Investigating population continuity with ancient DNA under a spatially explicit simulation framework

Ancient mitochondrial diversity reveals population homogeneity in Neolithic Greece and identifies population dynamics along the Danubian expansion axis

The aim of the study is to investigate mitochondrial diversity in Neolithic Greece and its relation to hunter-gatherers and farmers who populated the Danubian Neolithic expansion axis. We sequenced 42 mitochondrial palaeogenomes from Greece and analysed them together with European set of 328 mtDNA sequences dating from the Early to the Final Neolithic and 319 modern sequences. To test for population continuity through time in Greece, we use an original structured population continuity test that simulates DNA from different periods by explicitly considering the spatial and temporal dynamics of populations. We explore specific scenarios of the mode and tempo of the European Neolithic expansion along the Danubian axis applying spatially explicit simulations coupled with Approximate Bayesian Computation. We observe a striking genetic homogeneity for the maternal line throughout the Neolithic in Greece whereas population continuity is rejected between the Neolithic and present-day Greeks. Along the Danubian expansion axis, our best-fitting scenario supports a substantial decrease in mobility and an increasing local hunter-gatherer contribution to the gene-pool of farmers following the initial rapid Neolithic expansion. Οur original simulation approach models key demographic parameters rather than inferring them from fragmentary data leading to a better understanding of this important process in European prehistory.

Simulated patterns of mitochondrial diversity are consistent with partial population turnover in Bronze Age Central Europe

OBJECTIVES

The analysis of ancient mitochondrial DNA from osteological remains has challenged previous conclusions drawn from the analysis of mitochondrial DNA from present populations, notably by revealing an absence of genetic continuity between the Neolithic and modern populations in Central Europe. Our study investigates how to reconcile these contradictions at the mitochondrial level using a modeling approach.

MATERIALS AND METHODS

We used a spatially explicit computational framework to simulate ancient and modern DNA sequences under various evolutionary scenarios of post Neolithic demographic events and compared the genetic diversity of the simulated and observed mitochondrial sequences. We investigated which-if any-scenarios were able to reproduce statistics of genetic diversity similar to those observed, with a focus on the haplogroup N1a, associated with the spread of early Neolithic farmers.

RESULTS

Demographic fluctuations during the Neolithic transition or subsequent demographic collapses after this period, that is, due to epidemics such as plague, are not sufficient to explain the signal of population discontinuity detected on the mitochondrial DNA in Central Europe. Only a scenario involving a substantial genetic input due to the arrival of migrants after the Neolithic transition, possibly during the Bronze Age, is compatible with observed patterns of genetic diversity.

DISCUSSION

Our results corroborate paleogenomic studies, since out of the alternative hypotheses tested, the best one that was able to recover observed patterns of mitochondrial diversity in modern and ancient Central European populations was one were immigration of populations from the Pontic steppes during the Bronze Age was explicitly simulated.

Human ancient DNA analyses reveal the high burden of tuberculosis in Europeans over the last 2,000 years

Tuberculosis (TB), usually caused by Mycobacterium tuberculosis bacteria, is the first cause of death from an infectious disease at the worldwide scale, yet the mode and tempo of TB pressure on humans remain unknown. The recent discovery that homozygotes for the P1104A polymorphism of TYK2 are at higher risk to develop clinical forms of TB provided the first evidence of a common, monogenic predisposition to TB, offering a unique opportunity to inform on human co-evolution with a deadly pathogen. Here, we investigate the history of human exposure to TB by determining the evolutionary trajectory of the TYK2 P1104A variant in Europe, where TB is considered to be the deadliest documented infectious disease. Leveraging a large dataset of 1,013 ancient human genomes and using an approximate Bayesian computation approach, we find that the P1104A variant originated in the common ancestors of West Eurasians ∼30,000 years ago. Furthermore, we show that, following large-scale population movements of Anatolian Neolithic farmers and Eurasian steppe herders into Europe, P1104A has markedly fluctuated in frequency over the last 10,000 years of European history, with a dramatic decrease in frequency after the Bronze Age. Our analyses indicate that such a frequency drop is attributable to strong negative selection starting ∼2,000 years ago, with a relative fitness reduction on homozygotes of 20%, among the highest in the human genome. Together, our results provide genetic evidence that TB has imposed a heavy burden on European health over the last two millennia.

Sometimes hidden but always there: the assumptions underlying genetic inference of demographic histories

Demographic processes directly affect patterns of genetic variation within contemporary populations as well as future generations, allowing for demographic inference from patterns of both present-day and past genetic variation. Advances in laboratory procedures, sequencing and genotyping technologies in the past decades have resulted in massive increases in high-quality genome-wide genetic data from present-day populations and allowed retrieval of genetic data from archaeological material, also known as ancient DNA. This has resulted in an explosion of work exploring past changes in population size, structure, continuity and movement. However, as genetic processes are highly stochastic, patterns of genetic variation only indirectly reflect demographic histories. As a result, past demographic processes need to be reconstructed using an inferential approach. This usually involves comparing observed patterns of variation with model expectations from theoretical population genetics. A large number of approaches have been developed based on different population genetic models that each come with assumptions about the data and underlying demography. In this article I review some of the key models and assumptions underlying the most commonly used approaches for past demographic inference and their consequences for our ability to link the inferred demographic processes to the archaeological and climate records. This article is part of the theme issue 'Cross-disciplinary approaches to prehistoric demography'.

SPLATCHE3: simulation of serial genetic data under spatially explicit evolutionary scenarios including long-distance dispersal

SUMMARY

SPLATCHE3 simulates genetic data under a variety of spatially explicit evolutionary scenarios, extending previous versions of the framework. The new capabilities include long-distance migration, spatially and temporally heterogeneous short-scale migrations, alternative hybridization models, simulation of serial samples of genetic data and a large variety of DNA mutation models. These implementations have been applied independently to various studies, but grouped together in the current version.

AVAILABILITY AND IMPLEMENTATION

SPLATCHE3 is written in C++ and is freely available for non-commercial use from the website http://www.splatche.com/splatche3. It includes console versions for Linux, MacOs and Windows and a user-friendly GUI for Windows, as well as detailed documentation and ready-to-use examples.

Inference of natural selection from ancient DNA

Evolutionary processes, including selection, can be indirectly inferred based on patterns of genomic variation among contemporary populations or species. However, this often requires unrealistic assumptions of ancestral demography and selective regimes. Sequencing ancient DNA from temporally spaced samples can inform about past selection processes, as time series data allow direct quantification of population parameters collected before, during, and after genetic changes driven by selection. In this Comment and Opinion, we advocate for the inclusion of temporal sampling and the generation of paleogenomic datasets in evolutionary biology, and highlight some of the recent advances that have yet to be broadly applied by evolutionary biologists. In doing so, we consider the expected signatures of balancing, purifying, and positive selection in time series data, and detail how this can advance our understanding of the chronology and tempo of genomic change driven by selection. However, we also recognize the limitations of such data, which can suffer from postmortem damage, fragmentation, low coverage, and typically low sample size. We therefore highlight the many assumptions and considerations associated with analyzing paleogenomic data and the assumptions associated with analytical methods.

FST between archaic and present-day samples

The increasing abundance of DNA sequences obtained from fossils calls for new population genetics theory that takes account of both the temporal and spatial separation of samples. Here, we exploit the relationship between Wright's F_ST and average coalescence times to develop an analytic theory describing how F_ST depends on both the distance and time separating pairs of sampled genomes. We apply this theory to several simple models of population history. If there is a time series of samples, partial population replacement creates a discontinuity in pairwise F_ST values. The magnitude of the discontinuity depends on the extent of replacement. In stepping-stone models, pairwise F_ST values between archaic and present-day samples reflect both the spatial and temporal separation. At long distances, an isolation by distance pattern dominates. At short distances, the time separation dominates. Analytic predictions fit patterns generated by simulations. We illustrate our results with applications to archaic samples from European human populations. We compare present-day samples with a pair of archaic samples taken before and after a replacement event.

Bayesian estimation of partial population continuity using ancient DNA and spatially explicit simulations

The retrieval of ancient DNA from osteological material provides direct evidence of human genetic diversity in the past. Ancient DNA samples are often used to investigate whether there was population continuity in the settlement history of an area. Methods based on the serial coalescent algorithm have been developed to test whether the population continuity hypothesis can be statistically rejected by analysing DNA samples from the same region but of different ages. Rejection of this hypothesis is indicative of a large genetic shift, possibly due to immigration occurring between two sampling times. However, this approach is only able to reject a model of full continuity model (a total absence of genetic input from outside), but admixture between local and immigrant populations may lead to partial continuity. We have recently developed a method to test for population continuity that explicitly considers the spatial and temporal dynamics of populations. Here, we extended this approach to estimate the proportion of genetic continuity between two populations, using ancient genetic samples. We applied our original approach to the question of the Neolithic transition in Central Europe. Our results confirmed the rejection of full continuity, but our approach represents an important step forward by estimating the relative contribution of immigrant farmers and of local hunter-gatherers to the final Central European Neolithic genetic pool. Furthermore, we show that a substantial proportion of genes brought by the farmers in this region were assimilated from other hunter-gatherer populations along the way from Anatolia, which was not detectable by previous continuity tests. Our approach is also able to jointly estimate demographic parameters, as we show here by finding both low density and low migration rate for pre-Neolithic hunter-gatherers. It provides a useful tool for the analysis of the numerous ancient DNA data sets that are currently being produced for many different species.

The female ancestor's tale: Long-term matrilineal continuity in a nonisolated region of Tuscany

OBJECTIVES

With the advent of ancient DNA analyses, it has been possible to disentangle the contribution of ancient populations to the genetic pool of the modern inhabitants of many regions. Reconstructing the maternal ancestry has often highlighted genetic continuity over several millennia, but almost always in isolated areas. Here we analyze North-western Tuscany, a region that was a corridor of exchanges between Central Italy and the Western Mediterranean coast.

MATERIALS AND METHODS

We newly obtained mitochondrial HVRI sequences from 28 individuals, and after gathering published data, we collected genetic information for 119 individuals from the region. Those span five periods during the last 5,000 years: Prehistory, Etruscan age, Roman age, Renaissance, and Present-day. We used serial coalescent simulations in an approximate Bayesian computation framework to test for continuity between the mentioned groups.

RESULTS

Our analyses always favor continuity over discontinuity for all groups considered, with the Etruscans being part of the genealogy. Moreover, the posterior distributions of the parameters support very small female effective population sizes.

CONCLUSIONS

The observed signals of long-term genetic continuity and isolation are in contrast with the history of the region, conquered several times (Etruscans, Romans, Lombards, and French). While the Etruscans appear as a local population, intermediate between the prehistoric and the other samples, we suggest that the other conquerors-arriving from far-had a consistent social or sex bias, hence only marginally affecting the maternal lineages. At the same time, our results show that long-term genealogical continuity is not necessarily linked to geographical isolation.