Between 5,000 and 6,000 years ago, many Neolithic societies declined throughout western Eurasia due to a combination of factors that are still largely debated. Here, we report the discovery and genome reconstruction of Yersinia pestis, the etiological agent of plague, in Neolithic farmers in Sweden, pre-dating and basal to all modern and ancient known strains of this pathogen. We investigated the history of this strain by combining phylogenetic and molecular clock analyses of the bacterial genome, detailed archaeological information, and genomic analyses from infected individuals and hundreds of ancient human samples across Eurasia. These analyses revealed that multiple and independent lineages of Y. pestis branched and expanded across Eurasia during the Neolithic decline, spreading most likely through early trade networks rather than massive human migrations. Our results are consistent with the existence of a prehistoric plague pandemic that likely contributed to the decay of Neolithic populations in Europe.
We have evolved in the interpretation of the plague from 1) a Corded Ware-driven disease, to 2) a steppe disease that was spread by Yamna and Corded Ware, and now 3) a (potentially) Trypillia-driven disease that spread to the west earlier than Yamna and Corded Ware, but probably also later east and west with both.
At least it still seems that the plague and its demographic consequences were a good reason for the expansion of Indo-Europeans and Uralians into Europe, as we thought…
Featured image, from the paper: “The predicted model of early dispersion of Y. pestis during Neolithic and Bronze Age was built by integrating phylogenetic information of Y. pestis strains from this period (Figure 1E), their divergence times (Figure 3), the geographic locations, carbon dating and genotypes of the individuals, and the archaeological record. The model suggests that early Y. pestis strains likely emerged and spread from mega-settlements in Eastern Europe (built by the Trypillia Culture) into Europe and the Eurasian steppe, most likely through human interaction networks. This was facilitated by wheeled and animal-powered transports, which are schematized in the map with red lines with arrows pointing in both senses. Our model builds upon a previous model (Andrades Valtuena et al., 2017) that proposed the spread of plague to be associated with large-scale human migrations (blue line).”
Overall, 96 samples ranging from Slovenian littoral to Lower Styria were genotyped for 713,599 markers using the OmniExpress 24-V1 BeadChips (Figure 1), genetic data were obtained from Esko et al. (2013). After removing related individuals, 92 samples were left. The Slovenian dataset has been subsequently merged with the Human Origin dataset (Lazaridis et al., 2016) for a total of 2163 individuals.
First, Y chromosome genetic diversity was assessed. A total of 52 Y chromosomes were analyzed for 195 SNPs. The majority of individuals (25, 48.1%) belong to the haplogroup R1a1a1a (R-M417) while the second major haplogroup is represented by R1b (R-M343) including 15 individuals (28.8%). Twelve samples are assigned to haplogroup I (I M170): five and two samples belong to haplogroup I2a (I L460) and I1 (I M253), respectively, while the remaining five samples did not have enough information to be further assigned.
Considering the unbalanced sample size of the Slovenian population compared to the other populations included in the dataset, a subset of 20 Slovenian individuals randomly sampled was used.
All Slovenian samples group together with Hungarians, Czechs, and some Croatians (“Central-Eastern European” cluster) as also suggested by the PCA. All Basque individuals with few French and Spanish cluster together (“Basque” cluster) while a “Northern-European” cluster is made of the majority of French, English, Icelanders, Norwegians, and Orcadians. Five populations contributed to the “Eastern-European” cluster including Belarusians, Estonians, Lithuanians, Mordovians, and Russians. Western and South Europe is split into two cluster: the first (“Western European” cluster) includes all Spanish individuals, few French, and some Italians (North Italy) while the second (“Southern-European” cluster) groups Sicilians, Greeks, some Croatians, Romanians, and some Italians (North Italy).
Admixture Pattern and Migration
All Slovenian individuals share common pattern of genetic ancestry, as revealed by ADMIXTURE analysis. The three major ancestry components are the North East and North West European ones (light blue and dark blue, respectively, Figure 3), followed by a South European one (dark green, Figure 3). Contribution from the Sardinians and Basque are present in negligible amount. The admixture pattern of Slovenians mimics the one suggested by the neighboring Eastern European populations, but it is different from the pattern suggested by North Italian populations even though they are geographically close.
Using ALDER, the most significant admixture event was obtained with Russians and Sardinians as source populations and it happened 135 ± 9.31 generations ago (Z-score = 11.54). (…) When tested for multiple admixture events (MALDER), we obtained evidence for one admixture event 165.391 ± 17.1918 generations ago corresponding to ∼2620 BCE (CI: 3101–2139) considering a generation time of 28 years (Figure 4), with Kalmyk and Sardinians as sources.
We then modeled the Slovenian population as target of admixture of ancient individuals from Haak et al. (2015) while computing the f3(Ancient 1, Ancient 2, Slovenian) statistic. The most significant signal was obtained with Yamnaya and HungaryGamba_EN (Z-score = -10.66), followed by MA1 with LBK_EN (Z-score -9.7) and Yamnaya with Stuttgart (Z-score = -8.6) used as possible source populations (Supplementary Figure 5).
We found a significant signal of admixture by using both pairs as ancient sources. Specifically, for the pair Yamnaya and Hungary_EN the admixture event is dated at 134.38 ± 23.69 generations ago (Z-score = 5.26, p-value of 1.5e-07) while for Yamnaya and LBK_EN at 153.65 ± 22.19 generations ago (Z-score = 6.92, p-value 4.4e-12). Outgroup f3 with Yamnaya put Slovenian population close to Hungarians, Czechs, and English, indicating a similar shared drift between these population with the Steppe populations (Supplementary Figure 6).
Not that any of this would come as a surprise, but:
PCA keeps supporting the common cluster of certain West, South, and East Slavs in a “Central-Eastern European” cluster, distinct from the “North-Eastern European” cluster formed by modern Finno-Ugrians, as well as ancient Finno-Ugrians of north-eastern Europe who were only recently Slavicized.
Admixture supports the same ancient ‘western’ (a core West+South+East Slavic) cluster, and the admixture event with Yamna + Hungary_EN is logically a proxy for Yamna Hungary being at the core of ancestral Central-East population movements related to Bell Beakers in the mid- to late 3rd millennium.
I don’t know where exactly this impulse for the theory of Russia being the cradle of Slavs comes from today (although there are some obvious political trends to revive 19th c. ideas), but it was always clear for everyone, including Russians, that East Slavs had migrated to the east and north and assimilated indigenous Finno-Ugrians, apart from Turkic-, Iranian-, and Caucasian-speaking peoples to the east. Genetics is only confirming what was clear from other disciplines long ago.
We report genome-wide ancient DNA from 49 individuals forming four parallel time transects in Belize, Brazil, the Central Andes, and the Southern Cone, each dating to at least ∼9,000 years ago. The common ancestral population radiated rapidly from just one of the two early branches that contributed to Native Americans today. We document two previously unappreciated streams of gene flow between North and South America. One affected the Central Andes by ∼4,200 years ago, while the other explains an affinity between the oldest North American genome associated with the Clovis culture and the oldest Central and South Americans from Chile, Brazil, and Belize. However, this was not the primary source for later South Americans, as the other ancient individuals derive from lineages without specific affinity to the Clovis-associated genome, suggesting a population replacement that began at least 9,000 years ago and was followed by substantial population continuity in multiple regions.
The D4h3a mtDNA haplogroup has been hypothesized to be a marker for an early expansion into the Americas along the Pacific coast (Perego et al., 2009). However, its presence in two Lapa do Santo individuals and Anzick-1 (Rasmussen et al., 2014) makes this hypothesis unlikely.
The patterns we observe on the Y chromosome also force us to revise our understanding of the origins of present-day variation. Our ancient DNA analysis shows that the Q1a2a1b-CTS1780 haplogroup, which is currently rare, was present in a third of the ancient South Americas. In addition, our observation of the currently extremely rare C2b haplogroup at Lapa do Santo disproves the suggestion that it was introduced after 6,000 BP (Roewer et al., 2013).
(…) Our discovery that the Clovis-associated Anzick-1 genome at ∼12,800 BP shares distinctive ancestry with the oldest Chilean, Brazilian, and Belizean individuals supports the hypothesis that an expansion of people who spread the Clovis culture in North America also affected Central and South America, as expected if the spread of the Fishtail Complex in Central and South America and the Clovis Complex in North America were part of the same phenomenon (direct confirmation would require ancient DNA from a Fishtail-context) (Pearson, 2017). However, the fact that the great majority of ancestry of later South Americans lacks specific affinity to Anzick-1 rules out the hypothesis of a homogeneous founding population. Thus, if Clovis-related expansions were responsible for the peopling of South America, it must have been a complex scenario involving arrival in the Americas of sub-structured lineages with and without specific Anzick-1 affinity, with the one with Anzick-1 affinity making a minimal long-term contribution. While we cannot at present determine when the non-Anzick-1 associated lineages first arrived in South America, we can place an upper bound on the date of the spread to South America of all the lineages represented in our sampled ancient genomes as all are ANC-A and thus must have diversified after the ANC-A/ANC-B split estimated to have occurred ∼17,500–14,600 BP (Moreno-Mayar et al., 2018a).
Studies of the peopling of the Americas have focused on the timing and number of initial migrations. Less attention has been paid to the subsequent spread of people within the Americas. We sequenced 15 ancient human genomes spanning Alaska to Patagonia; six are ≥10,000 years old (up to ~18× coverage). All are most closely related to Native Americans, including an Ancient Beringian individual, and two morphologically distinct “Paleoamericans.” We find evidence of rapid dispersal and early diversification, including previously unknown groups, as people moved south. This resulted in multiple independent, geographically uneven migrations, including one that provides clues of a Late Pleistocene Australasian genetic signal, and a later Mesoamerican-related expansion. These led to complex and dynamic population histories from North to South America.
The Australasian signal is not present in USR1 or Spirit Cave, but only appears in Lagoa Santa. None of these individuals has UPopA/Mesoamerican-related admixture, which ap-parently dampened the Australasian signature in South American groups, such as the Karitiana. These findings suggest the Australasian signal, possibly present in a structured ancestral NA population, was absent in NA prior to the Spirit Cave/Lagoa Santa split. Groups carrying this signal were either already present in South America when the ancestors of Lagoa Santa reached the region, or Australasian-related groups arrived later but before 10.4 ka (the Lagoa Santa 14C age). That this signal has not been previously documented in North America implies that an earlier group possessing it had disappeared, or a later-arriving group passed through North America without leaving any genetic trace. If such a signal is ultimately detected in North America it could help determine when groups bear-ing Australasian ancestry arrived, relative to the divergence of SNA groups.
Although we detect the Australasian signal in one of the Lagoa Santa individuals identified as a “Paleoamerican,” it is absent in other “Paleoamericans” (2, 10), including Spirit Cave with its strong genetic affinities to Lagoa Santa. This indicates the “Paleoamerican” cranial form is not associated with the Australasian genetic signal, as previously suggested (6), or any other specific NA clade (2). The cause of this cranial form, if it is representative of broader population pat-terns, evidently did not result from separate ancestry, but likely multiple factors, including isolation and drift and non-stochastic mechanisms.
The peopling of the Andean highlands above 2500 m in elevation was a complex process that included cultural, biological, and genetic adaptations. Here, we present a time series of ancient whole genomes from the Andes of Peru, dating back to 7000 calendar years before the present (BP), and compare them to 42 new genome-wide genetic variation datasets from both highland and lowland populations. We infer three significant features: a split between low- and high-elevation populations that occurred between 9200 and 8200 BP; a population collapse after European contact that is significantly more severe in South American lowlanders than in highland populations; and evidence for positive selection at genetic loci related to starch digestion and plausibly pathogen resistance after European contact. We do not find selective sweep signals related to known components of the human hypoxia response, which may suggest more complex modes of genetic adaptation to high altitude.
The positions of non-Tagar Iron Age groups in the MDS plot were correlated with their geographic position within the Eurasian steppe belt and with frequencies of Western and Eastern Eurasian mtDNA lineages in their gene pools. Series from chronological Tagar stages (similar to the overall Tagar series) were located within the genetic variability (in terms of mtDNA) of Scythian World nomadic groups (Figs 5 and 6; S4 and S6 Tables). Specifically, the Early Tagar series was more similar to western nomads (North Pontic Scythians), while the Middle Tagar was more similar to the Southern Siberian populations of the Scythian period. The Late Tagar group (Tes`culture) belonging to the Early Xiongnu period had the “western-most” location on the MDS plot with the maximal genetic difference from Xiongnu and other eastern nomadic groups (but see Discussion concerning the low sample size for the Tes`series).
In a comparison of our Tagar series with modern populations in Eurasia, we detected similarity between the Tagar group and some modern Turkic-speaking populations (with the exception of the Indo-Iranian Tajik population) (Fig 7; S2 Table). Among the modern Turkic-speaking groups, populations from the western part of the Eurasian steppe belt, such as Bashkirs from the Volga-Ural region and Siberian Tatars from the West Siberian forest-steppe zone, were more similar to the Tagar group than modern Turkic-speaking populations of the Altay-Sayan mountain system (including the Khakassians from the Minusinsk basin) (Fig 7).
Mitochondrial DNA diversity and genetic relationships of the Tagar population
Our results are not inconsistent with the assumption of a probable role of gene flow due to the migration from Western Eurasia to the Minusinsk basin in the Bronze Age in the formation of the genetic composition of the Tagar population. Particularly, we detected many mtDNA lineages/clusters with probable West Eurasian origin that were dominant in modern populations of different parts of Europe, Caucasus, and the Near East (such as K and HV6) in our Tagar series based on a phylogeographic analysis.
We detected relatively low genetic distances between our Tagar population and two Bronze Age populations from the Minusinsk basin—the Okunevo culture population (pre-Andronovo Bronze Age) and Andronovo culture population, followed by Afanasievo population from the Minusinsk Basin and Middle Bronze Age population from the Mongolian Altai Mountains (the region adjacent to the Minusinsk basin) (Figs 3 and 6; S3 and S5 Tables). Among West Eurasian part of our Tagar series we also observed haplogroups/sub-haplogroups and haplotypes shared with Early and Middle Bronze Age populations from Minusinsk Basin and western part of Eurasian steppe belt (Fig 4; S5 Table). Thus, our results suggested a potentially significant role of the genetic components, introduced by migrants from Western Eurasia during the Bronze Age, in the formation of the genetic composition of the Tagar population. It is necessary to note the relatively small size of available mtDNA samples from the Bronze Age populations of Minusinsk basin; accordingly, additional mtDNA data for these populations are required to further confirm our inference.
Another substantial part of the mtDNA pool of the Tagar and other eastern populations of the Scythian World is typical of populations in Southern Siberia and adjacent regions of Central Asia (autochthonous Central Asian mtDNA clusters). Most of these components belong to the East Eurasian cluster of mtDNA haplogroups. Moreover, the role of each of these components in the formation of the genetic composition of subsequent (to the present) populations in South Siberia and Central Asia could be very different. In this regard, cluster C4a2a (and its subcluster C4a2a1), and haplogroup A8 are of particular interest.
Genetic features of successive Tagar groups
We compared successive Tagar groups (Early, Middle, and Late Tagar) with each other and with other Iron Age nomadic populations to evaluate changes in the mtDNA pool structure. Despite the genetic similarity between the Early and Middle Tagar series and Scythian World nomadic groups (Figs 5 and 6; S4 and S6 Tables), there were some peculiarities. For example, the Early Tagar series was more similar to North Pontic Classic Scythians, while the Middle Tagar samples were more similar to the Southern Siberian populations of the Scythian period (i.e., completely synchronous populations of regions neighboring the Minusinsk basin, such as the Pazyryk population from the Altay Mountains and Aldy-Bel population from Tuva).
We observed differences in the mtDNA pool structure between the Early and the Middle chronological stages of the Tagar culture population, as evidenced by the change in the ratio of Western to Eastern Eurasian mtDNA components. The contribution of Eastern Eurasian lineages increased from about one-third (34.8%) in the Early Tagar group to almost one-half (45.8%) in the Middle Tagar group.
At the level of mtDNA haplogroups, we detected a decrease in the diversity of phylogenetic clusters during the transition from the Early Tagar to the Middle Tagar. This decline in diversity equally affected the West Eurasian and East Eurasian components of the Tagar mtDNA pool. It should be noted that this decrease can be partially explained by the smaller number of Middle Tagar than Early Tagar samples. Under a simple binomial approximation the mtDNA clusters, observed at frequencies of 6.3% and 11.7%, could be lost by chance in our Early (N = 46) and Middle (N = 24) Tagar samples, respectively. However, the simultaneous lack of several such clusters, with a total frequency in the gene pool of the Early group of 34.8%, is unlikely.
The observed reduction in the genetic distance between the Middle Tagar population and other Scythian-like populations of Southern Siberia(Fig 5; S4 Table), in our opinion, is primarily associated with an increase in the role of East Eurasian mtDNA lineages in the gene pool (up to nearly half of the gene pool) and a substantial increase in the joint frequency of haplogroups C and D (from 8.7% in the Early Tagar series to 37.5% in the Middle Tagar series). These features are characteristic of many ancient and modern populations of Southern Siberia and adjacent regions of Central Asia, including the Pazyryk population of the Altai Mountains. We did not obtain strong evidence for an intensification of genetic contact between the population of the Minusinsk basin and the Altai Mountains in the Middle Tagar period compared with the Early Tagar period. Although, several archaeologists have found evidence for the intensification of contact at the level of material culture, namely, a cultural influence of the population of the Altai Mountains (represented by the Pazyryk population) on the population of the Minusinsk basin (the Saragash Tagar group) [6, 71, 72].
Another important issue is the change in the genetic structure of the Tagar population during the transition from the Middle (Saragash) to the Late (Tes`) stage. The Late Tagar stage refers to the Xiongnu period. Many archaeologists suggest that the formation of the Tes`stage involved the direct cultural influence of the Xiongnu and/or related groups of nomads from more eastern regions of Central Asia [71, 73]. Some archaeologists have even suggested renaming the Tes`stage in the Tes`culture , emphasizing the role of new eastern cultural elements. If this influence also existed at the genetic level, then we would expect to observe new genetic elements in the Tes`gene pool, particularly those of East Eurasian origin.
Just a reminder of the recent session in ISBA 8 on expanding Scythians (and also Mongolians and Turks) spreading Siberian ancestry, usually (wrongly) identified as “Uralic-Yeniseian” based on modern populations (similar to how steppe ancestry is wrongly identified as “Indo-European”), see the following graphic including the Tagar population:
And also the poster by Alexander M. Kim et al. Yeniseian hypotheses in light of genome-wide ancient DNA from historical Siberia:
The relevance of ancient DNA data to debates in historical linguistics is an emphatic strand in much recent work on the archaeogenetics of Eurasia, where the discussion has focused heavily on Indo-European (Haak et al. 2015; Narasimhan et al. 2018; de Barros Damgaard et al. 2018a,b). We present new genome-wide ancient DNA data from a historical Siberian individual in relation to Yeniseian, an isolated language “microfamily” (Vajda 2014) that nonetheless sits at the center of numerous controversial proposals in historical linguistics and cultural interaction. Yeniseian’s sole surviving representative is Ket, a critically endangered language fluently spoken by only a few dozen individuals near the Middle Yenisei River of Central Siberia.
In strong contrast to the present-day picture, river names and argued substrate influences and loanwords in languages outside the current range of Yeniseian, as well as direct records from the Russian colonial period, indicate that speakers of extinct Yeniseian languages had a formerly much broader presence in the taiga of Central Siberia as well as further south in the mountainous Altai-Sayan region – and perhaps even further afield in Inner Asia (Vajda 2010; Gorbachov 2017; Blažek 2016). The consilience of these proposals with genetic data is not straightforward (Flegontov et al. 2015, 2017) and faces a major obstacle in the lack of genetic information from verifiable speakers of Yeniseian languages other than the Kets, who have had complex ongoing interactions with speakers of non-Yeniseian languages such as the Samoyedic Selkups. We attempt to remedy this with new historical Siberian aDNA data, orienting our search for common denominators and systematic difference in a broader landscape of concordance, discordance, and uncertainty at the interface of diachronic linguistics and genetics.
Our analysis of divergence times suggests the population lineage ancestral to modern-day Sardinia was effectively isolated from the mainland European populations ~140–250 generations ago, corresponding to ~4,300–7,000 years ago assuming a generation time of 30 years and a mutation rate of 1.25 × 10−8 per basepair per generation. (…) in terms of relative values, the divergence time between Northern and Southern Europeans is much more recent than either is to Sardinia, signaling the relative isolation of Sardinia from mainland Europe.
We documented fine-scale variation in the ancient population ancestry proportions across the island. The most remote and interior areas of Sardinia—the Gennargentu massif covering the central and eastern regions, including the present-day province of Ogliastra— are thought to have been the least exposed to contact with outside populations. We found that pre-Neolithic hunter-gatherer and Neolithic farmer ancestries are enriched in this region of isolation. Under the premise that Ogliastra has been more buffered from recent immigration to the island, one interpretation of the result is that the early populations of Sardinia were an admixture of the two ancestries, rather than the pre-Neolithic ancestry arriving via later migrations from the mainland. Such admixture could have occurred principally on the island or on the mainland before the hypothesized Neolithic era influx to the island. Under the alternative premise that Ogliastra is simply a highly isolated region that has differentiated within Sardinia due to genetic drift, the result would be interpreted as genetic drift leading to a structured pattern of pre-Neolithic ancestry across the island, in an overall background of high Neolithic ancestry.
We found Sardinians show a signal of shared ancestry with the Basque in terms of the outgroup f3 shared-drift statistics. This is consistent with long-held arguments of a connection between the two populations, including claims of Basque-like, non-Indo-European words among Sardinian placenames. More recently, the Basque have been shown to be enriched for Neolithic farmer ancestry and Indo-European languages have been associated with steppe population expansions in the post-Neolithic Bronze Age. These results support a model in which Sardinians and the Basque may both retain a legacy of pre-Indo-European Neolithic ancestry. To be cautious, while it seems unlikely, we cannot exclude that the genetic similarity between the Basque and Sardinians is due to an unsampled pre-Neolithic population that has affinities with the Neolithic representatives analyzed here.
While we can confirm that Sardinians principally have Neolithic ancestry on the autosomes, the high frequency of two Y-chromosome haplogroups (I2a1a1 at ~39% and R1b1a2 at ~18%) that are not typically affiliated with Neolithic ancestry is one challenge to this model. Whether these haplogroups rose in frequency due to extensive genetic drift and/or reflect sex-biased demographic processes has been an open question. Our analysis of X chromosome versus autosome diversity suggests a smaller effective size for males, which can arise due to multiple processes, including polygyny, patrilineal inheritance rules, or transmission of reproductive success. We also find that the genetic ancestry enriched in Sardinia is more prevalent on the X chromosome than the autosome, suggesting that male lineages may more rapidly trace back to the mainland. Considering that the R1b1a2 haplogroup may be associated with post-Neolithic steppe ancestry expansions in Europe, and the recent timeframe when the R1b1a2 lineages expanded in Sardinia, the patterns raise the possibility of recent male-biased steppe ancestry migration to Sardinia, as has been reported among mainland Europeans at large (though see Lazaridis and Reich and Goldberg et al.). Such a recent influx is difficult to square with the overall divergence of Sardinian populations observed here.
Once again, haplogroup R1b1a2 (M269), and only R1b1a2, related to male-biased, steppe-related Indo-European migrations…just sayin’.
NOTE. In fact, the increase in Neolithic ancestry found in south-west Ireland with expanding Bell Beakers (likely Proto-Beakers), coupled with the finding of I2a subclades in Megalithic cultures of western Europe, would support this replacement after the Cardial and Epi-Cardial expansions, which were initially associated with G2a lineages.
I am not convinced about a survival of Palaeo-Sardo after the Bell Beaker expansion, though, since there is no clear-cut cultural divide (and posterior continuity) of pre-Beaker archaeological cultures after the arrival of Bell Beakers in the island that could be identified with the survival of Neolithic languages.
We may have to wait for ancient DNA to show a potential expansion of Neolithic ancestry from the west, maybe associated with the emergence of the Nuragic civilization (potentially linked with contemporaneous Megalithic cultures in Corsica and in the Balearic Islands, and thus with an Iberian rather than a Basque stock), although this is quite speculative at this moment in linguistic, archaeological, and genetic terms.
Nevertheless, it seems that the association of a Basque-Iberian language with the Neolithic expansion from Anatolia (see Villar’s latest book on the subject) is somehow strengthened by this paper. However, it is unclear when, how, and where expanding G2a subclades were replaced by native I2 lineages.
The Alemanni were a confederation of Germanic tribes that inhabited the eastern Upper Rhine basin and surrounding region (Fig. 1) (1). Roman ethnographers mentioned the Alemanni, but historical records from the 3rd to the 6th century CE contain no regular description of these tribes (2). The upheaval that occurred during the European Migration Period (Völkerwanderung) partly explains the interchangeability of nomenclature with the contemporaneous Suebi people of the same region and periods of geographic discontinuity in the historical record (3). This diverse nomenclature reflects centuries of interactions between Romans and other Germanic groups such as the Franks, Burgundians, Thuringians, Saxons, and Bavarians. With the defeat of the Alemanni by Clovis I of the Franks in 497 CE, Alamannia became a subsumed Duchy of the Merovingian Kingdom. This event solidified the naming of the inhabitants of this region as Alemanni (3). From the 5th to the 8th century CE, integration between the Franks and the Alemanni was reflected by changed burial practices, with households (familia) buried in richly furnished graves (Adelsgrablege) (4). The splendor of these Adelsgräber served to demonstrate the kinship structure, wealth, and status of the familia and also the power of the Franks (Personenverbandstaaten, a system of power based on personal relations rather than fixed territory). Because inclusion in familia during the Merovingian period was not necessarily based on inheritance or provenance, debate continues on the symbolism of these burial rites (5).
The 7th century CE Alemannic burial site at Niederstotzingen in southern Germany, used circa 580 to 630 CE, represents the best-preserved example of such an Alemannic Adelsgrablege. (…)
Strontium and oxygen isotope data from the enamel showed that most individuals are local rather than migrants (Table 1, table S2, and fig. S2), except for individuals 10 and 3B. (…)
Analysis of uniparental markers
mtDNA haplogroups were successfully assigned to all 13 individuals (Table 1). Notably, there are three groups of individuals that share, among the assigned positions, identical haplotypes: individuals 4, 9, and 12B in haplogroup X2b4; individuals 1 and 3A in haplogroup K1a; and individuals 2 and 5 in haplogroup K1a1b2a1a.
Most individuals belong to the R1b haplogroup (individuals 1, 3A, 3C, 6, 9, 12A, 12B, and 12C), which has the highest frequency (>70%) in modern western European populations (20). Five individuals (1, 3A, 9, 12B, and 12C) share the same marker (Z319) defining haplogroup R1b1a2a1a1c2b2b1a1 [=ISOGG R1b1a1a2a1a1c2b2b1a1a] (…) individuals 1, 3A, and 6 have R1b lineage and marker Z347 (R1b1a2a1a1c2b2b) [=ISOGG R1b1a1a2a1a1c2b2b], which belongs to the same male ancestral lineage as marker Z319 [i.e. all R1b-U106]. Individual 3B instead carries NRY haplogroup G2a2b1, which is rare in modern north, west, and east European populations (<5%), only reaching common abundance in the Caucasus (>70%), southern Europe, and the Near East (10 to 15%)
Genomically, the individuals buried at Niederstotzingen can be split into two groups: Niederstotzingen North (1, 3A, 6, 9, 12B, and 12C), who have genomic signals that most resemble modern northern and eastern European populations, and Niederstotzingen South (3B and 3C), who most resemble modern-day Mediterraneans, albeit with recent common ancestry to other Europeans. Niederstotzingen North is composed of those buried with identifiable artifacts: Lombards (individual 6), Franks (individual 9), and Byzantines (individuals 3A and 12B), all of whom have strontium and oxygen isotope signals that support local provenance (fig. S2) (8). Just two individuals, 3B (Niederstotzingen South) and 10 (no sufficient autosomal data, with R1 Y-haplogroup), have nonlocal strontium isotope signals. The δ18O values suggest that individuals 10 and 3B may have originated from a higher-altitude region, possibly the Swiss-German Alpine foothills (8). Combined with the genome affinity of individual 3B to southern Europeans, these data provide direct evidence for incoming mobility at the site and for contact that went beyond exchange of grave goods (4). Familia had holdings across the Merovingian Kingdom and traveled long distances to maintain them; these holdings could have extended from northern Italy to the North Sea. Nobles displayed and accrued power by recruiting outside individuals into the household as part of their traveling retinue. Extravagant burial rites of these familia are symbolic evidence of the Frankish power systems based on people Personenverbandstaaten imposed from the 5th until the 8th century CE (4). The assignment of grave goods and the burial pattern do not follow any apparent pattern with respect to genetic origin or provenance, suggesting that relatedness and fellowship were held in equal regard at this burial.
Both kinship estimates show first-degree relatedness for pairs 1/3A, 1/6, 1/9, 3A/9, and 9/12B and second-degree relatedness for 1/12B, 3A/6, 3A/12B, and 6/9. Except for 12C, all of the Niederstotzingen North individuals are detectably and closely related. The Niederstotzingen South individuals are not detectably related to each other or any other members of the cohort. (…)
We demonstrated that five of the individuals (1, 3A, 6, 9, and 12B) were kin to at least second degree (Fig. 3 and tables S15 and S16); four of these were buried with distinguishable grave goods (discussed above and in fig. S1). These data show that at Niederstotzingen, at least in death, diverse cultural affiliations could be appropriated even within the same family across just two generations. This finding is somewhat similar to the burial of the Frankish King Childeric in the 5th century CE with a combination of Frankish and Byzantine grave goods that symbolized both his provenance and military service to the Romans (4). The burial of three unrelated individuals (3B, 3C, and 12C) in multiple graves beside the rest of the cohort would imply that this Alemannic group buried their dead based on a combination of familial ties and fellowship. One explanation could be that they were adopted as children from another region to be trained as warriors, which was a common practice at the time; these children were raised with equal regard in the familia (2, 4).
The 7th century CE burial in Niederstotzingen represents the best-preserved example of an Alemannic Adelsgrablege. The observation that burial of the remains was close to a Roman crossroads, orientated in a considered way, and associated with rich grave goods points to a noble gravesite of an Alemannic familia with external cultural influences. The high percentage of males in the burial site suggests that this site was intended for a ranked warrior group, meaning that the individuals are not representative of the population existing in 7th century CE Alemannia. The kinship estimates show that kinship structure was organized around the familia, which is defined by close association of related and unrelated individuals united for a common purpose. The apparent kinship structure is consistent with the hypothesized Personenverbandstaaten, which was a system by which Merovingian nobles enforced rule in the Duchies of Alemannia, Thuringia, Burgundy, and elsewhere. Beyond the origin of the grave goods, we show isotopic and genetic evidence for contact with communities external to the region and evidence for shared ancestry between northern and southern Europeans. This finding invites debate on the Alemannic power system that may have been highly influenced by mobility and personal relations.
Experienced researchers, particularly those interested in population structure and historical inference, typically present STRUCTURE results alongside other methods that make different modelling assumptions. These include TreeMix, ADMIXTUREGRAPH, fineSTRUCTURE, GLOBETROTTER, f3 and D statistics, amongst many others. These models can be used both to probe whether assumptions of the model are likely to hold and to validate specific features of the results. Each also comes with its own pitfalls and difficulties of interpretation. It is not obvious that any single approach represents a direct replacement as a data summary tool. Here we build more directly on the results of STRUCTURE/ADMIXTURE by developing a new approach, badMIXTURE, to examine which features of the data are poorly fit by the model. Rather than intending to replace more specific or sophisticated analyses, we hope to encourage their use by making the limitations of the initial analysis clearer.
The default interpretation protocol
Most researchers are cautious but literal in their interpretation of STRUCTURE and ADMIXTURE results, as caricatured in Fig. 1, as it is difficult to interpret the results at all without making several of these assumptions. Here we use simulated and real data to illustrate how following this protocol can lead to inference of false histories, and how badMIXTURE can be used to examine model fit and avoid common pitfalls.
STRUCTURE and ADMIXTURE are popular because they give the user a broad-brush view of variation in genetic data, while allowing the possibility of zooming down on details about specific individuals or labelled groups. Unfortunately it is rarely the case that sampled data follows a simple history comprising a differentiation phase followed by a mixture phase, as assumed in an ADMIXTURE model and highlighted by case study 1. Naïve inferences based on this model (the Protocol of Fig. 1) can be misleading if sampling strategy or the inferred value of the number of populations K is inappropriate, or if recent bottlenecks or unobserved ancient structure appear in the data. It is therefore useful when interpreting the results obtained from real data to think of STRUCTURE and ADMIXTURE as algorithms that parsimoniously explain variation between individuals rather than as parametric models of divergence and admixture.
For example, if admixture events or genetic drift affect all members of the sample equally, then there is no variation between individuals for the model to explain. Non-African humans have a few percent Neanderthal ancestry, but this is invisible to STRUCTURE or ADMIXTURE since it does not result in differences in ancestry profiles between individuals. The same reasoning helps to explain why for most data sets—even in species such as humans where mixing is commonplace—each of the K populations is inferred by STRUCTURE/ADMIXTURE to have non-admixed representatives in the sample. If every individual in a group is in fact admixed, then (with some exceptions) the model simply shifts the allele frequencies of the inferred ancestral population to reflect the fraction of admixture that is shared by all individuals.
Several methods have been developed to estimate K, but for real data, the assumption that there is a true value is always incorrect; the question rather being whether the model is a good enough approximation to be practically useful. First, there may be close relatives in the sample which violates model assumptions. Second, there might be “isolation by distance”, meaning that there are no discrete populations at all. Third, population structure may be hierarchical, with subtle subdivisions nested within diverged groups. This kind of structure can be hard for the algorithms to detect and can lead to underestimation of K. Fourth, population structure may be fluid between historical epochs, with multiple events and structures leaving signals in the data. Many users examine the results of multiple K simultaneously but this makes interpretation more complex, especially because it makes it easier for users to find support for preconceptions about the data somewhere in the results.
In practice, the best that can be expected is that the algorithms choose the smallest number of ancestral populations that can explain the most salient variation in the data. Unless the demographic history of the sample is particularly simple, the value of K inferred according to any statistically sensible criterion is likely to be smaller than the number of distinct drift events that have practically impacted the sample. The algorithm uses variation in admixture proportions between individuals to approximately mimic the effect of more than K distinct drift events without estimating ancestral populations corresponding to each one. In other words, an admixture model is almost always “wrong” (Assumption 2 of the Core protocol, Fig. 1) and should not be interpreted without examining whether this lack of fit matters for a given question.
Because STRUCTURE/ADMIXTURE accounts for the most salient variation, results are greatly affected by sample size in common with other methods. Specifically, groups that contain fewer samples or have undergone little population-specific drift of their own are likely to be fit as mixes of multiple drifted groups, rather than assigned to their own ancestral population. Indeed, if an ancient sample is put into a data set of modern individuals, the ancient sample is typically represented as an admixture of the modern populations (e.g., ref. 28,29), which can happen even if the individual sample is older than the split date of the modern populations and thus cannot be admixed.
This paper was already available as a preprint in bioRxiv (first published in 2016) and it is incredible that it needed to wait all this time to be published. I found it weird how reviewers focused on the “tone” of the paper. I think it is great to see files from the peer review process published, but we need to know who these reviewers were, to understand their whiny remarks… A lot of geneticists out there need to develop a thick skin, or else we are going to see more and more delays based on a perceived incorrect tone towards the field, which seems a rather subjective reason to force researchers to correct a paper.
A potential hindrance to our advice to upgrade from PCA graphs to PCA biplots is that the SNPs are often so numerous that they would obscure the Items if both were graphed together. One way to reduce clutter, which is used in several figures in this article, is to present a biplot in two side-by-side panels, one for Items and one for SNPs. Another stratagem is to focus on a manageable subset of SNPs of particular interest and show only them in a biplot in order to avoid obscuring the Items. A later section on causal exploration by current methods mentions several procedures for identifying particularly relevant SNPs.
One of several data transformations is ordinarily applied to SNP data prior to PCA computations, such as centering by SNPs. These transformations make a huge difference in the appearance of PCA graphs or biplots. A SNPs-by-Items data matrix constitutes a two-way factorial design, so analysis of variance (ANOVA) recognizes three sources of variation: SNP main effects, Item main effects, and SNP-by-Item (S×I) interaction effects. Double-Centered PCA (DC-PCA) removes both main effects in order to focus on the remaining S×I interaction effects. The resulting PCs are called interaction principal components (IPCs), and are denoted by IPC1, IPC2, and so on. By way of preview, a later section on PCA variants argues that DC-PCA is best for SNP data. Surprisingly, our literature survey did not encounter even a single analysis identified as DC-PCA.
The axes in PCA graphs or biplots are often scaled to obtain a convenient shape, but actually the axes should have the same scale for many reasons emphasized recently by Malik and Piepho . However, our literature survey found a correct ratio of 1 in only 10% of the articles, a slightly faulty ratio of the larger scale over the shorter scale within 1.1 in 12%, and a substantially faulty ratio above 2 in 16% with the worst cases being ratios of 31 and 44. Especially when the scale along one PCA axis is stretched by a factor of 2 or more relative to the other axis, the relationships among various points or clusters of points are distorted and easily misinterpreted. Also, 7% of the articles failed to show the scale on one or both PCA axes, which leaves readers with an impressionistic graph that cannot be reproduced without effort. The contemporary literature on PCA of SNP data mostly violates the prohibition against stretching axes.
The percentage of variation captured by each PC is often included in the axis labels of PCA graphs or biplots. In general this information is worth including, but there are two qualifications. First, these percentages need to be interpreted relative to the size of the data matrix because large datasets can capture a small percentage and yet still be effective. For example, for a large dataset with over 107,000 SNPs for over 6,000 persons, the first two components capture only 0.3693% and 0.117% of the variation, and yet the PCA graph shows clear structure (Fig 1A in ). Contrariwise, a PCA graph could capture a large percentage of the total variation, even 50% or more, but that would not guarantee that it will show evident structure in the data. Second, the interpretation of these percentages depends on exactly how the PCA analysis was conducted, as explained in a later section on PCA variants. Readers cannot meaningfully interpret the percentages of variation captured by PCA axes when authors fail to communicate which variant of PCA was used.
Five simple recommendations for effective PCA analysis of SNP data emerge from this investigation.
Use the SNP coding 1 for the rare or minor allele and 0 for the common or major allele.
Use DC-PCA; for any other PCA variant, examine its augmented ANOVA table.
Report which SNP coding and PCA variant were selected, as required by contemporary standards in science for transparency and reproducibility, so that readers can interpret PCA results properly and reproduce PCA analyses reliably.
Produce PCA biplots of both Items and SNPs, rather than merely PCA graphs of only Items, in order to display the joint structure of Items and SNPs and thereby to facilitate causal explanations. Be aware of the arch distortion when interpreting PCA graphs or biplots.
Produce PCA biplots and graphs that have the same scale on every axis.
I read the referenced paper Biplots: Do Not Stretch Them!, by Malik and Piepho (2018), and even though it is not directly applicable to the most commonly available PCA graphs out there, it is a good reminder of the distorting effects of stretching. So for example quite recently in Krause-Kyora et al. (2018), where you can see Corded Ware and BBC samples from Central Europe clustering with samples from Yamna:
NOTE. This is related to a vertical distorsion (i.e. horizontal stretching), but possibly also to the addition of some distant outlier sample/s.
The so-called ‘Yamnaya’ ancestry
Every time I read papers like these, I remember commenters who kept swearing that genetics was the ultimate science that would solve anthropological problems, where unscientific archaeology and linguistics could not. Well, it seems that, like radiocarbon analysis, these promising developing methods need still a lot of refinement to achieve something meaningful, and that they mean nothing without traditional linguistics and archaeology… But we already knew that.
Also, if this is happening in most peer-reviewed publications, made by professional geneticists, in journals of high impact factor, you can only wonder how many more errors and misinterpretations can be found in the obscure market of so many amateur geneticists out there. Because amateur geneticist is a commonly used misnomer for people who are not geneticists (since they don’t have the most basic education in genetics), and some of them are not even ‘amateurs’ (because they are selling the outputs of bioinformatic tools)… It’s like calling healers ‘amateur doctors’.
NOTE. While everyone involved in population genetics is interested in knowing the truth, and we all have our confirmation (and other kinds of) biases, for those who get paid to tell people what they want to hear, and who have sold lots of wrong interpretations already, the incentives of ‘being right’ – and thus getting involved in crooked and paranoid behaviour regarding different interpretations – are as strong as the money they can win or loose by promoting themselves and selling more ‘product’.
As a reminder of how badly these wrong interpretations of genetic results – and the influence of the so-called ‘amateurs’ – can reflect on research groups, yet another turn of the screw by the Copenhagen group, in the oral presentations at Languages and migrations in pre-historic Europe (7-12 Aug 2018), organized by the Copenhagen University. The common theme seems to be that Bell Beaker and thus R1b-L23 subclades do represent a direct expansion from Yamna now, as opposed to being derived from Corded Ware migrants, as they supported before.
NOTE. Yes, the “Yamna → Corded Ware → Únětice / Bell Beaker” migration model is still commonplace in the Copenhagen workgroup. Yes, in 2018. Guus Kroonen had already admitted they were wrong, and it was already changed in the graphic representation accompanying a recent interview to Willerslev. However, since there is still no official retraction by anyone, it seems that each member has to reject the previous model in their own way, and at their own pace. I don’t think we can expect anyone at this point to accept responsibility for their wrong statements.
I love the newly invented arrows of migration from Yamna to the north to distinguish among dialects attributed by them to CWC groups, and the intensive use of materials from Heyd’s publications in the presentation, which means they understand he was right – except for the fact that they are used to support a completely different theory, radically opposed to those defended in Heyd’s model…
Now added to the Copenhagen’s unending proposals of language expansions, some pearls from the oral presentation:
Corded Ware north of the Carpathians of R1a lineages developed Germanic;
R1b borugh [?] Italo-Celtic;
the increase in steppe ancestry on north European Bell Beakers mean that they “were a continuation of the Yamnaya/Corded Ware expansion”;
“Corded Ware groups  stopped their expansion and took over the Bell Beaker package before migrating to England” [yep, it literally says that];
Italo-Celtic expanded to the UK and Iberia with Bell Beakers [I guess that included Lusitanian in Iberia, but not Messapian in Italy; or the opposite; or nothing like that, who knows];
2nd millennium BC Bronze Age Atlantic trade systems expanded Proto-Celtic [yep, trade systems expanded the language]
1st millennium BC expanded Gaulish with La Tène, including a “Gaulish version of Celtic to Ireland/UK” [hmmm, datBritish Gaulish indeed].
You know, because, why the hell not? A logical, stable, consequential, no-nonsense approach to Indo-European migrations, as always.
Also, compare still more invented arrows of migrations, from Mikkel Nørtoft’s Introducing the Homeland Timeline Map, going against Kristiansen’s multiple arrows, and even against the own recent fantasy map series in showing Bell Beakers stem from Yamna instead of CWC (or not, you never truly know what arrows actually mean):
I really, really loved that perennial arrow of migration from Volosovo, ca. 4000-800 BC (3000+ years, no less!), representing Uralic?, like that, without specifics – which is like saying, “somebody from the eastern forest zone, somehow, at some time, expanded something that was not Indo-European to Finland, and we couldn’t care less, except for the fact that they were certainly not R1a“.
This and Kristiansen’s arrows are the most comical invented migration routes of 2018; and that is saying something, given the dozens of similar maps that people publish in forums and blogs each week.
It’s hard to accept that this is a series of presentations made by professional linguists, archaeologists, and geneticists, as stated by the official website, and still harder to imagine that they collaborate within the same professional workgroup, which includes experienced geneticists and academics.
I propose the following video to close future presentations introducing innovative ideas like those above, to help the audience find the appropriate mood:
The Eurasian steppes reach from the Ukraine in Europe to Mongolia and China. Over the past 5000 years, these flat grasslands were thought to be the route for the ebb and flow of migrant humans, their horses, and their languages. de Barros Damgaard et al. probed whole-genome sequences from the remains of 74 individuals found across this region. Although there is evidence for migration into Europe from the steppes, the details of human movements are complex and involve independent acquisitions of horse cultures. Furthermore, it appears that the Indo-European Hittite language derived from Anatolia, not the steppes. The steppe people seem not to have penetrated South Asia. Genetic evidence indicates an independent history involving western Eurasian admixture into ancient South Asian peoples.
According to the commonly accepted “steppe hypothesis,” the initial spread of Indo-European (IE) languages into both Europe and Asia took place with migrations of Early Bronze Age Yamnaya pastoralists from the Pontic-Caspian steppe. This is believed to have been enabled by horse domestication, which revolutionized transport and warfare. Although in Europe there is much support for the steppe hypothesis, the impact of Early Bronze Age Western steppe pastoralists in Asia, including Anatolia and South Asia, remains less well understood, with limited archaeological evidence for their presence. Furthermore, the earliest secure evidence of horse husbandry comes from the Botai culture of Central Asia, whereas direct evidence for Yamnaya equestrianism remains elusive.
We investigated the genetic impact of Early Bronze Age migrations into Asia and interpret our findings in relation to the steppe hypothesis and early spread of IE languages. We generated whole-genome shotgun sequence data (~1 to 25 X average coverage) for 74 ancient individuals from Inner Asia and Anatolia, as well as 41 high-coverage present-day genomes from 17 Central Asian ethnicities.
We show that the population at Botai associated with the earliest evidence for horse husbandry derived from an ancient hunter-gatherer ancestry previously seen in the Upper Paleolithic Mal’ta (MA1) and was deeply diverged from the Western steppe pastoralists. They form part of a previously undescribed west-to-east cline of Holocene prehistoric steppe genetic ancestry in which Botai, Central Asians, and Baikal groups can be modeled with different amounts of Eastern hunter-gatherer (EHG) and Ancient East Asian genetic ancestry represented by Baikal_EN.
In Anatolia, Bronze Age samples, including from Hittite speaking settlements associated with the first written evidence of IE languages, show genetic continuity with preceding Anatolian Copper Age (CA) samples and have substantial Caucasian hunter-gatherer (CHG)–related ancestry but no evidence of direct steppe admixture.
In South Asia, we identified at least two distinct waves of admixture from the west, the first occurring from a source related to the Copper Age Namazga farming culture from the southern edge of the steppe, who exhibit both the Iranian and the EHG components found in many contemporary Pakistani and Indian groups from across the subcontinent. The second came from Late Bronze Age steppe sources, with a genetic impact that is more localized in the north and west.
Our findings reveal that the early spread of Yamnaya Bronze Age pastoralists had limited genetic impact in Anatolia as well as Central and South Asia. As such, the Asian story of Early Bronze Age expansions differs from that of Europe. Intriguingly, we find that direct descendants of Upper Paleolithic hunter-gatherers of Central Asia, now extinct as a separate lineage, survived well into the Bronze Age. These groups likely engaged in early horse domestication as a prey-route transition from hunting to herding, as otherwise seen for reindeer. Our findings further suggest that West Eurasian ancestry entered South Asia before and after, rather than during, the initial expansion of western steppe pastoralists, with the later event consistent with a Late Bronze Age entry of IE languages into South Asia. Finally, the lack of steppe ancestry in samples from Anatolia indicates that the spread of the earliest branch of IE languages into that region was not associated with a major population migration from the steppe.
I think the wording of the abstract is weird, but consequent with their samples and results, so probably just clickbait / citebait for Indian journalists and social networks, or maybe a new attempt to ‘show respect for the sensibilities of Indians’ related to the artificially magnified “AIT vs. OIT” controversy, that is only present in India.