The traditional way to estimate variance components (VC) is based on the animal model using a pedigree-based relationship matrix (A) (A-AM). After genomic selection was introduced into breeding programs, it was anticipated that VC estimates from A-AM would be biased because the effect of selection based on genomic information is not captured. The single-step method (H-AM), which uses an H matrix as (co)variance matrix, can be used as an alternative to estimate VC. Here, we compared VC estimates from A-AM and H-AM and investigated the effect of genomic selection, genotyping strategy and genotyping proportion on the estimation of VC from the two methods, by analyzing a dataset from a commercial broiler line and a simulated dataset that mimicked the broiler population. VC estimates from H-AM were severely overestimated with a high proportion of selective genotyping, and overestimation increased as proportion of genotyping increased in the analysis of both commercial and simulated data. This bias in H-AM estimates arises when selective genotyping is used to construct the H-matrix, regardless of whether selective genotyping is applied or not in the selection process. For simulated populations under genomic selection, estimates of genetic variance from A-AM were also significantly overestimated when the effect of genomic selection was strong. Our results suggest that VC estimates from H-AM under random genotyping have the expected values. Predicted breeding values from H-AM were inflated when VC estimates were biased, and inflation differed between genotyped and ungenotyped animals, which can lead to suboptimal selection decisions. We conclude that VC estimates from H-AM are biased with selective genotyping, but are close to expected values with random genotyping.VC estimates from A-AM in populations under genomic selection are also biased but to a much lesser degree. Therefore, we recommend the use of H-AM with random genotyping to estimate VC for populations under genomic selection. Our results indicate that it is still possible to use selective genotyping in selection, but then VC estimation should avoid the use of genotypes from one side only of the distribution of phenotypes. Hence, a dual genotyping strategy may be needed to address both selection and VC estimation.

中文翻译：

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基因组选择和基因分型策略对使用不同关系矩阵估算动物模型方差成分的影响。

估计方差分量（VC）的传统方法是基于动物模型，使用基于系谱的关系矩阵（A）（A-AM）。在将基因组选择引入育种程序后，可以预料，由于未捕获基于基因组信息的选择效果，因此来自A-AM的VC估计会产生偏差。使用H矩阵作为（协）方差矩阵的单步方法（H-AM）可以用作估计VC的替代方法。在这里，我们比较了A-AM和H-AM的VC估计值，并通过分析商业肉鸡生产线的数据集和模拟数据集，研究了基因组选择，基因分型策略和基因分型比例对这两种方法对VC估计的影响。模仿了肉鸡种群。来自H-AM的VC估计被严重高估，而选择性基因分型的比例很高，而在商业数据和模拟数据分析中，随着基因分型的比例增加，高估率也随之增加。当使用选择性基因分型来构建H矩阵时，无论选择过程中是否应用了选择性基因分型，H-AM估计都会出现这种偏差。对于基因组选择下的模拟种群，当基因组选择的效果很强时，来自A-AM的遗传方差估计值也被高估了。我们的结果表明，在随机基因分型下，来自H-AM的VC估计值具有预期值。当VC估计值有偏差时，来自H-AM的预测繁殖值会被夸大，而基因型和非基因型动物之间的通货膨胀也有所不同，这可能会导致选择决策不理想。我们得出的结论是，H-AM的VC估计值与选择性基因分型有偏差，但随机基因分型的结果接近预期值.A-AM在基因组选择人群中的VC估计值也有偏差，但程度要小得多。因此，我们建议将H-AM与随机基因分型一起用于估算在基因组选择下的人群的VC。我们的结果表明，仍然可以在选择中使用选择性基因分型，但是VC估计应避免仅从表型分布的一侧使用基因型。因此，可能需要双重基因分型策略来解决选择和VC估计。在基因组选择下的群体中，A-AM对VC的估计也有偏差，但程度要小得多。因此，我们建议将H-AM与随机基因分型一起用于估算在基因组选择下的人群的VC。我们的结果表明，仍然可以在选择中使用选择性基因分型，但是VC估计应避免仅从表型分布的一侧使用基因型。因此，可能需要双重基因分型策略来解决选择和VC估计。在基因组选择下的群体中，A-AM对VC的估计也有偏差，但程度要小得多。因此，我们建议将H-AM与随机基因分型一起用于估算在基因组选择下的人群的VC。我们的结果表明，仍然可以在选择中使用选择性基因分型，但是VC估计应避免仅从表型分布的一侧使用基因型。因此，可能需要双重基因分型策略来解决选择和VC估计。但是VC估计应该避免仅从表型分布的一侧使用基因型。因此，可能需要双重基因分型策略来解决选择和VC估计。但是VC估计应该避免仅从表型分布的一侧使用基因型。因此，可能需要双重基因分型策略来解决选择和VC估计。