Data from: An equation to predict the accuracy of genomic values by combining data from multiple traits, populations, or environments

Predicting the accuracy of estimated genomic values using genome-wide marker information is an important step in designing training populations. Currently, different deterministic equations are available to predict accuracy within populations, but not for multi-population scenarios where data from multiple breeds, lines or environments is combined. Therefore, our objective was to develop and validate a deterministic equation to predict the accuracy of genomic values when different populations are combined in one training population. The input parameters of the derived prediction equation are the number of individuals and the heritability from each of the populations in the training population, the genetic correlations between the populations, i.e., the correlation between allele substitution effects of quantitative trait loci, the effective number of chromosome segments across predicted and training populations, and the proportion of the genetic variance in the predicted population captured by the markers in each of the training populations. Validation was performed based on real genotype information of 1033 Holstein Friesian cows that were divided in three different populations by combining half-sib families in the same population. Phenotypes were simulated for multiple scenarios, differing in heritability within populations and in genetic correlations between the populations. Results showed that the derived equation can accurately predict the accuracy of estimating genomic values for different scenarios of multi-population genomic prediction. Therefore, the derived equation can be used to investigate the potential accuracy of different multi-population genomic prediction scenarios and to decide on the most optimal design of training populations.

利用全基因组标记信息（genome-wide marker information）预测基因组估计值（estimated genomic values）的准确性，是设计训练群体（training populations）过程中的关键环节。当前已有多种确定性方程（deterministic equations）可用于群体内部的准确性预测，但尚未有适用于整合多个品种、品系或环境数据的多群体场景（multi-population scenarios）的同类方程。为此，本研究旨在开发并验证一种确定性方程，用于当不同群体被整合至同一训练群体时，基因组估计值准确性的预测。所推导的预测方程的输入参数包括：训练群体中各群体的个体数与遗传力（heritability）、群体间的遗传相关（genetic correlations，即数量性状基因座（quantitative trait loci, QTL）的等位基因替代效应（allele substitution effects）间的相关）、预测群体与训练群体间的染色体片段有效数目（effective number of chromosome segments），以及各训练群体的标记所捕获的预测群体遗传方差占比。本研究以1033头荷斯坦弗里生牛的真实基因型数据为基础开展验证：通过将半同胞家系（half-sib families）归组，将其划分为3个不同的群体。针对群体内遗传力及群体间遗传相关各不相同的多种场景，模拟了表型（phenotypes）数据。结果表明，所推导的方程可准确预测多群体基因组预测（multi-population genomic prediction）不同场景下的基因组估计值估算准确性。因此，该方程可用于探究不同多群体基因组预测场景的潜在准确性，并辅助确定最优的训练群体设计方案。