Environmental data provide marginal benefit for predicting climate adaptation
Climate change poses a major challenge for both natural and cultivated species. Genomic tools are increasingly used in both conservation and breeding to identify adaptive loci that can be used to guide management in future climates. Here, we study the utility of climate and genomic data for identify...
| Autores principales: | , , , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Public Library of Science
2025
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| Acceso en línea: | https://hdl.handle.net/10568/179105 |
| _version_ | 1855513767529414656 |
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| author | Li, Forrest Gates, Daniel J. Buckler, Edward Hufford, Matthew Janzen, Garrett Rellán-Álvarez, Rubén Rodríguez-Zapata, Fausto Romero Navarro, J. Alberto Sawers, Ruairidh Snodgrass, Samantha Sonder, Kai Willcox, Martha Hearne, Sarah Ross-Ibarra, Jeffrey Runcie, Daniel |
| author_browse | Buckler, Edward Gates, Daniel J. Hearne, Sarah Hufford, Matthew Janzen, Garrett Li, Forrest Rellán-Álvarez, Rubén Rodríguez-Zapata, Fausto Romero Navarro, J. Alberto Ross-Ibarra, Jeffrey Runcie, Daniel Sawers, Ruairidh Snodgrass, Samantha Sonder, Kai Willcox, Martha |
| author_facet | Li, Forrest Gates, Daniel J. Buckler, Edward Hufford, Matthew Janzen, Garrett Rellán-Álvarez, Rubén Rodríguez-Zapata, Fausto Romero Navarro, J. Alberto Sawers, Ruairidh Snodgrass, Samantha Sonder, Kai Willcox, Martha Hearne, Sarah Ross-Ibarra, Jeffrey Runcie, Daniel |
| author_sort | Li, Forrest |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Climate change poses a major challenge for both natural and cultivated species. Genomic tools are increasingly used in both conservation and breeding to identify adaptive loci that can be used to guide management in future climates. Here, we study the utility of climate and genomic data for identifying promising alleles using common gardens of a large, geographically diverse sample of traditional maize varieties to evaluate multiple approaches. First, we used genotype data to predict environmental characteristics of germplasm collections to identify varieties that may be pre-adapted to target environments. Second, we used environmental GWAS (envGWAS) to identify loci associated with historical divergence along climatic gradients. Finally, we compared the value of environmental data and envGWAS-prioritized loci to genomic data for prioritizing traditional varieties. We find that maize yield traits are best predicted by genome-wide relatedness and population structure, and that incorporating envGWAS-identified variants or environment-of-origin provide little additional predictive information. While our results suggest that environmental data provide limited benefit in predicting fitness-related phenotypes, environmental GWAS is nonetheless a potentially powerful approach to identify individual novel loci associated with adaptation, especially when coupled with high density genotyping. |
| format | Journal Article |
| id | CGSpace179105 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Public Library of Science |
| publisherStr | Public Library of Science |
| record_format | dspace |
| spelling | CGSpace1791052025-12-20T02:00:37Z Environmental data provide marginal benefit for predicting climate adaptation Li, Forrest Gates, Daniel J. Buckler, Edward Hufford, Matthew Janzen, Garrett Rellán-Álvarez, Rubén Rodríguez-Zapata, Fausto Romero Navarro, J. Alberto Sawers, Ruairidh Snodgrass, Samantha Sonder, Kai Willcox, Martha Hearne, Sarah Ross-Ibarra, Jeffrey Runcie, Daniel climate change adaptation environment genome-wide association studies maize genetic diversity (as resource) abiotic stress Climate change poses a major challenge for both natural and cultivated species. Genomic tools are increasingly used in both conservation and breeding to identify adaptive loci that can be used to guide management in future climates. Here, we study the utility of climate and genomic data for identifying promising alleles using common gardens of a large, geographically diverse sample of traditional maize varieties to evaluate multiple approaches. First, we used genotype data to predict environmental characteristics of germplasm collections to identify varieties that may be pre-adapted to target environments. Second, we used environmental GWAS (envGWAS) to identify loci associated with historical divergence along climatic gradients. Finally, we compared the value of environmental data and envGWAS-prioritized loci to genomic data for prioritizing traditional varieties. We find that maize yield traits are best predicted by genome-wide relatedness and population structure, and that incorporating envGWAS-identified variants or environment-of-origin provide little additional predictive information. While our results suggest that environmental data provide limited benefit in predicting fitness-related phenotypes, environmental GWAS is nonetheless a potentially powerful approach to identify individual novel loci associated with adaptation, especially when coupled with high density genotyping. 2025-06 2025-12-19T22:24:10Z 2025-12-19T22:24:10Z Journal Article https://hdl.handle.net/10568/179105 en Open Access application/pdf Public Library of Science Li, F., Gates, D. J., Buckler, E. S., Hufford, M. B., Janzen, G. M., Rellán-Álvarez, R., Rodríguez-Zapata, F., Romero Navarro, J. A., Sawers, R. J. H., Snodgrass, S. J., Sonder, K., Willcox, M. C., Hearne, S. J., Ross-Ibarra, J., & Runcie, D. E. (2025). Environmental data provide marginal benefit for predicting climate adaptation. PLoS Genetics, 21(6), e1011714. https://doi.org/10.1371/journal.pgen.1011714 |
| spellingShingle | climate change adaptation environment genome-wide association studies maize genetic diversity (as resource) abiotic stress Li, Forrest Gates, Daniel J. Buckler, Edward Hufford, Matthew Janzen, Garrett Rellán-Álvarez, Rubén Rodríguez-Zapata, Fausto Romero Navarro, J. Alberto Sawers, Ruairidh Snodgrass, Samantha Sonder, Kai Willcox, Martha Hearne, Sarah Ross-Ibarra, Jeffrey Runcie, Daniel Environmental data provide marginal benefit for predicting climate adaptation |
| title | Environmental data provide marginal benefit for predicting climate adaptation |
| title_full | Environmental data provide marginal benefit for predicting climate adaptation |
| title_fullStr | Environmental data provide marginal benefit for predicting climate adaptation |
| title_full_unstemmed | Environmental data provide marginal benefit for predicting climate adaptation |
| title_short | Environmental data provide marginal benefit for predicting climate adaptation |
| title_sort | environmental data provide marginal benefit for predicting climate adaptation |
| topic | climate change adaptation environment genome-wide association studies maize genetic diversity (as resource) abiotic stress |
| url | https://hdl.handle.net/10568/179105 |
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