Enhancing Genomic Prediction Accuracy with a Single-Step Genomic Best Linear Unbiased Prediction Model Integrating Genome-Wide Association Study Results

• Published in: Animals (Basel) Vol. 15; no. 9; p. 1268
• Main Authors: Pang, Zhixu, Wang, Wannian, Huang, Pu, Zhang, Hongzhi, Zhang, Siying, Yang, Pengkun, Qiao, Liying, Liu, Jianhua, Pan, Yangyang, Yang, Kaijie, Liu, Wenzhong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.04.2025; MDPI
• Subjects: Accuracy; Cell division; Dairy cattle; Datasets; Females; Genetic markers; Genomes; genomic prediction; Genomics; pseudo QTNs; simulated dataset; ssGBLUP; weighted genomic relationship matrix; weighted genomic relationship matrix; simulated dataset; genomic prediction; pseudo QTNs; ssGBLUP
• ISSN: 2076-2615; 2076-2615
• 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.