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 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 data 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.

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Publication Details

Journal
PLoS Genetics
Published
2025-06-09
DOI
https://doi.org/10.1371/journal.pgen.1011714
Citations
10
Primary Topic
Climate change impacts on agriculture
Type
article
Field-Weighted Citation Impact
30.27

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article

Environmental data provide marginal benefit for predicting climate adaptation

Matthew B. Hufford, Daniel J. Gates, Edward S. Buckler, Rubén Rellán‐Álvarez et al.
10 citations
PLoS Genetics
Climate change impacts on agriculture
30.27
article

Environmental data provide marginal benefit for predicting climate adaptation

Matthew B. Hufford, Daniel J. Gates, Edward S. Buckler, Rubén Rellán‐Álvarez, Samantha J. Snodgrass, Garrett M. Janzen, Ruairidh J. H. Sawers, Martha C. Willcox, Forrest Li, Kai Sonder, Daniel E. Runcie, Sarah Hearne, Jeffrey Ross‐Ibarra, Fausto Rodríguez-Zapata, J. Alberto Romero Navarro
article en
10 citations

Abstract

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 data 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.

PLoS GeneticsVol. 21(6)
Pennsylvania State University (US), United States Department of Agriculture (US), North Carolina State University (US), Iowa State University (US), Cornell University (US), Centro Internacional de Mejoramiento de Maíz Y Trigo (MX), Robert W. Holley Center for Agriculture & Health (US), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (MX), University of California, Davis (US)
National Science Foundation, U.S. Department of Agriculture, Bill and Melinda Gates Foundation
Climate action
Openalex Percentile: Top 0%
Climate change impacts on agriculture
30.27
DOIOpen Access PDFOpenAlex
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