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 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.Author summary: Populations of natural and cultivated plant and animal populations will be affected by more extreme climate events such as drought and flooding in the future. We explore whether characterization of the environment-of-origin of each accession in a large sample of traditional maize germplasm can be used to accelerate conservation and breeding efforts for adaptation. We compare the utility of genotype and environmental data for predicting fitness of individuals in a number of common garden trials. We find that environment-of-origin data and genome scans for loci that associate with abiotic environmental variables provide surprisingly little benefit to prioritizing accessions for improvement, despite clear evidence of environmental adaptation in these accessions. These results provide important practical insight into the use of gene banks for climate adaptation.