Editor's evaluation: Stable population structure in Europe since the Iron Age, despite high mobility

Full text Figures and data Side by side Abstract Editor's evaluation Introduction Results Discussion Materials and methods Appendix 1 Data availability References Decision letter Author response Article and author information Abstract Ancient DNA research in the past decade has revealed that European population structure changed dramatically in the prehistoric period (14,000–3000 years before present, YBP), reflecting the widespread introduction of Neolithic farmer and Bronze Age Steppe ancestries. However, little is known about how population structure changed from the historical period onward (3000 YBP - present). To address this, we collected whole genomes from 204 individuals from Europe and the Mediterranean, many of which are the first historical period genomes from their region (e.g. Armenia and France). We found that most regions show remarkable inter-individual heterogeneity. At least 7% of historical individuals carry ancestry uncommon in the region where they were sampled, some indicating cross-Mediterranean contacts. Despite this high level of mobility, overall population structure across western Eurasia is relatively stable through the historical period up to the present, mirroring geography. We show that, under standard population genetics models with local panmixia, the observed level of dispersal would lead to a collapse of population structure. Persistent population structure thus suggests a lower effective migration rate than indicated by the observed dispersal. We hypothesize that this phenomenon can be explained by extensive transient dispersal arising from drastically improved transportation networks and the Roman Empire's mobilization of people for trade, labor, and military. This work highlights the utility of ancient DNA in elucidating finer scale human population dynamics in recent history. Editor's evaluation This important study provides an impressive dataset containing more than 200 novel ancient human genome sequences and a creative, robust, novel approach for studying human migration across time. The authors' conclusions are well supported by the data, and the methods used are convincing and solid. This paper will be of great interest to population geneticists and other scholars in the field of paleogenomics. https://doi.org/10.7554/eLife.79714.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Ancient DNA (aDNA) sequencing has provided immense insight into previously unanswered questions about human population history. Initially, sequencing efforts were focused on identifying the main ancestry groups and transitions during prehistoric times, for which there is no written record. Recently, aDNA sampling has expanded to more recent times, allowing the study of movements of people using genetic data alongside the well-studied historical record. However, we lack a comprehensive assessment of historical genetic structure, including characterizing genetic heterogeneity and interactions across regions. Integrating historical period genetics will be instrumental to better understanding the development of European and Mediterranean population structure from prehistoric to present-day. Prehistoric ancient genomes have allowed disentangling the movements of people and technologies across two major demographic transitions in prehistoric western Eurasia: first the farming transition ~7500 BCE (Lazaridis et al., 2014; Skoglund et al., 2012), and later the Bronze Age Steppe migrations ~3500 BCE (Haak et al., 2015). Over the course of generations, genetically differentiated peoples across western Eurasia came together and admixed. As a result, most present-day European genomes can be modeled as a three-way mixture of these prehistoric groups: Western Hunter-Gatherers, Neolithic farmers, and Bronze Age Herders from the Steppe (Haak et al., 2015; Lazaridis et al., 2014) with minor contributions from other groups (Antonio et al., 2019; Fernandes et al., 2020; Lazaridis et al., 2016; Morrison et al., 2020; Mathieson et al., 2018). These ancestry components are present at different proportions across western Eurasia, leading to a pattern where the genetic structure of Europe mirrors its geography (Novembre et al., 2008). Given that the major ancestry components of present-day west Eurasians were largely established by the end of the Bronze Age, it is unclear how and what types of demographic processes impacted the genetic make-up of western Eurasia over the last ~3000 years, from the end of the Bronze Age to present-day. Recent studies of historical period genomes from individual regions shed light on this question; they paint a picture of heterogeneity and mobility, rather than of stable population structure. In the city of Rome alone, the population was dynamic and harbored a large diversity of ancestries from across Europe and the Mediterranean from the Iron Age (~1000 BCE) through the Imperial Roman period (27 BCE-300 CE; Antonio et al., 2019). Historical genomes from the Iberian Peninsula also highlight gene flow from across the Mediterranean (Olalde et al., 2019). These regional reports fit well with archaeological and historical records. By the Iron Age, sea travel was already common, enabling peoples from across the Mediterranean to come into contact for trade (Abulafia, 2011; Broodbank, 2013). Subsequently, the Roman Empire leveraged its organization, labor force, and military prowess to build upon existing waterways and roads throughout Europe and create a united Mediterranean for the only time in history (Beard, 2015; Harper, 2017; Symonds, 2017). Not only did the Empire provide a means for movement, it also provided a reason for individuals to move. Empire building activities, broadly categorized into military, labor, and trade, pulled in people and resources from inside and outside the Empire (Scheidel, 2019). We sequenced 204 new historical period genomes from across Europe and the Mediterranean to more comprehensively investigate the Roman Empire's impact on the genetic landscape suggested by these regional reports of heterogeneous, mobile populations. By analyzing genetic similarities between individuals across historical Eurasia, we were able to quantify individual movements during this time. Based on population genetic simulations, we explore potential explanations of how population structure may be maintained in the face of frequent individual dispersal. Results 204 new historical genomes from Europe and the Mediterranean We collected whole genomes from 204 individuals across 53 archaeological sites in 18 countries spanning Europe and the Mediterranean (Figure 1—figure supplement 1), 26 of these individuals were recently reported (Moots et al., 2022). This collection includes the first historical genomes (Iron Age and later, i.e. after 1000 BCE) from present-day Armenia, Algeria, Austria, and France. Dates for 126 samples were directly determined through radiocarbon dating, and were used alongside archaeological contexts to infer dates for the remaining samples. DNA was extracted from either the powdered cochlear portion of the petrous bone (n=203) or from teeth (n=1). Libraries were partially treated with uracil-DNA glycosylase (UDG) and screened for ancient DNA damage patterns, high endogenous DNA content, and low contamination. We performed whole genome sequencing to a median depth of 0.92 x (0.16x to 2.38x). For downstream integration with published data, pseudohaploid genotypes were called for the 1240 k SNP panel (Mathieson et al., 2015), resulting in a median of 685,058 SNPs (167,000–1,029,345) per sample. We analyzed newly reported genomes in conjunction with 2033 present-day genomes, 1998 prehistoric genomes, and 764 published historical period genomes (Clemente et al., 2021; Kovacevic et al., 2014, Mallick et al., 2023; Pagani et al., 2016; Saupe et al., 2021; Žegarac et al., 2021, primary AADR sources cited in Materials and methods). Genomes were grouped by regions and time periods (Figure 1) and analyzed using principal component analysis (PCA) and qpAdm modeling (Haak et al., 2015). Figure 1 with 1 supplement see all Download asset Open asset Timeline of new and published genomes. (A) 204 newly reported genomes (black circles) are shown alongside published genomes (gray circles), ordered by time and region (colored the same way as in B). (B) Sampling locations of newly reported (black) and published (gray) genomes are indicated by diamonds, sized according to the number of genomes at each location. Local historical population structure varies across regions To investigate historical population structure, we categorized the data into 14 geographical regions, split into three sub-periods of the historical period: Iron Age (1000–1 BCE), Imperial Rome & Late Antiquity (1–700 CE), and Medieval Ages & Early Modern (700–1950 CE). We then characterized inter-individual heterogeneity within these spatio-temporal groups by examining (1) variation of projections onto a PCA space of present-day genomes (Figure 2—figure supplement 1), (2) genetic groups identified by qpAdm and clustering across time within a region, and (3) admixture modeling of genetic groups. A majority of regions have highly heterogeneous populations in at least one historical time period (Figure 2—figure supplement 2). This is illustrated by both the visual spread in PCA and the genetically distinct clusters of individuals based on pairwise modeling with qpAdm (Haak et al., 2015; Harney et al., 2021). On average, we identified 10 genetic clusters within each region present during the historical period, with a minimum of two and a maximum of 23. With genetically similar samples grouped together, we have more power relative to individual-level analyses when performing admixture modeling on clusters of interest using qpAdm. Regional vignettes reveal various patterns of historical population structure. In Armenia, for example, the population is highly homogeneous at any given time (Figure 2). After the Copper Age, there are two distinct genetic clusters, separated by a temporal split around 772–403 BCE (Figure 2BC). The earlier cluster (C1) includes newly reported samples (n=5) from Beniamin and published ones (n=6) from five other sites. This cluster cannot be modeled by any single source of ancestry using existing data. The later cluster (C3), which contains newly reported samples (n=12) from Beniamin dating between 403 BCE-500 CE, is genetically similar to present-day Armenians (excluding two Kurdish individuals; Figure 2C). Despite the split, there is evidence of partial continuity between the earlier and later clusters: the later (C3) can be modeled using around 50% of the earlier cluster (C1) and an additional source of Steppe ancestry. Historical genomes from Northern Europe, particularly newly reported genomes from Lithuania and Poland, exhibit a similar level of homogeneity (Figure 2—figure supplement 2). Figure 2 with 3 supplements see all Download asset Open asset Armenia: two homogeneous genetic clusters distinguished by a temporal shift. (A) Sampling locations of ancient genomes (open circles) colored by their genetic cluster identified using qpAdm modeling. (B) Date ranges for the genomes: each line represents the 95% confidence interval for the radiocarbon date or the upper and lower limit of the inferred date, and the point represents the midpoint of that range. (C) Projections of the genomes onto a PCA of present-day genomes (gray points labeled by their population). Present-day genomes from Armenia are shown with dark gray open circles. In contrast to the homogeneity of the Armenian population, most of the regions, including Italy, Southeastern Europe, and Western Europe, had strikingly heterogeneous populations. Newly collected samples reinforce previous findings of high heterogeneity in Rome, including a large portion of the population having affinities for present-day Near Eastern populations (Antonio et al., 2019; Posth et al., 2021; Figure 3—figure supplement 1). Interestingly, Southeastern European and Western European individuals during the Imperial Roman & Late Antiquity period also exhibit high heterogeneity, on par with that of contemporaneous Italy (Figures 3 and 4). Figure 3 with 1 supplement see all Download asset Open asset Southeastern Europe: highly heterogeneous Imperial Roman and Late Antiquity period population. (A) Sampling locations of genetic clusters are represented by a single point per location. Outlier ancestries are black stars, all others are open circles colored by genetic cluster. (B) Colored bars span the minimum and maximum of the date ranges of samples (95% confidence interval from radiocarbon dating or archaeological range). Points are the mean of an individual's date range. (C) Projections of the ancient genomes onto a PCA of present-day genomes (gray points). Population labels for the PCA reference space are shown in Figure 2C. Present-day genomes from Southeastern Europe are shown with dark gray open circles. Figure 4 Download asset Open asset Western Europe: heterogeneous Imperial Roman and Late Antiquity period population. (A) Sampling locations of genetic clusters are represented by a single point per location. Outlier ancestries are black stars, all others are open circles colored by genetic cluster. (B) Colored bars span the minimum and maximum of the date ranges of samples (95% confidence interval from radiocarbon dating or archaeological range). Points are the mean of an individual's date range. (C) Projections of the ancient genomes onto a PCA of present-day genomes (gray points). Population labels for the PCA reference space are shown in Figure 2C. Present-day genomes from Southeastern Europe are shown with dark gray open circles. Furthermore, these ancestries are often shared across regions. In Southeastern Europe, a core group of individuals have ancestry similar to that of present-day and contemporaneous Central Europeans (C6), while other clusters have ancestry similar to that of Northern Europeans (C7) and Eastern Mediterraneans (C8) (Figure 3C). These ancestry groups are found in contemporaneous Italy and Western Europe as well (Figure 4C, Figure 3—figure supplement 1). We also observe individuals of eastern nomadic ancestry, similar to that of Sarmatian individuals previously reported, in both Western Europe (C8, n=2) and Southeastern Europe (C2, n=2). Overall, we see remarkable local genetic heterogeneity as well as cross-regional similarities which point to common ancestry sources and, on a broader scale, demographic events affecting different regions in similar ways. At least 7-11% of historical individuals are ancestry outliers The high regional genetic heterogeneity with long range, cross-regional similarities suggests historical populations were highly mobile. We therefore sought to quantify the amount of movement during the historical period by estimating the proportion of individuals who are ancestry outliers with respect to all individuals found in the same region. We considered an individual an outlier if they belonged to an ancestry cluster that is underrepresented (consisting of fewer than 5% of individuals in a region or at most two individuals) within their sampling region from the Bronze Age up to present-day. To focus on first-generation migrants as well as long-range movements, we further identified outlier individuals who can be modeled as 100% (i.e. 'one-component model') of a majority ancestry cluster found in a different region. In total, we identified 11% of individuals as outliers, and could connect 7% of individuals to a putative source in a different region (Figure 5A). Based on the regions where these outliers and their sources originated, we created a network to illustrate their movements (Figure 5B). This network reveals the interconnectedness of Europe and the Mediterranean during the historical period. For example, as discussed above, the Armenian population is quite homogeneous (Figure 2). Unsurprisingly, no outliers were found within Armenia; however, we found outlier individuals in the Levant and Italy who can be putatively traced back to Armenia according to their ancestry (Figure 5C; blue outgoing arrows from Armenia). In contrast, the heterogeneous population in Italy connects it to many other regions, with bi-directional movement in most cases. In North Africa, outliers found in Iron Age Tunisia (Moots et al., 2022) indicate movements from many regions in Europe, and North African-like outliers were found in Italy and Austria (Western Europe). North African ancestry in Italy is supported by a single previously reported individual from the Imperial Roman period (R132; Antonio et al., 2019). Similar North African ancestry in Western Europe is supported by a single individual, R10667, from Wels, Austria, a site located on the frontier of the Roman Empire (C28 in Figure 4). This individual from Austria can be modeled using Canary Islander individuals from the Medieval Ages or an Iron Age outlier (distinguished by having more sub-Saharan ancestry) from Kerkouane, a Punic city near Carthage in modern-day Tunisia. Figure 5 with 3 supplements see all Download asset Open asset Ancestry outliers and their potential sources. (A) The proportions of outliers in each region were determined by individual pairwise qpAdm modeling followed by clustering. (B) Sources were inferred by one component qpAdm modeling of resulting clusters with all genetic clusters in the dataset. In the network visualizations, nodes are regions and directed edges are drawn from sources to outliers (i.e. potential migrants). The full network of source to outlier is shown. (C) Examples of individual regions are shown in greater detail. The 7% estimate for outliers with source should be considered conservative for the proportion of 'non-local' individuals. There are several cases where a cluster comprises more than 5% of the individuals in the region, but are clearly of a different ancestry than the majority and seem to be transient (only found in a single sub-period of the historical period). For example, in Southeastern Europe (Figure 3B), Imperial Roman & Late Antiquity individuals in C8 are (1) of distant ancestry (Near Eastern) and (2) not found in previous or subsequent time periods. However, since there are five individuals in this cluster, it does not meet our strict criteria for outlier consideration. Additionally, many clusters of underrepresented ancestry cannot be modeled as one-component models because they are recently admixed (i.e. require two or more ancestry components) or of ancestry not sampled elsewhere. Thus, we expect the actual proportion of individuals involved in long distance movements to be higher than reported here. Spatial population structure is relatively stable in the last 3,000 years The remarkable amount of heterogeneity and mobility in the historical period leads to the question of what impact this might have had on population structure over time. To investigate this, we sought to quantify the overall change in population structure across time, from prehistoric to present-day. To assess the spatial structure of population differentiation, we calculated FST across groups of individuals on a sliding spatial grid in each time period and related it to their mean geographic distance. In each time period, we recovered the classical pattern of isolation-by-distance (Figure 6A), where individuals closer in geographic space are also more similar genetically. Across time periods, we see a large decrease in overall FST from the Mesolithic & Neolithic periods to the Bronze Age (approximately 10,000–2300 BCE), coinciding with the major prehistoric migrations (Haak et al., 2015; Lazaridis et al., 2014). From the Bronze Age onward, however, FST does not decrease further with time, indicating that the level of genetic differentiation across space is relatively stable from the Bronze Age to present-day. Figure 6 Download asset Open asset Relatively stable population structure from Bronze Age to present-day. (A) Overall genetic differentiation between populations (measured by FST) and its relationship to geographical distance (spatial structure) is similar from Bronze Age onward. Confidence intervals were calculated through a bootstrap procedure, using 200 bootstrap replicates. (B) In PC space, each genome is represented by a point, colored based on their origin (for present-day individuals) or sampling location (for historical samples). The PC space is established by present-day samples (bottom), onto which either historical period (middle) or prehistoric genomes (top) were projected. For projections, the present-day samples are shown in gray, and their extent is visualized by a gray polygon. To assess not only the amount, but also the structure of geographic population differentiation, we compared the 'genetic maps' of historical period and present-day genomes. To construct these 'maps', we performed principal component analysis on 829 present-day European and Mediterranean genomes sampled across geographical space (Figure 6B, bottom) and projected historical period genomes onto the same PC space. Echoing close correspondence between genetic structure and geographic space in present-day Europeans (Novembre et al., 2008), we recovered similar spatial structure for historical samples as well, although noisier due to a narrower sampling distribution and higher local genetic heterogeneity (Figure 6B, middle). The similarity in structure between present-day and historical period is especially striking in comparison to a projection of prehistoric genomes, which correspondence to the present-day PCA as well as to geographic space (Figure 6B, our analyses indicate that European and Mediterranean population structure has relatively stable over the last This the is it for stable population structure to be maintained in the of long-range To address this, we populations in space. In these simulations, spatial population structure is established through local and which we to the spatial differentiation observed in Europe (Figure Figure and Figure supplement maximum FST of We then allowed a proportion of the population to the migration we observed in the data during the historical period (Figure supplement 2). with long-range dispersal as low as we observe FST over years with a time of as individuals differentiated genetically across space (Figure At FST dramatically within as spatial structure to the point that it is in the first two principal components (Figure These indicate that under a spatial population genetics we would expect structure to collapse by present-day given the of movement we Figure with 2 supplements see all Download asset Open asset of population structure with and long-range dispersal. (A) A of spatial structure is established by dispersal rate to a maximum FST of across the spatial and visualized using In to this either (B) or (C) of individuals and the is by analyzing spatial FST through time, as well as PCA after of long-range dispersal. Discussion In we observed largely stable spatial population structure across western Eurasia and high mobility of people by local genetic heterogeneity and cross-regional These two are with each other under standard population genetics A for this is that our did not some of human and population In the migration both movement and with local However, in human populations migration can be more people not where they and is not We hypothesize that in the historical period there was an of movement and compared to prehistoric For the spread of and Steppe ancestry, we that these prehistoric migrations would of years to the et al., 2015; et al., 2015; Lazaridis et al., In contrast, in the historical period, there were travel networks of roads and waterways as well as for cross-Mediterranean and movement (Abulafia, 2011; 2015; Broodbank, Symonds, 2017). This people to travel on the of or well within their (Figure supplements 2 and 2015). The Roman Empire is particularly important in understanding how transient mobility could a of this period. the of the existing and new expanded as for trade and the frontier as military established and in local which sought or et al., 2019). To these human the local population was thus in people from either or (e.g. to a historical the the of migrants in to the rate over the a process which would to individuals as migrants transient with the Roman Empire's highly travel networks et al., may the genetically heterogeneous especially the frontier regions (e.g. and With transient mobility as the main to the observed heterogeneity, it unclear what additional demographic processes to the of spatial genetic structure. The collapse of the Empire involved a of and of followed by 2015; et al., the Empire trade and movement, there may have little for individuals to in these regions. this is we would expect a in local genetic heterogeneity after the collapse of the we not have this period sampled to assess this The lack of samples is further by the that ancient DNA from archaeological which to be in a in the city for example, will more than a more for This it to comprehensively address in more genetic data from both and contexts across the historical period will be a in understanding how spatial population structure was Furthermore, it could the of other historical events and as the and during the Based on genetic analyses and the historical we hypothesize that both the of transient migrants which to population heterogeneity, as well as by heterogeneous, but local populations could have overall of genetic structure from the Iron Age to present-day. This work highlights the utility of ancient DNA in population dynamics through genetic through time and the of historical contexts to these Materials and methods collection and archaeological sites a The archaeological for ancient individuals reported in this study is in were written by the of individual-level are where Sampling was performed to across Europe and the Mediterranean, as to site level We particularly focused on regions where there was no published data for the Imperial Roman and Late Antiquity at the time of sampling (e.g. Austria, the Armenia, and North we to samples in the Imperial Roman and Late Antiquity period (approximately 1 CE), some samples outside of this period due to availability lack of date at the time of sampling to radiocarbon Date for individuals and time periods a of time periods and were by (1) the geographic

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