A very “Yamnaya-like” East Bell Beaker from France, probably R1b-L151

bell-beaker-expansion

Interesting report by Bernard Sécher on Anthrogenica, about the Ph.D. thesis of Samantha Brunel from Institut Jacques Monod, Paris, Paléogénomique des dynamiques des populations humaines sur le territoire Français entre 7000 et 2000 (2018).

NOTE. You can visit Bernard Sécher’s blog on genetic genealogy.

A summary from user Jool, who was there, translated into English by Sécher (slight changes to translation, and emphasis mine):

They have a good hundred samples from the North, Alsace and the Mediterranean coast, from the Mesolithic to the Iron Age.

There is no major surprise compared to the rest of Europe. On the PCA plot, the Mesolithic are with the WHG, the early Neolithics with the first farmers close to the Anatolians. Then there is a small resurgence of hunter-gatherers that moves the Middle Neolithics a little closer to the WHGs.

From the Bronze Age, they have 5 samples with autosomal DNA, all in Bell Beaker archaeological context, which are very spread on the PCA. A sample very high, close to the Yamnaya, a little above the Corded Ware, two samples right in the Central European Bell Beakers, a fairly low just above the Neolithic package, and one last full in the package. The most salient point was that the Y chromosomes of their 12 Bronze Age samples (all Bell Beakers) are all R1b, whereas there was no R1b in the Neolithic samples.

Finally they have samples of the Iron Age that are collected on the PCA plot close to the Bronze Age samples. They could not determine if there is continuity with the Bronze Age, or a partial replacement by a genetically close population.

PCA-caucasus-yamna
Image modified from Wang et al. (2018). Samples projected in PCA of 84 modern-day West Eurasian populations (open symbols). Previously known clusters have been marked and referenced. Marked and labelled are interesting samples; In red, likely position of late Yamna Hungary / early East Bell Beakers An EHG and a Caucasus ‘clouds’ have been drawn, leaving Pontic-Caspian steppe and derived groups between them. See the original file here. To understand the drawn potential Caucasus Mesolithic cluster, see above the PCA from Lazaridis et al. (2018).

The sample with likely high “steppe ancestry“, clustering closely to Yamna (more than Corded Ware samples) is then probably an early East Bell Beaker individual, probably from Alsace, or maybe close to the Rhine Delta in the north, rather than from the south, since we already have samples from southern France from Olalde et al. (2018) with high Neolithic ancestry, and samples from the Rhine with elevated steppe ancestry, but not that much.

This specific sample, if confirmed as one of those reported as R1b (then likely R1b-L151), as it seems from the wording of the summary, is key because it would finally link Yamna to East Bell Beaker through Yamna Hungary, all of them very “Yamnaya-like”, and therefore R1b-L151 (hence also R1b-L51) directly to the steppe, and not only to the Carpathian Basin (that is, until we have samples from late Repin or West Yamna…)

NOTE. The only alternative explanation for such elevated steppe ancestry would be an admixture between a ‘less Yamnaya-like’ East Bell Beaker + a Central European Corded Ware sample like the Esperstedt outlier + drift, but I don’t think that alternative is the best explanation of its position in the PCA closer to Yamna in any of the infinite parallel universes, so… Also, the sample from Esperstedt is clearly a late outlier likely influenced by Yamna vanguard settlers from Hungary, not the other way round…

Unexpectedly, then, fully Yamnaya-like individuals are found not only in Yamna Hungary ca. 3000-2500 BC, but also among expanding East Bell Beakers later than 2500 BC. This leaves us with unexplained, not-at-all-Yamnaya-like early Corded Ware samples from ca. 2900 BC on. An explanation based on admixture with locals seems unlikely, seeing how Corded Ware peoples continue a north Pontic cluster, being thus different from Yamna and their ancestors since the Neolithic; and how they remained that way for a long time, up to Sintashta, Srubna, Andronovo, and even later samples… A different, non-Indo-European community it is, then.

olalde_pca2
Image modified from Olalde et al. (2018). PCA of 999 Eurasian individuals. Marked is the Espersted Outlier with the approximate position of Yamna Hungary, probably the source of its admixture. Different Bell Beaker clines have been drawn, to represent approximate source of expansions from Central European sources into the different regions. In red, likely zone of Yamna Hungary and reported early East Bell Beaker individual from France.

Let’s wait and see the Ph.D. thesis, when it’s published, and keep observing in the meantime the absurd reactions of denial, anger, bargaining, and depression (stages of grief) among BBC/R1b=Vasconic and CWC/R1a=Indo-European fans, as if they had lost something (?). Maybe one of these reactions is actually the key to changing reality and going back to the 2000s, who knows…

Featured image: initial expansion of the East Bell Beaker Group, by Volker Heyd (2013).

Related

“Steppe ancestry” step by step: Khvalynsk, Sredni Stog, Repin, Yamna, Corded Ware

dzudzuana_pca-large

Wang et al. (2018) is obviously a game changer in many aspects. I have already written about the upcoming Yamna Hungary samples, about the new Steppe_Eneolithic and Caucasus Eneolithic keystones, and about the upcoming Greece Neolithic samples with steppe ancestry.

An interesting aspect of the paper, hidden among so many relevant details, is a clearer picture of how the so-called Yamnaya or steppe ancestry evolved from Samara hunter-gatherers to Yamna nomadic pastoralists, and how this ancestry appeared among Proto-Corded Ware populations.

anatolia-neolithic-steppe-eneolithic
Image modified from Wang et al. (2018). Marked are in orange: equivalent Steppe_Maykop ADMIXTURE; in red, approximate limit of Anatolia_Neolithic ancestry found in Yamna populations; in blue, Corded Ware-related groups. “Modelling results for the Steppe and Caucasus cluster. Admixture proportions based on (temporally and geographically) distal and proximal models, showing additional Anatolian farmer-related ancestry in Steppe groups as well as additional gene flow from the south in some of the Steppe groups as well as the Caucasus groups.”

Please note: arrows of “ancestry movement” in the following PCAs do not necessarily represent physical population movements, or even ethnolinguistic change. To avoid misinterpretations, I have depicted arrows with Y-DNA haplogroup migrations to represent the most likely true ethnolinguistic movements. Admixture graphics shown are from Wang et al. (2018), and also (the K12) from Mathieson et al. (2018).

1. Samara to Early Khvalynsk

The so-called steppe ancestry was born during the Khvalynsk expansion through the steppes, probably through exogamy of expanding elite clans (eventually all R1b-M269 lineages) originally of Samara_HG ancestry. The nearest group to the ANE-like ghost population with which Samara hunter-gatherers admixed is represented by the Steppe_Eneolithic / Steppe_Maykop cluster (from the Northern Caucasus Piedmont).

Steppe_Eneolithic samples, of R1b1 lineages, are probably expanded Khvalynsk peoples, showing thus a proximate ancestry of an Early Eneolithic ghost population of the Northern Caucasus. Steppe_Maykop samples represent a later replacement of this Steppe_Eneolithic population – and/or a similar population with further contribution of ANE-like ancestry – in the area some 1,000 years later.

PCA-caucasus-steppe-samara

This is what Steppe_Maykop looks like, different from Steppe_Eneolithic:

steppe-maykop-admixture

NOTE. This admixture shows how different Steppe_Maykop is from Steppe_Eneolithic, but in the different supervised ADMIXTURE graphics below Maykop_Eneolithic is roughly equivalent to Eneolithic_Steppe (see orange arrow in ADMIXTURE graphic above). This is useful for a simplified analysis, but actual differences between Khvalynsk, Sredni Stog, Afanasevo, Yamna and Corded Ware are probably underestimated in the analyses below, and will become clearer in the future when more ancestral hunter-gatherer populations are added to the analysis.

2. Early Khvalynsk expansion

We have direct data of Khvalynsk-Novodanilovka-like populations thanks to Khvalynsk and Steppe_Eneolithic samples (although I’ve used the latter above to represent the ghost Caucasus population with which Samara_HG admixed).

We also have indirect data. First, there is the PCA with outliers:

PCA-khvalynsk-steppe

Second, we have data from north Pontic Ukraine_Eneolithic samples (see next section).

Third, there is the continuity of late Repin / Afanasevo with Steppe_Eneolithic (see below).

3. Proto-Corded Ware expansion

It is unclear if R1a-M459 subclades were continuously in the steppe and resurged after the Khvalynsk expansion, or (the most likely option) they came from the forested region of the Upper Dnieper area, possibly from previous expansions there with hunter-gatherer pottery.

Supporting the latter is the millennia-long continuity of R1b-V88 and I2a2 subclades in the north Pontic Mesolithic, Neolithic, and Early Eneolithic Sredni Stog culture, until ca. 4500 BC (and even later, during the second half).

Only at the end of the Early Eneolithic with the disappearance of Novodanilovka (and beginning of the steppe ‘hiatus’ of Rassamakin) is R1a to be found in Ukraine again (after disappearing from the record some 2,000 years earlier), related to complex population movements in the north Pontic area.

NOTE. In the PCA, a tentative position of Novodanilovka closer to Anatolia_Neolithic / Dzudzuana ancestry is selected, based on the apparent cline formed by Ukraine_Eneolithic samples, and on the position and ancestry of Sredni Stog, Yamna, and Corded Ware later. A good alternative would be to place Novodanilovka still closer to the Balkan outliers (i.e. Suvorovo), and a source closer to EHG as the ancestry driven by the migration of R1a-M417.

PCA-sredni-stog-steppe

The first sample with steppe ancestry appears only after 4250 BC in the forest-steppe, centuries after the samples with steppe ancestry from the Northern Caucasus and the Balkans, which points to exogamy of expanding R1a-M417 lineages with the remnants of the Novodanilovka population.

steppe-ancestry-admixture-sredni-stog

4. Repin / Early Yamna expansion

We don’t have direct data on early Repin settlers. But we do have a very close representative: Afanasevo, a population we know comes directly from the Repin/late Khvalynsk expansion ca. 3500/3300 BC (just before the emergence of Early Yamna), and which shows fully Steppe_Eneolithic-like ancestry.

afanasevo-admixture

Compared to this eastern Repin expansion that gave Afanasevo, the late Repin expansion to the west ca. 3300 BC that gave rise to the Yamna culture was one of colonization, evidenced by the admixture with north Pontic (Sredni Stog-like) populations, no doubt through exogamy:

PCA-repin-yamna

This admixture is also found (in lesser proportion) in east Yamna groups, which supports the high mobility and exogamy practices among western and eastern Yamna clans, not only with locals:

yamnaya-admixture

5. Corded Ware

Corded Ware represents a quite homogeneous expansion of a late Sredni Stog population, compatible with the traditional location of Proto-Corded Ware peoples in the steppe-forest/forest zone of the Dnieper-Dniester region.

PCA-latvia-ln-steppe

We don’t have a comparison with Ukraine_Eneolithic or Corded Ware samples in Wang et al. (2018), but we do have proximate sources for Abashevo, when compared to the Poltavka population (with which it admixed in the Volga-Ural steppes): Sintashta, Potapovka, Srubna (with further Abashevo contribution), and Andronovo:

sintashta-poltavka-andronovo-admixture

The two CWC outliers from the Baltic show what I thought was an admixture with Yamna. However, given the previous mixture of Eneolithic_Steppe in north Pontic steppe-forest populations, this elevated “steppe ancestry” found in Baltic_LN (similar to west Yamna) seems rather an admixture of Baltic sub-Neolithic peoples with a north Pontic Eneolithic_Steppe-like population. Late Repin settlers also admixed with a similar population during its colonization of the north Pontic area, hence the Baltic_LN – west Yamna similarities.

NOTE. A direct admixture with west Yamna populations through exogamy by the ancestors of this Baltic population cannot be ruled out yet (without direct access to more samples), though, because of the contacts of Corded Ware with west Yamna settlers in the forest-steppe regions.

steppe-ancestry-admixture-latvia

A similar case is found in the Yamna outlier from Mednikarovo south of the Danube. It would be absurd to think that Yamna from the Balkans comes from Corded Ware (or vice versa), just because the former is closer in the PCA to the latter than other Yamna samples. The same error is also found e.g. in the Corded Ware → Bell Beaker theory, because of their proximity in the PCA and their shared “steppe ancestry”. All those theories have been proven already wrong.

NOTE. A similar fallacy is found in potential Sintashta→Mycenaean connections, where we should distinguish statistically that result from an East/West Yamna + Balkans_BA admixture. In fact, genetic links of Mycenaeans with west Yamna settlers prove this (there are some related analyses in Anthrogenica, but the site is down at this moment). To try to relate these two populations (separated more than 1,000 years before Sintashta) is like comparing ancient populations to modern ones, without the intermediate samples to trace the real anthropological trail of what is found…Pure numbers and wishful thinking.

Conclusion

Yamna and Corded Ware show a similar “steppe ancestry” due to convergence. I have said so many times (see e.g. here). This was clear long ago, just by looking at the Y-chromosome bottlenecks that differentiate them – and Tomenable noticed this difference in ADMIXTURE from the supplementary materials in Mathieson et al. (2017), well before Wang et al. (2018).

This different stock stems from (1) completely different ancestral populations + (2) different, long-lasting Y-chromosome bottlenecks. Their similarities come from the two neighbouring cultures admixing with similar populations.

If all this does not mean anything, and each lab was going to support some pre-selected archaeological theories from the 1960s or the 1980s, coupled with outdated linguistic models no matter what – Anthony’s model + Ringe’s glottochronological tree of the early 2000s in the Reich Lab; and worse, Kristiansen’s CWC-IE + Germano-Slavonic models of the 1940s in the Copenhagen group – , I have to repeat my question again:

What’s (so much published) ancient DNA useful for, exactly?

Related

Dzudzuana, Sidelkino, and the Caucasus contribution to the Pontic-Caspian steppe

hunter-gatherer-pottery

It has been known for a long time that the Caucasus must have hosted many (at least partially) isolated populations, probably helped by geographical boundaries, setting it apart from open Eurasian areas.

David Reich writes in his book the following about India:

The genetic data told a clear story. Around a third of Indian groups experienced population bottlenecks as strong or stronger than the ones that occurred among Finns or Ashkenazi Jews. We later confirmed this finding in an even larger dataset that we collected working with Thangaraj: genetic data from more than 250 jati groups spread throughout India (…)

Rather than an invention of colonialism as Dirks suggested, long-term endogamy as embodied in India today in the institution of caste has been overwhelmingly important for millennia. (…)

The Han Chinese are truly a large population. They have been mixing freely for thousands of years. In contrast, there are few if any Indian groups that are demographically very large, and the degree of genetic differentiation among Indian jati groups living side by side in the same village is typically two to three times higher than the genetic differentiation between northern and southern Europeans. The truth is that India is composed of a large number of small populations.

There is little doubt now, based on findings spanning thousands of years, that the Mesolithic and Neolithic Caucasus hosted various very small populations, even if the ancestral components may be reduced to the few known to date (such as ANE, EHG, AME*, ENA, CHG, and other “deep” ancestral components).

NOTE. I will call the ancestral component of Dzudzuana/Anatolian hunter-gatherers Ancient Middle Easterner (AME), to give a clear idea of its likely extension during the Late Upper Palaeolithic, and to avoid using the more simplistic Dzudzuana, unless it is useful to mention these specific local samples.

dzudzuana-pca
Image modified from Lazaridis et al. (2018), including Caucasus, Don-Volga-Ural, and North Pontic Mesolithic-Neolithic populations. “Ancient West Eurasian population structure. (a) Geographical distribution of key ancient West Eurasian populations. (b) Temporal distribution of key ancient West Eurasian populations (approximate date in ky BP). (c) PCA of key ancient West Eurasians, including additional populations (shown with grey shells), in the space of outgroup f4-statistics (Methods).”

Genetic labs have a strong fixation with ancestry. I guess the use of complex statistical methods gives professionals and laymen alike the feeling of dealing with “Science”, as opposed to academic fields where you have to interpret data. I think language reveals a lot about the way people think, and the fact that ancestral components are called ‘lineages’ – while not wrong per se – is a clear symptom of the lack of interest in the true lineages: Y-DNA haplogroups.

Y-DNA bottlenecks

It has become quite clear that male-biased migrations are often the ones which can be confidently followed for actual population movements and ethnolinguistic identification, at least until the Iron Age. The frequently used Palaeolithic clusters offer a clear example of why ancestry does not represent what some people believe: They merely give a basic idea of sizeable population replacements by distant peoples.

Both concepts are important: sizeable and distant peoples. For example, during the Upper Palaeolithic in Europe there was a sizeable population replacement of the Aurignacian Goyet cluster by the Gravettian Vestonice cluster (probably from populations of far eastern Russia) coupled with the arrival of haplogroup I, although during the thousands of years that this material culture lasted, the previously expanded C1a2 lineages did not disappear, and there were probably different resurgence and admixture events.

Haplogroup I certainly expanded with the Gravettian culture to Iberia, where the Goyet ancestry did not change much – probably because of male-driven migrations -, to the extent that during the Magdalenian expansions haplogroup I expanded with an ancestry closer to Goyet, in what is called a ‘resurge’ of the Goyet cluster – even though there is a clear replacement of male lines.

The Villabruna (WHG) cluster is another good example. It probably spread with haplogroup R1b-L754, which – based on the extra ‘East Asian’ affinity of some samples and on modern samples from the Middle East – came probably from the east through a southern route, and not too long before the expansion of WHG likely from around the Black Sea, although this is still unclear. The finding of haplogroup I in samples of mostly WHG ancestry could confuse people that do not care about timing, sub-structured populations, and gene flow.

palaeolithic-expansions-reich
Image from David Reich’s Who We Are and How We Got Here. Having migrated out of Africa and the Near East, modern human pioneer populations spread throughout Eurasia (1). By at least thirty-nine thousand years ago, one group founded a lineage of European hunter-gatherers that persisted largely uninterrupted for more than twenty thousand years (2). Eventually, groups derived from an eastern branch of this founding population of European huntergatherers spread west (3), displaced previous groups, and were eventually themselves pushed out of northern Europe by the spread of glacial ice, shown at its maximum extent (top right). As the glaciers receded, western Europe was repeopled from the southwest (4) by a population that had managed to persist for tens of thousands of years and was related to an approximately thirty-five-thousand-year old individual from far western Europe. A later human migration, following the first strong warming period, had an even larger impact, with a spread from the southeast (5) that not only transformed the population of western Europe but also homogenized the populations of Europe and the Near East. At a single site—Goyet Caves in Belgium—ancient DNA from individuals spread over twenty thousand years reflects these transformations, with representatives from the Aurignacian, Gravettian, and Magdalenian periods.

NOTE. If you don’t understand why ‘clusters’ that span thousands of years don’t really matter for the many Palaeolithic population expansions that certainly happened among hunter-gatherers in Europe, just take a look at what happened with Bell Beakers expanding from Yamna into western Europe within 500 years.

If we don’t thread carefully when talking about population migrations, these terms are bound to confuse people. Just as the fixation on “steppe ancestry” – which marks the arrival in Chalcolithic Europe of peoples from the Pontic-Caspian region – has confused a lot of researchers to this day.

When I began to write about the Indo-European demic diffusion model, my concern was to find a single spot where a North-West Indo-European proto-language could have expanded from ca. 2000 BC (our most common guesstimate). Based on the 2015 papers, and in spite of their conclusions, I thought it had become clear that Corded Ware was not it, and it was rather Bell Beakers. I assumed that Uralic was spoken to the north (as was the traditional belief), and thus Corded Ware expanded from the forest zone, hence steppe ancestry would also be found there with other R1a lineages.

With the publication of Mathieson et al. (2017) and Olalde et al. (2017), I changed my mind, seeing how “steppe ancestry” did in fact appear quite late, hence it was likely to be the result of very specific population movements, probably directly from the Caucasus. Later, Mathieson published in a revision the sample from Alexandria of hg R1a-M417 (probably R1a-Z645, possibly Z93+), which further supported the idea that the migration of Corded Ware peoples started near the North Pontic forest-steppe (as I included in a the next revision).

The question remains the same I repeated recently, though: where do the extra Caucasus components (i.e. beyond EHG) of Eneolithic Ukraine/Corded Ware and Khvalynsk/Yamna come from?

Steppe ancestry: “EHG” + “CHG”?

About EHG ancestry

From Lazaridis et al. (2018):

Considering 2-way mixtures, we can model Karelia_HG as deriving 34 ± 2.8% of its ancestry from a Villabruna-related source, with the remainder mainly from ANE represented by the AfontovaGora3 (AG3) sample from Lake Baikal ~17kya.

AG3 was likely of haplogroup Q1a (as reported by YFull, see Genetiker), and probably the ANE ancestry found in Eastern Europe accompanied a Palaeolithic migration of Q1a2-M25 (formed ca. 22600 BC, TMRCA ca. 14300 BC).

NOTE. You can read more about the expansion of Q lineages during the Palaeolithic.

Combined with what we know about the Eneolithic Steppe and Caucasus populations – it is likely that ANE ancestry remained the most important component of some of the small ghost populations of the Caucasus until their emergence with the Lola culture.

pca-caucasus-dzudzuana
Image modified from Wang et al. (2018). Samples projected in PCA of 84 modern-day West Eurasian populations (open symbols). Previously known clusters have been marked and referenced. Marked and labelled are the Balkan samples referenced in this text An EHG and a Caucasus ‘clouds’ have been drawn, leaving Pontic-Caspian steppe and derived groups between them. See the original file here. To understand the drawn potential Caucasus Mesolithic cluster, see above the PCA from Lazaridis et al. (2018).

The first sample we have now attributed to the EHG cluster is Sidelkino, from the Samara region (ca. 9300 BC), mtDNA U5a2. In Damgaard et al. (Science 2018), Yamnaya could be modelled as a CHG population related to Kotias Klde (54%) and the remaining from ANE population related to Sidelkino (>46%), with the following split events:

  1. A split event, where the CHG component of Yamnaya splits from KK1. The model inferred this time at 27 kya (though we note the larger models in Sections S2.12.4 and S2.12.5 inferred a more recent split time).
  2. A split event, where the ANE component of Yamnaya splits from Sidelkino. This was inferred at about about 11 kya.
  3. A split event, where the ANE component of Yamnaya splits from Botai. We inferred this to occur 17 kya. Note that this is above the Sidelkino split time, so our model infers Yamnaya to be more closely related to the EHG Sidelkino, as expected.
  4. An ancestral split event between the CHG and ANE ancestral populations. This was inferred to occur around 40 kya.

Other samples classified as of the EHG cluster:

  • Popovo2 (ca. 6250 BC) of hg J1, mtDNA U4d – Po2 and Po4 from the same site (ca. 6550 BC) show continuity of mtDNA.
  • Karelia_HG, from Juzhnii Oleni Ostrov (ca. 6300 BC): I0211/UzOO40 (ca. 6300 BC) of hg J1(xJ1a), mtDNA U4a; and I0061/UzOO74 of hg R1a1(xR1a1a), mtDNA C1
  • UzOO77 and UzOO76 from Juzhnii Oleni Ostrov (ca. 5250 BC) of mtDNA R1b.
  • Samara_HG from Lebyanzhinka (ca. 5600 BC) of hg R1b1a, mtDNA U5a1d.

From the analysis of Lazaridis et al. (2018), we have some details about their admixture:

dzudzuana-admixture-sidelkino
Image modified from Lazaridis et al. (2018). Modeling present-day and ancient West-Eurasians. Mixture proportions computed with qpAdm (Supplementary Information section 4). The proportion of ‘Mbuti’ ancestry represents the total of ‘Deep’ ancestry from lineages that split prior to the split of Ust’Ishim, Tianyuan, and West Eurasians and can include both ‘Basal Eurasian’ and other (e.g., Sub-Saharan African) ancestry. (Left) ‘Conservative’ estimates. Each population 367 cannot be modeled with fewer admixture events than shown. (Right) ‘Speculative’ estimates. The highest number of sources (≤5) with admixture estimates within [0,1] are shown for each population. Some of the admixture proportions are not significantly different from 0 (Supplementary Information section 4).

About Anatolia_Neolithic ancestry

About the enigmatic Anatolia_Neolithic-related ancestry found in Pontic-Caspian steppe samples, this is what Wang et al. (2018) had to say:

We focused on model of mixture of proximal sources such as CHG and Anatolian Chalcolithic for all six groups of the Caucasus cluster (Eneolithic Caucasus, Maykop and Late Makyop, Maykop-Novosvobodnaya, Kura-Araxes, and Dolmen LBA), with admixture proportions on a genetic cline of 40-72% Anatolian Chalcolithic related and 28-60% CHG related (Supplementary Table 7). When we explored Romania_EN and Greece_Neolithic individuals as alternative southeast European sources (30-46% and 36-49%), the CHG proportions increased to 54-70% and 51-64%, respectively. We hypothesize that alternative models, replacing the Anatolian Chalcolithic individual with yet unsampled populations from eastern Anatolia, South Caucasus or northern Mesopotamia, would probably also provide a fit to the data from some of the tested Caucasus groups.

Also:

The first appearance of ‘Near Eastern farmer related ancestry’ in the steppe zone is evident in Steppe Maykop outliers. However, PCA results also suggest that Yamnaya and later groups of the West Eurasian steppe carry some farmer related ancestry as they are slightly shifted towards ‘European Neolithic groups’ in PC2 (Fig. 2D) compared to Eneolithic steppe. This is not the case for the preceding Eneolithic steppe individuals. The tilting cline is also confirmed by admixture f3-statistics, which provide statistically negative values for AG3 as one source and any Anatolian Neolithic related group as a second source

yamnaya-caucasus-dzudzuana
Modified image from Wang et al. (2018). In blue, Yamna-related populations. In red, Corded Ware-related populations, and two elevated Anatolia_Neolithic values in Yamna. Notice how only GAC-related admixture increases the Anatolian_N-related ancestry in the Yamna outlier from Ozero, and the late Yamna sample from Hungary, related to the homogeneous Yamna population. “Supplementary Table 14. P values of rank=3 and admixture proportions in modelling Steppe ancestry populations as a four-way admixture of distal sources EHG, CHG, Anatolian_Neolithic and WHG using 14 outgroups.Left populations: Steppe cluster, EHG, CHG, WHG, Anatolian_Neolithic. Right populations: Mbuti.DG, Ust_Ishim.DG, Kostenki14, MA1, Han.DG, Papuan.DG, Onge.DG, Villabruna, Vestonice16, ElMiron, Ethiopia_4500BP.SG, Karitiana.DG, Natufian, Iran_Ganj_Dareh_Neolithic.”

Detailed exploration via D-statistics in the form of D(EHG, steppe group; X, Mbuti) and D(Samara_Eneolithic, steppe group; X, Mbuti) show significantly negative D values for most of the steppe groups when X is a member of the Caucasus cluster or one of the Levant/Anatolia farmer-related groups (Supplementary Figs. 5 and 6). In addition, we used f- and D-statistics to explore the shared ancestry with Anatolian Neolithic as well as the reciprocal relationship between Anatolian- and Iranian farmer-related ancestry for all groups of our two main clusters and relevant adjacent regions (Supplementary Fig. 4). Here, we observe an increase in farmer-related ancestry (both Anatolian and Iranian) in our Steppe cluster, ranging from Eneolithic steppe to later groups. In Middle/Late Bronze Age groups especially to the north and east we observe a further increase of Anatolian farmer related ancestry consistent with previous studies of the Poltavka, Andronovo, Srubnaya and Sintashta groups and reflecting a different process not especially related to events in the Caucasus.

(…) Surprisingly, we found that a minimum of four streams of ancestry is needed to explain all eleven steppe ancestry groups tested, including previously published ones (Fig. 2; Supplementary Table 12). Importantly, our results show a subtle contribution of both Anatolian farmer-related ancestry and WHG-related ancestry (Fig.4; Supplementary Tables 13 and 14), which was likely contributed through Middle and Late Neolithic farming groups from adjacent regions in the West. The discovery of a quite old AME ancestry has rendered this probably unnecessary, because this admixture from an Anatolian-like ghost population could be driven even by small populations from the Caucasus.

yamna-caucasus-cwc-anatolia-neolithic
Image modified from Wang et al. (2018). Marked are: in red, approximate limit of Anatolia_Neolithic ancestry found in Yamna populations; in blue, Corded Ware-related groups. “Modelling results for the Steppe and Caucasus 1128 cluster. Admixture proportions based on (temporally and geographically) distal and proximal models, showing additional Anatolian farmer-related ancestry in Steppe groups as well as additional gene flow from the south in some of the Steppe groups as well as the Caucasus groups (see also Supplementary Tables 10, 14 and 20).”

NOTE. For a detailed account of the possibilities regarding this differential admixture in the North Pontic area in contrast to the Don-Volga-Ural region, you can read the posts Sredni Stog, Proto-Corded Ware, and their “steppe admixture”, and Corded Ware culture origins: The Final Frontier.

While it is not yet fully clear, the increased Anatolian_Neolithic-like ancestry in Ukraine_Eneolithic samples (see below) makes it unlikely that all such ancestry in Corded Ware groups comes from a GAC-related contribution. It is likely that at least part of it represents contributions from populations of the Caucasus, based on the mostly westward population movements in the steppe from ca. 4600 BC on, including the Suvorovo-Novodanilovka expansion, and especially the Kuban-Maykop expansion during the final Eneolithic into the North Pontic area.

NOTE. Since CHG-like groups from the Caucasus may have combinations of AME and ANE ancestry similar to Yamna (which may thus appear as ‘steppe ancestry’ in the North Pontic area), it is impossible to interpret with precision the following ADMIXTURE graphic:

ukraine-whg-ehg-steppe
Modified image from Mathieson et al. (2018). Supervised ADMIXTURE analysis, modelling each ancient individual (one per row) as a mixture of population clusters constrained to contain northwestern-Anatolian Neolithic (grey), Yamnaya from Samara (yellow), EHG (pink) and WHG (green) populations. Dates in parentheses indicate approximate range of individuals in each population.

North-Eastern Technocomplex

The East Asian contribution to samples from the WHG samples (like Loschbour or La Braña), as specified in Fu et al. (2016), does not seem to be related to Baikal_EN, and appears possibly (in the ADMIXTURE analysis) integrated into he Villabruna component. I guess this implies that the shared alleles with East Asians are quite early, and potentially due to the expansion of R1b-L754 from the East.

It would be interesting to know the specific material culture Sidelkino belonged to – i.e. if it was related to the expansion of the North-Eastern Technocomplex – , and its Y-DNA. The Post-Swiderian expansion into eastern Europe, probably associated with the expansion of R1b-P297 lineages (including R1b-M73, found later in Botai and in Baltic HG) is supposed to have begun during the 11th millennium BC, but migrations to the Urals and beyond are probably concentrated in the 9th millennium, so this sample is possibly slightly early for R1b.

NOTE. User Rozenfeld at Anthrogenica posted this, which I think is interesting (in case anyone wants to try a Y-SNP call):

there is something strange with Sidelkino EHG: first, its archaeological context is not described in the supplementary. Second, its sex is not listed in the supplementary tables. Third, after looking for info about this sample, I found that: “Сиделькино-3. Для снятия вопроса о половой принадлежности индивида была проведена генетическая экспертиза, выявившая принадлежность останков мужчине.”(translation: Sidelkino-3. To resolve the question about sex of the remains, the genetic analysis was conducted, which showed that remains belonged to male), source: http://static.iea.ras.ru/books/7487_Traditsii.pdf

So either they haven’t mentioned his Y-DNA in the paper for some reason, or there are more than one Sidelkino sample and the male one has not yet been published. The coverage of the Sidelkino sample from the paper is 2.9, more than enough to tell Y-DNA haplogroup.

zaliznyak-post-swiderian
The map of spreading of Post-Swiderian and Post-Krasnosillian sites in Mesolithic of Eastern Europe in the 8th millennia BC. From Zaliznyak (see here).

My speculative guess right now about specific population movements in far eastern Europe, based on the few data we have:

  • The expansion of the North-Eastern Technocomplex first around the 9th millennium BC, most likely expanded R1b-P279 ca. 11300 BC, judging by its TMRCA, with both R1b-M73 (TMRCA 5300) and R1b-M269 (TMRCA 4400 BC) info (with extra El Mirón ancestry) back, and thus Eurasiatic.
  • The expansion of haplogroup J1 to the north may have happened before or after the R1b-P279 expansion. Judging by the increase in AG3-related ancestry near Karelia compared to Baltic_HG, it is possible that it expanded just after R1b-P279 (hence possibly J1-Y6304? TMRCA 9700 BC). Its long-lasting presence in the Caucasus is supported by the Satsurblia (ca. 11300 BC) and the Dolmen BA (ca. 1300 BC) samples.
  • The expansion of R1a-M17 ca. 6600 BC is still likely to have happened from the east, based on the R1a-M17 samples found in Baikalic cultures slightly later (ca. 5300 BC). The presence of elevated Baikal_EN ancestry in Karelia HG and in Samara HG, and the finding of R1a-M417 samples in the Forest Zone after the Mesolithic suggests a connection with the expansion of Hunter-Gatherer pottery, from the Elshanka culture in the Samara region northward into the Forset Zone and westward into the North Pontic area.
  • The expansion of R1b-M73 ca. 5300 BC is likely to be associated with the emergence of a group east of the Urals (related to the later Botai culture, and potentially Pre-Yukaghir). Its presence in a Narva sample from Donkalnis (ca. 5200 BC) suggest either an early split and spread of both R1b-P297 lineages (M73 and M269) through Eastern Europe, or maybe a back-migration with hunter-gatherer pottery.
  • R1b-M269 spread successfully ca. 4400 BC (and R1b-L23 ca. 4100 BC, both based on TMRCA), and this successful expansion is probably to be associated with the Khvalynsk-Novodanilovka expansion. We already know that Samara_HG ca. 5600 was R1b1a, so it is likely that R1b-M269 appeared (or ‘resurged’) in the Volga-Ural region shortly after the expansion of R1a-M17, whose expansion through the region may be inferred by the additional AG3 and Baikal_EN ancestry. Interesting from Samara_HG compared to the previous Sidelkino sample is the introduction of more El Mirón-related ancestry, typical of WHG populations (and thus proper of Baltic groups).

NOTE. The TMRCA dates are obviously gross approximations, because a) the actual rate of mutation is unknown and b) TMRCA estimates are based on the convergence of lineages that survived. The potential finding of R1a-Z645 (possibly Z93+) in Ukraine Eneolithic (ca. 4000 BC), and the potential finding of R1b-L23 in Khvalynsk ca. 4250 BC complicates things further, in terms of dates and origins of any subclade.

The question thus remains as it was long ago: did R1b-M269 lineages expand (‘return’) from the east, near the Urals, or directly from the north? Were they already near Samara at the same time as the expansion of hunter-gatherer pottery, and were not much affected by it? Or did they ‘resurge’ from populations admixed with Caucasus-related ancestry after the expansion of R1a-M17 with this pottery (since there are different stepped expansions from the Samara region)? We could even ask, did R1a-M17 really expand from the east, i.e. are the dates on Baikalic subclades from Moussa et al. (2016) reliable? Or did R1a-M17 expand from some pockets in the Pontic-Caspian steppe, taking over the expansion of HG pottery at some point?

hunger-gatherer-pottery
Early Neolithic cultures in eastern and central Europe: 1–Yelshanian; 2–North Caspian; 3–Rakushechnyj Yar; 4–Surskian; 5–Dnieper-Donetsian; 6– Bug-Dniesterian; 7–Upper Volga; 8–Narvian; 9–Linear Pottery. White arrows: expansion of early farming; black arrows: spread of pottery-making traditions. From Dolukhanov et al. (2009).

Maglemose-related migrations

The most interesting aspect from the new paper (regarding Indo-Uralic migrations) is that Ancestral Middle Easterner ancestry will probably be a better proxy for the Anatolia_Neolithic component found in Ukraine Mesolithic to Eneolithic, and possibly also for some of the “more CHG-like” component found among Pontic-Caspian steppe populations, all likely derived from different admixture events with groups from the Caucasus.

NOTE. Even the supposed gene flow of Neolithic Iranian ancestry into the Caucasus can be put into question, since that means possibly a Dzudzuana-like population with greater “deep ancestry” proportion than the one found in CHG, which may still be found within the Caucasus.

If it was not clear already that following ‘steppe ancestry’ wherever it appears is a rather lame way of following Indo-European migrations, every single sample from the Caucasus and their admixture with Pontic-Caspian steppe populations will probably show that “steppe ancestry” is in fact formed by a variety of steppe-related ancestral components, impossible to follow coherently with a single population. Exactly what is happening already with the Siberian ancestry.

If the paper on the Dzudzuana samples has shown something, is that the expansion of an ANE-like population shook the entire Caucasus area up to the Zagros Mountains, creating this ANE – AME cline that are CHG and Iran_N, with further contributions of “deep ancestries” (probably from the south) complicating the picture further.

If this happens with few known samples, and we know of an ANE-like ghost population in the Caucasus (appearing later in the Lola culture), we can already guess that the often repeated “CHG component” found in Ukraine_Eneolithic and Khvalynsk will not be the same (except the part mediated by the Novodanilovka expansion).

This ANE-like expansion happened probably in the Late Upper Palaeolithic, and reached Northern Europe probably after the expansion of the Villabruna cluster (ca. 12000 BC), judging by the advance of AG3-like and ENA-like ancestry in later WHG samples.

The population movements during the Mesolithic and Early Neolithic in the North Pontic area are quite complicated: the extra AME ancestry is probably connected to the admixture with populations from the Caucasus, while the close similarity of Ukraine populations with Scandinavian ones (with an increase in Villabruna ancestry from Mesolithic to Neolithic samples), probably reveal population movements related to the expansion of Maglemose-related groups.

maglemose-mesolithic
Etno-cultural situation in Central and Eastern Europe in the Late Mesolithic — Early Neolithic (VI—V Mill. BC) (after Конча 2004: 201, карта 1; made after ideas by L. L. Zaliznyak). Legend: 1 — Maglemose circle in the VII Mill. BC (after Gr. Clark); 2—7 — Mesolithic cultures of the Post-Maglemose tradition, VI Mill. BC (after S. Kozłowsky, L. L. Zaliznyak): 2 — de Leyen-Wartena; 3 — Oldesloe — Godenaa; 4 — Chojnice — Peńki; 5 — Janisłavice; 6 — finds of Janisłavice artefacts outside of the main area; 7 — Donets culture; 8 — directions of the settling of Janisłavice people (after S. Kozłowsky and L. L. Zaliznyak); 9 — the south border of Mesolithic and Early Neolithic cultures of post-Swidrian and post-Arensburgian traditions; 10 — northern border of settlement of the Balkan-Danubian farmers; 11 — Bug- Dniester culture; 12 — Neolithic cultures emerged on the ethno-cultural basis of post-Maglemose: Э — Ertebölle-Ellerbeck, Н — Neman, Д — Dnieper-Donets, М — Mariupol (western variants). From Klein (2017).

These Maglemose-related groups were probably migrants from the north-west, originally from the Northern European Plains, who occupied the previous Swiderian territory, and then expanded into the North Pontic area. The overwhelming presence of I2a (likely all I2a2a1b1b) lineages in Ukraine Neolithic supports this migration.

The likely picture of Mesolithic-Neolithic migrations in the North Pontic area right now is then:

  1. Expansion of R1a-M459 from the east ca. 12000 BC – probably coupled with AG3 and also some Baikal_EN ancestry. First sample is I1819 from Vasilievka (ca. 8700 BC), another is from Dereivka ca. 6900 BC.
  2. Expansion of R1b-V88 from the Balkans in the west ca. 9700 BC, based on its TMRCA and also the Balkan hunter-gatherer population overwhemingly of this haplogroup from the 10th millennium until the Neolithic. First sample is I1734 from Vasilievka (ca. 7252 BC), which suggests that it replaced the male population there, based on their similar EHG-like adxmixture (and lack of sizeable WHG increase), and shared mtDNA U5b2, U5a2.
  3. Expansion of I2a-Y5606 probably ca. 6800 based on its TMRCA with Janislawice culture. Supporting this is the increase in WHG contribution to Neolithic samples, including the spread of U4 subclades compared to the previous period.
  4. Expansion of R1a-M17 starting probably ca. 6600 BC in the east (see above).

NOTE. The first sample of haplogroup I appears in the Mesolithic: I1763 (ca. 8100 BC) of haplogroup I2a1, probably related to an older Upper Palaeolithic expansion.

janislawice
Distribution of archeological cultures in the North Pontic Region during the Mesolithic (7th – 6th millennium BCE). Dotted, dashed and solid lines with corresponding arrows indicate alternative models of the spread of the Grebenyky culture groups. (After Bryuako IV., Samojlova TL., Eds, Drevnie kul’tury Severo-­‐Zapadnogo Prichernomor’ya, Odessa: SMIL, 2013.) Nikitin – Ivanova 2017.

Conclusion

It is becoming more and more clear with each new paper that – unless the number of very ancient samples increases – the use of Y-chromosome haplogroups remains one of the most important tools for academics; this is especially so in the steppes, in light of the diversity found in populations from the Caucasus. A clear example comes from the Yamna – Corded Ware similarities:

After the publication of the 2015 papers, it was likely that Yamna expanded with haplogroup R1b-L23, but it has only become crystal clear that Yamna expanded through the steppes into Bell Beakers, now that we have data about the strict genetic homogeneity of the whole Yamna population from west to east (including Afanasevo), in contrast with contemporary Corded Ware peoples which expanded from a different forest-steppe population.

The presence of haplogroups Q and R1a-M459 (xM17) in Khvalynsk along with a R1b1a sample, which some interpreted as being akin to modern ‘mixed’ populations in the past, is likely to point instead to a period of Khvalynsk-Novodanilovka expansion with R1b-M269, where different small populations from the steppe were being integrated into the common Khvalynsk stock, but where differences are seen in material culture surrounding their burials, as supported by the finding of R1b1 in the Kuban area already in the first half of the 5th millennium. The case would be similar to the early ‘mixed’ Icelandic population.

Only after the emergence of the Samara culture (in the second half of the 6th millennium BC), with a sample of haplogroup R1b1a, starts then the obvious connection with Early Proto-Indo-Europeans; and only after the appearance of late Sredni Stog and haplogroup R1a-M417 (ca. 4000 BC) is its connection with Uralic also clear. In previous population movements, I think more haplogroups were involved in migrations of small groups, and only some communities among them were eventually successful, expanding to be dominant, creating ever growing cultures during their expansions.

Indeed, if you think in terms of Uralic and Indo-European just as converging languages, and forget their potential genetic connection, then the genetic + linguistic picture becomes simplified, and the upper frontier of the 6th millennium BC with a division North Pontic (Mariupol) vs. Volga-Ural (Samara) is enough. However, tracing their movements backwards – with cultural expansions from west to east (with the expansion of farming), and earlier east to west (with hunter-gatherer pottery), and still earlier west to east (with the north-eastern technocomplex), offers an interesting way to prove their potential connection to macrofamilies, at least in terms of population movements.

corded-ware-uralic-qpgraph
Modified image from Tambets et al. (2018) Proportions of ancestral components in studied European and Siberian populations and the tested qpGraph model. a The qpGraph model fitting the data for the tested populations. Colour codes for the terminal nodes: pink—modern populations (‘Population X’ refers to test population) and yellow—ancient populations (aDNA samples and their pools). Nodes coloured other than pink or yellow are hypothetical intermediate populations. We putatively named nodes which we used as admixture sources using the main recipient among known populations. The colours of intermediate nodes on the qpGraph model match those on the admixture proportions panel. The NeolL (Neolithic Levant) ancestry selected in this qpGraph is likely to correspond (at least in part) to a specific Dzudzuana-like component present in the CHG-like population that admixed in the North Pontic area.

I am quite convinced right now that it would be possible to connect the expansion of R1b-L754 subclades with a speculative Nostratic (given the R1b-V88 connection with Afroasiatic, and the obvious connection of R1b-L297 with Eurasiatic). Paradoxically, the connection of an Indo-Uralic community in the steppes (after the separation of Yukaghir) with any lineage expansion (R1a-M17, R1b-M269, or even Q, I or J1) seems somehow blurrier than one year ago, possibly just because there are too many open possibilities.

David Reich says about the admixture with Neanderthals, which he helped discover:

At the conclusion of the Neanderthal genome project, I am still amazed by the surprises we encountered. Having found the first evidence of interbreeding between Neanderthals and modern humans, I continue to have nightmares that the finding is some kind of mistake. But the data are sternly consistent: the evidence for Neanderthal interbreeding turns out to be everywhere. As we continue to do genetic work, we keep encountering more and more patterns that reflect the extraordinary impact this interbreeding has had on the genomes of people living today.

I think this is a shared feeling among many of us who have made proposals about anything, to fear that we have made a gross, evident mistake, and constantly look for flaws. However, it seems to me that geneticists are more preoccupied with being wrong in their developed statistical methods, in the theoretical models they are creating, and not so much about errors in the true ancient ethnolinguistic picture human population genetics is (at least in theory) concerned about. Their publications are, after all, constantly associating genetic finds with cultures and (whenever possible) languages, so this aspect of their research should not be taken lightly.

Seeing how David Anthony or Razib Khan (among many others) have changed their previously preferred migration models as new data was published, and they continue to be respected in their own fields, I guess we can be confident that professionals with integrity are going to accept whatever new picture appears. While I don’t think that genetic finds can change what we can reconstruct with comparative grammar, I am also ready to revise guesstimates and routes of expansion of certain dialects if R1a-Z645 is shown to have accompanied Late Proto-Indo-Europeans during their expansion with Yamna, and later integrated somehow with Corded Ware.

However, taking into account the obsession of some with an ancestral, uninterrupted R1a—Indo-European association, and the lack of actual political repercussion of Neanderthal admixture, I think the most common nightmare that all genetic researchers should be worried about is to keep inflating this “Yamnaya ancestry”-based hornet’s nest, which has been constantly stirred up for the past two years, by rejecting it – or, rather, specifying it into its true complex nature.

This succession of corrections and redefinitions, coupled with the distinct Y-DNA bottleneck of each steppe population, will eventually lead to a completely different ethnolinguistic picture of the Pontic-Caspian region during the Eneolithic, which is likely to eventually piss off not only reasonable academics stubbornly attached to the CWC-IE idea, but also a part of those interested in daydreaming about their patrilineal ancestors.

Sometimes it’s better to just rip off the band-aid once and for all…

Featured image from The oldest pottery in hunter-gatherer communitiesand models of Neolithisation of Eastern Europe (2015), by Andrey Mazurkevich and Ekaterina Dolbunova.

Related

Interesting is today’s post in Ancient DNA Era: Is Male-driven Genetic Replacement always meaning Language-shift?

Corded Ware—Uralic (I): Differences and similarities with Yamna

indo-european-uralic-migrations-corded-ware

This is the first of four posts on the Corded Ware—Uralic identification:

I was reading The Bronze Age Landscape in the Russian Steppes: The Samara Valley Project (2016), and I was really surprised to find the following excerpt by David W. Anthony:

The Samara Valley links the central steppes with the western steppes and is a north-south ecotone between the pastoral steppes to the south and the forest-steppe zone to the north [see figure below]. The economic contrast between pastoral steppe subsistence, with its associated social organizations, and forest-zone hunting and fishing economies probably explains the shifting but persistent linguistic border between forest-zone Uralic languages to the north (today largely displaced by Russian) and a sequence of steppe languages to the south, recently Turkic, before that Iranian, and before that probably an eastern dialect of Proto-Indo-European (Anthony 2007). The Samara Valley represents several kinds of borders, linguistic, cultural, and ecological, and it is centrally located in the Eurasian steppes, making it a critical place to examine the development of Eurasian steppe pastoralism.

uralic-languages-forest-zone-volga
Language map of the middle Volga-Ural region. After “Geographical Distribution of the Uralic Languages” by Finno-Ugrian Society, Helsinki, 1993.

Khokhlov (translated by Anthony) further insists on the racial and ethnic divide between both populations, Abashevo to the north, and Poltavka to the south, during the formation of the Abashevo – Sintashta-Potapovka community that gave rise to Proto-Indo-Iranians:

Among all cranial series in the Volga-Ural region, the Potapovka population represents the clearest example of race mixing and probably ethnic mixing as well. The cultural advancements seen in this period might perhaps have been the result of the mixing of heterogeneous groups. Such a craniometric observation is to some extent consistent with the view of some archaeologists that the Sintashta monuments represent a combination of various cultures (principally Abashevo and Poltavka, but with other influences) and therefore do not correspond to the basic concept of an archaeological culture (Kuzmina 2003:76). Under this option, the Potapovka-Sintashta burial rite may be considered, first, a combination of traits to guarantee the afterlife of a selected part of a heterogeneous population. Second, it reflected a kind of social “caste” rather than a single population. In our view, the decisive element in shaping the ethnic structure of the Potapovka-Sintashta monuments was their extensive mobility over a fairly large geographic area. They obtained knowledge of various cultures from the populations with whom they interacted.

steppe-lmba-sintashta-potapovka-filatovka
Late Middle Bronze Age cultures with the Proto-Indo-Iranian Sintashta-Potapovka-Filatovka group (shaded). After Anthony (2007 Figure 15.5), from Anthony (2016).

Interesting is also this excerpt about the predominant population in the Abashevo – Sintashta-Potapovka admixture (which supports what Chetan said recently, although this does not seemed backed by Y-DNA haplogroups found in the richest burials), coupled with the sign of incoming “Uraloid” peoples from the east, found in both Sintashta and eastern Abashevo:

The socially dominant anthropological component was Europeoid, possibly the descendants of Yamnaya. The association of craniofacial types with archaeological cultures in this period is difficult, primarily because of the small amount of published anthropological material of the cultures of steppe and forest belt (Balanbash, Vol’sko-Lbishche) and the eastern and southern steppes (Botai-Tersek). The crania associated with late MBA western Abashevo groups in the Don-Volga forest zone were different from eastern Abashevo in the Urals, where the expression of the Old Uraloid craniological complex was increased. Old Uraloid is found also on a single skull of Vol’sko-Lbishche culture (Tamar Utkul VII, Kurgan 4). Potentially related variants, including Mongoloid features, could be found among the Seima-Turbino tribes of the forest-steppe zone, who mixed with Sintashta and Abashevo. In the Sintashta Bulanova cemetery from the western Urals, some individuals were buried with implements of Seima-Turbino type (Khalyapin 2001; Khokhlov 2009; Khokhlov and Kitov 2009). Previously, similarities were noted between some individual skulls from Potapovka I and burials of the much older Botai culture in northern Kazakhstan (Khokhlov 2000a). Botai-Tersek is, in fact, a growing contender for the source of some “eastern” cranial features.

khvalynsk-yamna-srubna-facial-reconstruction
Facial reconstructions based on skulls from (a) Khvalynsk II Grave 24, a young adult male; (b) Poludin Grave 6, Yamnaya culture, a mature male (both by A. I. Nechvaloda); and (c) Luzanovsky cemetery, Srubnaya culture (by L. T. Yablonsky). In Khokhlov (2016).

The wave of peoples associated with “eastern” features can be seen in genetics in the Sintashta outliers from Narasimhan et al. (2018), and it probably will be eventually seen in Abashevo, too. These may be related to the Seima-Turbino international network – but most likely it is directly connected to Sintashta through the starting Andronovo and Seima-Turbino horizons, by admixing of prospective groups and small-scale back-migrations.

Corded Ware – Yamna similarities?

So, if peoples of north-eastern Europe have been assumed for a long time to be Uralic speakers, what is happening with the Corded Ware = IE obsession? Is it Gimbutas’ ghost possessing old archaeologists? Probably not.

It is about certain cultural similarities evident at first sight, which have been traditionally interpreted as a sign of cultural diffusion or migration. Not dissimilar to the many Bell Beaker models available, where each archaeologist is pushing certain differences, mixing what seemed reasonable, what still might seem reasonable, and what certainly isn’t anymore after the latest ancient DNA data.

kurgan-expansion
“European dialect” expansion of Proto-Indo-European according to Gimbutas (1963)

The initial models of Gimbutas, Kristiansen, or Anthony – which are known to many today – were enunciated in the infancy of archaeological studies in the regions, during and just after the fall of the USSR, and before many radiocarbon dates that we have today were published (with radiocarbon dating being still today in need of refinement), so it is only logical that gross mistakes were made.

We have similar gross mistakes related to the origins of Bell Beakers, and studying them was certainly easier than studying eastern data.

  • Gimbutas believed – based mainly on Kurgan-like burials – that Bell Beaker formed from a combination of Yamna settlers with the Vučedol culture, so she was not that far from the truth.
  • The expansion of Corded Ware from peoples of the North Pontic forest-steppe area, proposed by Gimbutas and later supported also by Kristiansen (1989) as the main Indo-European expansion – , is probably also right about the approximate origins of the culture. Only its ‘Indo-European’ nature is in question, given the differences with Khvalynsk and Yamna evolution.
  • Anthony only claimed that Yamna migrants settled in the Balkans and along the Danube into the Hungarian steppes. He never said that Corded Ware was a Yamna offshoot until after the first genetic papers of 2015 (read about his newest proposal). He initially claimed that only certain neighbouring Corded Ware groups “adopted” Indo-European (through cultural diffusion) because of ‘patron-client’ relationships, and was never preoccupied with the fate of Corded Ware and related cultures in the east European forest zone and Finland.

So none of them was really that far from the true picture; we might say a lot people are more way off the real picture today than the picture these three researchers helped create in the 1990s and 2000s. Genetics is just putting the last nail in the coffin of Corded Ware as a Yamna offshoot, instead of – as we believed in the 2000s – to Vučedol and Bell Beaker.

So let’s revise some of these traditional links between Corded Ware and Yamna with today’s data:

Archaeology

Even more than genetics – at least until we have an adequate regional and temporary sampling – , archaeological findings lead what we have to know about both cultures.

It is essential to remember that Corded Ware, starting ca. 3000/2900 BC in east-central Europe, has been proposed to be derived from Early Yamna, which appeared suddenly in the Pontic-Caspian steppes ca. 3300 BC (probably from the late Repin expansion), and expanded to the west ca. 3000.

Early Yamna is in turn identified as the expanding Late Proto-Indo-European community, which has been confirmed with the recent data on Afanasevo, Bell Beaker, and Sintashta-Potapovka and derived cultures.

The question at hand, therefore, is if Corded Ware can be considered an offshoot of the Late PIE community, and thus whether the CWC ethnolinguistic community – proven in genetics to be quite homogeneous – spoke a Late PIE dialect, or if – alternatively – it is derived from other neighbouring cultures of the North Pontic region.

NOTE. The interpretation of an Indo-Slavonic group represented by a previous branching off of the group is untenable with today’s data, since Indo-Slavonic – for those who support it – would itself be a branch of Graeco-Aryan, and Palaeo-Balkan languages expanded most likely with West Yamna (i.e. R1b-L23, mainly R1b-Z2103) to the south.

The convoluted alternative explanation would be that Corded Ware represents an earlier, Middle PIE branch (somehow carrying R1a??) which influences expanding Late PIE dialects; this has been recently supported by Kortlandt, although this simplistic picture also fails to explain the Uralic problem.

Kurgans: The Yamna tradition was inherited from late Repin, in turn inherited from Khvalynsk-Novodanilovka proto-Kurgans. As for the CWC tradition, it is unclear if the tumuli were built as a tradition inherited from North and West Pontic cultures (in turn inherited or copied from Khvalynsk-Novodanilovka), such as late Trypillia, late Kvityana, late Dereivka, late Sredni Stog; or if they were built because of the spread of the ‘Transformation of Europe’, set in motion by the Early Yamna expansion ca. 3300-3000 BC (as found in east-central European cultures like Coţofeni, Lizevile, Șoimuș, or the Adriatic Vučedol). My guess is that it inherits an older tradition than Yamna, with an origin in east-central Europe, because of the mound-building distribution in the North Pontic area before the Yamna expansion, but we may never really know.

pit-graves-central-europe-cwc
Distribution of Pit-Grave burials west of the Black Sea likely dating to the 2nd half of the IVth millennium BC (triangles: side-crouched burials; filled circles: supine extended burials; open circles: suspected). Frînculeasa, Preda, and Heyd (2015)

Burial rite: Yamna features (with regional differences) single burials with body on its back, flexed upright knees, poor grave goods, common orientation east-west (heads to the west) inherited from Repin, in turn inherited from Khvalynsk-Novodanilovka. CWC tradition – partially connected to Złota and surrounding east-central European territories (in turn from the Khvalynsk-Novodanilovka expansion) – features single graves, body in fetal position, strict gender differentiation – men on the right, women on the left -, looking to the south, graves with standardized assemblages (objects representing affirmation of battle, hunting, and feasting). The burial rites clearly represent different ideologies.

pit-grave-burial-schemes
Left: Pit-Grave burial types expanded with Khvalynsk-Novodanilovka. Right: Pit-Grave burial types associated with the Yamna expansion and influence. Frînculeasa, Preda, and Heyd (2015)

Corded decoration: Corded ware decoration appears in the Balkans during the 5th millennium, and represents a simple technique whereby a cord is twisted, or wrapped around a stick, and then pressed directly onto the fresh surface of a vessel leaving a characteristic decoration. It appears in many groups of the 5th and 4th millennium BC, but it was Globular Amphorae the culture which popularized the drinking vessels and their corded ornamentation. It appears thus in some regional groups of Yamna, but it becomes the standard pottery only in Corded Ware (especially with the A-horizon), which shows continuity with GAC pottery.

corded-ware-first-horizon
Origins of the first Corded Ware horizon (5th millennium BC) after the Khvalynsk-Novodanilovka expansion. Corded Ware (circles) and horse-head scepters (rectangles) and other steppe elements (triangles). Image from Bulatović (2014).

Economy: Yamna expands from Repin (and Repin from Khvalynsk-Novodanilovka) as a nomadic or semi-nomadic purely pastoralist society (with occasional gathering of wild seeds), which naturally thrives in the grasslands of the Pontic-Caspian, lower Danube and Hungarian steppes. Corded Ware shows agropastoralism (as late Eneolithic forest-steppe and steppe groups of eastern Europe, such as late Trypillian, TRB, and GAC groups), inhabits territories north of the loess line, with heavy reliance of hunter-gathering depending on the specific region.

Cattle herding: Interestingly, both west Yamna and Corded Ware show more reliance on cattle herding than other pastoralist groups, which – contrasted with the previous Eneolithic herding traditions of the Pontic-Caspian steppe, where sheep-goats predominate – make them look alike. However, the cattle-herding economy of Yamna is essential for its development from late Repin and its expansion through the steppes (over western territories practising more hunter-gathering and sheep-goat herding economy), and it does not reach equally the Volga-Ural region, whose groups keep some of the old subsistence economy (read more about the late Repin expansion). Corded Ware, on the other hand, inherits its economic strategy from east European groups like TRB, GAC, and especially late Trypillian communities, showing a predominance of cattle herding within an agropastoral community in the forest-steppe and forest zones of Volhynia, Podolia, and surrounding forest-steppe and forest regions.

yamna-scheme
Scheme of interlinked socio-economic-ideological innovations forming the Yamnaya. Frînculeasa, Preda, and Heyd (2015)

Horse riding: Horse riding and horse transport is proven in Yamna (and succeeding Bell Beaker and Sintashta), assumed for late Repin (essential for cattle herding in the seas of grasslands that are the steppes, without nearby water sources), quite likely during the Khvalynsk expansion (read more here), and potentially also for Samara, where the predominant horse symbolism of early Khvalynsk starts. Corded Ware – like the north Pontic forest-steppe and forest areas during the Eneolithic – , on the other hand, does not show a strong reliance on horse riding. The high mobility and short-term settlements characteristic of Corded Ware, that are often associated with horse riding by association with Yamna, may or may not be correct, but there is no need for horses to explain their herding economy or their mobility, and the north-eastern European areas – the one which survived after Bell Beaker expansion – did certainly not rely on horses as an essential part of their economy.

NOTE: I cannot think of more supposed similarities right now. If you have more ideas, please share in the comments and I will add them here.

Genetic similarities

EHG: This is the clearest link between both communities. We thought it was related to the expansion of ANE-related ancestry to the west into WHG territory, but now it seems that it will be rather WHG expanding into ANE territory from the Pontic-Caspian region to the east (read more on recent Caucasus Neolithic, on , and on Caucasus HG).

NOTE. Given how much each paper changes what we know about the Palaeolithic, the origin and expansion of the (always developing) known ancestral components and specific subclades (see below) is not clear at all.

CHG: This is the key link between both cultures, which will delimit their interaction in terms of time and space. CHG is intermediate between EHG and Iran N (ca. 8000 BC). The ancestry is thus linked to the Caucasus south of the steppe before the emergence of North Pontic (western) and Don-Volga-Ural (eastern) communities during the Mesolithic. The real question is: when we have more samples from the steppe and the Caucasus during the Neolithic, how many CHG groups are we going to find? Will the new specific ancestral components (say CHG1, CHG2, CHG3, etc.) found in Yamna (from Khvalynsk, in the east) and Corded Ware (probably from the North Pontic forest-steppe) be the same? My guess is, most likely not, unless they are mediated by the Khvalynsk-Novodanilovka expansion (read more on CHG in the Caucasus).

yamnaya-chg-ancestry
Formation of Yamna and CHG contribution, in Damgaard et al. (Science 2018). A 10-leaf model based on combining the models in Fig. S16 and Fig. S19 and re-estimating the model parameters.

WHG/EEF: This is the obvious major difference – known today – in the formation of both communities in the steppe, and shows the different contacts that both groups had at least since the Eneolithic, i.e. since the expansion of Repin with its renewed Y-DNA bottleneck, and probably since before the early Khvalynsk expansion (read more on Yamna-Corded Ware differences contrasting with Yamna-Afanasevo, Yamna-Bell Beaker, and Yamna-Sintashta similarities).

NOTE 1. Some similarities between groups can be seen depending on the sampled region; e.g. Baltic groups show more similarities with southern Pontic-Caspian steppe populations, probably due to exogamy.

yamna-corded-ware-diff-qpgraph
Tested qpGraph model in Tambets et al. (2018). The qpGraph model fitting the data for the tested populations. “Colour codes for the terminal nodes: pink—modern populations (‘Population X’ refers to test population) and yellow—ancient populations (aDNA samples and their pools). Nodes coloured other than pink or yellow are hypothetical intermediate populations. We putatively named nodes which we used as admixture sources using the main recipient among known populations. The colours of intermediate nodes on the qpGraph model match those on the admixture proportions panel.”

NOTE 2. We have this information on the differences in “steppe ancestry” between Yamna and Corded Ware, compared to previous studies, because now we have more samples of neighbouring, roughly contemporaneous Eneolithic groups, to analyse the real admixture processes. This kind of fine scale studies is what is going to show more and more differences between Khvalynsk-Yamna and Sredni Stog-Corded Ware as more data pours in. The evolution of both communities in archaeology and in PCA (see below) is probably witness to those differences yet to be published.

R1: Even though some people try very hard to think in terms of “R1” vs. (Caucasus) J or G or any other upper clade, this is plainly wrong. It is possible, given what we know now, that Q1a2-M242 expanded ANE ancestry to the west ca. 13000 BC, while R1b-P279 expanded WHG ancestry to the east with the expansion of post-Swiderian cultures, creating EHG as a WHG:ANE cline. The role of R1a-M459 is unknown, but it might be related to any of these migrations, or others (plural) along northern Eurasia (read more on the expansion of R1b-P279, on Palaeolithic Q1a2, and on R1a-M417).

NOTE. I am inclined to believe in a speculative Mesolithic-Early Neolithic community involving Eurasiatic movements accross North Eurasia, and Indo-Uralic movements in its western part, with the last intense early Uralic-PIE contacts represented by the forming west (Mariupol culture) and east (Don-Volga-Ural cultures, including Samara) communities developing side by side. Before their known Eneolithic expansions, no large-scale Y-DNA bottleneck is going to be seen in the Pontic-Caspian steppe, with different (especially R1a and R1b subclades) mixed among them, as shown in North Pontic Neolithic, Samara HG, and Khvalynsk samples.

PCA-trypillia-greece-neolithic-outlier-anatolian
Image modified from Wang et al. (2018). Samples projected in PCA of 84 modern-day West Eurasian populations (open symbols). Previously known clusters have been marked and referenced. Marked and labelled are the Balkan samples referenced in this text An EHG and a Caucasus ‘clouds’ have been drawn, leaving Pontic-Caspian steppe and derived groups between them. See the original file here.

Corded Ware and ‘steppe ancestry’

If we take a look at the evolution of Corded Ware cultures, the expansion of Bell Beakers – dominated over most previous European cultures from west to east Europe – influenced the development of the whole European Bronze Age, up to Mierzanowice and Trzciniec in the east.

The only relevant unscathed CWC-derived groups, after the expansion of Sintashta-Potapovka as the Srubna-Andronovo horizon in the Eurasian steppes, were those of the north-eastern European forest zone: between Belarus to the west, Finland to the north, the Urals to the east, and the forest-steppe region to the south. That is, precisely the region supposed to represent Uralic speakers during the Bronze Age.

This inconsistency of steppe ancestry and its relation with Uralic (and Balto-Slavic) peoples was observed shortly after the publication of the first famous 2015 papers by Paul Heggarty, of the Max-Planck Institute for Evolutionary Anthropology (read more):

Haak et al. (2015) make much of the high Yamnaya ancestry scores for (only some!) Indo-European languages. What they do not mention is that those same results also include speakers of other languages among those with the highest of all scores for Yamnaya ancestry. Only these are languages of the Uralic family, not Indo-European at all; and their Yamnaya-ancestry signals are far higher than in many branches of Indo-European in (southern) Europe. Estonian ranks very high, while speakers of the very closely related Finnish are curiously not shown, and nor are the Saami. Hungarian is relevant less directly since this language arrived only c. 900 AD, but also high.

uralic-steppe-ancestry

These data imply that Uralic-speakers too would have been part of the Yamnaya > Corded Ware movement, which was thus not exclusively Indo-European in any case. And as well as the genetics, the geography, chronology and language contact evidence also all fit with a Yamnaya > Corded Ware movement including Uralic as well as Balto-Slavic.

Both papers fail to address properly the question of the Uralic languages. And this despite — or because? — the only Uralic speakers they report rank so high among modern populations with Yamnaya ancestry. Their linguistic ancestors also have a good claim to have been involved in the Corded Ware and Yamnaya cultures, and of course the other members of the Uralic family are scattered across European Russia up to the Urals.

NOTE. Although the author was trying to support the Anatolian hypothesis – proper of glottochronological studies often published from the Max Planck Institute – , the question remains equally valid: “if Proto-Indo-European expands with Corded Ware and steppe ancestry, what is happening with Uralic peoples?”

For my part, I claimed in my draft that ancestral components were not the only relevant data to take into account, and that Y-DNA haplogroups R1a and R1b (appearing separately in CWC and Yamna-Bell Beaker-Afanasevo), together with their calculated timeframes of formation – and therefore likely expansion – did not fit with the archaeological and linguistic description of the spread of Proto-Indo-European and its dialects.

In fact, it seemed that only one haplogroup (R1b-M269) was constantly and consistenly associated with the proposed routes of Late PIE dialectal expansions – like Anthony’s second (Afanasevo) and third (Lower Danube, Balkan) waves. What genetics shows fits seamlessly with Mallory’s association of the North-West Indo-European expansion with Bell Beakers (read here how archaeologists were right).

balanovksy-yamnaya-ancestry
Map of the much beloved steppe (or “Yamnaya”) ancestry in modern populations, by Balanovsky. Modified from Klejn (2017).

More precise inconsistencies were observed after the publication of Olalde et al. (2017) and Mathieson et al. (2017), by Volker Heyd in Kossinna’s smile (2017). Letting aside the many details enumerated (you can read a summary in my latest draft), this interesting excerpt is from the conclusion:

NOTE. An open access ealier draft version of the paper is offered for download by the author.

Simple solutions to complex problems are never the best choice, even when favoured by politicians and the media. Kossinna also offered a simple solution to a complex prehistoric problem, and failed therein. Prehistoric archaeology has been aware of this for a century, and has responded by becoming more differentiated and nuanced, working anthropologically, scientifically and across disciplines (cf. Müller 2013; Kristiansen 2014), and rejecting monocausal explanations. The two aDNA papers in Nature, powerful and promising as they are for our future understanding, also offer rather straightforward messages, heavily pulled by culture-history and the equation of people with culture. This admittedly is due partly to the restrictions of the medium that conveys them (and despite the often relevant additional detail given as supplementary information, which is unfortunately not always given full consideration).

While I have no doubt that both papers are essentially right, they do not reflect the complexity of the past. It is here that archaeology and archaeologists contributing to aDNA studies find their role; rather than simply handing over samples and advising on chronology, and instead of letting the geneticists determine the agenda and set the messages, we should teach them about complexity in past human actions and interactions. If accepted, this could be the beginning of a marriage made in heaven, with the blessing smile of Gustaf Kossinna, and no doubt Vere Gordon Childe, were they still alive, in a reconciliation of twentieth- and twenty-first-century approaches. For us as archaeologists, it could also be the starting point for the next level of a new archaeology.

heyd-yamnaya-expansion
Main distribution of Yamnaya kurgans in the Pontic-Caspian steppe of modern day Russia, Ukraine, and Kazakhstan, and its western branch in modern south-east European countries of Romania, Bulgaria, Serbia, and Hungary, with numbers of excavated kurgans and graves given. Picture: Volker Heyd (2018).

The question was made painfully clear with the publication of Olalde et al. (2018) & Mathieson et al. (2018), where the real route of Yamna expansion into Europe was now clearly set through the steppes into the Carpathian basin, later expanded as Bell Beakers.

This has been further confirmed in more recent papers, such as Narasimhan et al. (2018), Damgaard et al. (2018), or Wang et al. (2018), among others.

However, the discussion is still dominated by political agendas based on prevalent Y-DNA haplogroups in modern countries and ethnic groups.

Related

Haplogroup R1a and CWC ancestry predominate in Fennic, Ugric, and Samoyedic groups

uralic-languages

Open access Genes reveal traces of common recent demographic history for most of the Uralic-speaking populations, by Tambets et al. Genome Biology (2018).

Interesting excerpts (emphasis mine):

Methods

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 [7]. 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 [23], 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 [18]. 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.

uralic-pca
Principal component analysis (PCA) and genetic distances of Uralic-speaking populations. a PCA (PC1 vs PC2) of the Uralic-speaking populations.

We next used ADMIXTURE [48], 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.

siberian-ancestry
Population structure of Uralic-speaking populations inferred from ADMIXTURE analysis on autosomal SNPs in Eurasian context. a Individual ancestry estimates for populations of interest for selected number of assumed ancestral populations (K3, K6, K9, K11). Ancestry components discussed in a main text (k2, k3, k5, k6, k9, k11) are indicated and have the same colours throughout. The names of the Uralic-speaking populations are indicated with blue (Finno-Ugric) or orange (Samoyedic). The full bar plot is presented in Additional file 3: Figure S3. b Frequency map of component k9

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.

uralic-modern-europe

Identity-by-descent

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).

ibd-uralic-genetics
Share of ~ 1–2 cM identity-by-descent (IBD) segments within and between regional groups of Uralic speakers. For each Uralic-speaking population representing lines in this matrix, we performed permutation test to estimate if it shows higher IBD segment sharing with other population (listed in columns) as compared to their geographic control group. Empty rectangles indicate no excess IBD sharing, rectangles filled in blue indicate comparisons when statistically significant excess IBD sharing was detected between one Uralic-speaking population with another Uralic-speaking population (listed in columns), rectangles filled in green mark the comparisons when a Uralic-speaking population shows excess IBD sharing with a non-Uralic-speaking population. For each tested Uralic speaker (matrix rows) populations in the control group that were used to generate permuted samples are indicated using small circles. For example, the rectangle filled in blue for Vepsians and Komis (A) implies that the Uralic-speaking Vepsians share more IBD segments with the Uralic-speaking Komis than the geographic control group for Vepsians, i.e. populations indicated with small circles (Central and North Russians, Swedes, Latvians and Lithuanians). The rectangle filled in green for Vepsians and Dolgans shows that the Uralic-speaking Vepsians share more IBD segments with the non-Uralic-speaking Dolgans than the geographic control group

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. [55]. 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 [48] 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).

yamna-cwc-qpgraph-admixture-uralic
Proportions of ancestral components in studied European and Siberian populations and the tested qpGraph model. a The qpGraph model fitting the data for the tested populations. Colour codes for the terminal nodes: pink—modern populations (‘Population X’ refers to test population) and yellow—ancient populations (aDNA samples and their pools). Nodes coloured other than pink or yellow are hypothetical intermediate populations. We putatively named nodes which we used as admixture sources using the main recipient among known populations. The colours of intermediate nodes on the qpGraph model match those on the admixture proportions panel. b Admixture proportions (%) of ancestral components. We calculated the admixture proportions summing up the relative shares of a set of intermediate populations to explain the full spectrum of admixture components in the test population. We further did the same for the intermediate node CWC’ and present the proportions of the mixing three components in the stacked column bar of CWC’. Colour codes for ancestral components are as follows: dark green—Western hunter gatherer (WHG’); light green—Eastern hunter gatherer (EHG’); grey—European early farmer (LBK’); dark blue—carriers of Corded Ware culture (CWC’); and dark grey—Siberian. CWC’ consists of three sub-components: blue—Caucasian hunter-gatherer in Yamnaya (CHGinY’); light blue—Eastern hunter-gatherer in Yamnaya (EHGinY’); and light grey—Neolithic Levant (NeolL’)

We then used the qpGraph software [48] 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.

Discussion

uralic-groups-haplogroup-r1a
Additional file 6: Table S5. Y chromosome haplogroup frequencies in Eurasia. Modified by me: in bold haplogroup N1c and R1a from Uralic-speaking populations, with those in red showing where R1a is the major haplogroup. Observe that all Uralic subgroups – Finno-Permic, Ugric, and Samoyedic – have some populations with a majority of R1a lineages.

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 [63] 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 [36]. 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 [38].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.

Comments

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.

estonians-hungarians-mordvinian
Selection of the PCA, with the group of Estonians, Mordovians, and Hungarians selected.

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:

yamna-corded-ware-qpgraph

#EDIT (22 sep 2018): I enjoyed re-reading it, and found this particular paragraph funny:

Despite the documented history of the migration of Magyars [63] 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.

They are so obsessed with finding a link to Siberian ancestry and N1c, and so convinced of Kristiansen’s idea of CWC=Indo-European, that they forgot to examine their own data from a critical point of view, and see the clear link between all Uralic peoples with Corded Ware ancestry and R1a-Z645 subclades… Here is a reminder about Hungarians and R1a-Z282, and about the expansion of R1a-Z645 with Uralic peoples.

Related

Neolithic and Bronze Age Anatolia, Urals, Fennoscandia, Italy, and Hungary (ISBA 8, 20th Sep)

jena-isba8

I will post information on ISBA 8 sesions today as I see them on Twitter (see programme in PDF, and sessions from yesterday).

Official abstracts are listed first (emphasis mine), then reports and images and/or link to tweets. Here is the list for quick access:

Russian colonization in Yakutia

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.

NOTE: For another interesting study on Yakutian tribes, see Relationships between clans and genetic kin explain cultural similarities over vast distances.

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):

finland-medieval-admixture

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.

Studies on hunter-gatherer pottery – appearing in eastern Europe before Middle Eastern Neolithic pottery – may be important to understand the arrival of R1a-M17 lineages to the region before ca. 7000 BC. Or not, right now it is not very clear what happened with R1b-P297 and R1a-M17, and with WHG—EHG—ANE ancestry

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.

Detail of the images:

mongol-bronze-age-pca

mongol-bronze-age-f4-ancestry

Viking Age town shows higher genetic diversity than Neolithic and Bronze Age

sigtuna-vikings

Open access Genomic and Strontium Isotope Variation Reveal Immigration Patterns in a Viking Age Town, by Krzewińska et al., Current Biology (2018).

Interesting excerpts (emphasis mine, some references deleted for clarity):

The town of Sigtuna in eastern central Sweden was one of the pioneer urban hubs in the vast and complex communicative network of the Viking world. The town that is thought to have been royally founded was planned and organized as a formal administrative center and was an important focal point for the establishment of Christianity [19]. The material culture in Sigtuna indicates that the town had intense international contacts and hosted several cemeteries with a Christian character. Some of them may have been used by kin-based groups or by people sharing the same sociocultural background. In order to explore the character and magnitude of mobility and migration in a late Viking Age town, we generated and analyzed genomic (n = 23) and strontium isotope (n = 31) data from individuals excavated in Sigtuna.

y-dna-vikings

The mitochondrial genomes were sequenced at 1.5× to 367× coverage. Most of the individuals were assigned to haplogroups commonly found in current-day Europeans, such as H, J, and U [14, 26, 27]. All of these haplotypes are present in Scandinavia today.

The Y chromosome haplogroups were assigned in seven males. The Y haplogroups include I1a, I2a, N1a, G2a, and R1b. Two identified lineages (I2a and N1a) have not been found in modern-day Sweden or Norway [28, 29]. Haplogroups I and N are associated with eastern and central Europe, as well as Finno-Ugric groups [30]. Interestingly, I2a was previously identified in a middle Neolithic Swedish hunter-gatherer dating to ca. 3,000 years BCE [31].

In Sigtuna, the genetic diversity in the late Viking Age was greater than the genetic diversity in late Neolithic and Bronze Age cultures (Unetice and Yamnaya as examples) and modern East Asians; it was on par with Roman soldiers in England but lower than in modern-day European groups (GBR and FIN; Figure 2B). Within the town, the group excavated at church 1 has somewhat greater diversity than that at cemetery 1. Interestingly, the diversity at church 1 is nearly as high as that observed in Roman soldiers in England, which is remarkable, since the latter was considered to be an exceptionally heterogeneous group in contemporary Europe [39].

pca-vikings
A PCA plot visualising all 23 individuals from Sigtuna used in ancient DNA analyses (m – males, f – females).

Different sex-related mobility patterns for Sigtuna inhabitants have been suggested based on material culture, especially ceramics. Building on design and clay analyses, some female potters in Sigtuna are thought to have grown up in Novgorod in Rus’ [40]. Moreover, historical sources mention female mobility in connection to marriage, especially among the elite from Rus’ and West Slavonic regions [41, 42]. Male mobility is also known from historical sources, often in connection to clergymen moving to the town [43].

Interestingly, we found a number of individuals from Sigtuna to be genetically similar to the modern-day human variation of eastern Europeans, and most harbor close genetic affinities to Lithuanians (Figure 2A). The strontium isotope ratios in 28 adult individuals with assigned biological sex and strontium values obtained from teeth (23 M1 and five M2) show that 70% of the females and 44% of the males from Sigtuna were non-locals (STAR Methods). The difference in migrant ratios between females and male mobility patterns was not statistically significant (Fisher’s exact test, p = 0.254 for 28 individuals and p = 0.376 for 16 individuals). Hence, no evidence of a sex-specific mobility pattern was found.

(…) As these social groups are not mirrored by our genetic or strontium data, this suggests that the inclusion in them was not based on kinship. Therefore, it appears as if socio-cultural factors, not biological bonds, governed where people were interred (i.e., the choice of cemetery).

diversity-yamna
Average pairwise genetic diversity measured in complete Sigtuna, St. Gertrud (church 1) and cemetery 1 (the Nunnan block) compared to both ancient and modern populations ranked by time period (Yamnaya, Unetice, and GBR-Roman, Roman Age individuals from Great Britain; GBR-AS, Anglo-Saxon individuals from Great Britain; GBR-IA, Iron Age individuals from Great Britain; JPT-Modern, presentday Japanese from Tokyo; FIN-Modern, present-day Finnish; GBR-Modern, present-day British; GIHModern, present-day Gujarati Indian from Houston, Texas). Error bars show ±2 SEs.

Interesting from this paper is the higher genetic (especially Y-DNA) diversity found in more recent periods (see e.g. here) compared to Neolithic and Bronze Age cultures, which is probably the reason behind some obviously wrong interpretations, e.g. regarding links between Yamna and Corded Ware populations.

The sample 84001, a “first-generation short-distance migrant” of haplogroup N1c-L392 (N1a in the new nomenclature) brings yet more proof of how:

  • Admixture changes completely within a certain number of generations. In this case, the N1c-L392 sample clusters within the genetic variation of modern Norwegians, near to the Skane Iron Age sample, and not with its eastern origin (likely many generations before).
  • This haplogroup appeared quite late in Fennoscandia but still managed to integrate and expand into different ethnolinguistic groups; in this case, this individual was probably a Viking of Nordic language, given its genetic admixture and its non-local (but neighbouring Scandinavian) strontium values.

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