The lack of improvement in prediction accuracy in multi-breed genomic evaluation can be due to the (a) inconsistency in allele substitution effects across breeds, (b) the absence of close family relationships between breeds and (c) between-breed differences in linkage disequilibrium (LD) between single nucleotide polymorphisms (SNPs) and quantitative trait loci that influence a trait across breeds. The objective of this study was to investigate the possibility of improvement in prediction accuracy by accounting for the LD phase differences in a multi-breed reference population. Prediction accuracy was compared in three different scenarios. Scenarios had separate or combined reference population. In the proposed method, when reference population of two breeds were combined, subset of common SNPs between two breeds with high and similar LD phases were identified. Then, A Bayesian Ridge regression model was used to estimated effects for two set of SNPs, SNPs with similar LD phase in both breeds and the remaining SNPs. Results showed that simple pooling of two reference population into a single reference population did not improve prediction accuracy compared to a separate reference population of each breed. However, accounting for LD phase differences in two breeds, improved prediction accuracy compared to separate training and simple pooling of reference populations. This can be appealing for small populations (e.g., beef) that often have relatively small reference population and a combined genetic evaluation is usually performed on multiple breeds.

多品种基因组评估中预测准确性的改善可能是由于（a）各个品种之间的等位基因替代效应不一致，（b）品种之间缺乏紧密的家族关系，以及（c）品种之间的连锁差异单核苷酸多态性（SNP）与影响整个品种性状的数量性状基因座之间的不平衡（LD）。这项研究的目的是通过考虑多品种参考种群中的LD相差来研究提高预测准确性的可能性。在三种不同情况下比较了预测准确性。方案具有单独或合并的参考人群。在建议的方法中，将两个品种的参考种群相结合，确定了两个具有较高和相似LD阶段的品种之间的常见SNP的子集。然后，使用贝叶斯岭回归模型评估两组SNP的效果，两个品种中具有相似LD相的SNP以及其余SNP。结果表明，与每个品种的单独参考种群相比，将两个参考种群简单合并为一个参考种群不会提高预测准确性。但是，考虑到两个品种的LD相差，与单独训练和简单合并参考种群相比，提高了预测准确性。这对于通常具有相对较小参考种群的小种群（例如牛肉）可能很有吸引力，并且通常对多个品种进行综合遗传评估。使用贝叶斯岭回归模型估计两组SNP的效果，两个品种中具有相似LD相的SNP以及其余SNP。结果表明，与每个品种的单独参考群体相比，将两个参考群体简单地合并为一个参考群体并不能提高预测准确性。但是，考虑到两个品种的LD相差，与单独训练和简单合并参考种群相比，提高了预测准确性。这对于通常具有相对较小参考种群的小种群（例如牛肉）可能很有吸引力，并且通常对多个品种进行综合遗传评估。使用贝叶斯岭回归模型估计两组SNP的效果，两个品种中具有相似LD相的SNP以及其余SNP。结果表明，与每个品种的单独参考群体相比，将两个参考群体简单地合并为一个参考群体并不能提高预测准确性。但是，考虑到两个品种的LD相差，与单独训练和简单合并参考种群相比，提高了预测准确性。这对于通常具有相对较小参考种群的小种群（例如牛肉）可能很有吸引力，并且通常对多个品种进行综合遗传评估。结果表明，与每个品种的单独参考群体相比，将两个参考群体简单地合并为一个参考群体并不能提高预测准确性。但是，考虑到两个品种的LD相差，与单独训练和简单合并参考种群相比，提高了预测准确性。这对于通常具有相对较小参考种群的小种群（例如牛肉）可能很有吸引力，并且通常对多个品种进行综合遗传评估。结果表明，与每个品种的单独参考群体相比，将两个参考群体简单地合并为一个参考群体并不能提高预测准确性。但是，考虑到两个品种的LD相差，与单独训练和简单合并参考种群相比，提高了预测准确性。这对于通常具有相对较小参考种群的小种群（例如牛肉）可能很有吸引力，并且通常对多个品种进行综合遗传评估。