We introduce a flexible and robust simulation-based framework to infer demographic parameters from the site frequency spectrum (SFS) computed on large genomic datasets. We show that our composite-likelihood approach allows one to study evolutionary models of arbitrary complexity, which cannot be tackled by other current likelihood-based methods. For simple scenarios, our approach compares favorably in terms of accuracy and speed with , the current reference in the field, while showing better convergence properties for complex models. We first apply our methodology to non-coding genomic SNP data from four human populations. To infer their demographic history, we compare neutral evolutionary models of increasing complexity, including unsampled populations. We further show the versatility of our framework by extending it to the inference of demographic parameters from SNP chips with known ascertainment, such as that recently released by Affymetrix to study human origins. Whereas previous ways of handling ascertained SNPs were either restricted to a single population or only allowed the inference of divergence time between a pair of populations, our framework can correctly infer parameters of more complex models including the divergence of several populations, bottlenecks and migration. We apply this approach to the reconstruction of African demography using two distinct ascertained human SNP panels studied under two evolutionary models. The two SNP panels lead to globally very similar estimates and confidence intervals, and suggest an ancient divergence (>110 Ky) between Yoruba and San populations. Our methodology appears well suited to the study of complex scenarios from large genomic data sets. We present a new likelihood-based method to infer the past demography of a set of populations from large genomic datasets. Our method can be applied to arbitrarily complex models as the likelihood is estimated by coalescent simulations. Under simple scenarios, our method behaves similarly to a widely used diffusion-based method while showing better convergence properties. In addition, our approach can be applied to very complex models including as many as a dozen populations, and still retrieve parameters very accurately in a reasonable time. We apply our approach to estimate the past demography of four human populations for which non-coding whole genome diversity is available, estimating the degree of European admixture of a southwest African American population and that of a Kenyan population with an unsampled East African population. We also show the versatility of our framework by inferring the demographic history of African populations from SNP chip data with known ascertainment bias, and find a very old divergence time (>110 Ky) between Yorubas from Western Africa and Sans from Southern Africa.

Northeastern Siberia has been inhabited by humans for more than 40,000 years but its deep population history remains poorly understood. Here we investigate the late Pleistocene population history of northeastern Siberia through analyses of 34 newly recovered ancient genomes that date to between 31,000 and 600 years ago. We document complex population dynamics during this period, including at least three major migration events: an initial peopling by a previously unknown Palaeolithic population of ‘Ancient North Siberians’ who are distantly related to early West Eurasian hunter-gatherers; the arrival of East Asian-related peoples, which gave rise to ‘Ancient Palaeo-Siberians’ who are closely related to contemporary communities from far-northeastern Siberia (such as the Koryaks), as well as Native Americans; and a Holocene migration of other East Asian-related peoples, who we name ‘Neo-Siberians’, and from whom many contemporary Siberians are descended. Each of these population expansions largely replaced the earlier inhabitants, and ultimately generated the mosaic genetic make-up of contemporary peoples who inhabit a vast area across northern Eurasia and the Americas.

How early human groups were organized: Sequencing ancient hominid remains has provided insights into the relatedness between individuals. However, it is not clear whether ancient humans bred among close relatives, as is common in some modern human cultures. Sikora et al. report genome sequences from four early humans buried close together in western Russia about 34,000 years ago (see the Perspective by Bergström and Tyler-Smith). The individuals clustered together genetically and came from a population with a small effective size, but they were not very closely related. Thus, these people may represent a single social group that was part of a larger mating network, similar to contemporary hunter-gatherers. The lack of close inbreeding might help to explain the survival advantage of anatomically modern humans. Science , this issue p. 659 ; see also p. 586

The idea that molecular data should contain information on the recent evolutionary history of populations is rather old. However, much of the work carried out today owes to the work of the statisticians and theoreticians who demonstrated that it was possible to detect departures from equilibrium conditions (e.g., panmictic population/mutation–drift equilibrium) and interpret them in terms of deviations from neutrality or stationarity. During the last 20 years the detection of population size changes has usually been carried out under the assumption that samples were obtained from populations that can be approximated by a Wright–Fisher model (i.e., assuming panmixia, demographic stationarity, etc.). However, natural populations are usually part of spatial networks and are interconnected through gene flow. Here we simulated genetic data at mutation and migration–drift equilibrium under an n-island and a stepping-stone model. The simulated populations were thus stationary and not subject to any population size change. We varied the level of gene flow between populations and the scaled mutation rate. We also used several sampling schemes. We then analyzed the simulated samples using the Bayesian method implemented in MSVAR, the Markov Chain Monte Carlo simulation program, to detect and quantify putative population size changes using microsatellite data. Our results show that all three factors (genetic differentiation/gene flow, genetic diversity, and the sampling scheme) play a role in generating false bottleneck signals. We also suggest an ad hoc method to counter this effect. The confounding effect of population structure and of the sampling scheme has practical implications for many conservation studies. Indeed, if population structure is creating “spurious” bottleneck signals, the interpretation of bottleneck signals from genetic data might be less straightforward than it would seem, and several studies may have overestimated or incorrectly detected bottlenecks in endangered species.

Of chimpanzees and bonobos: Modern non-African human genomes contain genomic remnants that suggest that there was interbreeding between ancient humans and archaic hominoid lineages. Now, de Manuel et al. show similar ancestral interbreeding between the ancestors of today's chimpanzees and bonobos (see the Perspective by Hoelzel). The study also provides population-specific genetic markers that may be valuable for conservation efforts. Science , this issue p. 477 ; see also p. 414