This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Enhancing Genomic Prediction Accuracy with a Single-Step Genomic Best Linear Unbiased Prediction Model Integrating Genome-Wide Association Study Results
by
, , , , and
Zhixu Pang
†,
Wannian Wang
†,
Pu Huang
,
Hongzhi Zhang
,
Siying Zhang
,
Pengkun Yang
,
Liying Qiao
,
Jianhua Liu
,
Yangyang Pan
,
Kaijie Yang
and
Wenzhong Liu
* College of Animal Science, Shanxi Agricultural University, Taigu, Jinzhong 030801, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Animals 2025, 15(9), 1268; https://doi.org/10.3390/ani15091268 (registering DOI)
Submission received: 31 March 2025
/
Revised: 24 April 2025
/
Accepted: 29 April 2025
/
Published: 29 April 2025
(This article belongs to the Section Animal Genetics and Genomics)
Simple Summary
Genomic selection (GS) is a powerful tool for improving the accuracy of genetic predictions in animal breeding. The single-step GBLUP (ssGBLUP) model integrates genomic, pedigree, and phenotypic data but assumes that all genetic markers contribute equally, which may limit its predictive power. To address this, we developed an enhanced model, ssGWABLUP, which incorporates genome-wide association study (GWAS) results to assign differential weights to markers. Furthermore, we introduced the ssGWABLUP_pQTNs model that integrates pseudo quantitative trait nucleotides (pQTNs) identified from GWAS into the weighted framework. Using both simulated and real datasets, we demonstrated that ssGWABLUP_pQTNs consistently outperforms ssGBLUP and other models, particularly for traits influenced by a small number of major genes. These findings suggest that integrating GWAS-derived information into ssGBLUP can enhance genomic prediction accuracy, providing a promising approach for improving genetic evaluations in livestock breeding programs.
Abstract
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.
