The Iron Age expansion of Southern Siberian groups and ancestry with Scythians

iron_age-sarmatians

Maternal genetic features of the Iron Age Tagar population from Southern Siberia (1st millennium BC), by Pilipenko et al. (2018).

Interesting excerpts (emphasis mine):

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).

tagar-archaeology
Location of Tagar archaeological sites from which samples for this study were obtained. Burial grounds: 1—Novaya Chernaya-1; 2—Podgornoe Ozero, Barsuchiha-1, Barsuchiha-6, Barsuchiha-7; 3—Perevozinskiy; 4—Ulug-Kyuzyur, Kichik-Kyuzyur, Sovetskaya Khakassiya; 5—Tepsey-3, Tepsey-8, Tepsey-9; 6—Dolgiy Kurgan. https://doi.org/10.1371/journal.pone.0204062.g001

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.

tagar-mtdna-tree
Phylogenetic tree of mtDNA lineages from the Tagar population. Color coding of the Tagar stages: orange—the Early Tagar stage; blue—the Middle Tagar Stage; green—the Late Tagar stage. Color of haplogroup labels: yellow—for Western Eurasian haplogroups; red—for Eastern Eurasian haplogroups. https://doi.org/10.1371/journal.pone.0204062.g002

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.

tagar-mtdna-fst
Results of multidimensional scaling based on matrix of Slatkin population differentiation (FST) according to frequencies of mtDNA haplogroup in Tagar populations and modern populations of Eurasia. Populations: Tagar (red pentagon) (this study); Mongolian-speaking populations: Khamnigans (Buryat Republic, Russia) [43]; Barghuts (Inner Mongolia, China) [44]; Buryats (Buryat Republic, Southern Siberia, Russia) [43]; Mongols (Mongolia) [45]. Turkic-speaking populations: Tuvinians (Tuva Republic, Russia) [43]; Tofalars (Irkutsk region, Russia) [46]; Altai-Kizhi ((Altai Republic, Russia) [43, 47]; Telenghits (Altai Republic, Russia) [43,47]; Tubalars (Altai Republic) [48]; Shors (Kemerovo region, Russia) [43, 47]; Khakassians (Khakassian Rupublic, Russia) [43, 46]; Altaian Kazakhs (Altai Republic) [49]; Kazakhs (Kazakhstan, Uzbekistan) [50, 51]; Kirghiz (Kyrgyzstan) [50, 51]; Uighurs (Kazakhstan and Xinjiang) [50, 52]; Siberian Tatars (Tyumen and Omsk regions, Russia) [53]; Tatars (Volga-Ural rigion, Russia) [54]; Bashkirs (Volga-Ural region, Russia) [55]; Uzbeks (Uzbekistan) [51, 56]; Turkmens (Turkmenistan) [51, 56]; Nogays [57]; Turkeys [58]; other populations: Evenks [43, 46]; Ulchi [59]; Koreans (South Korea) [43]; Han Chinese [60]; Zhuang (Guangxi, China) [61]; Tadjiks (Tadjikistan) [43, 51]; Iranians [60]; Russians [62]. https://doi.org/10.1371/journal.pone.0204062.g007

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 [71], 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.

Siberian ancestry

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:

siberian-genetic-component-chronology
Very important observation with implication of population turnover is that pre-Turkic Inner Eurasian populations’ Siberian ancestry appears predominantly “Uralic-Yeniseian” in contrast to later dominance of “Tungusic-Mongolic” sort (which does sporadically occur earlier). Alexander M. Kim

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.

Related

Modern Sardinians show elevated Neolithic farmer ancestry shared with Basques

sardinia-europe-relation

New paper (behind paywall), Genomic history of the Sardinian population, by Chiang et al. Nature Genetics (2018), previously published as a preprint at bioRxiv (2016).

#EDIT (18 Sep 2018): Link to read paper for free shared by the main author.

Interesting excerpts (emphasis mine):

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.

sardinia-pca
PCA results of merged Sardinian whole-genome sequences and the HGDP Sardinians. See below for a map of the corresponding regions.

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.

density-nuraghi-sardinia-genetics
Left: Geographical map of Sardinia. The provincial boundaries are given as black lines. The provinces are abbreviated as Cag (Cagliari), Cmp (Campidano), Car (Carbonia), Ori (Oristano), Sas (Sassari), Olb (Olbia-tempio), Nuo (Nuoro), and Ogl (Ogliastra). For sampled villages within Ogliastra, the names and abbreviations are indicated in the colored boxes. The color corresponds to the color used in the PCA plot (Fig. 2a). The Gennargentu region referred to in the main text is the mountainous area shown in brown that is centered in western Ogliastra and southeastern Nuoro.
Right: Density of Nuraghi in Sardinia, from Wikipedia.

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.

sardinian-admixture
Mixture proportions of the three-component ancestries among Sardinian populations. Using a method first presented in Haak et al. (Nature 522, 207–211, 2015), we computed unbiased estimates of mixture proportions without a parameterized model of relationships between the test populations and the outgroup populations based on f4 statistics. The three-component ancestries were represented by early Neolithic individuals from the LBK culture (LBK_EN), pre-Neolithic huntergatherers (Loschbour), and Bronze Age steppe pastoralists (Yamnaya). See Supplementary Table 5 for standard error estimates computed using a block jackknife.

Once again, haplogroup R1b1a2 (M269), and only R1b1a2, related to male-biased, steppe-related Indo-European migrations…just sayin’.

Interestingly, haplogroup I2a1a1 is actually found among northern Iberians during the Neolithic and Chalcolithic, and is therefore associated with Neolithic ancestry in Iberia, too, and consequently – unless there is a big surprise hidden somewhere – with the ancestry found today among Basques.

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.

Related

Early Medieval Alemannic graveyard shows diverse cultural and genetic makeup

alemannic-niederstotzingen

Open access Ancient genome-wide analyses infer kinship structure in an Early Medieval Alemannic graveyard, by O’Sullivan et al., Science (2018) 4(9):eaao1262

Interesting excerpts:

Introduction

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. (…)

alemannic-haplogroup

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%)

Genome-wide capture

alemannic-pca
PCA plot of Niederstotzingen individuals, modern west Eurasians, and selected ancient Europeans. Genome-wide ancient data were projected against modern west Eurasian populations. Colors on PCA indicate more general Eurasian geographic boundaries than countries: dark green, Caucasus; bright green, eastern Europe; yellow, Sardinia and Canary Islands; bright blue, Jewish diaspora; bright purple, western and central Europe; red, southern Europe; dark brown, west Asia; light purple, Spain; dark purple, Russia; pale green, Middle East; orange, North Africa. The transparent circles serve to highlight the genetic overlap between regions of interest.

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.

Kinship

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).

alemannic-family
Reconstruction of first- and second-degree relatedness among all related individuals. Bold black lines and blue lines indicate first- and second-degree relatedness, respectively. Dark blue squares are identified males with age-at-death estimates years old (y.o.), mtDNA haplotypes, and NRY haplogroups. Red circles represent unidentified females that passed maternal haplotypes to their offspring. The light square represents one male infant that shares its maternal haplotype with individuals 12B and 9. N.D., not determined.

Conclusion

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.

Texts and images distributed under the terms of the Creative Commons Attribution-NonCommercial license.

Related

Common pitfalls in human genomics and bioinformatics: ADMIXTURE, PCA, and the ‘Yamnaya’ ancestral component

invasion-from-the-steppe-yamnaya

Good timing for the publication of two interesting papers, that a lot of people should read very carefully:

ADMIXTURE

Open access A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots, by Daniel J. Lawson, Lucy van Dorp & Daniel Falush, Nature Communications (2018).

Interesting excerpts (emphasis mine):

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.

admixture-protocol
A protocol for interpreting admixture estimates, based on the assumption that the model underlying the inference is correct. If these assumptions are not validated, there is substantial danger of over-interpretation. The “Core protocol” describes the assumptions that are made by the admixture model itself (Protocol 1, 3, 4), and inference for estimating K (Protocol 2). The “Algorithm input” protocol describes choices that can further bias results, while the “Interpretation” protocol describes assumptions that can be made in interpreting the output that are not directly supported by model inference

Discussion

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.

admixture-pitfalls
Three scenarios that give indistinguishable ADMIXTURE results. a Simplified schematic of each simulation scenario. b Inferred ADMIXTURE plots at K= 11. c CHROMOPAINTER inferred painting palettes.

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.

PCA of SNP data

Open access Effective principal components analysis of SNP data, by Gauch, Qian, Piepho, Zhou, & Chen, bioRxiv (2018).

Interesting excerpts:

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 [3]. 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.

pca-how-to
DC-PCA biplot for oat data. The gradient in the CA-arranged matrix in Fig 13 is shown here for both lines and SNPs by the color scheme red, pink, black, light green, dark green.

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 [4]). 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.

Conclusion

Five simple recommendations for effective PCA analysis of SNP data emerge from this investigation.

  1. Use the SNP coding 1 for the rare or minor allele and 0 for the common or major allele.
  2. Use DC-PCA; for any other PCA variant, examine its augmented ANOVA table.
  3. 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.
  4. 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.
  5. 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.

pca-cwc-yamna-bbc
Principal Component Analysis (PCA) of the human Karsdorf and Sorsum samples together with previously published ancient populations projected on 27 modern day West Eurasian populations (not shown) based on a set of 1.23 million SNPs (Mathieson et al., 2015). https://doi.org/10.7554/eLife.36666.006

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.

So their lead archaeologist, Kristian Kristiansen, in The Indo-Europeanization of Europé (sic):

kristiansen-migrations
Kristiansen’s (2018) map of Indo-European migrations

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, dat British 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):

corded-ware-migrations
Nørtoft’s (2018) maps of Indo-European migrations.

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.

NOTE. You can read a more reasonable account of how haplogroup R1b-L51 and how R1-Z645 subclades expanded, and which dialects most likely expanded with them.

We don’t know where these scholars of the Danish workgroup stand at this moment, or if they ever had (or intended to have) a common position – beyond their persistent ideas of Yamnaya™ ancestral component = Indo-European and R1a must be Indo-European – , because each new publication changes some essential aspects without expressly stating so, and makes thus everything still messier.

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:

Related

“Steppe people seem not to have penetrated South Asia”

indo-iranian-sintashta-uralic-migrations

Open access structured abstract for The first horse herders and the impact of early Bronze Age steppe expansions into Asia from Damgaard et al. Science (2018) 360(6396):eaar7711.

Abstract (emphasis mine):

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.

INTRODUCTION
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.

RATIONALE
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.

damgaard-south-asia
Model-based admixture proportions for selected ancient and present-day individuals, assuming K = 6, shown with their corresponding geographical locations. Ancient groups are represented by larger admixture plots, with those sequenced in the present work surrounded by black borders and others used for providing context with blue borders. Present-day South Asian groups are represented by smaller admixture plots with dark red borders.

RESULTS
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.

CONCLUSION
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.

However, everything is possible, since it is brought to you by the same Danish group who proposed the Yamnaya ancestral component™, the CHG = Indo-European (and simultaneously EHG in Maykop = Anatolian??), and now also the CWC/R1a = Indo-European & Volosovo = Uralic

Here is the reaction of Narasimhan: Narasimhan has deleted the Tweet, it basically questioned the sentence that steppe people did not penetrate South Asia.

Related

Cystic fibrosis probably spread with expanding Bell Beakers

indo-european-uralic-bell-beaker-corded-ware-migrations

New paper (behind paywall) Estimating the age of p.(Phe508del) with family studies of geographically distinct European populations and the early spread of cystic fibrosis, by Farrell et al., European Journal of Human Genetics (2018).

Interesting excerpts (emphasis mine):

Our results revealed tMRCA average values ranging from 4725 to 1175 years ago and support the estimates of Serre et al. (3000–6000 years ago) [11], rather than Morral et al. (52,000 years ago) [6], but the latter figure was challenged by Kaplan et al. [26] because of disagreement with assumptions used in their calculations. In addition, the tMRCA values from western European regions reported herein refine the results of Fichou et al. [7] from a study of Breton CF patients in which the Estiage analysis suggested that the most common recent ancestor lived 115 generations ago. That tMRCA value, however, may have underestimated the age of p.(Phe508del) in Brittany due to consideration of all the haplotypes, even those that were reconstructed with ambiguities, as well as a potential bias associated with consanguinity due to including both haplotypes in homozygous families. In the more stringent Estiage analyses reported herein, those potential biases were avoided for all populations, leading to estimates of the oldest tMCRA values corresponding to the Early Bronze Age in western Europe, which is generally agreed to begin around 3000 BCE. This finding extends our results from a direct investigation of aDNA in teeth from Iron Age burials near Vienna around 350 BCE and allow us to conclude that p.(Phe508del) was present in that region long before then. More specifically, in the Austrian families studied, the Estiage data revealed a mean tMCRA value of 3575 years ago, which converts to 1558 BCE (Middle Bronze Age) [22].

Perhaps most remarkably, the estimated ages of p.(Phe508del) in the three western European regions (France, Ireland, and Denmark) were similar with closely overlapping 95% CI values. This observation is also in line with previously documented spatial autocorrelograms expressing genetic and geographical distance for these populations [24]. Such data provide more insight about the ancient origin of CF in our judgment—both when and where—and lead us to propose that CFTR p.(Phe508del) is derived from ancestors who lived in western Europe during the Bronze Age, as early as 2700 BCE, and that its relatively rapid dissemination occurred because of human migrations around the northwestern Atlantic trading routes [21] and then towards central and eastern Europe [22]. Diffusion from northwestern to central Europe in approximately 1000 years is consistent with the prominent Bronze Age migrations evident in the archeological record [21, 22] and from genomic studies of aDNA [27]. On the other hand, we are assuming a discrete origin of the principal CF-causing variant, but it is possible that p.(Phe508del) arose more than once or earlier, and then reached western Europe subsequently through Neolithic migrations.

cystic-fibrosis

[About Bell Beakers] (…) More specifically, their distinctive Bell Beaker pottery appeared and spread across western and central Europe beginning around 3000–2750 BCE and then disappeared between 2200 and 1800 BCE [22, 29]. Their migrations are linked to the advent of western and central European metallurgy, as they manufactured and traded metal goods, especially weapons, while traveling over long distances [30]. Most relevant to our study is the evidence that they migrated in a direction and over a time period that fits well with the pattern of tMRCA data we found for the p.(Phe508del) variant. Olalde et al. [29] have shown that both migration and cultural transmission played a major role in diffusion of the “Beaker Complex” and led to a “profound demographic transformation” of Britain after 2400 BCE. Moreover, the cultural elements that unite the widely distributed Beaker folk are so obvious that some have considered them a distinct ethnicity of Bronze Age people [33].

From our results, we propose the novel concept that large scale, long term west-to-east migrations of the Bell Beaker Europeans [22, 28–30] during the Bronze Age, could explain the dissemination of p.(Phe508del) in Europe and its documented northwest-to-southeast gradient [4].In fact, our tMRCA data show a temporal gradient also.

As you can see from the references, they consulted with Barry Cunliffe (or people accepting his theory), who is obsessed with Bell Beakers expanding Celtic languages from the British Isles. He is like the British equivalent of Danish scholar Kristian Kristiansen, and his obsession with Corded Ware = Indo-European (and Germanic = CWC Denmark), immutable no matter what genetic results might show.

The funny thing is, the interpretation of the paper is probably right. From what we can see in the data, it is quite possible that the disease spread with expanding Bell Beakers…only it spread from the East group in Hungary, i.e. from east to west. The regional difference in TMRCA and apparent west—east cline would point to the different expansions of affected lineages in the corresponding regions, and not to an origin in the British Isles.

Related

The importance of fine-scale studies for integrating palaeogenomics and archaeology

eurasian-genomes-published

Short review (behind paywall) The importance of fine-scale studies for integrating paleogenomics and archaeology, by Krishna R. Veeramah, Current Opinion in Genetics & Development (2018) 53:83-89.

Abstract (emphasis mine):

There has been an undercurrent of intellectual tension between geneticists studying human population history and archaeologists for almost 40 years. The rapid development of paleogenomics, with geneticists working on the very material discovered by archaeologists, appears to have recently heightened this tension. The relationship between these two fields thus far has largely been of a multidisciplinary nature, with archaeologists providing the raw materials for sequencing, as well as a scaffold of hypotheses based on interpretation of archaeological cultures from which the geneticists can ground their inferences from the genomic data. Much of this work has taken place in the context of western Eurasia, which is acting as testing ground for the interaction between the disciplines. Perhaps the major finding has not been any particular historical episode, but rather the apparent pervasiveness of migration events, some apparently of substantial scale, over the past ∼5000 years, challenging the prevailing view of archaeology that largely dismissed migration as a driving force of cultural change in the 1960s. However, while the genetic evidence for ‘migration’ is generally statistically sound, the description of these events as structured behaviours is lacking, which, coupled with often over simplistic archaeological definitions, prevents the use of this information by archaeologists for studying the social processes they are interested in. In order to integrate paleogenomics and archaeology in a truly interdisciplinary manner, it will be necessary to focus less on grand narratives over space and time, and instead integrate genomic data with other form of archaeological information at the level of individual communities to understand the internal social dynamics, which can then be connected amongst communities to model migration at a regional level. A smattering of recent studies have begun to follow this approach, resulting in inferences that are not only helping ask questions that are currently relevant to archaeologists, but also potentially opening up new avenues of research.

Interesting excerpts (emphasis mine, reference numbers removed for clarity):

There are two major, somewhat intertwined, problems that currently exist.

First, archaeologists are not critiquing whether the migrations identified by paleogenomics using sophisticated population genetic machinery are actually occurring. Instead, the technical criticism arrives in terms of how these migrations are being ascribed to specific cultures. In many paleogenomic papers, there is a tendency (and often an analytical and technical need) to associate samples with particular archaeological cultures, for which all samples are then treated as possessing some kind homogenous and pervasive social identity that is bound in space and time. The major critiques of this thus far have been directed to those studies examining Corded-Ware and Bell-Beaker-related individuals and their potential relationship to the Yamnaya [Vander Linden (2016), Heyd (2017), Furholt (2017)], but are applicable to many other ‘migration’ scenarios described in the recent literature. This is compounded by the use of sometimes small numbers of samples to represent certain cultures from a particular geographic area as representatives of the entire culture at a supra-regional level. Yet often these archaeological cultures such as Corded-Ware and Bell-Beaker themselves show considerable variability in space and time, and even within cemeteries, which is not factored into the genetic analysis.

From a population geneticists point of view, this kind of simplification is somewhat understandable and will often likely have very little impact on the final analysis, given that the primary goal is usually to use ancient samples to better understand modern genetic variation. Though there may be a specific historical interest in some of these past events, I would argue that the aim for most population geneticists at a higher level is to try and fit modern patterns of genetic variation using the simplest models possible that take into account past demographic events (for example fitting f-statistics using the ADMIXTUREGRAPH approach), as this is how we are trained. Although sharing an archaeological culture may not mean that a set of individuals are part of the same homogeneous social group in reality, this approach may be a good enough heuristic to find broad genetic connections compared to another group represented by a different culture, which can then ultimately help understand and model modern human population structure. However, for an archaeologists interested in the ancient individuals themselves and their social identity, this lumping is unsatisfactory, where sophisticated narratives of the individual migrants and their ancient communities are the intended goal.

eurasian-genomes
From the paper. Barplot showing cumulative number of ancient Eurasian genomes published on a yearly basis up to 8th July 2018. Includes samples undergoing both whole genome shotgun and SNP capture sequencing.

The second related problem is that ‘migration’ in the sense used currently in the paleogenomics literature lacks sufficient detail to be of much use for an archaeologists attempting to disentangle the complex social dynamics within and between communities. To truly understand the role of migration as a social process and its contribution towards cultural changes, it is necessary to describe it as a structured behaviour, rather than treating it as an explanatory ‘black box’. Are the migrations occurring as a result of short range waves-of-advance movements, or as long-distance movements via leapfrogging models or stream migrations along established routes dependent on key kinship networks. Are there return migrants, and are some subset of individuals more predisposed to migration driving the signals? Although such models were implemented in past studies (even with classical markers [1]) and are part of the population genetics literature, they are lacking in the current paleogenomics literature when discussing migration. The finding that there is an increase of 12.3% of ancestry type X in population A compared to the preceding population B that is suggestive of a migration, is not particularly useful for examining these kind of models. It is also unclear to what degree standard population genetic parameters estimated from genomic data such as effective population size, Ne, and gene flow are relevant to models studied in archaeology, given they reflect (somewhat undefined) long-term population sizes and average rates of movements over time, rather than reflecting any kind of reality of census size and mobility in the ancient communities the archaeologists are actually attempting to study.

The text goes on to talk about ways of studying fine-grained social dynamics of local cultures, such as:

define levels of genetic relatedness, but also in terms of material culture, age, sex, stress and activity indicators, stable isotopes for diet reconstruction (nitrogen, d13C and d15N, carbon, 13C/12C) and strontium and oxygen isotopes for mobility (87Sr/86Sr, d18O). Where possible, sites should be examined over multiple generations. In addition it will be incredibly useful to characterize the impact of disease in these communities, which is also proving to be a highly fruitful realm for paleogenomics.

I would say that the main problem is not the obvious limitations of palaeogenomics in terms of identifying prehistoric ethnolinguistic communities and their evolution, which is why it is just another tool to complement archaeology and linguistics. The main problem is the narrow understanding that some people have of the inherent limitations of palaeogenomics – especially when it interests them – , when publicizing simplistic conclusions based on these tools and their results. And I am not referring only to amateurs.

Related

Y-chromosome mixture in the modern Corsican population shows different migration layers

mesolithic-europe

Open access Prehistoric migrations through the Mediterranean basin shaped Corsican Y-chromosome diversity, by Di Cristofaro et al. PLOS One (2018).

Interesting excerpts:

This study included 321 samples from men throughout Corsica; samples from Provence and Tuscany were added to the cohort. All samples were typed for 92 Y-SNPs, and Y-STRs were also analyzed.

Haplogroup R represented approximately half of the lineages in both Corsican and Tuscan samples (respectively 51.8% and 45.3%) whereas it reached 90% in Provence. Sub-clade R1b1a1a2a1a2b-U152 predominated in North Corsica whereas R1b1a1a2a1a1-U106 was present in South Corsica. Both SNPs display clinal distributions of frequency variation in Europe, the U152 branch being most frequent in Switzerland, Italy, France and Western Poland. Calibrated branch lengths from whole Y chromosome sequencing [44,45] and ancient DNA studies [46] both indicated that R1a and R1b diversification began relatively recently, about 5 Kya, consistent with Bronze Age and Copper Age demographic expansion. TMRCA estimations are concordant with such expansion in Corsica.

corsica-haplogroups
Spatial frequency maps for haplogroups with frequencies above 3%, their Y-STR based phylogenetic networks in Corsican populations (Blue: North, Green: West, Orange: South, Black: Center and Purple: East) and their TMRCA (in years, +/- SE).

Haplogroup G reached 21.7% in Corsica and 13.3% in Tuscany. Sub-clade G2a2a1a2-L91 accounted for 11.3% of all haplogroups in Corsica yet was not present in Provence or in Tuscany. Thirty-four out of the 37 G2a2a1a2-L91 displayed a unique Y-STR profile, illustrated by the star-like profile of STR networks (Fig 1). G2a2a1a2-L91 and G2a2a-PF3147(xL91xM286) show their highest frequency in present day Sardinia and southern Corsica compared to low levels from Caucasus to Southern Europe, encompassing the Near and Middle East [21,47–50]. Ancient DNA results from Early and Middle Neolithic samples reported the presence of haplogroup G2a-P15 [51–53], consistent with gene flow from the Mediterranean region during the Neolithic transition. Td expansion time estimated by STR for P15-affiliated chromosomes was estimated to be 15,082+/-2217 years ago [49]. Ötzi, the 5,300-year-old Alpine mummy, was derived for the L91 SNP [21]. A genetic relationship between G haplogroups from Corsica and Sardinia is further supported by DYS19 duplication, reported in North Sardinia [14], and observed in the southern part of the Corsica in 9 out of 37 G2a2a1a2-L91 chromosomes and in 4 out of 5 G2a2a-PF3147(xL91xM286) chromosomes, 3 of which displayed an identical STR profile (S4 Table).

This lineage has a reported coalescent age estimated by whole sequencing in Sardinian samples of about 9,000 years ago. This could reflect common ancestors coming from the Caucasus and moving westward during the Neolithic period [48], whereas their continental counterparts would have been replaced by rapidly expanding populations associated with the Bronze Age [46,54,55]. Estimated TMRCA for L91 lineage in Corsica is 4529 +/- 853 years. G-L497 showed high frequencies in Corsica compared to Provence and Tuscany, and this haplogroup was common in Europe, but rare in Greece, Anatolia and the Middle East. Fifteen out of the 17 Corsican G2a2b2a1a1b-L497 displayed a unique Y-STR profile (S4 Table) with an estimated TMRCA of 6867 +/- 1294 years. Haplogroup G2a2b1-M406, associated with Impressed Ware Neolithic markers, along with J2a1-DYS445 = 6 and J2a1b1-M92 [22,49], had very low levels in Corsica. Conversely, G2a2b2a-P303was highly represented and seemed to be independent of the G2a2b1-M406 marker. The 7 G2a2b2a-P303(xL497xM527) Corsican chromosomes displayed a unique Y-STR profile (S4 Table).

pca-corsica
First and second axes of the PCA based on 12 Y-chromosome haplogroup frequencies in 83 west Mediterranean populations.

Haplogroup J, mainly represented by J2a1b-M67(xM92), displayed intermediate frequencies in Corsica compared to Tuscany and Provence. J2a1b-M67(xM92) derived STR network analysis displayed a quite homogeneous profile across the island with an estimated TMRCA of 2381 +/- 449 years (Fig 1) and individuals displaying M67 were peripheral compared to Northwestern Italians (S2 Fig). The haplogroup J2a1-Page55(xM67xM530), characteristic of non-Greek Anatolia [22], was found in the north-west of Corsica. Haplogroup J2a1-DYS445 = 6 was found in the north-west with DYS391 = 10 repeats, and in the far south with DYS391 = 9 repeats, the former was associated with Anatolian Greek samples, whereas the second was found in central Anatolia [22]. The 7 J2b2a-M241 displayed a unique Y-STR profile (S4 Table), they were only detected in the Cap Corse region, this sub-haplogroup shows frequency peaks in both the southern Balkans and northern-central Italy [56] and is associated with expansion from the Near East to the Balkans during Neolithic period [57].

Haplogroup E, mainly represented by E1b1b1a1b1a-V13, displayed intermediate frequencies in Corsica compared to Tuscany and Provence. E1b1b1a1b1a-V13 was thought to have initiated a pan-Mediterranean expansion 7,000 years ago starting from the Balkans [52] and its dispersal to the northern shore of the Mediterranean basin is consistent with the Greek Anatolian expansion to the western Mediterranean [22], characteristic of the region surrounding Alaria, and consistent with the TMRCA estimated in Corsica for this haplogroup. A few E1b1a-V38 chromosomes are also observed in the same regions as V13.

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