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dc.contributor.authorEstoup, Arnaud
dc.contributor.authorMarin, Jean-Michel
dc.contributor.authorRobert, Christian P.
dc.contributor.authorBeaumont, Mark A.
dc.contributor.authorSantos, Filipe
dc.contributor.authorGuillemaud, Thomas
dc.contributor.authorBalding, David
dc.contributor.authorCornuet, Jean-Marie
dc.date.accessioned2009-06-09T14:16:27Z
dc.date.available2009-06-09T14:16:27Z
dc.date.issued2008-04
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/225
dc.language.isoenen
dc.subjectStatistique bayésienneen
dc.subjectlogicielen
dc.subjectsimulationen
dc.subjectstatistiqueen
dc.subjectgénétiqueen
dc.subject.ddc519en
dc.titleInfering population history with DIY ABC : a user-friendly approach to Approximate Bayesian Computationen
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherINRIA;France
dc.contributor.editoruniversityotherThe University of Reading;Royaume-Uni
dc.contributor.editoruniversityotherImperial college London;Royaume-Uni
dc.contributor.editoruniversityotherINRA;France
dc.description.abstractenGenetic 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.en
dc.relation.isversionofjnlnameBioinformatics
dc.relation.isversionofjnlvol24en
dc.relation.isversionofjnlissue23en
dc.relation.isversionofjnldate2008-12
dc.relation.isversionofjnlpages2713-2719en
dc.relation.isversionofdoihttp://dx.doi.org/10.1093/bioinformatics/btn514en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherOxford University Pressen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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