LFMM

LFMM identifies correlations between genetic polymorphisms and environmental variables to detect signatures of local adaptation.

Key Features:

• Integration of Population Genetics and Ecological Modeling: LFMM correlates genetic polymorphisms with environmental variables to screen genomes for signs of local adaptation, capturing weak effects across numerous loci.
• Use of Unobserved Variables: LFMM models population structure via unobserved latent factors to control confounding from population history and isolation-by-distance when estimating gene-environment correlations.
• Computational Efficiency: LFMM implements fast algorithms for genome scans that aim to reduce false-positive associations compared to related methods.
• Application in Diverse Data Sets: LFMM has been applied to plant and human genetic data and can identify genes associated with development that correlate with climatic gradients.

Scientific Applications:

• Local adaptation scans: Identify loci involved in adaptive responses by estimating gene-environment correlations across the genome.
• Biodiversity conservation and climate change impacts: Inform studies on biodiversity conservation and the genetic basis of responses to climatic gradients.
• Human health and development genetics: Support investigations into genetic loci associated with development that correlate with environmental variables relevant to human health.

Methodology:

LFMM fits latent factor mixed models that introduce unobserved latent factors to model population structure, estimate random effects, and test correlations between genotype data and environmental variables using statistical learning algorithms for genome scans.