Environmental data provide marginal benefit for predicting climate adaptation.

Authors: LI, FOREST - University Of California; GATES, DANIEL - University Of California; Buckler Iv, Edward; HUFFORD, MATTHEW - Iowa State University; JANZEN, GARRETT - Iowa State University; RELLAN-ALVAREZ, RUBEN - North Carolina State University; RODRIGUEZ-ZAPATA, FAUSTO - North Carolina State University; ROMERO NAVARRO, J. ALBERTO - Cornell University; SAWERS, RAUIRIDH - Pennsylvania State University; SNODGRASS, SAMANTHA - University Of California; SONDER, KAI - International Maize & Wheat Improvement Center (CIMMYT); WILCOX, MARTHA - International Maize & Wheat Improvement Center (CIMMYT); HEARNE, SARAH - International Maize & Wheat Improvement Center (CIMMYT); ROSS-IBARRA, JEFFREY - University Of California; RUNCIE, DANIEL - University Of California

Submitted to: PLoS Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/1/2025
Publication Date: 6/9/2025
Citation: Li, F., Gates, D.J., Buckler Iv, E.S., Hufford, M.B., Janzen, G.M., Rellan-Alvarez, R., Rodriguez-Zapata, F., Romero Navarro, J., Sawers, R.J., Snodgrass, S.J., Sonder, K., Wilcox, M.C., Hearne, S.J., Ross-Ibarra, J., Runcie, D. 2025. Environmental data provide marginal benefit for predicting climate adaptation.. PLoS Genetics. https://doi.org/10.1371/journal.pgen.1011714.
DOI: https://doi.org/10.1371/journal.pgen.1011714

Interpretive Summary: 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.

Technical Abstract: Maintaining crop yields in the face of climate change is a major challenge facing plant breeding today. Considerable genetic variation exists in ex-situ collections of traditional crop varieties, but identifying adaptive loci and testing their agronomic performance in large populations in field trials is costly. Here, we study the utility of climate and genomic data for identifying promising traditional varieties to incorporate into maize breeding programs. To do so, we use phenotypic data from more than 4,000 traditional maize varieties grown in 13 trial environments. First, we used genotype data to predict environmental characteristics of germplasm collections to identify varieties that may be locally adapted to target environments. Second, we used environmental GWAS (envGWAS) to identify genetic loci associated with historical divergence along climatic gradients, such as the putative heat shock protein hsftf9 and the large-scale adaptive inversion Inv4m. 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 genomic data, and that envGWAS-identified variants provide little direct predictive information over patterns of population structure. We also find that adding environment-of-origin variables does not improve yield component prediction over kinship or population structure alone, but could be a useful selection proxy in the absence of sequencing data. While our results suggest little utility of environmental data for selecting traditional varieties to incorporate in breeding programs, environmental GWAS is nonetheless a potentially powerful approach to identify individual novel loci for maize improvement, especially when coupled with high density genotyping.