A new Approximate Bayesian Computation framework to distinguish among complex evolutionary models using whole-genome data

Inferring past demographic histories is crucial in population genetics, and the amount of complete genomes now available for many species should in principle facilitate this inference. In practice, however, the available inferential methods suffer from severe limitations. Although hundreds complete genomes can be simultaneously analyzed, complex demographic processes can easily exceed computational constraints, and there are no standard procedures to make sure that the estimates obtained are reliable. Here we present ABC-SeS, a new Approximate Bayesian Computation (ABC) framework, based on the Random Forest algorithm, to infer complex past population processes using complete genomes. All possible pairs of populations are compared, and the data are summarized by the full genomic distribution of the four mutually exclusive categories of segregating sites, a set of statistics fast to compute even from unphased genome data. We constructed an efficient ABC pipeline and tested how accurately it allows one to recognize the true model among models of increasing complexity, using simulated data and taking into account different sampling strategies in terms of number of individuals analyzed, number and size of the genetic loci considered. Once assessed the power of ABC-SeS in the comparison of even complex models, we applied it to the analysis of real data, testing models on the dispersal of anatomically modern humans out of Africa and exploring the evolutionary relationships of the three species of Orangutan inhabiting Borneo and Sumatra. The flexibility of our ABC framework, combined with the power provided by the set of statistics proposed pave the way for reliable inference of past population processes for any species for which high coverage genomes are available.