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Updated: Sep 19, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Environmental data provide marginal benefit for predicting climate adaptation.

Forrest Li^1,2, Daniel J Gates^1,3, Edward S Buckler^4,5

¹Department of Evolution and Ecology, University of California Davis, Davis, California, United States of America.

|June 9, 2025

Summary

This summary is machine-generated.

Genomic data best predicts maize yield, with environmental data offering little extra insight. Environmental Genome-Wide Association Studies (envGWAS) can still identify novel adaptation loci.

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Area of Science:

Genomics
Plant Breeding
Climate Change Adaptation

Background:

Climate change presents significant challenges to both wild and cultivated species.
Genomic tools are vital for identifying adaptive loci in conservation and breeding for future climates.
Traditional maize varieties offer a valuable resource for studying adaptation.

Purpose of the Study:

To evaluate the utility of climate and genomic data for identifying adaptive alleles in traditional maize varieties.
To compare different approaches for predicting pre-adaptation and identifying loci associated with climate adaptation.
To assess the value of environmental data and envGWAS-prioritized loci for variety selection.

Main Methods:

Utilized genotype data to predict environmental characteristics of germplasm collections.
Employed environmental Genome-Wide Association Studies (envGWAS) to identify loci linked to climatic gradients.
Compared the predictive power of genome-wide relatedness, population structure, envGWAS loci, and environment-of-origin data for maize yield traits.

Main Results:

Maize yield traits were most accurately predicted by genome-wide relatedness and population structure.
Incorporating envGWAS variants or environment-of-origin data provided minimal additional predictive value for yield.
Environmental GWAS proved effective in identifying loci associated with historical climate divergence.

Conclusions:

Environmental data alone offers limited benefit for predicting fitness-related phenotypes in maize.
Environmental GWAS is a powerful method for discovering novel loci associated with adaptation.
High-density genotyping combined with envGWAS enhances the identification of adaptive loci.