Landscape genomic tests for associations between loci and environmental gradients

Adaptation to local environments often occurs through natural selection acting on a large number of alleles, each having a weak phenotypic effect. One way to detect these alleles is to identify genetic polymorphisms that exhibit high correlation with environmental variables used as proxies for ecological pressures. Here we propose an integrated framework based on population genetics, ecological modeling and statistical learning techniques to screen genomes for signatures of local adaptation. These new algorithms introduce latent factor mixed models to population genetics, employing an approach based on probabilistic principal component analysis in which population structure is introduced via unobserved variables. These fast, 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 latent factor models 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 unusual correlations with climatic gradients.