Liu Yiyi, Zhang Yuling, Zhou Fuchen, Yao Zekai, Zhan Yuexin, Fan Zhenfei, Meng Xianglun, Zhang Zebin, Liu Langqing, Yang Jie, Wu Zhenfang, Cai Gengyuan, Zheng Enqin
National Engineering Research Center for Breeding Swine Industry, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China.
Animals (Basel). 2023 Dec 15;13(24):3871. doi: 10.3390/ani13243871.
Enhancing the accuracy of genomic prediction is a key goal in genomic selection (GS) research. Integrating prior biological information into GS methods using appropriate models can improve prediction accuracy for complex traits. Genome-wide association study (GWAS) is widely utilized to identify potential candidate loci associated with complex traits in livestock and poultry, offering essential genomic insights. In this study, a GWAS was conducted on 685 Duroc × Landrace × Yorkshire (DLY) pigs to extract significant single-nucleotide polymorphisms (SNPs) as genomic features. We compared two GS models, genomic best linear unbiased prediction (GBLUP) and genomic feature BLUP (GFBLUP), by using imputed whole-genome sequencing (WGS) data on 651 Yorkshire pigs. The results revealed that the GBLUP model achieved prediction accuracies of 0.499 for backfat thickness (BFT) and 0.423 for loin muscle area (LMA). By applying the GFBLUP model with GWAS-based SNP preselection, the average prediction accuracies for BFT and LMA traits reached 0.491 and 0.440, respectively. Specifically, the GFBLUP model displayed a 4.8% enhancement in predicting LMA compared to the GBLUP model. These findings suggest that, in certain scenarios, the GFBLUP model may offer superior genomic prediction accuracy when compared to the GBLUP model, underscoring the potential value of incorporating genomic features to refine GS models.
提高基因组预测的准确性是基因组选择（GS）研究的关键目标。使用适当的模型将先前的生物学信息整合到GS方法中，可以提高复杂性状的预测准确性。全基因组关联研究（GWAS）被广泛用于识别与畜禽复杂性状相关的潜在候选基因座，提供重要的基因组见解。在本研究中，对685头杜洛克×长白×大白（DLY）猪进行了GWAS，以提取显著的单核苷酸多态性（SNP）作为基因组特征。我们使用651头大白猪的推算全基因组测序（WGS）数据，比较了两种GS模型，即基因组最佳线性无偏预测（GBLUP）和基因组特征BLUP（GFBLUP）。结果显示，GBLUP模型对背膘厚度（BFT）的预测准确率为0.499，对腰肌面积（LMA）的预测准确率为0.423。通过应用基于GWAS的SNP预选的GFBLUP模型，BFT和LMA性状的平均预测准确率分别达到0.491和0.440。具体而言，与GBLUP模型相比，GFBLUP模型在预测LMA方面提高了4.8%。这些发现表明，在某些情况下，与GBLUP模型相比，GFBLUP模型可能提供更高的基因组预测准确性，突出了纳入基因组特征以优化GS模型的潜在价值。
