title: Amount of information needed for model choice in Approximate Bayesian Computation.
abstract: Approximate Bayesian Computation (ABC) has become a popular technique in evolutionary genetics for elucidating population structure and history due to its flexibility. The statistical inference framework has benefited from significant progress in recent years. In population genetics, however, its outcome depends heavily on the amount of information in the dataset, whether that be the level of genetic variation or the number of samples and loci. Here we look at the power to reject a simple constant population size coalescent model in favor of a bottleneck model in datasets of varying quality. Not only is this power dependent on the number of samples and loci, but it also depends strongly on the level of nucleotide diversity in the observed dataset. Whilst overall model choice in an ABC setting is fairly powerful and quite conservative with regard to false positives, detecting weaker bottlenecks is problematic in smaller or less genetically diverse datasets and limits the inferences possible in non-model organism where the amount of information regarding the two models is often limited. Our results show it is important to consider these limitations when performing an ABC analysis and that studies should perform simulations based on the size and nature of the dataset in order to fully assess the power of the study.
authors: Department of Ecology and Genetics, Program in Plant Ecology and Evolution, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden.
authors: Michael
authors: M
authors: Stocks
authors: Institut National de la Recherche Agronomique (INRA), UMR Agroécologie, Dijon, France.
authors: Mathieu
authors: M
authors: Siol
authors: Department of Ecology and Genetics, Program in Plant Ecology and Evolution, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden.
authors: Martin
authors: M
authors: Lascoux
authors: Institut National de la Recherche Agronomique (INRA), UMR Interactions Arbres-Microorganismes (IAM), Nancy, France; Institut de Recherche pour le Développement (IRD), UMR Diversité et Adaptation des Plantes Cultivées, Montpellier, France.
authors: Stéphane
authors: S
authors: De Mita
mesh_terms: D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D014644:Genetic Variation; D015233:Models, Statistical; D011156:Population Density; D025341:Principal Component Analysis