ArticlePLoS genetics2025
Environmental data provide marginal benefit for predicting climate adaptation.
Article in PLoS genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The trial behind it
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Who cites it
11 citing papers in PubMed.
- The MexMAGIC population reveals the genetic architecture of traits exhibiting clinal variation in Mexican native maize.The New phytologist · 2026Article
- Putatively Adaptive and Genome-Wide Genetic Variation Often Rank Populations as Similarly Distinctive.Molecular ecology · 2026Article
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Functional genomic map of local adaptation in sorghum to guide allele mining.bioRxiv : the preprint server for biology · 2026Article
- Variation in responses to temperature in admixed Populus genotypes predicts geographic shifts in regions where hybrids are favored.The New phytologist · 2026Article
- Editorial: Utilizing machine learning with phenotypic and genotypic data to enhance effective breeding in agricultural and horticultural crops.Frontiers in plant science · 2026Article
- Review
- Using landscape genomics to infer genomic regions involved in environmental adaptation of soybean genebank accessions.BMC plant biology · 2025Article
- Local Climate Adaptation in Chinese Indigenous Pig Genomes.Animals : an open access journal from MDPI · 2025Article
- Environmental data provide marginal benefit for predicting climate adaptation.PLoS genetics · 2025Article
Corrections and comments
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Authors and funding
15 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.