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Infering population history with DIY ABC : a user-friendly approach to Approximate Bayesian Computation

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Date
2008-04
Dewey
Probabilités et mathématiques appliquées
Sujet
Statistique bayésienne; logiciel; simulation; statistique; génétique
Journal issue
Bioinformatics
Volume
24
Number
23
Publication date
12-2008
Article pages
2713-2719
Publisher
Oxford University Press
DOI
http://dx.doi.org/10.1093/bioinformatics/btn514
URI
https://basepub.dauphine.fr/handle/123456789/225
Collections
  • CEREMADE : Publications
Metadata
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Author
Estoup, Arnaud
Marin, Jean-Michel
Robert, Christian P.
Beaumont, Mark A.
Santos, Filipe
Guillemaud, Thomas
Balding, David
Cornuet, Jean-Marie
Type
Article accepté pour publication ou publié
Abstract (EN)
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.

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