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

Authors: Cornuet, Jean-Marie; Santos, Filipe; Beaumont, Marc; Robert, Christian; Marin, Jean-Michel; Balding, David J.; Guillemaud, Thomas; +1 Authors

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

Abstract

AbstractSummary: 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@imperial.ac.ukSupplementary information: Supplementary data are also available at http://www.montpellier.inra.fr/CBGP/diyabc

Keywords

570, logiciel, [INFO.INFO-SE] Computer Science [cs]/Software Engineering [cs.SE], [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], génétique, 519, Evolution, Molecular, Statistique bayésienne, Population Groups, POPULATION GENETICS, HISTORY, Humans, Quantitative Biology - Populations and Evolution, MATHEMATICAL PROGRAMMING, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], BIOLOGIE DES POPULATIONS, [STAT.ME] Statistics [stat]/Methodology [stat.ME], Populations and Evolution (q-bio.PE), Bayes Theorem, statistique, HISTOIRE, GENETIQUE, simulation, Original Papers, STATISTICS, Probabilités et mathématiques appliquées, 004, POPULATION DYNAMICS, Genetics, Population, FOS: Biological sciences, [SDV.GEN.GPO] Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], [STAT.ME]Statistics [stat]/Methodology [stat.ME], Algorithms, Software

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    selected citations
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    611
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
611
Top 1%
Top 1%
Top 1%
Green
gold