from pedigree (DEBV). Loss of the accuracy of DEBV from the reference to the target population was modeled based on the effective number of independent chromosome segments in the reference population (Me). Measures of Me derived from the inverse of the variance of relationships and from the accuracies of GEBV and PEBV in the reference population, derived using either a Fisher information or a selection index approach, were compared by simulation. Using simulation, both the Fisher and the selection index approach correctly predicted accuracy in the target population over time, both with and without selection. The index approach, however, resulted in estimates of Me that were less affected by heritability, reference size, and selection, and which are, therefore, more appropriate as a population parameter. The variance of relationships underpredicted Me and was greatly affected by selection. A leave-one-out cross-validation approach was proposed to estimate required accuracies of EBV in the reference population. Aspects of the methods were validated using real data. A deterministic method was developed to predict the accuracy of GEBV in selection candidates in a closed breeding population. The population parameter Me that is required for these predictions can be derived from an available reference data set, and applied to other reference data sets and traits for that population. This method can be used to evaluate the benefit of genomic prediction and to optimize genomic selection breeding programs.

中文翻译：

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预测基因组预测的准确性

使用基因组预测设计育种计划需要数学模型。虽然可以使用基于谱系的育种值估计值 (PEBV) 进行选择的确定性模型，但这些模型尚未完全用于基因组选择，其中一个关键的缺失部分是选择候选者的基因组 EBV (GEBV) 的准确性。在这里，根据参考种群中 GEBV 和 PEBV 的准确性以及选择候选者与其在参考种群中最接近的祖先的距离，开发了一种确定性方法来预测封闭育种种群中的这种准确性。GEBV 的准确性被建模为 PEBV 和基于基因组关系偏离谱系 (DEBV) 的 EBV 的准确性的组合。基于参考群体 (Me) 中独立染色体片段的有效数量，对参考目标群体的 DEBV 准确性损失进行建模。通过模拟比较了从关系方差的倒数和参考群体中 GEBV 和 PEBV 的准确度得出的 Me 测量值，使用 Fisher 信息或选择指数方法得出。使用模拟，无论有没有选择，Fisher 和选择指数方法都正确预测了目标人群随时间推移的准确性。然而，指数方法导致对 Me 的估计受遗传力、参考大小和选择的影响较小，因此更适合作为总体参数。关系的差异低估了我，并受到选择的极大影响。提出了一种留一法交叉验证方法来估计参考人群中所需的 EBV 准确度。使用真实数据验证了方法的各个方面。开发了一种确定性方法来预测封闭育种种群中选择候选者中 GEBV 的准确性。这些预测所需的种群参数 Me 可以从可用的参考数据集导出，并应用于该种群的其他参考数据集和特征。该方法可用于评估基因组预测的益处并优化基因组选择育种程序。使用真实数据验证了方法的各个方面。开发了一种确定性方法来预测封闭育种种群中选择候选者中 GEBV 的准确性。这些预测所需的种群参数 Me 可以从可用的参考数据集导出，并应用于该种群的其他参考数据集和性状。该方法可用于评估基因组预测的益处并优化基因组选择育种程序。使用真实数据验证了方法的各个方面。开发了一种确定性方法来预测封闭育种种群中选择候选者中 GEBV 的准确性。这些预测所需的种群参数 Me 可以从可用的参考数据集导出，并应用于该种群的其他参考数据集和特征。该方法可用于评估基因组预测的益处并优化基因组选择育种程序。并应用于该人群的其他参考数据集和特征。该方法可用于评估基因组预测的益处并优化基因组选择育种程序。并应用于该人群的其他参考数据集和特征。该方法可用于评估基因组预测的益处并优化基因组选择育种程序。