Department of Animal Science, College of Animal Science, Zhejiang University, Hangzhou, China.
SciGene Biotechnology Co. Ltd, Hefei, China.
Anim Genet. 2024 Oct;55(5):714-724. doi: 10.1111/age.13465. Epub 2024 Aug 12.
The low heritability of reproduction traits such as total number born (TNB), number born alive (NBA) and adjusted litter weight until 21 days at weaning (ALW) poses a challenge for genetic improvement. In this study, we aimed to identify genetic variants that influence these traits and evaluate the accuracy of genomic selection (GS) using these variants as genomic features. We performed single-step genome-wide association studies (ssGWAS) on 17 823 Large White (LW) pigs, of which 2770 were genotyped by 50K single nucleotide polymorphism (SNP) chips. Additionally, we analyzed runs of homozygosity (ROH) in the population and tested their effects on the traits. The genomic feature best linear unbiased prediction (GFBLUP) was then carried out in an independent population of 350 LW pigs using identified trait-related SNP subsets as genomic features. As a result, our findings identified five, one and four SNP windows that explaining more than 1% of genetic variance for ALW, TNB, and NBA, respectively and discovered 358 hotspots and nine ROH islands. The ROH SSC1:21814570-27186456 and SSC11:7220366-14276394 were found to be significantly associated with ALW and NBA, respectively. We assessed the genomic estimated breeding value accuracy through 20 replicates of five-fold cross-validation. Our findings demonstrate that GFBLUP, incorporating SNPs located in effective ROH (p-value < 0.05) as genomic features, might enhance GS accuracy for ALW compared with GBLUP. Additionally, using SNPs explaining more than 0.1% of the genetic variance in ssGWAS for NBA as genomic features might improve the GS accuracy, too. However, it is important to note that the incorporation of inappropriate genomic features can significantly reduce GS accuracy. In conclusion, our findings provide valuable insights into the genetic mechanisms of reproductive traits in pigs and suggest that the ssGWAS and ROH have the potential to enhance the accuracy of GS for reproductive traits in LW pigs.
繁殖性状(如总产仔数(TNB)、产活仔数(NBA)和断奶 21 日调整窝重(ALW))的低遗传力给遗传改良带来了挑战。在这项研究中,我们旨在确定影响这些性状的遗传变异,并评估使用这些变异作为基因组特征的基因组选择(GS)的准确性。我们对 17823 头长白猪(LW)进行了单步全基因组关联研究(ssGWAS),其中 2770 头通过 50K 单核苷酸多态性(SNP)芯片进行了基因型分析。此外,我们还分析了群体中的纯合段(ROH),并测试了它们对性状的影响。然后,我们使用鉴定出的与性状相关的 SNP 子集作为基因组特征,在 350 头 LW 猪的独立群体中进行了最佳线性无偏预测(GFBLUP)分析。结果发现,有五个、一个和四个 SNP 窗口分别解释了 ALW、TNB 和 NBA 遗传方差的 1%以上,发现了 358 个热点和 9 个 ROH 岛。ROH SSC1:21814570-27186456 和 SSC11:7220366-14276394 分别与 ALW 和 NBA 显著相关。我们通过 20 次五重交叉验证评估了基因组估计育种值的准确性。我们的研究结果表明,GFBLUP 纳入了位于有效 ROH 中的 SNP(p 值<0.05)作为基因组特征,与 GBLUP 相比,可能会提高 ALW 的 GS 准确性。此外,使用 ssGWAS 中解释 NBA 遗传方差超过 0.1%的 SNP 作为基因组特征也可能提高 GS 准确性。然而,需要注意的是,纳入不合适的基因组特征会显著降低 GS 的准确性。总之,我们的研究结果为猪繁殖性状的遗传机制提供了有价值的见解,并表明 ssGWAS 和 ROH 有可能提高 LW 猪繁殖性状的 GS 准确性。