Metabolic marker-assisted genomic prediction improves hybrid breeding
Hybrid breeding is widely acknowledged as the most effective method for increasing crop yield, particularly in maize and rice. However, a major challenge in hybrid breeding is the selection of desirable combinations from the vast pool of potential crosses. Genomic selection (GS) has emerged as a pow...
| Autores principales: | , , , , , , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Cell Press
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/179152 |
| _version_ | 1855522127811182592 |
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| author | Xu, Yang Yang, Wenyan Qiu, Jie Zhou, Kai Yu, Guangning Zhang, Yuxiang Wang, Xin Jiao, Yuxin Wang, Xinyi Hu, Shujun Zhang, Xuecai Pengcheng Li Lu, Yue Chen, Rujia Tao, Tianyun Yang, Zefeng Xu, Yunbi Xu, Chenwu |
| author_browse | Chen, Rujia Hu, Shujun Jiao, Yuxin Lu, Yue Pengcheng Li Qiu, Jie Tao, Tianyun Wang, Xin Wang, Xinyi Xu, Chenwu Xu, Yang Xu, Yunbi Yang, Wenyan Yang, Zefeng Yu, Guangning Zhang, Xuecai Zhang, Yuxiang Zhou, Kai |
| author_facet | Xu, Yang Yang, Wenyan Qiu, Jie Zhou, Kai Yu, Guangning Zhang, Yuxiang Wang, Xin Jiao, Yuxin Wang, Xinyi Hu, Shujun Zhang, Xuecai Pengcheng Li Lu, Yue Chen, Rujia Tao, Tianyun Yang, Zefeng Xu, Yunbi Xu, Chenwu |
| author_sort | Xu, Yang |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Hybrid breeding is widely acknowledged as the most effective method for increasing crop yield, particularly in maize and rice. However, a major challenge in hybrid breeding is the selection of desirable combinations from the vast pool of potential crosses. Genomic selection (GS) has emerged as a powerful tool to tackle this challenge, but its success in practical breeding depends on prediction accuracy. Several strategies have been explored to enhance prediction accuracy for complex traits, such as the incorporation of functional markers and multi-omics data. Metabolome-wide association studies (MWAS) help to identify metabolites that are closely linked to phenotypes, known as metabolic markers. However, the use of preselected metabolic markers from parental lines to predict hybrid performance has not yet been explored. In this study, we developed a novel approach called metabolic marker-assisted genomic prediction (MM_GP), which incorporates significant metabolites identified from MWAS into GS models to improve the accuracy of genomic hybrid prediction. In maize and rice hybrid populations, MM_GP outperformed genomic prediction (GP) for all traits, regardless of the method used (genomic best linear unbiased prediction or extreme gradient boosting). On average, MM_GP demonstrated 4.6% and 13.6% higher predictive abilities than GP for maize and rice, respectively. MM_GP could also match or even surpass the predictive ability of M_GP (integrated genomic-metabolomic prediction) for most traits. In maize, the integration of only six metabolic markers significantly associated with multiple traits resulted in 5.0% and 3.1% higher average predictive ability compared with GP and M_GP, respectively. With advances in high-throughput metabolomics technologies and prediction models, this approach holds great promise for revolutionizing genomic hybrid breeding by enhancing its accuracy and efficiency. |
| format | Journal Article |
| id | CGSpace179152 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Cell Press |
| publisherStr | Cell Press |
| record_format | dspace |
| spelling | CGSpace1791522025-12-22T02:05:32Z Metabolic marker-assisted genomic prediction improves hybrid breeding Xu, Yang Yang, Wenyan Qiu, Jie Zhou, Kai Yu, Guangning Zhang, Yuxiang Wang, Xin Jiao, Yuxin Wang, Xinyi Hu, Shujun Zhang, Xuecai Pengcheng Li Lu, Yue Chen, Rujia Tao, Tianyun Yang, Zefeng Xu, Yunbi Xu, Chenwu genomics forecasting hybrids breeding metabolome association mapping marker-assisted selection maize genome-wide association studies Hybrid breeding is widely acknowledged as the most effective method for increasing crop yield, particularly in maize and rice. However, a major challenge in hybrid breeding is the selection of desirable combinations from the vast pool of potential crosses. Genomic selection (GS) has emerged as a powerful tool to tackle this challenge, but its success in practical breeding depends on prediction accuracy. Several strategies have been explored to enhance prediction accuracy for complex traits, such as the incorporation of functional markers and multi-omics data. Metabolome-wide association studies (MWAS) help to identify metabolites that are closely linked to phenotypes, known as metabolic markers. However, the use of preselected metabolic markers from parental lines to predict hybrid performance has not yet been explored. In this study, we developed a novel approach called metabolic marker-assisted genomic prediction (MM_GP), which incorporates significant metabolites identified from MWAS into GS models to improve the accuracy of genomic hybrid prediction. In maize and rice hybrid populations, MM_GP outperformed genomic prediction (GP) for all traits, regardless of the method used (genomic best linear unbiased prediction or extreme gradient boosting). On average, MM_GP demonstrated 4.6% and 13.6% higher predictive abilities than GP for maize and rice, respectively. MM_GP could also match or even surpass the predictive ability of M_GP (integrated genomic-metabolomic prediction) for most traits. In maize, the integration of only six metabolic markers significantly associated with multiple traits resulted in 5.0% and 3.1% higher average predictive ability compared with GP and M_GP, respectively. With advances in high-throughput metabolomics technologies and prediction models, this approach holds great promise for revolutionizing genomic hybrid breeding by enhancing its accuracy and efficiency. 2025-03 2025-12-21T22:46:45Z 2025-12-21T22:46:45Z Journal Article https://hdl.handle.net/10568/179152 en Open Access application/pdf Cell Press Plant Communications Shanghai Editorial Xu, Y., Yang, W., Qiu, J., Zhou, K., Yu, G., Zhang, Y., Wang, X., Jiao, Y., Wang, X., Hu, S., Zhang, X., Li, P., Lu, Y., Chen, R., Tao, T., Yang, Z., Xu, Y., & Xu, C. (2025). Metabolic marker-assisted genomic prediction improves hybrid breeding. Plant Communications, 6(3), 101199. https://doi.org/10.1016/j.xplc.2024.101199 |
| spellingShingle | genomics forecasting hybrids breeding metabolome association mapping marker-assisted selection maize genome-wide association studies Xu, Yang Yang, Wenyan Qiu, Jie Zhou, Kai Yu, Guangning Zhang, Yuxiang Wang, Xin Jiao, Yuxin Wang, Xinyi Hu, Shujun Zhang, Xuecai Pengcheng Li Lu, Yue Chen, Rujia Tao, Tianyun Yang, Zefeng Xu, Yunbi Xu, Chenwu Metabolic marker-assisted genomic prediction improves hybrid breeding |
| title | Metabolic marker-assisted genomic prediction improves hybrid breeding |
| title_full | Metabolic marker-assisted genomic prediction improves hybrid breeding |
| title_fullStr | Metabolic marker-assisted genomic prediction improves hybrid breeding |
| title_full_unstemmed | Metabolic marker-assisted genomic prediction improves hybrid breeding |
| title_short | Metabolic marker-assisted genomic prediction improves hybrid breeding |
| title_sort | metabolic marker assisted genomic prediction improves hybrid breeding |
| topic | genomics forecasting hybrids breeding metabolome association mapping marker-assisted selection maize genome-wide association studies |
| url | https://hdl.handle.net/10568/179152 |
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