Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education & Key Laboratory of Swine Genetics and Breeding of Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China.
Center for Quantitative Genetics and Genomics, Aarhus University, Tjele 8830, Denmark.
J Anim Sci. 2022 Jul 1;100(7). doi: 10.1093/jas/skac174.
Joint genomic evaluation by combining data recordings and genomic information from different pig herds and populations is of interest for pig breeding companies because the efficiency of genomic selection (GS) could be further improved. In this work, an efficient strategy of joint genomic evaluation combining data from multiple pig populations is investigated. Total teat number (TTN), a trait that is equally recorded on 13,060 American Yorkshire (AY) populations (14.68 teats) and 10,060 Danish Yorkshire (DY) pigs (14.29 teats), was used to explore the feasibility and accuracy of GS combining datasets from different populations. We first estimated the genetic correlation (rg) of TTN between AY and DY pig populations (rg = 0.79, se = 0.23). Then we employed the genome-wide association study to identify quantitative trait locus (QTL) regions that are significantly associated with TTN and investigate the genetic architecture of TTN in different populations. Our results suggested that the genomic regions controlling TTN are slightly different in the two Yorkshire populations, where the candidate QTL regions were on SSC 7 and SSC 8 for the AY population and on SSC 7 for the DY population. Finally, we explored an optimal way of genomic prediction for TTN via three different genomic best linear unbiased prediction models and we concluded that when TTN across populations are regarded as different, but correlated, traits in a multitrait model, predictive abilities for both Yorkshire populations improve. As a conclusion, joint genomic evaluation for target traits in multiple pig populations is feasible in practice and more accurate, provided a proper model is used.
结合不同猪群和群体的数据记录和基因组信息进行联合基因组评估对养猪公司具有重要意义，因为基因组选择（GS）的效率可以进一步提高。在这项工作中，研究了一种结合多个猪群体数据进行联合基因组评估的有效策略。总奶头数（TTN）是一个在 13060 个美国约克夏（AY）群体（14.68 个奶头）和 10060 个丹麦约克夏（DY）猪（14.29 个奶头）上同等记录的性状，用于探索从不同群体结合数据集进行 GS 的可行性和准确性。我们首先估计了 AY 和 DY 猪群体之间 TTN 的遗传相关系数（rg）（rg = 0.79，se = 0.23）。然后，我们采用全基因组关联研究来鉴定与 TTN 显著相关的数量性状基因座（QTL）区域，并研究不同群体中 TTN 的遗传结构。我们的结果表明，控制 TTN 的基因组区域在两个约克夏群体中略有不同，其中候选 QTL 区域在 AY 群体的 SSC7 和 SSC8 上，而在 DY 群体的 SSC7 上。最后，我们通过三种不同的基因组最佳线性无偏预测模型探索了 TTN 基因组预测的最佳方法，我们得出的结论是，当跨群体的 TTN 被视为不同的，但相关的，多性状模型中的性状时，两个约克夏群体的预测能力都会提高。总之，在实践中，对多个猪群体的目标性状进行联合基因组评估是可行的，只要使用适当的模型，就可以更准确。
