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Bioinformatics Advance Access originally published online on October 7, 2008
Bioinformatics 2008 24(23):2713-2719; doi:10.1093/bioinformatics/btn514
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation

Jean-Marie Cornuet 1,2,*, Filipe Santos 2, Mark A. Beaumont 3, Christian P. Robert 4, Jean-Michel Marin 5, David J. Balding 1, Thomas Guillemaud 6 and Arnaud Estoup 2

1Department of Epidemiology and Public Health, Imperial College, St Mary's Campus, Norfolk Place, London W2 1PG, UK, 2Centre de Biologie et de Gestion des Populations, INRA, Campus International de Baillarguet, CS 30016 34988 Montferrier-sur-Lez, France, 3School of Biological Sciences, Lyle Building, The University of Reading Whiteknights, Reading RG6 6AS, UK, 4CEREMADE, Université Paris-Dauphine, Place Delattre de Tassigny, 75775 Paris cedex 16, 5INRIA Saclay, Projet select, Université Paris-Sud, Laboratoire de Mathématiques (Bât. 425), 91400 Orsay and 6UMR 1301 I.B.S.V. INRA-UNSA-CNRS, 400 Route des Chappes, BP 167 - 06903 Sophia Antipolis cedex. France

*To whom correspondence should be addressed.


  Abstract

Summary: Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract part of this information but they require sophisticated statistical techniques that have been made available to the biologist community (through computer programs) only for simple and standard situations typically involving a small number of samples. We propose here a computer program (DIY ABC) for inference based on approximate Bayesian computation (ABC), in which scenarios can be customized by the user to fit many complex situations involving any number of populations and samples. Such scenarios involve any combination of population divergences, admixtures and population size changes. DIY ABC can be used to compare competing scenarios, estimate parameters for one or more scenarios and compute bias and precision measures for a given scenario and known values of parameters (the current version applies to unlinked microsatellite data). This article describes key methods used in the program and provides its main features. The analysis of one simulated and one real dataset, both with complex evolutionary scenarios, illustrates the main possibilities of DIY ABC.

Availability: The software DIY ABC is freely available at http://www.montpellier.inra.fr/CBGP/diyabc.

Contact: j.cornuet{at}imperial.ac.uk

Supplementary information: Supplementary data are also available at http://www.montpellier.inra.fr/CBGP/diyabc

Associate Editor: Martin Bishop

Received on June 17, 2008; revised on September 5, 2008; accepted on October 2, 2008

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