Demographic Inference Using Skyline Plots on Approximate Bayesian Computation

<p>Navascués & Burgarella (2012). Demographic inference using skyline plots on approximate Bayesian computation.SMBE Dublin 2012</p> <p> </p> <p>Bayesian Skyline Plots are representations of the posterior probability density of the effective population size in function of time. A model with<br>several periods of constant population size is used to explore the demographies that best explain the data. Instead of estimating the posterior<br>probability density for parameters of the model (i.e. size at each interval) the probability density of population size in function of time (i.e. size at<br>each generation) is estimated. This method is currently implemented in BEAST (Drummond & Rambaut 2007 BMC Evol Biol), which uses an<br>MCMC estimation of likelihood under the coalescent.<br>We have implemented the same approach under an Approximate Bayesian Computation analysis. The purpose of this is to facilitate analysis<br>of dataset were the MCMC approach is inappropriate, such as analysis of DNA sequences with intragenic recombination. The performance of<br>the method has been evaluated with simulated datasets (see table). Simulations for the ABC procedure were performed with FASTSIMCOAL<br>(Excoffier & Foll 2011 Bioinformatics), using a model with 10 periods of constant size. Sample were of 50 diploid individuals typed at 100<br>microsatellite loci. Summary statistics were calculated with ARLSUMSTATS (from Arlequin). Relative root of mean squared error, relative bias<br>and proportion of times the true value fall outside the 95%HPD interval were estimated from 50 simulations of target data.</p> <p>