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Updated: May 12, 2026
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Testing for associations between loci and environmental gradients using latent factor mixed models.
Eric Frichot1, Sean D Schoville, Guillaume Bouchard
1TIMC-IMAG UMR 5525, Université Joseph Fourier Grenoble, Centre National de la Recherche Scientifique, Grenoble, France.
Molecular Biology and Evolution
|April 2, 2013
Summary
New algorithms identify genes linked to local adaptation by correlating genetic data with environmental factors. This approach improves accuracy in genome scans for evolutionary studies.
Area of Science:
- Evolutionary biology
- Population genetics
- Ecological modeling
Background:
- Local adaptation is driven by natural selection on numerous genes with small effects.
- Identifying genes under local adaptation requires correlating genetic variation with environmental pressures.
Purpose of the Study:
- To develop novel algorithms for detecting signatures of local adaptation in genomes.
- To improve the accuracy of genome scans by accounting for population structure.
Main Methods:
- Developed algorithms using population genetics, ecological modeling, and statistical learning.
- Implemented algorithms in the latent factor mixed model (LFMM) program.
- Accounted for population structure using unobserved variables.
Main Results:
- LFMM efficiently estimates population structure and gene-environment correlations.
- The new algorithms reduce false-positive associations in genome scans.
- Applied models to plant and human data, identifying genes correlated with climatic gradients.
Conclusions:
- LFMM provides a robust method for detecting local adaptation.
- Identified genes related to development show strong correlations with climate.
- This approach enhances our understanding of evolutionary processes.
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