Enhancing across-population genomic prediction for maize hybrids
In crop breeding, genomic selection (GS) serves as a powerful tool for predicting unknown phenotypes by using genome-wide markers, aimed at enhancing genetic gain for quantitative traits. However, in practical applications of GS, predictions are not always made within populations or for individuals...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
MDPI
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/170017 |
| _version_ | 1855524474240106496 |
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| author | Guangning Yu Furong Li Xin Wang Yuxiang Zhang Kai Zhou Wenyan Yang Xiusheng Guan Xuecai Zhang Chenwu Xu Yang Xu |
| author_browse | Chenwu Xu Furong Li Guangning Yu Kai Zhou Wenyan Yang Xin Wang Xiusheng Guan Xuecai Zhang Yang Xu Yuxiang Zhang |
| author_facet | Guangning Yu Furong Li Xin Wang Yuxiang Zhang Kai Zhou Wenyan Yang Xiusheng Guan Xuecai Zhang Chenwu Xu Yang Xu |
| author_sort | Guangning Yu |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | In crop breeding, genomic selection (GS) serves as a powerful tool for predicting unknown phenotypes by using genome-wide markers, aimed at enhancing genetic gain for quantitative traits. However, in practical applications of GS, predictions are not always made within populations or for individuals that are genetically similar to the training population. Therefore, exploring possibilities and effective strategies for across-population prediction becomes an attractive avenue for applying GS technology in breeding practices. In this study, we used an existing maize population of 5820 hybrids as the training population to predict another population of 523 maize hybrids using the GBLUP and BayesB models. We evaluated the impact of optimizing the training population based on the genetic relationship between the training and breeding populations on the accuracy of across-population predictions. The results showed that the prediction accuracy improved to some extent with varying training population sizes. However, the optimal size of the training population differed for various traits. Additionally, we proposed a population structure-based across-population genomic prediction (PSAPGP) strategy, which integrates population structure as a fixed effect in the GS models. Principal component analysis, clustering, and Q-matrix analysis were used to assess the population structure. Notably, when the Q-matrix was used, the across-population prediction exhibited the best performance, with improvements ranging from 8 to 11% for ear weight, ear grain weight and plant height. This is a promising strategy for reducing phenotyping costs and enhancing maize hybrid breeding efficiency. |
| format | Journal Article |
| id | CGSpace170017 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1700172025-12-08T10:29:22Z Enhancing across-population genomic prediction for maize hybrids Guangning Yu Furong Li Xin Wang Yuxiang Zhang Kai Zhou Wenyan Yang Xiusheng Guan Xuecai Zhang Chenwu Xu Yang Xu genomics population population structure maize hybrids In crop breeding, genomic selection (GS) serves as a powerful tool for predicting unknown phenotypes by using genome-wide markers, aimed at enhancing genetic gain for quantitative traits. However, in practical applications of GS, predictions are not always made within populations or for individuals that are genetically similar to the training population. Therefore, exploring possibilities and effective strategies for across-population prediction becomes an attractive avenue for applying GS technology in breeding practices. In this study, we used an existing maize population of 5820 hybrids as the training population to predict another population of 523 maize hybrids using the GBLUP and BayesB models. We evaluated the impact of optimizing the training population based on the genetic relationship between the training and breeding populations on the accuracy of across-population predictions. The results showed that the prediction accuracy improved to some extent with varying training population sizes. However, the optimal size of the training population differed for various traits. Additionally, we proposed a population structure-based across-population genomic prediction (PSAPGP) strategy, which integrates population structure as a fixed effect in the GS models. Principal component analysis, clustering, and Q-matrix analysis were used to assess the population structure. Notably, when the Q-matrix was used, the across-population prediction exhibited the best performance, with improvements ranging from 8 to 11% for ear weight, ear grain weight and plant height. This is a promising strategy for reducing phenotyping costs and enhancing maize hybrid breeding efficiency. 2024 2025-01-26T22:13:35Z 2025-01-26T22:13:35Z Journal Article https://hdl.handle.net/10568/170017 en Open Access application/pdf MDPI Yu, G., Li, F., Wang, X., Zhang, Y., Zhou, K., Yang, W., Guan, X., Zhang, X., Xu, C., & Xu, Y. (2024). Enhancing Across-Population Genomic Prediction for Maize Hybrids. Plants, 13(21), 3105. https://doi.org/10.3390/plants13213105 |
| spellingShingle | genomics population population structure maize hybrids Guangning Yu Furong Li Xin Wang Yuxiang Zhang Kai Zhou Wenyan Yang Xiusheng Guan Xuecai Zhang Chenwu Xu Yang Xu Enhancing across-population genomic prediction for maize hybrids |
| title | Enhancing across-population genomic prediction for maize hybrids |
| title_full | Enhancing across-population genomic prediction for maize hybrids |
| title_fullStr | Enhancing across-population genomic prediction for maize hybrids |
| title_full_unstemmed | Enhancing across-population genomic prediction for maize hybrids |
| title_short | Enhancing across-population genomic prediction for maize hybrids |
| title_sort | enhancing across population genomic prediction for maize hybrids |
| topic | genomics population population structure maize hybrids |
| url | https://hdl.handle.net/10568/170017 |
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