Abstract The objective of this study was to determine whether the linear regression (LR) method could be used to validate genomic threshold models. Statistics for the LR method were computed from estimated breeding values (EBVs) using the whole and truncated data sets with variances from the reference and validation populations. The method was tested using simulated and real chicken data sets. The simulated data set included 10 generations of 4,500 birds each; genotypes were available for the last three generations. Each animal was assigned a continuous trait, which was converted to a binary score assuming an incidence of failure of 7%. The real data set included the survival status of 186,596 broilers (mortality rate equal to 7.2%) and genotypes of 18,047 birds. Both data sets were analysed using best linear unbiased predictor (BLUP) or single‐step GBLUP (ssGBLUP). The whole data set included all phenotypes available, whereas in the partial data set, phenotypes of the most recent generation were removed. In the simulated data set, the accuracies based on the LR formulas were 0.45 for BLUP and 0.76 for ssGBLUP, whereas the correlations between true breeding values and EBVs (i.e. true accuracies) were 0.37 and 0.65, respectively. The gain in accuracy by adding genomic information was overestimated by 0.09 when using the LR method compared to the true increase in accuracy. However, when the estimated ratio between the additive variance computed based on pedigree only and on pedigree and genomic information was considered, the difference between true and estimated gain was <0.02. Accuracies of BLUP and ssGBLUP with the real data set were 0.41 and 0.47, respectively. This small improvement in accuracy when using ssGBLUP with the real data set was due to population structure and lower heritability. The LR method is a useful tool for estimating improvements in accuracy of EBVs due to the inclusion of genomic information when traditional validation methods as k‐fold validation and predictive ability are not applicable.

中文翻译：

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使用线性回归方法验证阈值模型的单步 GBLUP 基因组预测：在鸡死亡率中的应用

摘要 本研究的目的是确定线性回归 (LR) 方法是否可用于验证基因组阈值模型。LR 方法的统计数据是根据估计育种值 (EBV) 计算得出的，使用完整和截断的数据集，并具有与参考和验证群体的差异。该方法使用模拟和真实的鸡肉数据集进行了测试。模拟数据集包括 10 代，每代 4,500 只鸟；基因型可用于最后三代。每只动物都被分配了一个连续性状，假设失败率为 7%，则将其转换为二进制分数。真实数据集包括186,596只肉鸡的存活状态（死亡率等于7.2%）和18,047只鸡的基因型。使用最佳线性无偏预测器 (BLUP) 或单步 GBLUP (ssGBLUP) 分析两个数据集。整个数据集包括所有可用的表型，而在部分数据集中，最近一代的表型被删除。在模拟数据集中，基于 LR 公式的 BLUP 和 ssGBLUP 的准确度分别为 0.45 和 0.76，而真实育种值与 EBV（即真实准确度）之间的相关性分别为 0.37 和 0.65。与准确度的真正提高相比，使用 LR 方法时通过添加基因组信息获得的准确度高估了 0.09。然而，当考虑仅基于谱系计算的加性方差与谱系和基因组信息之间的估计比率时，真实增益和估计增益之间的差异<0.02。BLUP 和 ssGBLUP 在真实数据集上的准确率分别为 0.41 和 0.47。将 ssGBLUP 与真实数据集一起使用时，准确性的这种小幅提高是由于种群结构和较低的遗传力。当传统的验证方法如 k 折验证和预测能力不适用时，由于包含基因组信息，LR 方法是估计 EBV 准确性改进的有用工具。