Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design
Genomic prediction (GP) in plant breeding has the potential to predict and identify the best-performing hybrids based on the genotypes of their parental lines. In a GP experiment, 34 elite inbred lines were selected to make 285 single-cross hybrids in a partial-diallel cross design. These lines repr...
| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/137827 |
| _version_ | 1855521127832485888 |
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| author | Ping Luo Houwen Wang Zhiyong Ni Ruisi Yang Fei Wang Hongjun Yong Lin Zhang Zhiqiang Zhou Wei Song Mingshun Li Jie Yang Jianfeng Weng Zhaodong Meng Degui Zhang Jienan Han Yong Chen Runze Zhang Liwei Wang Meng Zhao Wenwei Gao Xiaoyu Chen Wenjie Li Zhuanfang Hao Junjie Fu Xuecai Zhang Xinhai Li |
| author_browse | Degui Zhang Fei Wang Hongjun Yong Houwen Wang Jianfeng Weng Jie Yang Jienan Han Junjie Fu Lin Zhang Liwei Wang Meng Zhao Mingshun Li Ping Luo Ruisi Yang Runze Zhang Wei Song Wenjie Li Wenwei Gao Xiaoyu Chen Xinhai Li Xuecai Zhang Yong Chen Zhaodong Meng Zhiqiang Zhou Zhiyong Ni Zhuanfang Hao |
| author_facet | Ping Luo Houwen Wang Zhiyong Ni Ruisi Yang Fei Wang Hongjun Yong Lin Zhang Zhiqiang Zhou Wei Song Mingshun Li Jie Yang Jianfeng Weng Zhaodong Meng Degui Zhang Jienan Han Yong Chen Runze Zhang Liwei Wang Meng Zhao Wenwei Gao Xiaoyu Chen Wenjie Li Zhuanfang Hao Junjie Fu Xuecai Zhang Xinhai Li |
| author_sort | Ping Luo |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Genomic prediction (GP) in plant breeding has the potential to predict and identify the best-performing hybrids based on the genotypes of their parental lines. In a GP experiment, 34 elite inbred lines were selected to make 285 single-cross hybrids in a partial-diallel cross design. These lines represented a mini-core collection of Chinese maize germplasm and comprised 18 inbred lines from the Stiff Stalk heterotic group and 16 inbred lines from the Non-Stiff Stalk heterotic group. The parents were genotyped by sequencing and the 285 hybrids were phenotyped for nine yield and yield-related traits at two locations in the summer sowing area (SUS) and three locations in the spring sowing area (SPS) in the main maize-producing regions of China. Multiple GP models were employed to assess the accuracy of trait prediction in the hybrids. By ten-fold cross-validation, the prediction accuracies of yield performance of the hybrids estimated by the genomic best linear unbiased prediction (GBLUP) model in SUS and SPS were 0.51 and 0.46, respectively. The prediction accuracies of the remaining yield-related traits estimated with GBLUP ranged from 0.49 to 0.86 and from 0.53 to 0.89 in SUS and SPS, respectively. When additive, dominance, epistasis effects, genotype-by-environment interaction, and multi-trait effects were incorporated into the prediction model, the prediction accuracy of hybrid yield performance was improved. The ratio of training to testing population and size of training population optimal for yield prediction were determined. Multiple prediction models can improve prediction accuracy in hybrid breeding. |
| format | Journal Article |
| id | CGSpace137827 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1378272025-12-08T10:11:39Z Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design Ping Luo Houwen Wang Zhiyong Ni Ruisi Yang Fei Wang Hongjun Yong Lin Zhang Zhiqiang Zhou Wei Song Mingshun Li Jie Yang Jianfeng Weng Zhaodong Meng Degui Zhang Jienan Han Yong Chen Runze Zhang Liwei Wang Meng Zhao Wenwei Gao Xiaoyu Chen Wenjie Li Zhuanfang Hao Junjie Fu Xuecai Zhang Xinhai Li maize genetics hybrids performance assessment Genomic prediction (GP) in plant breeding has the potential to predict and identify the best-performing hybrids based on the genotypes of their parental lines. In a GP experiment, 34 elite inbred lines were selected to make 285 single-cross hybrids in a partial-diallel cross design. These lines represented a mini-core collection of Chinese maize germplasm and comprised 18 inbred lines from the Stiff Stalk heterotic group and 16 inbred lines from the Non-Stiff Stalk heterotic group. The parents were genotyped by sequencing and the 285 hybrids were phenotyped for nine yield and yield-related traits at two locations in the summer sowing area (SUS) and three locations in the spring sowing area (SPS) in the main maize-producing regions of China. Multiple GP models were employed to assess the accuracy of trait prediction in the hybrids. By ten-fold cross-validation, the prediction accuracies of yield performance of the hybrids estimated by the genomic best linear unbiased prediction (GBLUP) model in SUS and SPS were 0.51 and 0.46, respectively. The prediction accuracies of the remaining yield-related traits estimated with GBLUP ranged from 0.49 to 0.86 and from 0.53 to 0.89 in SUS and SPS, respectively. When additive, dominance, epistasis effects, genotype-by-environment interaction, and multi-trait effects were incorporated into the prediction model, the prediction accuracy of hybrid yield performance was improved. The ratio of training to testing population and size of training population optimal for yield prediction were determined. Multiple prediction models can improve prediction accuracy in hybrid breeding. 2023-12 2024-01-16T23:02:36Z 2024-01-16T23:02:36Z Journal Article https://hdl.handle.net/10568/137827 en Open Access application/pdf Elsevier Luo, P., Wang, H., Ni, Z., Yang, R., Wang, F., Yong, H., Zhang, L., Zhou, Z., Song, W., Li, M., Yang, J., Weng, J., Meng, Z., Zhang, D., Han, J., Chen, Y., Zhang, R., Wang, L., Zhao, M., … Li, X. (2023). Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design. The Crop Journal, 11(6), 1884–1892. https://doi.org/10.1016/j.cj.2023.09.009 |
| spellingShingle | maize genetics hybrids performance assessment Ping Luo Houwen Wang Zhiyong Ni Ruisi Yang Fei Wang Hongjun Yong Lin Zhang Zhiqiang Zhou Wei Song Mingshun Li Jie Yang Jianfeng Weng Zhaodong Meng Degui Zhang Jienan Han Yong Chen Runze Zhang Liwei Wang Meng Zhao Wenwei Gao Xiaoyu Chen Wenjie Li Zhuanfang Hao Junjie Fu Xuecai Zhang Xinhai Li Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design |
| title | Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design |
| title_full | Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design |
| title_fullStr | Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design |
| title_full_unstemmed | Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design |
| title_short | Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design |
| title_sort | genomic prediction of yield performance among single cross maize hybrids using a partial diallel cross design |
| topic | maize genetics hybrids performance assessment |
| url | https://hdl.handle.net/10568/137827 |
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