Department of Animal Science, School of Agriculture and Biology, Shanghai Jiaotong University, 800# Dongchuan Road, Shang, East 200240, China.
Department of Animal Science, College of Animal Sciences, Zhejiang University, 866# Yuhangtang Road, Hangzhou, East 310058, China.
Animal. 2023 Oct;17(10):100980. doi: 10.1016/j.animal.2023.100980. Epub 2023 Sep 7.
Genomic prediction (GP) has greatly advanced animal and plant breeding over the past two decades. GP in joint populations is a feasible method to improve the accuracy of genomic estimated breeding values in small populations. However, there is still a need to understand the factors that influence GP in joint populations. This study used simulated data and real data from Duroc pig populations to examine the impact of linkage disequilibrium (LD), causal variants effect sizes (CVESs), and minor allele frequencies (MAF) of SNPs on the accuracy of genomic prediction in joint populations. Three prediction methods were used: genomic best linear unbiased prediction (GBLUP), single-step GBLUP and multi-trait GBLUP. Results from the simulated datasets showed that the accuracies of GP in joint populations were always higher than those in a single population when only LD inconsistencies existed. However, single-step GBLUP accuracy in joint populations decreased as the correlation of MAF between populations decreased, while the accuracy of GBLUP is consistently higher in joint populations than in a single population. As the correlation of CVES between populations decreased, the accuracy of both GBLUP and single-step GBLUP in joint populations declined. Analysis of real Duroc populations showed low genetic correlation, similar to the simulated relationship between the most distant populations. In most cases in Duroc populations, GP have higher accuracies in joint populations than in individual population. In conclusion, the consistency of CVES plays a more important role in multi-population GP. The genetic relatedness of the Duroc populations is so weak that the prediction accuracy of GP in joint populations is reduced in some traits. Multi-trait GBLUP is a competitive method for the joint breeding evaluation.
基因组预测 (GP) 在过去二十年极大地推动了动植物的育种工作。联合群体中的 GP 是提高小群体中基因组估计育种值准确性的可行方法。然而，仍需要了解影响联合群体中 GP 的因素。本研究使用模拟数据和杜洛克猪群体的真实数据，研究了连锁不平衡 (LD)、因果变异效应大小 (CVES) 和单核苷酸多态性 (SNP) 的次要等位基因频率 (MAF) 对联合群体中基因组预测准确性的影响。使用了三种预测方法：基因组最佳线性无偏预测 (GBLUP)、一步法 GBLUP 和多性状 GBLUP。模拟数据集的结果表明，当仅存在 LD 不一致时，联合群体中的 GP 准确性总是高于单一群体。然而，随着群体间 MAF 相关性的降低，联合群体中单步 GBLUP 的准确性降低，而 GBLUP 的准确性始终高于单一群体。随着群体间 CVES 相关性的降低，联合群体中 GBLUP 和单步 GBLUP 的准确性都降低。对真实杜洛克群体的分析表明遗传相关性较低，与模拟的最远距离群体之间的关系相似。在杜洛克群体的大多数情况下，联合群体中的 GP 具有比单个群体更高的准确性。总之，CVES 的一致性在多群体 GP 中起着更重要的作用。杜洛克群体的遗传关系非常弱，以至于在某些性状中，联合群体中 GP 的预测准确性降低。多性状 GBLUP 是联合育种评估的一种有竞争力的方法。
