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

J.M Cornuet, F Santos, M.A Beaumont, C.P Robert, J.M Marin, D.J Balding, T Guillemaud, A Estoup

Research output: Contribution to journalArticle (Academic Journal)

479 Citations (Scopus)

Abstract

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.
Translated title of the contributionInferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation
Original languageEnglish
Pages (from-to)2713 - 2719
Number of pages7
JournalBioinformatics
Volume24
Issue number23
DOIs
Publication statusPublished - Dec 2008

Bibliographical note

Publisher: Oxford University Press

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    Cornuet, J. M., Santos, F., Beaumont, M. A., Robert, C. P., Marin, J. M., Balding, D. J., Guillemaud, T., & Estoup, A. (2008). Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation. Bioinformatics, 24 (23), 2713 - 2719. https://doi.org/10.1093/bioinformatics/btn514