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Post by Admin on May 28, 2022 21:57:47 GMT
In contrast to the homogeneity of the Armenian population, most of the regions, including Italy, Southeastern Central 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 Central 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. Southeastern Central Europe: highly heterogeneous Imperial Roman and Late Antique period population. (A) Genetic clusters of ancient genomes (open circles) and outliers (black stars) identified using qpWave and clustering, with characteristics of each genetic group shown in a table. (B) Sampling locations of ancient genomes (open circles) colored by their genetic cluster. (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 Central Europe are shown with black open circles.  Figure 4. Western Europe: heterogeneous Imperial Roman and Late Antique period population. (A) Genetic clusters of ancient genomes (open circles) and outliers (black stars) were identified using qpWave and clustering, with characteristics of each genetic group shown in a table. (B) Sampling locations of ancient genomes (open circles) colored by their genetic cluster. (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. Furthermore, these ancestries are often shared across regions. In Southeastern Central Europe, a core group of individuals have ancestry similar to that of present-day and contemporaneous Central Europeans (C10), while other clusters have ancestry similar to that of Northern Europeans (C4) and Eastern Mediterraneans (C6) (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 outliers of eastern nomadic ancestry, similar to that of Sarmatian individuals previously reported, in both Western Europe (C5, n=2) and Southeastern Central Europe (C1, 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. |
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Post by Admin on May 29, 2022 19:46:06 GMT
At least 8% 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 (less than 5%, or fewer than 2 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% of another ancestry cluster found in a different region (henceforth “outliers with source”). When there were multiple valid one-component models, we performed model competition to identify the best source. In total, we identified 8% of individuals as outliers with source (Figure 5A). Of the valid sources for these outliers, we selected only clusters that were non-outliers within their own regions, and created a network to illustrate movements between outliers and source locations (Figure 5C). 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, Italy, and North Africa 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 reciprocal 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 (C18 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. Ancestry outliers and their potential sources. (A) The proportions of outliers in each region were determined by individual pairwise qpWave 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 estimate of 8% 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 Central Europe (Figure 3B), Imperial Roman & Late Antiquity individuals in C6 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 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. |
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Post by Admin on May 30, 2022 18:42:26 GMT
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 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. 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. (B and C) 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). 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 all 1713 present-day European and Mediterranean genomes sampled across geographical space (Figure 6B) 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 6C).Together, our analyses indicate that European and Mediterranean population structure has been relatively stable over the last 3,000 years. This raises the question: Is it surprising for stable population structure to be maintained in the presence of ∼8% long-range migration? To address this, we simulated Wright-Fisher populations evolving neutrally in continuous space. In these simulations, spatial population structure is established through local mate choice and limited dispersal, which we calibrated to approximately match the spatial differentiation observed in historical-period Europe (Figure 6A, Figure 7A and Figure 7 - figure supplement 1, maximum FST of ∼0.03). We then allowed a proportion of the population to disperse longer distances, empirically matching the migration distances we observed in the data during the historical period (Figure 7 - figure supplement 2). Even with long-range dispersal as low as 4%, we observe decreasing FST over 120 generations (∼3000 years with a generation time of 25 years) as individuals become less differentiated genetically across space (Figure 7B). At 8%, FST decreases dramatically within 120 generations as spatial structure collapses to the point that it is hardly detectable in the first two principal components (Figure 7C). These simulations indicate that under a basic spatial population genetics model we would expect structure to collapse by present-day given the levels of movement we observe.  Figure 7. Simulation of population structure with and without long-range dispersal. (A) A base model of spatial structure is established by calibrating per-generation dispersal rate to generate a maximum FST of ∼0.03 across the maximal spatial distance, and visualized using PCA. In addition to this base dispersal, either 4% (B) or 8% (C) of individuals disperse longer distances, and the effect is tracked by analyzing spatial FST through time, as well as PCA after 120 generations of long-range dispersal. |
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Post by Admin on May 31, 2022 17:49:38 GMT
Discussion
In summary, we observed largely stable spatial population structure across western Eurasia and high mobility of people evidenced by local genetic heterogeneity and cross-regional connections. These two observations are seemingly incompatible with each other under standard population genetics assumptions.
A possible explanation for this apparent paradox is that our simulations did not capture some key features of human behaviors and population dynamics. In the populations we simulate, migration implies both movement and reproduction with local random mate choice. However, in real human populations migration can be more complex: people do not necessarily reproduce where they migrate, and reproduction is not necessarily random. We hypothesize that in the historical period there was an increasing decoupling of movement and reproduction, compared to prehistoric times. For the spread of Farmer and Steppe ancestry, we know that these prehistoric migrations would take hundreds of years to traverse the continent (Allentoft et al., 2015; Haak et al., 2015; Lazaridis et al., 2016). In contrast, in the historical period, there were dense travel networks through roads and waterways as well as clear incentives for cross-Mediterranean and cross-continental movement (Abulafia, 2011; Beard, 2015; Broodbank, 2013; Symonds, 2017). This enabled people to travel cross-continental distances on the order of weeks or months, well within their lifetimes (Figure 5 - figure supplement 2, 3) (Scheidel, 2015).
The Roman Empire is particularly important in understanding how transient mobility could become a unique hallmark of this period. During the expansion of the Empire, existing and new cities quickly expanded as hubs for trade and labor. Urban-military complexes emerged along the frontier as military forces established themselves and drew in local communities which sought protection or economic benefit (Séguy, 2019). To support these rapidly growing economic city-centers, human capital beyond the local population was necessary, thus drawing in people from far away places either freely or forcibly (e.g. slavery, military). According to a longstanding historical hypothesis, the Urban Graveyard Effect, the influx of migrants in city-centers disproportionately contributed to death rate over birth rate; a process which would contribute to observing individuals as “transient” migrants (de Ligt and Tacoma, 2016). Long-range, transient migration, combined with the Roman Empire’s highly efficient travel networks (Cherry, 2007; Oleson, 2008; Scheidel, 2015) may explain the genetically heterogeneous populations, especially along the frontier regions (e.g. Serbia, Croatia, and Austria).
With transient mobility as the main contributor to the observed heterogeneity, it remains unclear what additional demographic processes contributed to the maintenance of spatial genetic structure. The collapse of the Empire involved a loss of urban-military complexes and depopulation of cities, followed by ruralization (Burgess, 2007; Dey, 2015; Roymans et al., 2020). Without the Empire incentivising trade and movement, there may be little motive for individuals to remain in these now remote regions.
If this hypothesis is true, we would expect a reduction in local genetic heterogeneity after the collapse of the Empire. Unfortunately, we do not have this period sampled densely enough to assess this comprehensively. The lack of samples is further amplified by the fact that ancient DNA comes from archaeological excavations, which tend to be enriched in urban areas; a stone mausoleum in the city center, for example, will produce more surface scatter than a wood farmhouse, making urban areas more likely for excavation (Bowes, 2011). This makes it difficult to comprehensively address differences in rural versus urban demography. Collecting more genetic data from both urban and rural contexts across the historical period will be a valuable future step in understanding how spatial population structure was maintained. Furthermore, it could elucidate the role of other historical events and peoples, such as the Franks, Lombards, Visigoths, and Huns, during the Migration Period.
Based on genetic analyses and the rich historical record, we hypothesize that both the loss of transient migrants which contributed to population heterogeneity, as well as repopulation by less heterogeneous, but temporally stable, local populations could have helped maintain overall stability of genetic structure from the Iron Age to present-day. This work highlights the utility of ancient DNA in revealing complex population dynamics through direct genetic observations through time and the importance of integrating historical contexts to understand these complexities.
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Post by Admin on May 31, 2022 21:58:17 GMT
 Figure 1 - figure supplement 1. Detailed map of locations for newly reported samples. Each circle represents a location, the size of the circle corresponds to the number of individuals sampled from that location. Circles are colored by their time period: Bronze Age is green (Pian Sultano), Iron Age is yellow (two recently reported sites Tarquinia and Kerkouane), Imperial Rome and Late Antiquity is dark blue, Medieval Ages and Early Modern are light blue (Palazzo della Cancelleria, Velić, Gardun, Mirine-Fulfinum). Note that the Bronze Age and Iron Age sites were recently reported in (Moots et al., 2022).  Figure 2 - figure supplement 1. Principal component analysis of present-day genomes from Europe and the Mediterranean. PCA was performed on 829 individuals (480,712 snps) using smartpca v1600. The following parameters were used: 5 outlier iterations (numoutlieriter), 10 principal components along which to remove outliers (numoutlierevec), altnormstyle set to NO, with least squares projection turned on (lsqproject set to YES).  Figure 2 - figure supplement 2. Ancestry clusters identified within regions. Each row displays data from a single study region. The first column shows a map with the sampling locations for the individuals, while columns two through four show the individuals projected onto a PCA space of present-day genomes (gray points) (populations are labeled in the far right panel in row 1 and in Figure 2 - figure supplement 1). Individual ancient genomes in the map and PCA panels are colored by ancestry clusters identified using qpWave. Colors are not matched across regions. Star points are putative outliers, i.e. individuals with ancestry that is underrepresented in the region. They are not colored by ancestry clusters so as to reduce visual clutter. |
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