This work focuses on the effects of variable amount of genomic information in the Bayesian estimation of unknown variance components associated with single-step genomic prediction. We propose a quantitative criterion for the amount of genomic information included in the model and use it to study the relative effect of genomic data on efficiency of sampling from the posterior distribution of parameters of the single-step model when conducting a Bayesian analysis with estimating unknown variances. The rate of change of estimated variances was dependent on the amount of genomic information involved in the analysis, but did not depend on the Gibbs updating schemes applied for sampling realizations of the posterior distribution. Simulation revealed a gradual deterioration of convergence rates for the locations parameters when new genomic data were gradually added into the analysis. In contrast, the convergence of variance components showed continuous improvement under the same conditions. The sampling efficiency increased proportionally to the amount of genomic information. In addition, an optimal amount of genomic information in variance-covariance matrix that guaranty the most (computationally) efficient analysis was found to correspond a proportion of animals genotyped ***0.8. The proposed criterion yield a characterization of expected performance of the Gibbs sampler if the analysis is subject to adjustment of the amount of genomic data and can be used to guide researchers on how large a proportion of animals should be genotyped in order to attain an efficient analysis.

中文翻译：

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基因组信息对未知方差混合线性模型贝叶斯分析效率的相对影响

这项工作侧重于可变数量的基因组信息在与单步基因组预测相关的未知方差分量的贝叶斯估计中的影响。我们提出了模型中包含的基因组信息量的定量标准，并使用它来研究基因组数据对从单步模型参数的后验分布采样效率的相对影响，当进行贝叶斯分析时，估计未知数差异。估计方差的变化率取决于分析中涉及的基因组信息量，但不取决于应用于后验分布采样实现的 Gibbs 更新方案。模拟显示，当新的基因组数据逐渐添加到分析中时，位置参数的收敛速度会逐渐恶化。相比之下，方差分量的收敛在相同条件下表现出持续改善。采样效率与基因组信息量成比例地增加。此外，发现保证最（计算上）有效分析的方差-协方差矩阵中的最佳基因组信息量对应于基因分型为 ***0.8 的动物比例。