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Related Experiment Video

Updated: Feb 10, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Predicting genotype environmental range from genome-environment associations.

Stéphanie Manel¹, Marco Andrello¹, Karine Henry²

¹EPHE, PSL Research University, CNRS, UM, SupAgro, IND, INRA, UMR 5175 CEFE, Montpellier, France.

Molecular Ecology

|May 18, 2018

Summary

This study introduces a novel method to predict species environmental ranges using genetic markers. Aridity predictions in beets show wild varieties possess greater stress resistance, offering potential for crop improvement.

Keywords:

genome scan genome-environment association landscape genomics predictive landscape genetics

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Published on: July 27, 2021

Area of Science:

Population Genetics
Genomics
Plant Breeding

Background:

Genome-environment association studies typically identify local adaptation markers individually.
Recent research suggests combining single-locus associations for predictive environmental modeling.
Predicting species' environmental niches from genetic data remains an active research area.

Purpose of the Study:

To develop and validate a novel approach for predicting species' environmental ranges from genetic markers.
To apply this method to predict aridity in wild and cultivated beet populations.
To assess the potential of wild beet genotypes for improving cultivated varieties' aridity tolerance.

Main Methods:

Utilized genome-environment association methods to identify alleles linked to aridity.
Developed a predictive model using the fraction of aridity-associated alleles (I) per individual.
Validated the model on independent wild beet individuals and predicted aridity in cultivated beets.

Main Results:

Identified 66 alleles associated with aridity in beet populations.
The fraction I accurately predicted aridity values in wild beets and was applied to cultivated beets.
Wild beets exhibited higher aridity tolerance than cultivated beets, indicated by wider aridity range and median values.

Conclusions:

The developed method successfully predicts environmental variables (aridity) from genotype data.
Wild beet populations demonstrate superior adaptation to arid conditions compared to cultivated varieties.
Wild beet genetic resources hold significant potential for enhancing cultivated beet drought and aridity resistance.