Leveraging automated machine learning for environmental data-driven genetic analysis and genomic prediction in maize hybrids
Genotype, environment, and genotype-by-environment (GxE) interactions play a critical role in shaping crop phenotypes. Here, a large-scale, multi-environment hybrid maize dataset is used to construct and validate an automated machine learning framework that integrates environmental and genomic data...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Wiley-VCH Verlag
2025
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| Online Access: | https://hdl.handle.net/10568/179136 |
| _version_ | 1855522108114731008 |
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| author | He, Kunhui Yu, Tingxi Gao, Shang Chen, Shoukun Li, Liang Zhang, Xuecai Huang, Changling Xu, Yunbi Wang, Jiankang Boddupalli, Prasanna Hearne, Sarah Li, Xinhai Li, Huihui |
| author_browse | Boddupalli, Prasanna Chen, Shoukun Gao, Shang He, Kunhui Hearne, Sarah Huang, Changling Li, Huihui Li, Liang Li, Xinhai Wang, Jiankang Xu, Yunbi Yu, Tingxi Zhang, Xuecai |
| author_facet | He, Kunhui Yu, Tingxi Gao, Shang Chen, Shoukun Li, Liang Zhang, Xuecai Huang, Changling Xu, Yunbi Wang, Jiankang Boddupalli, Prasanna Hearne, Sarah Li, Xinhai Li, Huihui |
| author_sort | He, Kunhui |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Genotype, environment, and genotype-by-environment (GxE) interactions play a critical role in shaping crop phenotypes. Here, a large-scale, multi-environment hybrid maize dataset is used to construct and validate an automated machine learning framework that integrates environmental and genomic data for improved accuracy and efficiency in genetic analyses and genomic predictions. Dimensionality-reduced environmental parameters (RD_EPs) aligned with developmental stages are applied to establish linear relationships between RD_EPs and traits to assess the influence of environment on phenotype. Genome-wide association study identifies 539 phenotypic plasticity trait-associated markers (PP-TAMs), 223 environmental stability TAMs (Main-TAMs), and 92 GxE-TAMs, revealing distinct genetic bases for PP and GxE interactions. Training genomic prediction models with both TAMs and RD_EPs increase prediction accuracy by 14.02% to 28.42% over that of genome-wide marker approaches. These results demonstrate the potential of utilizing environmental data for improving genetic analysis and genomic selection, offering a scalable approach for developing climate-adaptive maize varieties. |
| format | Journal Article |
| id | CGSpace179136 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Wiley-VCH Verlag |
| publisherStr | Wiley-VCH Verlag |
| record_format | dspace |
| spelling | CGSpace1791362025-12-22T02:05:31Z Leveraging automated machine learning for environmental data-driven genetic analysis and genomic prediction in maize hybrids He, Kunhui Yu, Tingxi Gao, Shang Chen, Shoukun Li, Liang Zhang, Xuecai Huang, Changling Xu, Yunbi Wang, Jiankang Boddupalli, Prasanna Hearne, Sarah Li, Xinhai Li, Huihui environment data genetics marker-assisted selection genotype environment interaction machine learning maize hybrids Genotype, environment, and genotype-by-environment (GxE) interactions play a critical role in shaping crop phenotypes. Here, a large-scale, multi-environment hybrid maize dataset is used to construct and validate an automated machine learning framework that integrates environmental and genomic data for improved accuracy and efficiency in genetic analyses and genomic predictions. Dimensionality-reduced environmental parameters (RD_EPs) aligned with developmental stages are applied to establish linear relationships between RD_EPs and traits to assess the influence of environment on phenotype. Genome-wide association study identifies 539 phenotypic plasticity trait-associated markers (PP-TAMs), 223 environmental stability TAMs (Main-TAMs), and 92 GxE-TAMs, revealing distinct genetic bases for PP and GxE interactions. Training genomic prediction models with both TAMs and RD_EPs increase prediction accuracy by 14.02% to 28.42% over that of genome-wide marker approaches. These results demonstrate the potential of utilizing environmental data for improving genetic analysis and genomic selection, offering a scalable approach for developing climate-adaptive maize varieties. 2025-05-08 2025-12-21T21:19:03Z 2025-12-21T21:19:03Z Journal Article https://hdl.handle.net/10568/179136 en Open Access application/pdf Wiley-VCH Verlag He, K., Yu, T., Gao, S., Chen, S., Li, L., Zhang, X., Huang, C., Xu, Y., Wang, J., Prasanna, B. M., Hearne, S., Li, X., & Li, H. (2025). Leveraging automated machine learning for environmental data-driven genetic analysis and genomic prediction in maize hybrids. Advanced Science, 12(17), 2412423. https://doi.org/10.1002/advs.202412423 |
| spellingShingle | environment data genetics marker-assisted selection genotype environment interaction machine learning maize hybrids He, Kunhui Yu, Tingxi Gao, Shang Chen, Shoukun Li, Liang Zhang, Xuecai Huang, Changling Xu, Yunbi Wang, Jiankang Boddupalli, Prasanna Hearne, Sarah Li, Xinhai Li, Huihui Leveraging automated machine learning for environmental data-driven genetic analysis and genomic prediction in maize hybrids |
| title | Leveraging automated machine learning for environmental data-driven genetic analysis and genomic prediction in maize hybrids |
| title_full | Leveraging automated machine learning for environmental data-driven genetic analysis and genomic prediction in maize hybrids |
| title_fullStr | Leveraging automated machine learning for environmental data-driven genetic analysis and genomic prediction in maize hybrids |
| title_full_unstemmed | Leveraging automated machine learning for environmental data-driven genetic analysis and genomic prediction in maize hybrids |
| title_short | Leveraging automated machine learning for environmental data-driven genetic analysis and genomic prediction in maize hybrids |
| title_sort | leveraging automated machine learning for environmental data driven genetic analysis and genomic prediction in maize hybrids |
| topic | environment data genetics marker-assisted selection genotype environment interaction machine learning maize hybrids |
| url | https://hdl.handle.net/10568/179136 |
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