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There is a wide consensus in considering Africa as the birthplace of anatomically modern humans (AMH), but the dispersal pattern and the main routes followed by our ancestors to colonize the world are still matters of debate. It is still an open question whether AMH left Africa through a single process, dispersing almost simultaneously over Asia and Europe, or in two main waves, first through the Arab Peninsula into southern Asia and Australo-Melanesia, and later through a northern route crossing the Levant. The development of new methodologies for inferring population history and the availability of worldwide high-coverage whole-genome sequences did not resolve this debate. In this work, we test the two main out-of-Africa hypotheses through an Approximate Bayesian Computation approach, based on the Random-Forest algorithm. We evaluated the ability of the method to discriminate between the alternative models of AMH out-of-Africa, using simulated data. Once assessed that the models are distinguishable, we compared simulated data with real genomic variation, from modern and archaic populations. This analysis showed that a model of multiple dispersals is four-fold as likely as the alternative single-dispersal model. According to our estimates, the two dispersal processes may be placed, respectively, around 74,000 and around 46,000 years ago.

Inferring past demographic histories is crucial in population genetics, and the amount of complete genomes now available should in principle facilitate this inference. In practice, however, the available inferential methods suffer from severe limitations. Although hundreds complete genomes can be simultaneously analysed, complex demographic processes can easily exceed computational constraints, and the procedures to evaluate the reliability of the estimates contribute to increase the computational effort. Here we present an approximate Bayesian computation framework based on the random forest algorithm (ABC-RF), to infer complex past population processes using complete genomes. To this aim, we propose to summarize the data by the full genomic distribution of the four mutually exclusive categories of segregating sites (FDSS), a statistic fast to compute from unphased genome data and that does not require the ancestral state of alleles to be known. 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 analysed, number and size of the genetic loci considered. We also compared the FDSS with the unfolded and folded site frequency spectrum (SFS), and for these statistics we highlighted the experimental conditions maximizing the inferential power of the ABC-RF procedure. We finally analysed real data sets, 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.

Inferring past demographic histories is crucial in population genetics, and the amount of complete genomes now available 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 the procedures to evaluate the reliability of the estimates contribute to increase the computational effort. Here we present an Approximate Bayesian Computation (ABC) framework, based on the Random Forest algorithm, to infer complex past population processes using complete genomes. To do this, we propose to summarize the data by the full genomic distribution of the four mutually exclusive categories of segregating sites (FDSS), a statistic fast to compute 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. We tested the power of theFDSSto be informative about even complex evolutionary histories and compared the results with those obtained summarizing the data through the unfolded Site Frequency Spectrum, thus highlighting for both statistics the experimental conditions maximizing the inferential power. Finally, we analyzed two datasets, testing models (a) on the dispersal of anatomically modern humans out of Africa and (b) the evolutionary relationships of the three species of Orangutan inhabiting Borneo and Sumatra.

Extensive sequencing of modern and ancient human genomes has revealed that contemporary populations can be explained as the result of recent mixing of a few distinct ancestral genetic lineages1. But the small number of aDNA samples that predate the Last Glacial Maximum means that the origins of these lineages are not well understood. Here, we circumvent the limited sampling by modelling explicitly the effect of climatic changes and terrain on population demography and migrations through time and space, and show that these factors are sufficient to explain the divergence among ancestral lineages. Our reconstructions show that the sharp separation between African and Eurasian lineages is a consequence of only a few limited periods of connectivity through the arid Arabian peninsula, which acted as the gate out of the Arican continent. The subsequent spread across Eurasia was then mostly shaped by mountain ranges, and to a lesser extent deserts, leading to the split of European and Asians, and the further diversification of these two groups. A high tolerance to cold climates allowed the persistence at high latitudes even during the Last Glacial Maximum, maintaining a pocket in Beringia that led to the later, rapid colonisation of the Americas. The advent of food production was associated with an increase in movement2, but mountains and climate have been shown to still play a major role even in this latter period3,4, affecting the mixing of the ancestral lineages that we have shown to be shaped by those two factors in the first place.