Data from: Enhanced computational methods for quantifying the effect of geographic and environmental isolation on genetic differentiation

1. In a recent paper, Bradburd et al. (Evolution, 67, 2013, 3258) proposed
a model to quantify the relative effect of geographic and environmental
distance on genetic differentiation. Here, we enhance this method in
several ways. 2. We modify the covariance model so as to fit better with
mainstream geostatistical models and avoid mathematically ill-behaved
covariance functions. We extend the model – initially implemented only for
co-dominant bi-allelic markers such as single nucleotide polymorphisms –
to encompass highly polymorphic markers such as microsatellites. We
implement and test a model selection procedure that allows users to assess
which model (e.g. with or without an environment effect) is most suited.
We code all our MCMC algorithms in a mix of compiled languages which
allows us to decrease computing time by at least one order of magnitude.
We propose an approximate inference and model selection method allowing us
to deal with genomic data sets (several hundred thousands loci). 3. We
also illustrate the potential of the method by re-analysing three data
sets, namely harbour porpoises in Europe, coyotes in California and
herrings in the Baltic Sea. 4. The computer program developed here is
freely available as an r package called sunder. It takes as input
georeferenced allele counts at the individual or population level for
co-dominant markers. Program homepage:
http://www2.imm.dtu.dk/~gigu/Sunder/.