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

Filippo Botta, Casper Eriksen, Michaël C. Fontaine, Gilles Guillot

Research output: Contribution to journalJournal articleResearchpeer-review


Motivation In a recent paper, Bradburd et al. [2013] proposed a model to quantify the relative effect of geographic and environmental distance on genetic differentiation. Here, we enhance this method in several ways.
Results (i) We modify the covariance model so as to fit better with mainstream geostatistical models and avoid mathematically illbehaved covariance functions, (ii) we extend the model - initially implemented only for co-dominant bi-allelic markers such as SNPs - to encompass highly polymorphic markers such as microsatellites, (iii) 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, (iv) we extend the program to handle several environmental variables jointly, (v) 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, (vi) we propose an approximate inference and model selection method allowing to deal with a large number of loci. We also illustrate the potential of the method by re-analyzing three datasets relative to harbour porpoises in Europe, coyotes in California and herrings in the Baltic Sea.
Availability The computer program developed here is freely available as an R package called SUNDER. It takes as input geo-referenced allele counts at the individual or population level for co-dominant
markers. Programe homepage: www2.imm.dtu.dk/˜gigu/Sunder
Original languageEnglish
JournalMethods in Ecology and Evolution
Issue number11
Pages (from-to)1270-1277
Number of pages7
Publication statusPublished - 2015

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