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


BACKGROUND
The software DIY ABC is freely available at http://www.montpellier.inra.fr/CBGP/diyabc.
Department of Epidemiology and Public Health, Imperial College, St Mary's Campus, Norfolk Place, London W2 1PG, UK. j.cornuet@imperial.ac.uk
Jean-Marie
JM
Cornuet
Filipe
F
Santos
Mark A
MA
Beaumont
Christian P
CP
Robert
Jean-Michel
JM
Marin
David J
DJ
Balding
Thomas
T
Guillemaud
Arnaud
A
Estoup
D000465:Algorithms; D001499:Bayes Theorem; D019143:Evolution, Molecular; D005828:Genetics, Population / Q000379:methods*; D006801:Humans; D044382:Population Groups / Q000235:genetics; D012984:Software