Single-step genomic best linear unbiased prediction (GBLUP) involves a joint analysis of individuals with genotype information, and their ancestors, descendants, or contemporaries, without recorded genotypes. Livestock applications typically represent populations with fewer individuals with genotypes relative to the number not genotyped. Most breeding programmes are structured, consisting of a nucleus tier in which selection drives genetic gains that are propagated through descendants that represent parents in multiplier and commercial tiers. In some cases, the genotypes in the nucleus tier are proprietary to a breeding company, and not publicly available for a whole industry analysis. Bayesian inference involves combining a defined description of prior information with new information to generate a posterior distribution that contains all available information on parameters of interest. A natural extension of Bayesian analysis would be to use information from the posterior distribution to define the prior distribution in a subsequent analysis. We derive the mixed model equations for inference on breeding values for non genotyped individuals in that subset of the population that is of current interest, using only data on the performance of current individuals and their immediate pedigree, along with prior information defined from the posterior distribution of an external BLUP or single-step GBLUP analysis of the ancestors of the current population. Identical estimates of breeding values and their prediction error covariances for current animals of interest in the multiplier or commercial tier can be obtained without requiring neither the genomic relationship matrix nor genotypes of any of their ancestors in the nucleus tier, as can be obtained from a single analysis using pedigree, performance, and genomic information from all tiers. The Bayesian analysis of the current population does not require explicit information on unselected genotyped animals in the external population.

中文翻译：

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一种在祖先基因组关系矩阵不可用时为非基因型后代获得精确单步GBLUP的方法

单步基因组最佳线性无偏预测 (GBLUP) 涉及对具有基因型信息的个体及其祖先、后代或同时代人进行联合分析，而没有记录的基因型。家畜应用通常代表具有基因型的个体相对于未进行基因分型的人数较少的群体。大多数育种计划都是结构化的，包括一个核心层，其中选择驱动遗传增益，这些遗传增益通过代表倍增器和商业层的父母的后代传播。在某些情况下，核心层的基因型是育种公司专有的，不能公开用于整个行业的分析。贝叶斯推理涉及将先验信息的定义描述与新信息相结合，以生成包含所有可用参数的后验分布。贝叶斯分析的一个自然扩展是使用来自后验分布的信息来定义后续分析中的先验分布。我们仅使用有关当前个体的表现及其直接谱系的数据以及从后验分布定义的先验信息，推导出当前感兴趣的该群体子集中非基因型个体的育种值的混合模型方程对当前种群的祖先进行外部 BLUP 或单步 GBLUP 分析。对于当前感兴趣的动物在乘数或商业层中的育种值及其预测误差协方差的相同估计可以在不需要基因组关系矩阵或其任何祖先在核层中的基因型的情况下获得，如可以从单个使用来自所有层级的谱系、性能和基因组信息进行分析。当前种群的贝叶斯分析不需要外部种群中未选择的基因分型动物的明确信息。