ArticleMolecular biology and evolution2013
Testing for associations between loci and environmental gradients using latent factor mixed models.
Article in Molecular biology and evolution, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 330 papers, 1 of them a synthesis that pooled it.
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Who cites it
330 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Genetic Structure and Signs of Local Adaptation in Icelandic Brown Trout Populations.Ecology and evolution · 2026Article
- Integrating landscape genomics and machine learning to analyze the genetic structure and specific molecular markers of Chinese dairy goats (Capra hircus).Journal of animal science and biotechnology · 2026Article
- Environmental Association Analyses in Plants and the Influence of Methodological Decisions.Molecular ecology · 2026Review
- Putatively Adaptive and Genome-Wide Genetic Variation Often Rank Populations as Similarly Distinctive.Molecular ecology · 2026Article
- How Did a Size-Divergent Water Strider Species Group Exhibiting an East Asia-Eastern North America Disjunction Originate and Diversify?Molecular ecology · 2026Article
- Integrating whole-genome resequencing data reveals adaptive selection signatures under extreme environments of goat populations in China.BMC genomics · 2026Article
- Comparative plastome analysis and plastomic phylogeny of Moraceae with expanded species-level sampling.BMC plant biology · 2026Article
- Adaptation across an extreme elevational gradient in Andean leaf-eared mice, the world's highest-dwelling mammal.Science (New York, N.Y.) · 2026Article
- Ancient inversion polymorphisms are locally adaptive in a widespread butterfly species.Science advances · 2026Article
- Haplotype-resolved genome assembly sheds light on the evolutionary history of autohexaploid Tripidium arundinaceum.Nature communications · 2026Article
- Natural variation inScience advances · 2026Article
- Bringing environment back into human evolution: why human genetics needs genome-environment association studies.Human genetics · 2026Review
- Different Paths, Similar Pressures: Divergent Drivers of Genetic Diversity Despite Convergent Genomic Signatures of Selection in Response to Urban Intensity in Two Oligolectic Bee Species.Molecular ecology · 2026Article
- Decoding the genomic basis of adaptive capacity and vulnerability in the high-altitudePlant diversity · 2026Article
- Not Just a Matter of Space: Integrating Ecological Niche Modeling With Genotype-Environment Associations Suggests High Maladaptation Risks Under Climate Change for a Microendemic Malagasy Frog.Ecology and evolution · 2026Article
- Re-Interpreting Genetic Offset: Quantifying the Least Required Evolutionary Rate Under Climate Change at the Mediterranean Range Margin of European Beech.Evolutionary applications · 2026Article
270 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adaptation to local environments often occurs through natural selection acting on a large number of loci, each having a weak phenotypic effect. One way to detect these loci is to identify genetic polymorphisms that exhibit high correlation with environmental variables used as proxies for ecological pressures. Here, we propose new algorithms based on population genetics, ecological modeling, and statistical learning techniques to screen genomes for signatures of local adaptation. Implemented in the computer program "latent factor mixed model" (LFMM), these algorithms employ an approach in which population structure is introduced using unobserved variables. These fast and computationally efficient algorithms detect correlations between environmental and genetic variation while simultaneously inferring background levels of population structure. Comparing these new algorithms with related methods provides evidence that LFMM can efficiently estimate random effects due to population history and isolation-by-distance patterns when computing gene-environment correlations, and decrease the number of false-positive associations in genome scans. We then apply these models to plant and human genetic data, identifying several genes with functions related to development that exhibit strong correlations with climatic gradients.
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