  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.
