Inference on population history and model checking using DNA sequence and microsatellite data with the software DIYABC (v1.0)

We used controlled simulated data sets generated under evolutionary scenarios involving various divergence and admixture events to evaluate the effect of mixing autosomal microsatellite, mtDNA and/or nuclear autosomal DNA sequence data on inferences. This evaluation included the comparison of competing scenarios and the quantification of their relative support, and the estimation of parameter posterior distributions under a given scenario. We also considered a set of scenarios often compared when making ABC inferences on the routes of introduction of invasive species to illustrate the interest of the new model checking option of DIYABC to assess model misfit.Our new developments of the integrated software DIYABC should be particularly useful to make inference on complex evolutionary scenarios involving both recent and ancient historical events and using various types of molecular markers in diploid or haploid organisms. They offer a handy way for non-expert users to achieve model checking computation within an ABC framework, hence filling up a gap of ABC analysis. The software DIYABC V1.0 is freely available at http://www1.montpellier.inra.fr/CBGP/diyabc webcite.Natural populations are often characterized by complex demographic histories. Their effective sizes and ranges change over time leading to fission and fusion processes that leave signatures on their genetic constitution and structure. One promising prospect of current biology is that molecular data will help us to reveal the complex demographic processes that have acted on populations. The extensive availability of different molecular markers and increased computer power has promoted the development of inferential methods and associated software that have begun to fulfil these expectations [1,2].Approximate Bayesian computation (ABC) is a recent flexible class of Monte-Carlo algorithms for performing model-based inference [3]. Estimations associated with demographic and genetic models often imply a full like