Pang Zhixu, Wang Wannian, Huang Pu, Zhang Hongzhi, Zhang Siying, Yang Pengkun, Qiao Liying, Liu Jianhua, Pan Yangyang, Yang Kaijie, Liu Wenzhong
College of Animal Science, Shanxi Agricultural University, Taigu, Jinzhong 030801, China.
Animals (Basel). 2025 Apr 29;15(9):1268. doi: 10.3390/ani15091268.
Genomic selection (GS) is a genetic breeding method that uses genome-wide marker information to improve the accuracy of the prediction of complex traits. The single-step GBLUP (ssGBLUP) model, which integrates pedigree, phenotypic, and genomic data, has improved genomic prediction. However, ssGBLUP assumes that all markers contribute equally to genetic variance, which can limit its predictive accuracy, especially for traits controlled by major genes. To overcome this limitation, we integrate results from genome-wide association studies (GWAS) into an enhanced ssGBLUP framework, termed single-step genome-wide association assisted BLUP (ssGWABLUP). Our approach assigns differential weights to markers on the basis of their GWAS results, thereby increasing the contribution of effective markers while diminishing the influence of ineffective ones during the construction of the genomic relationship matrix. By incorporating pseudo quantitative trait nucleotides (pQTNs) as covariates, we aim to capture the effects of markers closely associated with major causal variants, leading to the development of the ssGWABLUP_pQTNs. Compared with weighted ssGBLUP (WssGBLUP), the ssGWABLUP model demonstrated superior accuracy and dispersion across different genetic architectures. We then compared the performance of our proposed ssGWABLUP_pQTNs model against both ssGBLUP and ssGWABLUP across various genetic scenarios. Our results demonstrate that ssGWABLUP_pQTNs outperforms other models in terms of prediction accuracy, particularly in scenarios with simpler genetic architectures. Additionally, evaluation using pig dataset confirmed the effectiveness of ssGWABLUP_pQTNs, highlighting its potential for practical breeding applications. The incorporation of pQTNs and a weighted genomic relationship matrix presents a promising and potentially scalable approach to further enhance genomic prediction, with potential implications for improving the accuracy of genomic selection in breeding programs.
基因组选择（GS）是一种遗传育种方法，它利用全基因组标记信息来提高复杂性状预测的准确性。单步GBLUP（ssGBLUP）模型整合了系谱、表型和基因组数据，改进了基因组预测。然而，ssGBLUP假定所有标记对遗传方差的贡献相同，这可能会限制其预测准确性，特别是对于由主基因控制的性状。为克服这一限制，我们将全基因组关联研究（GWAS）的结果整合到一个增强的ssGBLUP框架中，称为单步全基因组关联辅助BLUP（ssGWABLUP）。我们的方法根据标记的GWAS结果为其分配不同的权重，从而在构建基因组关系矩阵时增加有效标记的贡献，同时减少无效标记的影响。通过纳入伪数量性状核苷酸（pQTNs）作为协变量，我们旨在捕捉与主要因果变异密切相关的标记的效应，从而开发出ssGWABLUP_pQTNs。与加权ssGBLUP（WssGBLUP）相比，ssGWABLUP模型在不同遗传结构上表现出更高的准确性和离散度。然后，我们在各种遗传场景下比较了我们提出的ssGWABLUP_pQTNs模型与ssGBLUP和ssGWABLUP的性能。我们的结果表明，ssGWABLUP_pQTNs在预测准确性方面优于其他模型，特别是在遗传结构较简单的场景中。此外，使用猪数据集进行的评估证实了ssGWABLUP_pQTNs的有效性，突出了其在实际育种应用中的潜力。纳入pQTNs和加权基因组关系矩阵为进一步提高基因组预测提供了一种有前景且可能可扩展的方法，对提高育种计划中基因组选择的准确性具有潜在意义。
