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

Jean-Marie Cornuet,Arnaud Estoup,Virgine Ravigné

doi:10.1186/1471-2105-11-401

Abstract

BackgroundApproximate Bayesian computation (ABC) is a recent flexible class of Monte-Carlo algorithms increasingly used to make model-based inference on complex evolutionary scenarios that have acted on natural populations. The software DIYABC offers a user-friendly interface allowing non-expert users to consider population histories involving any combination of population divergences, admixtures and population size changes. We here describe and illustrate new developments of this software that mainly include (i) inference from DNA sequence data in addition or separately to microsatellite data, (ii) the possibility to analyze five categories of loci considering balanced or non balanced sex ratios: autosomal diploid, autosomal haploid, X-linked, Y-linked and mitochondrial, and (iii) the possibility to perform model checking computation to assess the "goodness-of-fit" of a model, a feature of ABC analysis that has been so far neglected.ResultsWe 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.ConclusionsOur 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.

Highlights

Approximate Bayesian computation (ABC) is a recent flexible class of Monte-Carlo algorithms increasingly used to make model-based inference on complex evolutionary scenarios that have acted on natural populations
Summary statistics can be chosen for DNA sequence data among a set of 14 statistics detailed in the notice document available at http://www1.montpellier.inra.fr/CBGP/diyabc
We found that, depending on the parameter considered, cumulating the information provided by different markers translated into a decrease, an increase or, most frequently, an absence of noticeable variation of the relative median absolute errors (RMAE) values compared to that obtained with the most informative genetic marker (Figure 4 and see Additional file 2 for an illustration of the variation of RMAE values expected by chance between different replicates of 500 pseudo-observed data sets)

Summary

Introduction

Approximate Bayesian computation (ABC) is a recent flexible class of Monte-Carlo algorithms increasingly used to make model-based inference on complex evolutionary scenarios that have acted on natural populations. ABC methods were recently successfully used to make inference on complex models in population and evolutionary biology [6,7,8,9,10,11,12,13], infectious disease epidemiology [14] and system biology [15] Such inferences mainly include model selection among a finite set of models (evolutionary scenarios) and inferences on the posterior distribution of the parameter of interest under a given model. Useful when doing inferences, model checking is a feature of ABC analyses that has been so far neglected ([5]; but see [23,24,25])

Methods

Results

Conclusion