(…) the spread of agriculture in Europe was a result of the demic diffusion of early Anatolian farmers, it was discovered that the spread of agriculture to South Asia was mediated by a genetically completely different farmer population in the Zagros mountains in contemporary Iran (IF). The ANI-ASI cline itself was interpreted as a mixture of three components genetically related to Iranian agriculturalists, Onge and Early and Middle Bronze Age Steppe populations (Steppe_EMBA).
The first ever autosomal aDNA from South Asia comes from Northern Pakistan (Swat Valley, early Iron Age). This study presented altogether 362 aDNA samples from the broad South and Central Asia and contributes substantially to our understanding of the evolutionary past of South and Central Asia. The study redefines the three genetic strata that form the basis of the Indian Cline. The Indus Periphery (IP) component is composed of (varying proportions of): first, IF, second, Ancient Ancestral South Asians (AASI), which represents an ancient branch of human genetic variation in Asia arising from a population split contemporaneous with the splits of East Asian, Onge and Australian Aboriginal ancestors and third, West_Siberian Hunter gatherers (WS_HG).
The authors argue that IP could have formed the genetic base of the Indus Valley Civilization (IVC). Upon the collapse of the IVC IP contributes to the formation of both ASI and ANI. ASI is formed as IP admixes further with AASI. ANI in turn forms when IP admixes with the incoming Middle and Late Bronze Age Steppe (Steppe_MLBA) component, (rather than the Steppe_EMBA groups suggested earlier)
Dating of the arrival of the Austro-Asiatic speakers in South Asia-based on Y chromosome haplogroup O2a1-M95 expansion estimates yielded dates between 3000 and 2000 BCE . However, admixture LD decay-based approach on genome-wide data suggests the admixture between South Asian and incoming Austro-Asiatic speakers occurred slightly later between 1800 and 0 BCE (Tätte et al. submitted). It is interesting that while the mtDNA variants of the Mundas are completely South Asian, the Y chromosome variation is dominated at >60% by haplogroup O2a which is phylogeographically nested in East Asian-specific paternal lineages.
In India, the speakers of Tibeto-Burman (TB) languages live in the Seven Sisters States in Northeast India and in the very north of the country. Genetically they show a clear East Asian origin and around 20% of subsequent admixture with South Asians within the last 1000 years.The genetic flavour of East Asia in TB is different from that in Munda speakers as the best surrogates for the East Asian admixing component are contemporary Han Chinese.
I found the simplistic migration maps especially interesting to illustrate ancient population movements. The emergence of EHG is supposed to involve a WHG:ANE cline, though, and this isn’t clear from the map. Also, there is new information on what may be at the origin of WHG and Anatolian hunter-gatherers.
A total of 286 samples of Uralic-speaking individuals, of those 121 genotyped in this study, were analysed in the context of 1514 Eurasian samples (including 14 samples published for the first time) based on whole genome single nucleotide polymorphisms (SNPs) (Additional file 1: Table S1). All these samples, together with the larger sample set of Uralic speakers, were characterized for mtDNA and chrY markers.
The question as which material cultures may have co-spread together with proto-Uralic and Uralic languages depends on the time estimates of the splits in the Uralic language tree. Deeper age estimates (6,000 BP) of the Uralic language tree suggest a connection between the spread of FU languages from the Volga River basin towards the Baltic Sea either with the expansion of the Neolithic culture of Combed Ware, e.g. [6, 7, 17, 26] or with the Neolithic Volosovo culture . Younger age estimates support a link between the westward dispersion of Proto-Finno-Saamic and eastward dispersion of Proto-Samoyedic with a BA Sejma-Turbino (ST) cultural complex [14, 18, 27, 28] that mediated the diffusion of specific metal tools and weapons from the Altai Mountains over the Urals to Northern Europe or with the Netted Ware culture , which succeeded Volosovo culture in the west. It has been suggested that Proto-Uralic may have even served as the lingua franca of the merchants involved in the ST phenomenon . All these scenarios imply that material culture of the Baltic Sea area in Europe was influenced by cultures spreading westward from the periphery of Europe and/or Siberia. Whether these dispersals involved the spread of both languages and people remains so far largely unknown.
The population structure of Uralic speakers
To contextualize the autosomal genetic diversity of Uralic speakers among other Eurasian populations (Additional file 1: Table S1), we first ran the principal component (PC) analysis (Fig. 2a, Additional file 3: Figure S1). The first two PCs (Fig. 2a, Additional file 3: Figure S1A) sketch the geography of the Eurasian populations along the East-West and North-South axes, respectively. The Uralic speakers, along with other populations speaking Slavic and Turkic languages, are scattered along the first PC axis in agreement with their geographic distribution (Figs. 1 and 2a) suggesting that geography is the main predictor of genetic affinity among the groups in the given area. Secondly, in support of this, we find that FST-distances between populations (Additional file 3: Figure S2) decay in correlation with geographical distance (Pearson’s r = 0.77, p < 0.0001). On the UPGMA tree based on these FST-distances (Fig. 2b), the Uralic speakers cluster into several different groups close to their geographic neighbours.
We next used ADMIXTURE , which presents the individuals as composed of inferred genetic components in proportions that maximize Hardy-Weinberg and linkage equilibrium in the overall sample (see the ‘Methods’ section for choice of presented K). Overall, and specifically at lower values of K, the genetic makeup of Uralic speakers resembles that of their geographic neighbours. The Saami and (a subset of) the Mansi serve as exceptions to that pattern being more similar to geographically more distant populations (Fig. 3a, Additional file 3: S3). However, starting from K = 9, ADMIXTURE identifies a genetic component (k9, magenta in Fig. 3a, Additional file 3: S3), which is predominantly, although not exclusively, found in Uralic speakers. This component is also well visible on K = 10, which has the best cross-validation index among all tests (Additional file 3: S3B). The spatial distribution of this component (Fig. 3b) shows a frequency peak among Ob-Ugric and Samoyed speakers as well as among neighbouring Kets (Fig. 3a). The proportion of k9 decreases rapidly from West Siberia towards east, south and west, constituting on average 40% of the genetic ancestry of FU speakers in Volga-Ural region (VUR) and 20% in their Turkic-speaking neighbours (Bashkirs, Tatars, Chuvashes; Fig. 3a). The proportion of this component among the Saami in Northern Scandinavia is again similar to that of the VUR FU speakers, which is exceptional in the geographic context. It is also notable that North Russians, sampled from near the White Sea, differ from other Russians by sporting higher proportions of k9 (10–15%), which is similar to the values we observe in their Finnic-speaking neighbours. Notably, Estonians and Hungarians, who are geographically the westernmost Uralic speakers, virtually lack the k9 cluster membership.
We also tested the different demographic histories of female and male lineages by comparing outgroup f3 results for autosomal and X chromosome (chrX) data for pairs of populations (Estonians, Udmurts or Khanty vs others) with high versus low probability to share their patrilineal ancestry in chrY hg N (see the ‘Methods’ section, Additional file 3: Figure S13). We found a minor but significant excess of autosomal affinity relative to chrX for pairs of populations that showed a higher than 10% chance of two randomly sampled males across the two groups sharing their chrY ancestry in hg N3-M178, compared to pairs of populations where such probability is lower than 5% (Additional file 3: Figure S13).
In sum, these results suggest that most of the Uralic speakers may indeed share some level of genetic continuity via k9, which, however, also extends to the geographically close Turkic speakers.
We found that it is the admixture with the Siberians that makes the Western Uralic speakers different from the tested European populations (Additional file 3: Figure S4A-F, H, J, L). Differentiating between Estonians and Finns, the Siberians share more derived alleles with Finns, while the geographic neighbours of Estonians (and Finns) share more alleles with Estonians (Additional file 3: Figure S4M). Importantly, Estonians do not share more derived alleles with other Finnic, Saami, VUR FU or Ob-Ugric-speaking populations than Latvians (Additional file 3: Figure S4O). The difference between Estonians and Latvians is instead manifested through significantly higher levels of shared drift between Estonians and Siberians on the one hand and Latvians and their immediate geographic neighbours on the other hand. None of the Uralic speakers, including linguistically close Khanty and Mansi, show significantly closer affinities to the Hungarians than any non-FU population from NE Europe (Additional file 3: Figure S4R).
Time of Siberian admixture
The time depth of the Globetrotter (Fig. 5b) inferred admixture events is relatively recent—500–1900 AD (see also complementary ALDER results, in Additional file 13: Table S12 and Additional file 3: Figure S7)—and agrees broadly with the results reported in Busby et al. . A more detailed examination of the ALDER dates, however, reveals an interesting pattern. The admixture events detected in the Baltic Sea region and VUR Uralic speakers are the oldest (800–900 AD or older) followed by those in VUR Turkic speakers (∼1200–1300 AD), while the admixture dates for most of the Siberian populations (>1500 AD) are the most recent (Additional file 3: Figure S7). The West Eurasian influx into West Siberia seen in modern genomes was thus very recent, while the East Eurasian influx into NE Europe seems to have taken place within the first millennium AD (Fig. 5b, Additional file 3: Figure S7).
Affinities of the Uralic speakers with ancient Eurasians
We next calculated outgroup f3-statistics  to estimate the extent of shared genetic drift between modern and ancient Eurasians (Additional file 14: Table S13, Additional file 3: Figures S8-S9). Consistent with previous reports [45, 50], we find that the NE European populations including the Uralic speakers share more drift with any European Mesolithic hunter-gatherer group than Central or Western Europeans (Additional file 3: Figure S9A-C). Contrasting the genetic contribution of western hunter-gatherers (WHG) and eastern hunter-gatherers (EHG), we find that VUR Uralic speakers and the Saami share more drift with EHG. Conversely, WHG shares more drift with the Finnic and West European populations (Additional file 3: Figure S9A). Interestingly, we see a similar pattern of excess of shared drift between VUR and EHG if we substitute WHG with the aDNA sample from the Yamnaya culture (Additional file 3: Figure S9D). As reported before [2, 45], the genetic contribution of European early farmers decreases along an axis from Southern Europe towards the Ural Mountains (Fig. 6, Additional file 3: Figure S9E-F).
We then used the qpGraph software  to test alternative demographic scenarios by trying to fit the genetic diversity observed in a range of the extant Finno-Ugric populations through a model involving the four basic European ancestral components: WHG, EHG, early farmers (LBK), steppe people of Yamnaya/Corded Ware culture (CWC) and a Siberian component (Fig. 6, Additional file 3: Figure S10). We chose the modern Nganasans to serve as a proxy for the latter component because we see least evidence for Western Eurasian admixture (Additional file 3: Figure S3) among them. We also tested the Khantys for that proxy but the model did not fit (yielding f2-statistics, Z-score > 3). The only Uralic-speaking population that did not fit into the tested model with five ancestral components were Hungarians. The qpGraph estimates of the contributions from the Siberian component show that it is the main ancestry component in the West Siberian Uralic speakers and constitutes up to one third of the genomes of modern VUR and the Saami (Fig. 6). It drops, however, to less than 10% in most of NE Europe, to 5% in Estonians and close to zero in Latvians and Lithuanians.
One of the notable observations that stands out in the fineSTRUCTURE analysis is that neither Hungarians nor Estonians or Mordovians form genetic clusters with other Uralic speakers but instead do so with a broad spectrum of geographically adjacent samples. Despite the documented history of the migration of Magyars  and their linguistic affinity to Khantys and Mansis, who today live east of the Ural Mountains, there is nothing in the present-day gene pool of the sampled Hungarians that we could tie specifically to other Uralic speakers.
Perhaps even more surprisingly, we found that Estonians, who show close affinities in IBD analysis to neighbouring Finnic speakers and Saami, do not share an excess of IBD segments with the VUR or Siberian Uralic speakers. This is eIn this context, it is important to remind that the limited (5%, Fig. 6) East Eurasian impact in the autosomal gene pool of modern Estonians contrasts with the fact that more than 30% of Estonian (but not Hungarian) men carry chrY N3 that has an East Eurasian origin and is very frequent among NE European Uralic speakers . However, the spread of chrY hg N3 is not language group specific as it shows similar frequencies in Baltic-speaking Latvians and Lithuanians, and in North Russians, who in all our analyses are very similar to Finnic-speakers. The latter, however, are believed to have either significantly admixed with their Uralic-speaking neighbours or have undergone a language shift from Uralic to Indo-European .ven more striking considering that the immediate neighbours—Finns, Vepsians and Karelians—do.
With some exceptions such as Estonians, Hungarians and Mordovians, both IBD sharing and Globetrotter results suggest that there are detectable inter-regional haplotype sharing ties between Uralic speakers from West Siberia and VUR, and between NE European Uralic speakers and VUR. In other words, there is a fragmented pattern of haplotype sharing between populations but no unifying signal of sharing that unite all the studied Uralic speakers.
The paper is obviously trying to find a “N1c/Siberian ancestry = Uralic” link, but it shows (as previous papers using ancient DNA) that this identification is impossible, because it is not possible to identify “N1c=Siberian ancestry”, “N1c=Uralic”, or “Siberian ancestry = Uralic”. In fact, the arrival of N subclades and Siberian ancestry are late, both events (probably multiple stepped events) are unrelated to each other, and represent east-west demic diffusion waves (as well as founder effects) that probably coincide in part with the Scythian and Turkic (or associated) expansions, i.e. too late for any model of Proto-Uralic or Proto-Finno-Ugric expansion.
On the other hand, it shows interesting data regarding ancestry of populations that show increased Siberian influence, such as those easternmost groups admixed with Yeniseian-like populations (Samoyedic), those showing strong founder effects (Finnic), or those isolated in the Circum-Artic region with neighbouring Siberian peoples in Kola (Saami). All in all, Hungarians, Estonians and Mordovians seem to show the original situation better than the other groups, which is also reflected in part in Y-DNA, conserved as a majority of R1a lineages precisely in these groups. Just another reminder that CWC-related ancestry is found in every single Uralic group, and that it represents the main ancestral component in all non-Samoyedic groups.
The qpGraph shows the ancestor of Yamna (likely Khvalynsk) and Corded Ware stemming as different populations from a common (likely Neolithic) node – whose difference is based on the proportion of Anatolian-related ancestry – , that is, probably before the Indo-Hittite expansion; and ends with CWC groups forming the base for all Uralic peoples. Below is a detail of the qpGraph on the left, and my old guess (2017) on the right, for comparison:
#EDIT (22 sep 2018): I enjoyed re-reading it, and found this particular paragraph funny:
Despite the documented history of the migration of Magyars  and their linguistic affinity to Khantys and Mansis, who today live east of the Ural Mountains, there is nothing in the present-day gene pool of the sampled Hungarians that we could tie specifically to other Uralic speakers.
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.
Exploring the genomic impact of colonization in north-eastern Siberia, by Seguin-Orlando et al.
Yakutia is the coldest region in the northern hemisphere, with winter record temperatures below minus 70°C. The ability of Yakut people to adapt both culturally and biologically to extremely cold temperatures has been key to their subsistence. They are believed to descend from an ancestral population, which left its original homeland in the Lake Baykal area following the Mongol expansion between the 13th and 15th centuries AD. They originally developed a semi-nomadic lifestyle, based on horse and cattle breeding, providing transportation, primary clothing material, meat, and milk. The early colonization by Russians in the first half of the 17th century AD, and their further expansion, have massively impacted indigenous populations. It led not only to massive epidemiological outbreaks, but also to an important dietary shift increasingly relying on carbohydrate-rich resources, and a profound lifestyle transition with the gradual conversion from Shamanism to Christianity and the establishment of new marriage customs. Leveraging an exceptional archaeological collection of more than a hundred of bodies excavated by MAFSO (Mission Archéologique Française en Sibérie Orientale) over the last 15 years and naturally kept frozen by the extreme cold temperatures of Yakutia, we have started to characterize the (epi)genome of indigenous individuals who lived from the 16th to the 20th century AD. Current data include the genome sequence of approximately 50 individuals that lived prior to and after Russian contact, at a coverage from 2 to 40 fold. Combined with data from archaeology and physical anthropology, as well as microbial DNA preserved in the specimens, our unique dataset is aimed at assessing the biological consequences of the social and biological changes undergone by the Yakut people following their neolithisation by Russian colons.
Clio Der Sarkissian: Age, sex, geography and parental relatedness are not factors which influence oral microbial diversity in 124 individuals from 17thC Siberia #ISBA8
preliminary conclusions: no detectable impact of Russian colonization on Yakut oral microbiome diversity despite dietary and other societal changes (but perhaps calculus not adequately sensitive) pic.twitter.com/oO2OjqIHKg
Ancient DNA from a Medieval trading centre in Northern Finland
Using ancient DNA to identify the ancestry of individuals from a Medieval trading centre in Northern Finland, by Simoes et al.
Analyzing genomic information from archaeological human remains has proved to be a powerful approach to understand human history. For the archaeological site of Ii Hamina, ancient DNA can be used to infer the ancestries of individuals buried there. Situated approximately 30 km from Oulu, in Northern Finland, Ii Hamina was an important trade place since Medieval times. The historical context indicates that the site could have been a melting pot for different cultures and people of diversified genetic backgrounds. Archaeological and osteological evidence from different individuals suggest a rich diversity. For example, stable isotope analyses indicate that freshwater and marine fish was the dominant protein source for this population. However, one individual proved to be an outlier, with a diet containing relatively more terrestrial meat or vegetables. The variety of artefacts that was found associated with several human remains also points to potential differences in religious beliefs or social status. In this study, we aimed to investigate if such variation could be attributed to different genetic ancestries. Ten of the individuals buried in Ii Hamina’s churchyard, dating to between the 15th and 17th century AD, were screened for presence of authentic ancient DNA. We retrieved genome-wide data for six of the individuals and performed downstream analysis. Data authenticity was confirmed by DNA damage patterns and low estimates of mitochondrial contamination. The relatively recent age of these human remains allows for a direct comparison to modern populations. A combination of population genetics methods was undertaken to characterize their genetic structure, and identify potential familiar relationships. We found a high diversity of mitochondrial lineages at the site. In spite of the putatively distant origin of some of the artifacts, most individuals shared a higher affinity to the present-day Finnish or Late Settlement Finnish populations. Interestingly, different methods consistently suggested that the individual with outlier isotopic values had a different genetic origin, being more closely related to reindeer herding Saami. Here we show how data from different sources, such as stable isotopes, can be intersected with ancient DNA in order to get a more comprehensive understanding of the human past.
A closer look at the bottom left corner of the poster (the left columns are probably the new samples):
Plant resources processed in HG pottery from the Upper Volga
Multiple criteria for the detection of plant resources processed in hunter-gatherer pottery vessels from the Upper Volga, Russia, by Bondetti et al.
In Northern Eurasia, the Neolithic is marked by the adoption of pottery by hunter-gatherer communities. The degree to which this is related to wider social and lifestyle changes is subject to ongoing debate and the focus of a new research programme. The use and function of early pottery by pre-agricultural societies during the 7th-5th millennia BC is of central interest to this debate. Organic residue analysis provides important information about pottery use. This approach relies on the identification and isotopic characteristics of lipid biomarkers, absorbed into the pores of the ceramic or charred deposits adhering to pottery vessel surfaces, using a combined methodology, namely GC-MS, GC-c-IRMS and EA-IRMS. However, while animal products (e.g., marine, freshwater, ruminant, porcine) have the benefit of being lipid-rich and well-characterised at the molecular and isotopic level, the identification of plant resources still suffers from a lack of specific criteria for identification. In huntergatherer contexts this problem is exacerbated by the wide range of wild, foraged plant resources that may have been potentially exploited. Here we evaluate approaches for the characterisation of terrestrial plant food in pottery through the study of pottery assemblages from Zamostje 2 and Sakhtysh 2a, two hunter-gatherer settlements located in the Upper Volga region of Russia.
GC-MS analysis of the lipids, extracted from the ceramics and charred residues by acidified methanol, suggests that pottery use was primarily oriented towards terrestrial and aquatic animal products. However, while many of the Early Neolithic vessels contain lipids distinctive of freshwater resources, triterpenoids are also present in high abundance suggesting mixing with plant products. When considering the isotopic criteria, we suggest that plants were a major commodity processed in pottery at this time. This is supported by the microscopic identification of Viburnum (Viburnum Opulus L.) berries in the charred deposits on several vessels from Zamostje.
The study of Upper Volga pottery demonstrated the importance of using a multidisciplinary approach to determine the presence of plant resources in vessels. Furthermore, this informs the selection of samples, often subject to freshwater reservoir effects, for 14C dating.
Bronze Age population dynamics and the rise of dairy pastoralism on the eastern Eurasian steppe
Bronze Age population dynamics and the rise of dairy pastoralism on the eastern Eurasian steppe, by Warinner et al.
Recent paleogenomic studies have shown that migrations of Western steppe herders (WSH), beginning in the Eneolithic (ca. 3300-2700 BCE), profoundly transformed the genes and cultures of Europe and Central Asia. Compared to Europe, the eastern extent of this WSH expansion is not well defined. Here we present genomic and proteomic data from 22 directly dated Bronze Age khirigsuur burials from Khövsgöl, Mongolia (ca. 1380-975 BCE). Only one individual showed evidence of WSH ancestry, despite the presence of WSH populations in the nearby Altai-Sayan region for more than a millennium. At the same time, LCMS/ MS analysis of dental calculus provides direct protein evidence of milk consumption from Western domesticated livestock in 7 of 9 individuals. Our results show that dairy pastoralism was adopted by Bronze Age Mongolians despite minimal genetic exchange with Western steppe herders.
Comments on ancestry of the Deer Stone-Khirigsuur ancestry; one “eastern” outlier and a (late) “western” outlier – but in the main only low (2-7%) levels of western admixture (of “Sintashta” and not “Afanasievo” type) pic.twitter.com/9E3jCQKTlm
The Thoroughbred horse breed was developed primarily for racing, and has a significant contribution to the qualitative improvement of many other horse breeds. Despite the importance of Thoroughbred racehorses in historical, cultural, and economical viewpoints, there was no temporal and spatial dynamics of them using the mitogenome sequences. To explore this topic, the complete mitochondrial genome sequences of 14 Thoroughbreds and two Przewalski’s horses were determined. These sequences were analyzed together along with 151 previously published horse mitochondrial genomes from a range of breeds across the globe using a Bayesian coalescent approach as well as Bayesian inference and maximum likelihood methods. The racing horses were revealed to have multiple maternal origins and to be closely related to horses from one Asian, two Middle Eastern, and five European breeds. Thoroughbred horse breed was not directly related to the Przewalski’s horse which has been regarded as the closest taxon to the all domestic horses and the only true wild horse species left in the world. Our phylogenomic analyses also supported that there was no apparent correlation between geographic origin or breed and the evolution of global horses. The most recent common ancestor of the Thoroughbreds lived approximately 8,100–111,500 years ago, which was significantly younger than the most recent common ancestor of modern horses (0.7286 My). Bayesian skyline plot revealed that the population expansion of modern horses, including Thoroughbreds, occurred approximately 5,500–11,000 years ago, which coincide with the start of domestication. This is the first phylogenomic study on the Thoroughbred racehorse in association with its spatio-temporal dynamics. The database and genetic history information of Thoroughbred mitogenomes obtained from the present study provide useful information for future horse improvement projects, as well as for the study of horse genomics, conservation, and in association with its geographical distribution.
We carried out a Bayesian coalescent approach using extended mitochondrial genome sequences from 167 horses in order to further assess the timescale of horse domestication. Here, we first calculated the time of the most recent common ancestor of Thoroughbred horses. Our analysis revealed the age of the most recent common ancestor of the racing horse to be around 8,100–111,500 years old. This estimate is much younger than that of the most recent common ancestor of the global horses, which has been estimated at 0.7286 Mys old.
On the domestication time of modern horses, there have been several publications derived from both archaeological [49–51] and molecular [11–12, 23, 48] evidences. D’Andrade  reported that the origin of domestic horses was around 4,000 years ago. Ludwig et al.  stated the domestication time to be about 5,000 years ago, while Anthony  noted that horse rearing by humans may have occurred approximately 6,000 years ago. Subsequently, on the basis of mitochondrial genome sequences, Lippold et al.  and Achilli et al.  postulated domestication time to be about 6,000–8,000 and 6,000–7,000 years ago, respectively. Warmuth  dated domestication time to 5,500 years ago based on autosomal genotype data, while Orlando et al.  claimed that Przewalski’s and domestic horse populations diverged 38,000–72,000 years ago based on analysis of genome sequences. In contrast to the previous hypothesized date of horse domestication, the results of our Bayesian skyline plot (BSP) analysis depict a rapid expansion of the horse population approximately 5,500–11,000 years ago, which coincides with the start of domestication.
It seems that we will not have an update on horse aDNA from the ISBA 8, so we will have to make do with this for the moment.
The Phase 3 sequence data from 20 populations, comprising five populations for each of the four main geographical regions of Europe, East Asia, South Asia and Africa, were downloaded from the 1000 Genomes Project website (www.1000genomes.org/data, ), including whole mitochondrial genome data for 1999 individuals. We decided not to analyse populations from the Americas due to the region’s complex history of admixture [13,14].
The European populations were as follows: Finnish sampled in Finland (FIN); European Caucasians resident in Utah, USA (CEU); British in England and Scotland (GBR); an Iberian population from Spain (IBS) and Toscani from Italy (TSI). Representing East Asia were the Han Chinese in Beijing (CHB); Southern Han Chinese (CHS); Dai Chinese from Xishuangbanna, China (CDX); Kinh population from Ho Chi Minh City, Vietnam (KHV) and Japanese from Tokyo (JPT). The South Asian populations were Punjabi Indians from Lahore, Pakistan (PJL); Gujarati Indians in Houston, USA (GIH) as well as Indian Telugu sampled in the UK (ITU); Bengali from Bangladesh (BEB) and Sri Lankan Tamil from the UK (STU). (…)
We analysed our mtDNA data with the extended Bayesian skyline plot (EBSP) method, a Bayesian, non-parametric technique for inferring past population size fluctuations from genetic data. Building on the previous Bayesian skyline plot (BSP) approach, EBSP uses a piecewise-linear model and Markov chain Monte Carlo (MCMC) methods to reconstruct a populations’ demographic history  and is implemented in the software package BEAST v. 2.3.2 . Alignments for each of the 20 populations were loaded separately into the Bayesian Evolutionary Analysis Utility tool (BEAUti v. 2.3.2) in NEXUS format.
Regional demographic histories
The five European profiles are presented in figure 2. The four southerly populations all show profiles with a stable size up to approximately 14 ka followed by a sudden, rapid increase that becomes progressively less steep towards the present. There is also a north-south trend, with confidence intervals becoming broader towards the north, particularly for the oldest time-points. The Finnish population profile appears rather different, but this is to be expected both because it is so far north and because previous studies have identified Finns as a strong genetic outlier in Europe [19–22].
The five profiles for South Asia are shown in figure 3. All populations reveal a period of rapid growth approximately 45–40 ka which then slows. Near the present the two southerly populations, GIH and STU both show evidence of a decline. However, this may be due to these samples being drawn from populations no longer living on the subcontinent, with the downward trend capturing a bottleneck associated with moving to Europe/America, perhaps accentuated by the tendency for immigrant populations to group by region, religion and race .
After 568 AD the nomadic Avars settled in the Carpathian Basin and founded their empire, which was an important force in Central Europe until the beginning of the 9th century AD. The Avar elite was probably of Inner Asian origin; its identification with the Rourans (who ruled the region of today’s Mongolia and North China in the 4th-6th centuries AD) is widely accepted in the historical research.
Here, we study the whole mitochondrial genomes of twenty-three 7th century and two 8th century AD individuals from a well-characterised Avar elite group of burials excavated in Hungary. Most of them were buried with high value prestige artefacts and their skulls showed Mongoloid morphological traits.
The majority (64%) of the studied samples’ mitochondrial DNA variability belongs to Asian haplogroups (C, D, F, M, R, Y and Z). This Avar elite group shows affinities to several ancient and modern Inner Asian populations.
The genetic results verify the historical thesis on the Inner Asian origin of the Avar elite, as not only a military retinue consisting of armed men, but an endogamous group of families migrated. This correlates well with records on historical nomadic societies where maternal lineages were as important as paternal descent.
The mitochondrial genome sequences can be assigned to a wide range of the Eurasian haplogroups with dominance of the Asian lineages, which represent 64% of the variability: four samples belong to Asian macrohaplogroup C (two C4a1a4, one C4a1a4a and one C4b6); five samples to macrohaplogroup D (one by one D4i2, D4j, D4j12, D4j5a, D5b1), and three individuals to F (two F1b1b and one F1b1f). Each haplogroup M7c1b2b, R2, Y1a1 and Z1a1 is represented by one individual. One further haplogroup, M7 (probably M7c1b2b), was detected (sample AC20); however, the poor quality of its sequence data (2.19x average coverage) did not allow further analysis of this sample.
European lineages (occurring mainly among females) are represented by the following haplogroups: H (one H5a2 and one H8a1), one J1b1a1, three T1a (two T1a1 and one T1a1b), one U5a1 and one U5b1b (Table S1).
We detected two identical F1b1f haplotypes (AC11 female and AC12 male) and two identical C4a1a4 haplotypes (AC13 and AC15 males) from the same cemetery of Kunszállás; these matches indicate the maternal kinship of these individuals. There is no chronological difference between the female and the male from Grave 30 and 32 (AC11 and AC12), but the two males buried in Grave 28 and 52 (AC13 and AC15) are not contemporaries; they lived at least 2-3 generations apart.
The Avar period elite shows the lowest and non-significant genetic distances to ancient Central Asian populations dated to the Late Iron Age (Hunnic) and to the Medieval period, which is displayed on the ancient MDS plot (Fig. 4); these connections are also reflected on the haplogroup based Ward-type clustering tree (Fig. 3). Building of these large Central Asian sample pools is enabled by the small number of samples per cultural/ethnic group. Further mitogenomic data from Inner Asia are needed to specify the ancient genetic connections; however, genomic analyses are also set back by the state of archaeological research, i.e. the lack of human remains from the 4th-5th century Mongolia, which would be a particularly important region in the study of the Avar elite’s origin.
The investigated elite group from the Avar period elite also shows low genetic distances and phylogenetic connections to several Central and Inner Asian modern populations. Our results indicate that the source population of the elite group of the Avar Qaganate might have existed in Inner Asia (region of today’s Mongolia and North China) and the studied stratum of the Avars moved from there westwards towards Europe. Further genetic connections of the Avars to modern populations living to East and North of Inner Asia (Yakuts, Buryats, Tungus) probably indicate common source populations.
Sadly, no Y-DNA is available from this paper, although haplogroups Q, C2, or R1b (xM269) are probably to be expected, given the reported mtDNA. A replacement of the male population with subsequent migrations is obvious from the current distribution of Y-DNA haplogroups in the Carpathian Basin.
Hungarians and Corded Ware
Ancient Hungarians are important to understand the evolution, not only of Ugric, but also of Finno-Ugric peoples and their origin, since they show a genetic picture before more recent population expansions, genetic drift, and bottlenecks in eastern Europe.
In Ob-Ugric peoples, from the scarce data found in Pimenoff et al. (2018), we can see how Siberian N subclades expanded further after the separation of Magyars, evidenced by the inverted proportion of haplogroups R1a and N in modern Khantys and Mansis compared to Hungarians, and the diversity of N subclades compared to modern Fennic peoples.
Similarly to Hungarians, the situation of modern Estonians (where R1a and N subclades show approximately the same proportion, ca. 33%) is probably closer to Fennic peoples in Antiquity, not having undergone the latest strong founder effect evident in modern Finns after their expansion to the north.
In Semino et al. (2001) they found among 45 Palóc from Budapest and northern Hungary: 60% R1a, 13% R1b, 11% I, 9% E, 2% G, 2% J2.
In Csányi et al. (2008) Among 100 Hungarian men, 90 of whom from the Great Hungarian Plain: 30% R1a, 15% R1b, 13% I2a1, 13% J2, 9% E1b1b1a, 8% I1, 3% G2, 3% J1, 3% I*, 1% E*, 1% F*, 1% K*. Among 97 Székelys, in Romania: 20% R1b, 19% R1a, 17% I1, 11% J2, 10% J1, 8% E1b1b1a, 5% I2a1, 5% G2, 3% P*, 1% E*, 1% N.
In Pamjav et al. (2011), among 230 samples expected to include 6-8% Gypsy peoples: 26% R1a, 20% I2a, 19% R1b, 7% I, 6% J2, 5% H, 5% G2a, 5% E1b1b1a1, 3% J1, <1% N, <1% R2.
In Pamjav et al. (2017), from the Bodrogköz population: R1a-M458 (20.4%), I2a1-P37 (19%), R1b-M343 (15%), R1a-Z280 (14.3%), E1b-M78 (10.2%), and N1c-Tat (6.2%).
NOTE. The N1c-Tat found in Bodrogköz belongs to the N1c-VL29 subgroup, more frequent among Balto-Slavic peoples, which may suggest (yet again) an initial stage of the expansion of N subclades among Finno-Ugric peoples by the time of the Hungarian migration.
3.2% N (1.4% Z9136, 0.5% M2019/VL67, 0.5% Y7310, 0.9% Z16981)- note: only unrelated males are sampled
2.3% Q (1.2% YP789, 0.9% M346, 0.2% M242)
R1a-Z280 stands out in FDNA (which we have to assume has no geographic preference among modern Hungarians), while R1a-M458 is prevalent in the north, which probably points to its relationship with (at least West) Slavic populations.
NOTE. For more on the analysis of probability of the actual subclade, see here.
Bronze Age R1a-Z93 samples of central-east Europe – like the Balkans BA sample (ca. 1750-1625 BC) from Merichleri, of R1a1a1b2 subclade – correspond most likely to the expansion of Iranian-speaking peoples in the early 2nd millennium BC, probably to the westward expansion of the Srubna culture.
The specific subclade of King Béla III, on the other hand, probably corresponds to the more recent expansion of Magyar tribes settled in the region during the 9th century AD, so the specific subclade must have separated from those found in central-east Europe and in Andronovo during the Corded Ware expansion.
The study by Csányi et al. (2008), where the Tat C allele was found in 2 of 4 ancient samples, showed thus a potential 50:50 relationship of N1c in ancient Magyars, which is striking given the modern 1-3% a mere 1,000 years later, without any relevant population movement in between. This result remains to be reproduced with the current technology.
In fact, recent studies of ancient Magyars, from the 10th to the 12th century, have not shown any N1c sample, and have confirmed instead the ancient presence of R1a (two other samples, interred near Béla III), R1b (four samples), I2a (two samples) J1, and E1b, a mixed genetic picture which is more in line with what is expected.
So the question that I recently posed about east Corded Ware groups remains open: were Proto-Ugric peoples mainly of R1a-Z282 or R1a-Z93 subclades? Without ancient DNA from Middle Dnieper, Fatyanovo, Afanasevo, and the succeeding cultures (like Netted Ware) in north-eastern Europe, it is difficult to say.
It is very likely that they are going to show mainly a mixture of both R1a-Z282 and R1a-Z93 lineages, with later populations showing a higher proportion of R1a-Z280 subclades. Whether this mixture happened already during the Corded Ware period, or is the result of later developments, is still unknown. What is certain is that Hungarian N1a1a1a-L708 subclades belong to more recent additions of Siberian haplogroups to the Ugric stock, probably during the Iron Age, just centuries before the Magyar expansion.
Here we analyze North-western Tuscany, a region that was a corridor of exchanges between Central Italy and the Western Mediterranean coast.
We newly obtained mitochondrial HVRI sequences from 28 individuals, and after gathering published data, we collected genetic information for 119 individuals from the region. Those span five periods during the last 5,000 years: Prehistory, Etruscan age, Roman age, Renaissance, and Present-day. We used serial coalescent simulations in an approximate Bayesian computation framework to test for continuity between the mentioned groups.
In all cases, a simple model of a long-term genealogical continuity proved to fit the data better, and sometimes much better, than the alternative hypothesis of discontinuity.
The low number of samples analyzed requires some caution in the interpretation. Because we did not test for gene flow, it is at this stage impossible to reject it, but our results suggest at least significant levels of genealogical continuity. Moreover, as it has not been possible to obtain more precise information on the age of the Eneolithic samples, they were grouped together considering the average archaeological period of interest, which may cause a bias in the analyses. (…)
(…) clearly, our samples show high levels of continuity when considering the whole Tuscan region as a genetic reservoir during the Iron Age.
The posterior distributions of the parameters confirm a high degree of genetic isolation in the sampled population, with very small values for the female effective population sizes across time. Such values, in particular the Neolithic ones, are in accord with the estimates obtained in similar studies, both in Tuscany (Ghirotto et al., 2013) and in France (Rivollat et al., 2017).
Taken at their face value, our results do not show any major shift in the composition of the maternal ancestry of the population, across 50 centuries. This does not mean that no demographic process of relevance has affected the population, and indeed the higher diversity accumulating in time is the likely consequence of immigrating people, enriching the mitochondrial gene pool.
(…) the population of the current Lucca province appears to have retained very ancient mitochondrial features, despite occupying a geographical corridor between the Ligurian and the Tyrrhenian coast, and despite not showing the persistence of unique cultural traits through the centuries.
Another possibility is that that the different populations passing through the area (Etruscans, Romans, and Lombards) had a consistent social and/or sex bias. An example of similar patterns has been observed several times. Between the Late Neolithic and the Early Bronze Age, female exogamy in patrilocal society has been observed in Southern Germany (Knipper et al., 2017); during the Bronze Age the migrations toward Europe from the steppes appears to have consisted prevalently of males (Goldberg, Günther, Rosenberg, & Jakobsson, 2017); and in more recent periods in the Canary Islands, the female ancestry maintains a significant amount of autochthonous lineages, while the male ancestry was strongly influenced by the European colonization (Fregel et al., 2009, b).
It is well known that military invasions may not have a significant genetic impact upon the invaded population (Schiffels et al., 2016; Sokal, Oden, Walker, Di Giovanni, & Thomson, 1996;Weale,Weiss, Jager, Bradman, & Thomas, 2002), especially at the mitochondrial level, because of the limited size of a sustainable army, and of the fact that armies are generally composed mostly or only of males. Even if a substantial share of invaders decided to remain and settle the region, this form of gene flow would affect mostly or only the paternal lineages, rather than the maternal ones. We can also hypothesize the immigration of a number of people (e.g., Romans, Lombards) that may have acted as ruler of the region, remaining socially (and so genetically) separated by the local population, and leaving few (if any) traces in the gene pools of the local population.
If, however, male-biased expansions are also seen during the Iron Age – probably driven by particular subclades then – , this would certainly justify the continuity of admixture in certain regions in spite of these population expansions, and thus the importance of Y-DNA to track more recent language changes.
One of the most interesting details of the upcoming paper of Italic peoples will be the Y-DNA (and admixture) of Etruscans compared to other neighbouring peoples, given the known conflicting theories regarding their recent vs. older origin in the East before the historical record.