The accuracy of genomic prediction is partly determined by the size of the reference population. In Atlantic salmon breeding programs, four parallel populations often exist, thus offering the opportunity to increase the size of the reference set by combining these populations. By allowing a reduction in the number of records per population, multi-population prediction can potentially reduce cost and welfare issues related to the recording of traits, particularly for diseases. In this study, we evaluated the accuracy of multi- and across-population prediction of breeding values for resistance to amoebic gill disease (AGD) using all single nucleotide polymorphisms (SNPs) on a 55K chip or a selected subset of SNPs based on the signs of allele substitution effect estimates across populations, using both linear and nonlinear genomic prediction (GP) models in Atlantic salmon populations. In addition, we investigated genetic distance, genetic correlation estimated based on genomic relationships, and persistency of linkage disequilibrium (LD) phase across these populations. The genetic distance between populations ranged from 0.03 to 0.07, while the genetic correlation ranged from 0.19 to 0.99. Nonetheless, compared to within-population prediction, there was limited or no impact of combining populations for multi-population prediction across the various models used or when using the selected subset of SNPs. The estimates of across-population prediction accuracy were low and to some extent proportional to the genetic correlation estimates. The persistency of LD phase between adjacent markers across populations using all SNP data ranged from 0.51 to 0.65, indicating that LD is poorly conserved across the studied populations. Our results show that a high genetic correlation and a high genetic relationship between populations do not guarantee a higher prediction accuracy from multi-population genomic prediction in Atlantic salmon.

中文翻译：

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使用多个大西洋鲑鱼种群进行基因组预测的准确性

基因组预测的准确性部分取决于参考群体的大小。在大西洋鲑鱼育种计划中，通常存在四个平行种群，从而提供了通过组合这些种群来增加参考集大小的机会。通过减少每个群体的记录数量，多群体预测可以潜在地减少与性状记录相关的成本和福利问题，特别是疾病。在这项研究中，我们使用 55K 芯片上的所有单核苷酸多态性 (SNP) 或基于迹象选定的 SNP 子集，评估了抗阿米巴鳃病 (AGD) 育种值的多种群和跨种群预测的准确性使用大西洋鲑鱼种群中的线性和非线性基因组预测（GP）模型来估计跨种群的等位基因替代效应。此外，我们还研究了这些群体的遗传距离、基于基因组关系估计的遗传相关性以及连锁不平衡（LD）相的持续性。群体间遗传距离为0.03~0.07，遗传相关性为0.19~0.​​99。尽管如此，与群体内预测相比，在使用的各种模型中或使用选定的 SNP 子集时，组合群体进行多群体预测的影响有限或没有影响。跨人群预测准确性的估计值较低，并且在某种程度上与遗传相关性估计值成正比。使用所有 SNP 数据，跨群体的相邻标记之间 LD 相位的持久性范围为 0.51 至 0.65，表明 LD 在研究群体中保守性较差。我们的结果表明，种群之间的高遗传相关性和高遗传关系并不能保证大西洋鲑鱼多种群基因组预测的更高预测准确性。