Disentangling the effects of geographic and ecological isolation on genetic differentiation
Gideon Bradburd, Peter Ralph, Graham Coop
(Submitted on 13 Feb 2013)
Populations can be genetically isolated by geographic distance and by differences in their ecology or environment that decrease the rate of successful migration. Empirical studies often seek to investigate the relationship between genetic differentiation and some ecological variable(s) while accounting for geographic distance, but common approaches to this problem (e.g. the partial Mantel test) have a number of drawbacks. In this article, we present a Bayesian method that enables users to quantify the relative contributions of geographic distance and ecological distance to genetic differentiation between sampled populations or individuals. We model the allele frequencies in populations at a set of unlinked loci as spatial Gaussian processes, and model the covariance structure of pairs of populations as a decreasing function of both geographic and ecological distance between that pair. Parameters of the model are estimated using a Markov chain Monte Carlo algorithm. We have implemented this method, Bayesian Estimation of Differentiation in Alleles by Spatial Structure and Local Ecology (BEDASSLE), in a user-friendly format in the statistical platform R, and we demonstrate its utility with a simulation study and empirical applications to human and teosinte datasets.