Summary1 Based on population genomic and environmental data, genomewide ecological association studies aim at detecting allele frequencies that exhibit significant statistical association with ecological gradients. Ecological association studies can provide lists of genetic polymorphisms that are potentially involved in local adaptation to environmental conditions through natural selection. 2 Here, we present the R package LEA that enables users to run ecological association studies from the R command line. The package can perform analyses of population structure and genome scans for adaptive alleles from large genomic data sets. It derives advantages from R programming functionalities to adjust significance values for multiple testing issues and to visualize results. 3 This note also illustrates the main steps of ecological association studies and the typical use of LEA for analysing data sets based on R commands.

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.