A major unanswered question is whether expansion began with hunter-gatherer groups, perhaps as a result of the invention of particular technologies or behavioral innovations, or much more recently with the advent of agriculture. Early mtDNA studies suggested that humans experienced a burst of population growth between 30 and 130 thousand years ago —well before the start of agriculture. More recent results have extended the time frame for sub-Saharan African growth to 213–12 kya, depending in part on mtDNA haplogroup. However, it is populations—not haplogroups— that are subject to growth, and many present-day hunter-gatherer groups, including those in Africa, do not exhibit any mtDNA signal of demographic expansion at all. On the other hand, Y chromosome sequence data are compatible with a model of constant size for both hunter-gatherer and farming populations in Africa. Autosomal microsatellites tend to indicate an early start to population growth, but there is disagreement among studies on the time of expansion and whether or not the expansions involved African populations. Zhivotovsky et al. examined a large autosomal microsatellite dataset in 52 worldwide populations and concluded that African farmers, but not hunter-gatherers, exhibit the signal of population growth. Unfortunately, inferences of demographic parameters based on the above mentioned loci may be unreliable due to the possible confounding effects of natural selection or evolutionary stochasticity , best way to dry bud or uncertainty in our understanding of mutation rates or the underlying mutation process. A more reliable source of information regarding past population size change comes from multilocus nuclear sequence studies.
Once polymorphism data from multiple X-linked and autosomal loci began to appear, clear discrepancies with inferences based on both mtDNA and microsatellites emerged. For example, most non-African populations tend to have positive Tajima’s D values— reflecting possible contractions in Ne—while most African populations tend to have only slightly negative values. Indeed, the largest re-sequencing study to date that targets unlinked autosomal non-coding regions finds that patterns of neutral polymorphism in non-African populations reject the standard constant size model, and are most compatible with a range of bottleneck models invoking a large reduction in effective population size some time after the appearance of modern humans in Africa. In contrast, data from the sole African population examined, the Hausa of Cameroon, were compatible with demographic equilibrium, as well as with a set of recent population expansion models. In this paper, we expand upon the work of Voight et al. by analyzing a re-sequencing dataset comprised of 20 independentlyevolving autosomal non-coding regions in 7 human populations. Our sub-Saharan African populations include the San from Namibia, Biaka from the Central African Republic, Mandenka from Senegal, and Yorubans from Nigeria. Our multilocus analysis, which focuses on two summary statistics with power to detect population growth , follows a two-step approach. We employ a simulation-based method to test the hypothesis that populations experienced exponential growth after a period of constant size. When the hypothesis cannot be rejected, we then fit parameters of this two-phase growth model to our data using approximate Bayesian computation. As in previous studies, we find that the non-African data are not consistent with a simple growth model. On the other hand all four sub-Saharan African samples fit the two-phase growth model, and we are able to infer a range of onset times and growth rates for each population.
We sample sub-Saharan African populations that practice different subsistence strategies and then ask whether the inferred signals of population growth are shared between, or specific to, food-gathering or food-producing groups.Our understanding of population size changes in human prehistory has improved as our genetic datasets and analysis methods have become more sophisticated. Early studies of the pairwise mismatch distribution in mitochondrial DNA suggested dramatic increases in population size between 110 and 70 kya in sub-Saharan Africa. More recent coalescent studies have also favored 50- to 100-fold growth occurring between 213 and 12 kya. Conversely, modern surveys of nuclear sequence variation at unlinked loci have not provided clear evidence for rapid population growth from small ancestral size. For example, African populations usually exhibit slightly negative Tajima’s D values, while non-African populations tend to have positive Tajima’s D values. Different patterns of polymorphism in African and non-African populations have been interpreted as reflecting a history of bottleneck in the ancestry of non-Africans. Therefore, the question of when anatomically modern human populations began to expand in size is better addressed in sub-Saharan African populations because more recent demographic events likely obscure signals of population growth in the ancestors of nonAfrican groups. Bottlenecks, in particular, can mask the effects of earlier, as well as later, population growth.However, thus far, very few surveys of nuclear DNA sequence variation have been performed in sub-Saharan African populations, and interpretations drawn by existing studies have been complicated by the different populations and loci analyzed, the kinds of analyses performed, and the different growth models assumed.
The earliest studies considered only the few existing nuclear sequence data available in the literature at the time, and explored only a small set of growth model parameters. Later studies adopted a more explicit hypothesis-testing framework, but focused on only a single African population. For instance, Pluzhnikov et al. analyzed a large resequence dataset of noncoding autosomal regions for the Hausa of Cameroon . They determined that while observed summaries of the site frequency spectrum did not statistically reject a null model of constant size, they were consistent with a range of alternative growth models. Consequently, Voight et al. turned to a goodness-of-fit approach to determine better estimates of the time of onset of growth and the growth rate in the Hausa. By generating approximate likelihoods for the mean of observed summary statistics over a grid of parameter values, they determined that the Hausa best fit a growth model beginning ,1,000 generations ago with a per-generation growth rate a of 0.7561023 . Assuming a generation time of 25 years, this corresponds to an overall ,2-fold growth rate from ancestral to modern size beginning ,25 kya. Here, we extend these sorts of analyses to a greater range of African populations: two hunter-gathers, the San of Namibia and the Biaka of the Central African Republic; and two food producers, the Mandenka of Senegal and the Yorubans of Nigeria. All four groups show depressed values of Tajima’s D and Rozas’ R2 coupled with a high proportion of singleton mutations . These patterns of sequence polymorphism are suggestive of population growth. We therefore tested our multilocus African dataset to determine whether we could reject models of population growth, and adopted the best aspects of previous hypothesis-testing and inference approaches. We first employed hypothesis-testing to determine, by coalescent simulation, whether a range of growth models could be rejected in favor of constant size using the method pioneered by Pluzhnikov et al.. When growth could not be rejected, we fitted parameters of the two phase growth model to our data using approximate Bayesian computation . Thus, we conditioned simulations on each locus individually , cannabis grow setup and explored a continuous range of parameter values rather than restricting our search to a set of predetermined grid coordinates. All of our African populations best fit models with relatively low population growth beginning in the late Pleistocene . Even with ,112-kb of sequence data per individual, a large range of growth models are consistent with our 95% credible regions for t and a. We cannot, for instance, statistically distinguish different rates and times of growth among our four sub-Saharan African samples. However, our hunter-gather populations show a tendency towards slightly older and stronger growth than our food-producing populations . Furthermore, we detect a strongly negative, non-linear association between t and a . This effect, which has been identified previously, implies that sequence data from our four African populations are consistent either with weaker growth beginning earlier in the Late Pleistocene, or with stronger growth commencing more recently. Interestingly, we can reject an onset of population growth for the San during the Holocene , and therefore, growth in this population is not linked to the development of agriculture. Although we cannot reject an onset of growth associated with agriculture for the Biaka, Mandenka and Yorubans, our best fitting models do not favor this interpretation. Indeed, the limited size of our dataset gives us more power to infer older rather than more recent growth. We see little effect from the increased size of the dataset obtained for Yorubans. Even though we increased both the number of samples and the number of loci , estimates of the rate and timing of growth are comparable to those inferred for the Mandenka, and our 95% credible region is not appreciably smaller. This is interesting given that, under a model of population growth, expected values of Tajima’s D depend to some extent on sample size. With regard to the small increase in the number of loci in our Yoruban dataset, recent power analyses by Adams and Hudson suggest that orders of magnitude more data may be necessary to obtain growth model parameters with substantially greater accuracy, especially in models involving recent growth. Furthermore, the modern effective sizes we infer – on the order of 105 – are much smaller than regional census sizes.
This discrepancy partly reflects the fact that effective size is not a simple proxy for census size. However, another explanation also seems likely: under a model of exponential growth, the bulk of the population increase is weighted towards the present, and for the aforementioned reasons [28], we are not likely to capture the effects of substantial increases population size in modern times. Although population growth seems like a reasonable demographic model for human groups on non-genetic grounds [1,2,34], humans have likely experienced both population growth and population structure at some time in the past. The question is whether and to what extent either or both of these aspects of population history left a signature on patterns of variation. To explore the effects of alternate models of population structure on patterns of genetic variation, we use a coalescent simulation approach. In particular, we examine how Tajima’s D and Rozas’ R2 respond under models incorporating low-frequency gene flow in a structured population, recent admixture, and cryptic population structure . We assume a two-deme splitting model with i) a constant low level of gene flow, ii) a single admixture event occurring ,3 kya , and iii) population structure collapsing ,150 years ago . All of these processes produce very slight reductions in Tajima’s D and Rozas’ R2, but the mean deviations never exceed 0.27 and 0.011, respectively. To put these values in perspective, such deviations represent no more than 10% and 12% of the variance naturally observed for Tajima’s D and Rozas’ R2 under the corresponding standard neutral models with no gene flow, admixture, or cryptic population structure. Although these confounding factors may have caused our growth estimates to appear slightly older or stronger than they actually are, their effects are minor. Similarly, biases in our estimates of per-locus mutation and recombination rates are unlikely to have major effects on our inferences. For instance, elevated recombination would lead to a lower variance of Tajima’s D and Rozas’ R2, which would return growth estimates with less uncertainty, while elevated mutation rates would shorten our time frames, and hence return younger growth estimates. Estimates of growth rates under the isolation-with-migration model, which simultaneously accounts for population structure and gene flow, are consistent with our inference of an increase in the effective size of sub-Saharan African populations. Although growth rates are lower than suggested by ABC, we still infer that African populations experienced ,5-fold growth from ancestral sizes. While a simple two-phase growth model is too simplistic to fully describe African population history, it is interesting to note that a more complex model incorporating an ancient bottleneck does not fit African resequencing data. This is in marked contrast to the large reduction in population size that the same studies inferred for non-Africans. We therefore suggest that our growth estimates genuinely reflect a substantial increase in effective size among sub-Saharan African populations beginning in the Late Pleistocene. However, we note that these inferences could be complicated by other forms of population structure not accounted for in our models. While some authors have speculated that human populations underwent sudden expansions in population size in response to dramatic climatic events, technological inventions, or behavioral changes that took place earlier than 50 kya, our data are more consistent with a model of exponential growth beginning after 50 kya, but certainly before the Holocene.