Improving multi-trait genomic prediction by incorporating local genetic correlations
Abstract Genomic prediction holds significant potential for advancing precision medicine in humans, as well as accelerating genetic improvement in animals and plants. For multi-trait prediction, the conventional multi-trait models are primarily based on global genetic correlations between traits. Wi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-025-07721-9 |
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| author | Jun Teng Tingting Zhai Xinyi Zhang Changheng Zhao Wenwen Wang Hui Tang Chao Ning Yingli Shang Dan Wang Qin Zhang |
| author_facet | Jun Teng Tingting Zhai Xinyi Zhang Changheng Zhao Wenwen Wang Hui Tang Chao Ning Yingli Shang Dan Wang Qin Zhang |
| author_sort | Jun Teng |
| collection | DOAJ |
| description | Abstract Genomic prediction holds significant potential for advancing precision medicine in humans, as well as accelerating genetic improvement in animals and plants. For multi-trait prediction, the conventional multi-trait models are primarily based on global genetic correlations between traits. With the development of local genetic correlation (LGC) estimation methods, it is now possible to analyze LGCs confined to specific genomic regions and it is expected that incorporating LGCs into multi-trait prediction model would enhance the prediction ability. Here, we proposed three models to address this issue and evaluated their performances using simulated data and three real datasets from human, cow, and pig populations. Our results demonstrate that LGCs are heterogeneous across the genome and incorporating LGCs in multi-trait prediction would increase the prediction accuracy by an average of 12.76% ± 2.07% compared to conventional multi-trait genomic prediction method (MTGBLUP) in the real datasets. Our findings highlight the importance of considering LGCs in improving multi-trait genomic prediction. |
| format | Article |
| id | doaj-art-8e0d4d474ca34d9a9ea56642e3d97535 |
| institution | OA Journals |
| issn | 2399-3642 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj-art-8e0d4d474ca34d9a9ea56642e3d975352025-08-20T02:01:39ZengNature PortfolioCommunications Biology2399-36422025-02-018111410.1038/s42003-025-07721-9Improving multi-trait genomic prediction by incorporating local genetic correlationsJun Teng0Tingting Zhai1Xinyi Zhang2Changheng Zhao3Wenwen Wang4Hui Tang5Chao Ning6Yingli Shang7Dan Wang8Qin Zhang9Shandong Provincial Key Laboratory for Livestock Germplasm Innovation & Utilization, College of Animal Science and Technology, Shandong Agricultural UniversityNational Key Laboratory of Wheat Improvement, College of Life Science, Shandong Agricultural UniversityShandong Provincial Key Laboratory for Livestock Germplasm Innovation & Utilization, College of Animal Science and Technology, Shandong Agricultural UniversityShandong Provincial Key Laboratory for Livestock Germplasm Innovation & Utilization, College of Animal Science and Technology, Shandong Agricultural UniversityShandong Provincial Key Laboratory for Livestock Germplasm Innovation & Utilization, College of Animal Science and Technology, Shandong Agricultural UniversityShandong Provincial Key Laboratory for Livestock Germplasm Innovation & Utilization, College of Animal Science and Technology, Shandong Agricultural UniversityShandong Provincial Key Laboratory for Livestock Germplasm Innovation & Utilization, College of Animal Science and Technology, Shandong Agricultural UniversityCollege of Veterinary Medicine, Shandong Agricultural UniversityShandong Provincial Key Laboratory for Livestock Germplasm Innovation & Utilization, College of Animal Science and Technology, Shandong Agricultural UniversityShandong Provincial Key Laboratory for Livestock Germplasm Innovation & Utilization, College of Animal Science and Technology, Shandong Agricultural UniversityAbstract Genomic prediction holds significant potential for advancing precision medicine in humans, as well as accelerating genetic improvement in animals and plants. For multi-trait prediction, the conventional multi-trait models are primarily based on global genetic correlations between traits. With the development of local genetic correlation (LGC) estimation methods, it is now possible to analyze LGCs confined to specific genomic regions and it is expected that incorporating LGCs into multi-trait prediction model would enhance the prediction ability. Here, we proposed three models to address this issue and evaluated their performances using simulated data and three real datasets from human, cow, and pig populations. Our results demonstrate that LGCs are heterogeneous across the genome and incorporating LGCs in multi-trait prediction would increase the prediction accuracy by an average of 12.76% ± 2.07% compared to conventional multi-trait genomic prediction method (MTGBLUP) in the real datasets. Our findings highlight the importance of considering LGCs in improving multi-trait genomic prediction.https://doi.org/10.1038/s42003-025-07721-9 |
| spellingShingle | Jun Teng Tingting Zhai Xinyi Zhang Changheng Zhao Wenwen Wang Hui Tang Chao Ning Yingli Shang Dan Wang Qin Zhang Improving multi-trait genomic prediction by incorporating local genetic correlations Communications Biology |
| title | Improving multi-trait genomic prediction by incorporating local genetic correlations |
| title_full | Improving multi-trait genomic prediction by incorporating local genetic correlations |
| title_fullStr | Improving multi-trait genomic prediction by incorporating local genetic correlations |
| title_full_unstemmed | Improving multi-trait genomic prediction by incorporating local genetic correlations |
| title_short | Improving multi-trait genomic prediction by incorporating local genetic correlations |
| title_sort | improving multi trait genomic prediction by incorporating local genetic correlations |
| url | https://doi.org/10.1038/s42003-025-07721-9 |
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