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 sett

SUBMITTER: Stocks M 

PROVIDER: S-EPMC4069000 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature