Journal Article

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

Jean-Marie Cornuet, Filipe Santos, Mark A. Beaumont, Christian P. Robert, Jean-Michel Marin, David J. Balding, Thomas Guillemaud and Arnaud Estoup

in Bioinformatics

Volume 24, issue 23, pages 2713-2719
Published in print December 2008 | ISSN: 1367-4803
Published online October 2008 | e-ISSN: 1460-2059 | DOI:

More Like This

Show all results sharing this subject:

  • Bioinformatics and Computational Biology


Show Summary Details


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


Supplementary information: Supplementary data are also available at

Journal Article.  5886 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.