Enhancing Genomic Prediction Accuracy with a Single-Step Genomic Best Linear Unbiased Prediction Model Integrating Genome-Wide Association Study Results
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...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Animals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-2615/15/9/1268 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850275358492852224 |
|---|---|
| author | Zhixu Pang Wannian Wang Pu Huang Hongzhi Zhang Siying Zhang Pengkun Yang Liying Qiao Jianhua Liu Yangyang Pan Kaijie Yang Wenzhong Liu |
| author_facet | Zhixu Pang Wannian Wang Pu Huang Hongzhi Zhang Siying Zhang Pengkun Yang Liying Qiao Jianhua Liu Yangyang Pan Kaijie Yang Wenzhong Liu |
| author_sort | Zhixu Pang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d28489d1adc34ae4abbde383cc73d828 |
| institution | OA Journals |
| issn | 2076-2615 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj-art-d28489d1adc34ae4abbde383cc73d8282025-08-20T01:50:45ZengMDPI AGAnimals2076-26152025-04-01159126810.3390/ani15091268Enhancing Genomic Prediction Accuracy with a Single-Step Genomic Best Linear Unbiased Prediction Model Integrating Genome-Wide Association Study ResultsZhixu Pang0Wannian Wang1Pu Huang2Hongzhi Zhang3Siying Zhang4Pengkun Yang5Liying Qiao6Jianhua Liu7Yangyang Pan8Kaijie Yang9Wenzhong Liu10College of Animal Science, Shanxi Agricultural University, Taigu, Jinzhong 030801, ChinaCollege of Animal Science, Shanxi Agricultural University, Taigu, Jinzhong 030801, ChinaCollege of Animal Science, Shanxi Agricultural University, Taigu, Jinzhong 030801, ChinaCollege of Animal Science, Shanxi Agricultural University, Taigu, Jinzhong 030801, ChinaCollege of Animal Science, Shanxi Agricultural University, Taigu, Jinzhong 030801, ChinaCollege of Animal Science, Shanxi Agricultural University, Taigu, Jinzhong 030801, ChinaCollege of Animal Science, Shanxi Agricultural University, Taigu, Jinzhong 030801, ChinaCollege of Animal Science, Shanxi Agricultural University, Taigu, Jinzhong 030801, ChinaCollege of Animal Science, Shanxi Agricultural University, Taigu, Jinzhong 030801, ChinaCollege of Animal Science, Shanxi Agricultural University, Taigu, Jinzhong 030801, ChinaCollege of Animal Science, Shanxi Agricultural University, Taigu, Jinzhong 030801, ChinaGenomic 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.https://www.mdpi.com/2076-2615/15/9/1268genomic predictionssGBLUPpseudo QTNsweighted genomic relationship matrixsimulated dataset |
| spellingShingle | Zhixu Pang Wannian Wang Pu Huang Hongzhi Zhang Siying Zhang Pengkun Yang Liying Qiao Jianhua Liu Yangyang Pan Kaijie Yang Wenzhong Liu Enhancing Genomic Prediction Accuracy with a Single-Step Genomic Best Linear Unbiased Prediction Model Integrating Genome-Wide Association Study Results Animals genomic prediction ssGBLUP pseudo QTNs weighted genomic relationship matrix simulated dataset |
| title | Enhancing Genomic Prediction Accuracy with a Single-Step Genomic Best Linear Unbiased Prediction Model Integrating Genome-Wide Association Study Results |
| title_full | Enhancing Genomic Prediction Accuracy with a Single-Step Genomic Best Linear Unbiased Prediction Model Integrating Genome-Wide Association Study Results |
| title_fullStr | Enhancing Genomic Prediction Accuracy with a Single-Step Genomic Best Linear Unbiased Prediction Model Integrating Genome-Wide Association Study Results |
| title_full_unstemmed | Enhancing Genomic Prediction Accuracy with a Single-Step Genomic Best Linear Unbiased Prediction Model Integrating Genome-Wide Association Study Results |
| title_short | Enhancing Genomic Prediction Accuracy with a Single-Step Genomic Best Linear Unbiased Prediction Model Integrating Genome-Wide Association Study Results |
| title_sort | enhancing genomic prediction accuracy with a single step genomic best linear unbiased prediction model integrating genome wide association study results |
| topic | genomic prediction ssGBLUP pseudo QTNs weighted genomic relationship matrix simulated dataset |
| url | https://www.mdpi.com/2076-2615/15/9/1268 |
| work_keys_str_mv | AT zhixupang enhancinggenomicpredictionaccuracywithasinglestepgenomicbestlinearunbiasedpredictionmodelintegratinggenomewideassociationstudyresults AT wannianwang enhancinggenomicpredictionaccuracywithasinglestepgenomicbestlinearunbiasedpredictionmodelintegratinggenomewideassociationstudyresults AT puhuang enhancinggenomicpredictionaccuracywithasinglestepgenomicbestlinearunbiasedpredictionmodelintegratinggenomewideassociationstudyresults AT hongzhizhang enhancinggenomicpredictionaccuracywithasinglestepgenomicbestlinearunbiasedpredictionmodelintegratinggenomewideassociationstudyresults AT siyingzhang enhancinggenomicpredictionaccuracywithasinglestepgenomicbestlinearunbiasedpredictionmodelintegratinggenomewideassociationstudyresults AT pengkunyang enhancinggenomicpredictionaccuracywithasinglestepgenomicbestlinearunbiasedpredictionmodelintegratinggenomewideassociationstudyresults AT liyingqiao enhancinggenomicpredictionaccuracywithasinglestepgenomicbestlinearunbiasedpredictionmodelintegratinggenomewideassociationstudyresults AT jianhualiu enhancinggenomicpredictionaccuracywithasinglestepgenomicbestlinearunbiasedpredictionmodelintegratinggenomewideassociationstudyresults AT yangyangpan enhancinggenomicpredictionaccuracywithasinglestepgenomicbestlinearunbiasedpredictionmodelintegratinggenomewideassociationstudyresults AT kaijieyang enhancinggenomicpredictionaccuracywithasinglestepgenomicbestlinearunbiasedpredictionmodelintegratinggenomewideassociationstudyresults AT wenzhongliu enhancinggenomicpredictionaccuracywithasinglestepgenomicbestlinearunbiasedpredictionmodelintegratinggenomewideassociationstudyresults |