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


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.

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.

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).


R1a-Z280 lineages in Srubna; and first Palaeo-Balkan R1b-Z2103?


Scythian samples from the North Pontic area are far more complex than what could be seen at first glance. From the new Y-SNP calls we have now thanks to the publications at Molgen (see the spreadsheet) and in Anthrogenica threads, I think this is the basis to work with:

NOTE. I understand that writing a paper requires a lot of work, and probably statistical methods are the main interest of authors, editors, and reviewers. But it is difficult to comprehend how any user of open source tools can instantly offer a more complex assessment of the samples’ Y-SNP calls than professionals working on these samples for months. I think that, by now, it should be clear to everyone that Y-DNA is often as important (sometimes even more) than statistical tools to infer certain population movements, since admixture can change within few generations of male-biased migrations, whereas haplogroups can’t…


Srubna-Andronovo samples are as homogeneous as they always were, dominated by R1a-Z645 subclades and CWC-related (steppe_MLBA) ancestry.

The appearance of one (possibly two) R-Z280 lineages in this mixed Srubna-Alakul region of the southern Urals and this early (1880-1690 BC, hence rather Pokrovka-Alakul) points to the admixture of R1a-Z93 and R1a-Z280 already in Abashevo, which also explains the wide distribution of both subclades in the forest zones of Central Asia.

If Abashevo is the cornerstone of the Indo-Iranian / Uralic community, as it seems, the genetic admixture would initially be quite similar, undergoing in the steppes a reduction to haplogroup R1a-Z93 (obviously not complete), at the same time as it expanded to the west with Pokrovka and Srubna, and to the east with Petrovka and Andronovo. To the north, similar reductions will probably be seen following the Seima-Turbino phenomenon.

NOTE. Another R1a-Z280 has been found in the recent sample from Bronze Age Poland (see spreadsheet). As it appears right now in ancient and modern DNA, there seems to be a different distribution between subclades:

  • R1a-Z280 (formed ca. 2900 BC, TMRCA ca. 2600 BC) appears mainly distributed today to the east, in the forest and steppe regions, with the most ‘successful’ expansions possibly related to the spread of Abashevo- and Battle Axe-related cultures (Indo-Iranian and Uralic alike).
  • R1a-M458 (formed ca. 2700, TMRCA ca. 2700 BC) appears mainly distributed to the north, from central Europe to the east – but not in the steppe in aDNA, with the most ‘successful’ expansions to the west.

M458 lineages seem thus to have expanded in the steppe in sizeable numbers only after the Iranian expansions (see a map of modern R1a distributions) i.e. possibly with the expansion of Slavs, which supports the model whereby cultures from central-east Europe (like Trzciniec and Lusatian), accompanied mainly by M458 lineages, were responsible for the expansion of Proto-Balto-Slavic (and later Proto-Slavic).

The finding of haplogroup R1a-Z93, among them one Z2123, is no surprise at this point after other similar Srubna samples. As I said, the early Srubna expansion is most likely responsible for the Szólád Bronze Age sample (ca. 2100-1700 BC), and for the Balkans BA sample (ca. 1750-1625 BC) from Merichleri, due to incursions along the central-east European steppe.

Map of decorated bone/antler bridle cheek-pieces and whip handle equivalents. They are often local translations that remained faithful to the originals (from data in Piggott, 1965; Kristiansen & Larsson, 2005; David, 2007). Image from Vandkilde (2014).


Cimmerian samples from the west show signs of continuity with R1a-Z93 lineages. Nevertheless, the sample of haplogroup Q1a-Y558, together with the ‘Pre-Scythian’ sample of haplogroup N (of the Mezőcsát Culture) in Hungary ca. 980-830 BC, as well as their PCA, seem to depict an origin of these Pre-Scythian peoples in populations related to the eastern Central Asian steppes, too.

NOTE. I will write more on different movements (unrelated to Uralic expansions) from Central and East Asia to the west accompanied by Siberian ancestry and haplogroup N with the post of Ugric-Samoyedic expansions.


The Scythian of Z2123 lineage ca. 375-203 BC from the Volga (in Mathieson et al. 2015), together with the sample scy193 from Glinoe (probably also R1a-Z2123), without a date, as well as their common Steppe_MLBA cluster, suggest that Scythians, too, were at first probably quite homogeneous as is common among pastoralist nomads, and came thus from the Central Asian steppes.

The reduction in haplogroup variability among East Iranian peoples seems supported by the three new Late Sarmatian samples of haplogroup R1a-Z2124.

Approximate location of Glinoe and Glinoe Sad (with Starosilya to the south, in Ukrainian territory):

This initial expansion of Scythians does not mean that one can dismiss the western samples as non-Scythians, though, because ‘Scythian’ is a cultural attribution, based on materials. Confirming the diversity among western Scythians, a session at the recent ISBA 8:

Genetic continuity in the western Eurasian Steppe broken not due to Scythian dominance, but rather at the transition to the Chernyakhov culture (Ostrogoths), by Järve et al.

The long-held archaeological view sees the Early Iron Age nomadic Scythians expanding west from their Altai region homeland across the Eurasian Steppe until they reached the Ponto-Caspian region north of the Black and Caspian Seas by around 2,900 BP. However, the migration theory has not found support from ancient DNA evidence, and it is still unclear how much of the Scythian dominance in the Eurasian Steppe was due to movements of people and how much reflected cultural diffusion and elite dominance. We present new whole-genome results of 31 ancient Western and Eastern Scythians as well as samples pre- and postdating them that allow us to set the Scythians in a temporal context by comparing the Western Scythians to samples before and after within the Ponto-Caspian region. We detect no significant contribution of the Scythians to the Early Iron Age Ponto-Caspian gene pool, inferring instead a genetic continuity in the western Eurasian Steppe that persisted from at least 4,800–4,400 cal BP to 2,700–2,100 cal BP (based on our radiocarbon dated samples), i.e. from the Yamnaya through the Scythian period.

(…) Our results (…) support the hypothesis that the Scythian dominance was cultural rather than achieved through population replacement.

Detail of the slide with admixture of Scythian groups in Ukraine:


The findings of those 31 samples seem to support what Krzewińska et al. (2018) found in a tiny region of Moldavia-south-western Ukraine (Glinoi, Glinoi Sad, and Starosilya).

The question, then, is as follows: if Scythian dominance was “cultural rather than achieved through population replacement”…Where are the R1b-Z2103 from? One possibility, as I said in the previous post, is that they represent pockets of Iranian R1b lineages in the steppes descended from eastern Yamna, given that this haplogroup appears in modern populations from a wide region surrounding the steppes.

The other possibility, which is what some have proposed since the publication of the paper, is that they are related to Thracians, and thus to Palaeo-Balkan populations. About the previously published Thracian individuals in Sikora et al. (2014):

Geographic origin of ancient samples and ADMIXTURE results. (A) Map of Europe indicating the discovery sites for each of the ancient samples used in this study. (B) Ancestral population clusters inferred using ADMIXTURE on the HGDP dataset, for k = 6 ancestral clusters. The width of the bars of the ancient samples was increased to aid visualization. https://doi.org/10.1371/journal.pgen.1004353.g001

For the Thracian individuals from Bulgaria, no clear pattern emerges. While P192-1 still shows the highest proportion of Sardinian ancestry, K8 more resembles the HG individuals, with a high fraction of Russian ancestry.

Despite their different geographic origins, both the Swedish farmer gok4 and the Thracian P192-1 closely resemble the Iceman in their relationship with Sardinians, making it unlikely that all three individuals were recent migrants from Sardinia. Furthermore, P192-1 is an Iron Age individual from well after the arrival of the first farmers in Southeastern Europe (more than 2,000 years after the Iceman and gok4), perhaps indicating genetic continuity with the early farmers in this region. The only non-HG individual not following this pattern is K8 from Bulgaria. Interestingly, this individual was excavated from an aristocratic inhumation burial containing rich grave goods, indicating a high social standing, as opposed to the other individual, who was found in a pit.


The following are excerpts from A Companion to Ancient Thrace (2015), by Valeva, Nankov, and Graninger (emphasis mine):

Thracian settlements from the 6th c. BC on:

(…) urban centers were established in northeastern Thrace, whose development was linked to the growth of road and communication networks along with related economic and distributive functions. The early establishment of markets/emporia along the Danube took place toward the middle of the first millennium BCE (Irimia 2006, 250–253; Stoyanov in press). The abundant data for intensive trade discovered at the Getic village in Satu Nou on the right bank of the Danube provides another example of an emporion that developed along the main artery of communication toward the interior of Thrace (Conovici 2000, 75–76).

Undoubtedly the most prominent manifestation of centralization processes and stratification in the settlement system of Thrace arrives with the emergence of political capitals – the leading urban centers of various Thracian political formations.

Image from Volf at Vol_Vlad LiveJournal.

Their relationships with Scythians and Greeks

The Scythian presence south of the Danube must be balanced with a Thracian presence north of the river. We have observed Getae there in Alexander’s day, settled and raising grain. For Strabo the coastlands from the Danube delta north as far as the river and Greek city of Tyras were the Desert of the Getae (7.3.14), notable for its poverty and tracklessness beyond the great river. He seems to suggest also that it was here that Lysimachus was taken alive by Dromichaetes, king of the Getae, whose famous homily on poverty and imperialism only makes sense on the steppe beyond the river (7.3.8; cf. Diod. 21.12; further on Getic possessions above the Danube, Paus. 1.9 with Delev 2000, 393, who seems rather too skeptical; on poverty, cf. Ballesteros Pastor 2003). This was the kind of discourse more familiarly found among Scythians, proud and blunt in the strength of their poverty. However, as Herodotus makes clear, simple pastoralism was not the whole story as one advanced round into Scythia. For he observes the agriculture practiced north and west of Olbia. These were the lands of the Alizones and the people he calls the Scythian Ploughmen, not least to distinguish them from the Royal Scythians east of Olbia, in whose outlook, he says, these agriculturalist Scythians were their inferiors, their slaves (Hdt. 4.20). The key point here is that, as we began to see with the Getan grain-fields of Alexander’s day, there was scope for Thracian agriculturalists to maintain their lifestyles if they moved north of the Danube, the steppe notwithstanding. It is true that it is movement in the other direction that tends to catch the eye, but there are indications in the literary tradition and, especially, in the archaeological record that there was also significant movement northward from Thrace across the Danube and the Desert of the Getae beyond it.

Greek literary sources were not much concerned with Thracian migration into Scythia, but we should observe the occasional indications of that process in very different texts and contexts. At the level of myth, it is to be remembered that Amazons were regularly considered to be of Thracian ethnicity from Archaic times onward and so are often depicted in Thracian dress in Greek art (Bothmer 1957; cf. Sparkes 1997): while they are most familiar on the south coast of the Black Sea, east of Sinope, they were also located on the north coast, especially east of the Don (the ancient Tanais). Herodotus reports an origin-story of the Sauromatians there, according to which this people had been created by the union of some Scythian warriors with Amazons captured on the south coast and then washed up on the coast of Scythia (4.110). While the story is unhistorical, it is not without importance. First, it reminds us that passage north from the Danube was not the only way that Thracians, Thracian influence, and Thracian culture might find their way into Scythia. There were many more and less circuitous routes, especially by sea, that could bring Thrace into Scythia. Secondly, the myth offered some ideological basis for the Sauromatian settlement in Thrace that Strabo records, for Sauromatians might claim a Thracian origin through their Amazon forebears. Finally, rather as we saw that Heracles could bring together some of the peoples of the region, we should also observe that Ares, whose earthly home was located in Thrace by a strong Greek and Roman tradition, seems also to have been a deity of special significance and special cult among the Scythians. So much was appropriate, especially from a Classical perspective, in associations between these two peoples, whose fame resided especially in their capacity for war.

Scythians: cultures and findings (ca. 7th-4th/3rd c. BC). Greek colonies marked with concentric circles.

This broad picture of cultural contact, interaction, and osmosis, beyond simple conflict, provides the context for a range of archaeological discoveries, which – if examined separately – may seem to offer no more than a scatter of peculiarities. Here we must acknowledge especially the pioneering work of Melyukova, who has done most to develop thinking on Thracian–Scythian interaction. As she pointed out, we have a good example of Thracian–Scythian osmosis as early as the mid-seventh century bce at Tsarev Brod in northeastern Bulgaria, where a warrior’s burial combines elements of Scythian and Thracian culture (Melyukova 1965). For, while the manner of his burial and many of the grave goods find parallels in Scythia and not Thrace, there are also goods which would be odd in a Scythian burial and more at home in a Thracian one of this period (notably a Hallstatt vessel, an iron knife, and a gold diadem). Also interesting in this regard are several stone figures found in the Dobrudja which resemble very closely figures of this kind (baby) known from Scythia (Melyukova 1965, 37–38). They range in date from perhaps the sixth to the third centuries bce, and presumably were used there – as in Scythia – to mark the burials of leading Scythians deposited in the area. Is this cultural osmosis? We should probably expect osmosis to occur in tandem with the movement of artefacts, so that only good contexts can really answer such questions from case to case. However, the broad pattern is indicated by a range of factors. Particularly notable in this regard is the observable development of a Thraco-Scythian form of what is more familiar as “Scythian animal style,” a term which – it must be understood – already embraces a range of types as we examine the different examples of the style across the great expanse from Siberia to the western Ukraine. As Melyukova observes, Thrace shows both items made in this style among Scythians and, more numerous and more interesting, a Thracian tendency to adapt that style to local tastes, with observable regional distinctions within Thrace itself. Among the Getae and Odrysians the adaptation seems to have been at its height from the later fifth century to the mid-third century (Melyukova 1965, 38; 1979).

The absence of local animal style in Bulgaria before the fifth century bce confirms that we have cultural influences and osmosis at work here, though that is not to say that Scythian tradition somehow dominated its Thracian counterpart, as has been claimed (pace Melyukova 1965, 39; contrast Kitov 1980 and 1984). Of particular interest here is the horse-gear (forehead-covers, cheek-pieces, bridle fittings, and so on) which is found extensively in Romania and Bulgaria as well as in Scythia, both in hoarded deposits and in burials. This exemplifies the development of a regional animal style, not least in silver and bronze, which problematizes the whole issue of the place(s) of its production. Accordingly, the regular designation as “Thracian” of horse-gear from the rich fourth century Scythian burial of Oguz in the Ukraine becomes at least awkward and questionable (further, Fialko 1995). And let us be clear that this is no minor matter, nor even part of a broader debate about the shared development of toreutics among Thracians and Scythians (e.g., Kitov 1980 and 1984). A finely equipped horse of fine quality was a strong statement and striking display of wealth and the power it implied

(…) while Thracian pottery appears at Olbia, Scythian pottery among Thracians is largely confined to the eastern limits of what should probably be regarded as Getic territory, namely the area close to the west of the Dniester, from the sixth century bce. Rather exceptional then is the Scythian pottery noted at Istros, which has been explained as a consequence of the Scythian pursuit of the withdrawing army of Darius and, possibly, a continued Scythian grip on the southern Danube in its aftermath (Melyukova 1965, 34). The archaeology seems to show us, therefore, that the elite Thracians and Scythians were more open to adaptation and acculturation than were their lesser brethren.

Paleo-Balkan languages in Eastern Europe between 5th and 1st century BC. From Wikipedia.


(…) we see distinct peoples and organizations, for example as Sitalces’ forces line up against the Scythians. Much more striking, however, against that general background, are the various ways in which the two peoples and their elites are seen to interact, connect, and share a cultural interface. We see also in Scyles’ story how the Greek cities on the coast of Thrace and Scythia played a significant role in the workings of relationships between the two peoples. It is not simply that these cities straddled the Danube, but also that they could collaborate – witness the honors for Autocles, ca. 300 bce (SEG 49.1051; Ochotnikov 2006) – and were implicated with the interactions of the much greater non-Greek powers around them. At the same time, we have seen the limited reality of familiar distinctions between settled Thracians and nomadic Scythians and the limited role of the Danube too in dividing Thrace and Scythia. The interactions of the two were not simply matters of dynastic politics and the occasional shared taste for artefacts like horse-gear, but were more profoundly rooted in the economic matrix across the region, so that “Scythian” nomadism might flourish in the Dobrudja and “Thracian-style” agriculture and settlement can be traced from Thrace across the Danube as far as Olbia. All of that offers scant justification for the Greek tendency to run together Thracians and Scythians as much the same phenomenon, not least as irrational, ferocious, and rather vulgar barbarians (e.g., Plato, Rep. 435b), because such notions were the result of ignorance and chauvinism. However, Herodotus did not share those faults to any degree, so that we may take his ready movement from Scythians to Thracians to be an indication of the importance of interaction between the two peoples whom he had encountered not only as slaves in the Aegean world, but as powerful forces in their own lands (e.g., Hdt. 4.74, where Thracian usage is suddenly brought into his account of Scythian hemp). Similarly, Thucydides, who quite without need breaks off his disquisition on the Odrysians to remark upon political disunity among the Scythians (Thuc. 2.97, a favorite theme: cf. Hdt. 4.81; Xen., Cyr. 1.1.4). As we have seen throughout this discussion, there were many reasons why Thracians might turn the thoughts of serious writers to Scythians and vice versa.

It seems, following Sikora et al. (2014), that Thracian ‘common’ populations would have more Anatolian Neolithic ancestry compared to more ‘steppe-like’ samples. But there were important differences even between the two nearby samples published from Bulgaria, which may account for the close interaction between Scythians and Thracians we see in Krzewińska et al. (2018), potentially reflected in the differences between the Central, Southern and the South-Central clusters (possibly related to different periods rather than peoples??).

If these R1b-Z2103 were descended from Thracian elites, this would be the first proof of Palaeo-Balkan populations showing mainly R1b-Z2103, as I expect. Their appearance together with haplogroup I2a2a1b1 (also found in Ukraine Neolithic and in the Yamna outlier from Bulgaria) seem to support this regional continuity, and thus a long-lasting cultural and ethnic border roughly around the Danube, similar to the one found in the northern Caucasus.

However, since these samples are some 2,500 years younger than the Yamna expansion to the south, and they are archaeologically Scythians, it is impossible to say. In any case, it would seem that the main expansion of R1a-Z645 lineages to the south of the Danube – and therefore those found among modern Greeks – was mediated by the Slavic expansions centuries later.

Modified image from Krzewińska et al. (2018), with added Y-DNA haplogroups to each defined Scythian cluster and Sarmatians. Principal component analysis (PCA) plot visualizing 35 Bronze Age and Iron Age individuals presented in this study and in published ancient individuals in relation to modern reference panel from the Human Origins data set. See image with population references.

On the Northern cluster there is a sample of haplogroup R1b-P312 which, given its position on the PCA (apparently even more ‘modern Celtic’-like than the Hallstatt_Bylany sample from Damgaard et al. 2018), it seems that it could be the product of the previous eastward Hallstatt expansion…although potentially also from a recent one?:

Especially important in the archaeology of this interior is the large settlement at Nemirov in the wooded steppe of the western Ukraine, where there has been considerable excavation. This settlement’s origins evidently owe nothing significant to Greek influence, though the early east Greek pottery there (from ca. 650 bce onward: Vakhtina 2007) and what seems to be a Greek graffito hint at its connections with the Greeks of the coast, especially at Olbia, which lay at the estuary of the River Bug on whose middle course the site was located (Braund 2008). The main interest of the site for the present discussion, however, is its demonstrable participation in the broader Hallstatt culture to its west and south (especially Smirnova 2001). Once we consider Nemirov and the forest steppe in connection with Olbia and the other locations across the forest steppe and coastal zone, together with the less obvious movements across the steppe itself, we have a large picture of multiple connectivities in which Thrace bulks large.

Early Iron Age cultures of the Carpathian basin ca. 7-6th century BC, including steppe-related groups. Ďurkovič et al. (2018).

While the above description of clear-cut R1a-Steppe and R1b-Balkans is attractive (and probably more reliable than admixture found in scattered samples of unclear dates), the true ancient genetic picture is more complicated than that:

  • There is nothing in the material culture of the published western Scythians to distinguish the supposed Thracian elites.
  • We have the sample I0575, an Early Sarmatian from the southern Urals (one of the few available) of haplogroup R1b-Z2106, which supports the presence of R1b-Z2103 lineages among Eastern Iranian-speaking peoples.
  • We also have DA30, a Sarmatian of I2b lineage from the central steppes in Kazakhstan (ca. 47 BC – 24 AD).
  • Other Sarmatian samples of haplogroup R remain undefined.
  • There is R1a-Z93 in a late Sarmatian-Hun sample, which complicates the picture of late pastoralist nomads further.

Therefore, the possibility of hidden pockets of Iranian peoples of R1b-Z2103 (maybe also R1b-P312) lineages remains the best explanation, and should not be discarded simply because of the prevalent haplogroups among modern populations, or because of the different clusters found, or else we risk an obvious circular reasoning: “this sample is not (autosomically or in prevalent haplogroups) like those we already had from the steppe, ergo it is not from this or that steppe culture.” Hopefully, the upcoming paper by Järve et al. will help develop a clearer genetic transect of Iranian populations from the steppes.

All in all, the diversity among western Scythians represents probably one of the earliest difficult cases of acculturation to be studied with ancient DNA (obviously not the only one), since Scythians combine unclear archaeological data with limited and conflicting proto-historical accounts (also difficult to contrast with the wide confidence intervals of radiocarbon dates) with different evolving clusters and haplogroups – especially in border regions with strong and continued interactions of cultures and peoples.

With emerging complex cases like these during the Iron Age, I am happy to see that at least earlier expansions show clearer Y-DNA bottlenecks, or else genetics would only add more data to argue about potential cultural diffusion events, instead of solving questions about proto-language expansions once and for all…


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


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


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.

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


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.

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.

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.


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.

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’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):

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:


Reproductive success among ancient Icelanders stratified by ancestry


New paper (behind paywall), Ancient genomes from Iceland reveal the making of a human population, by Ebenesersdóttir et al. Science (2018) 360(6392):1028-1032.

Abstract and relevant excerpts (emphasis mine):

Opportunities to directly study the founding of a human population and its subsequent evolutionary history are rare. Using genome sequence data from 27 ancient Icelanders, we demonstrate that they are a combination of Norse, Gaelic, and admixed individuals. We further show that these ancient Icelanders are markedly more similar to their source populations in Scandinavia and the British-Irish Isles than to contemporary Icelanders, who have been shaped by 1100 years of extensive genetic drift. Finally, we report evidence of unequal contributions from the ancient founders to the contemporary Icelandic gene pool. These results provide detailed insights into the making of a human population that has proven extraordinarily useful for the discovery of genotype-phenotype associations.

Shared drift of ancient and contemporary Icelanders. (A) Scatterplot of D-statistics reflecting Iceland-specific drift. To aid interpretation, we included values for ancient British-Irish Islanders and a subset of contemporary individuals (who were correspondingly removed from the reference populations).

We estimated the mean Norse ancestry of the settlement population (24 pre-Christians and one early Christian) as 0.566 [95% confidence interval (CI) 0.431–0.702], with a nonsignificant difference betweenmales (0.579) and females (0.521). Applying the same ADMIXTURE analysis to each of the 916 contemporary Icelanders, we obtained a mean Norse ancestry of 0.704 (95% CI 0.699–0.709). Although not statistically significant (t test p = 0.058), this difference is suggestive. A similar difference ofNorse ancestry was observed with a frequency-based weighted least-squares admixture estimator (16), 0.625 [Mean squared error (MSE) = 0.083] versus 0.74 (MSE = 0.0037). Finally, the D-statistic test D(YRI, X; Gaelic, Norse) also revealed a greater affinity between Norse and contemporary Icelanders (0.0004, 95% CI 0.00008–0.00072) than between Norse and ancient Icelanders (−0.0002, 95% CI −0.00056–0.00015). This observation raises the possibility that reproductive success among the earliest Icelanders was stratified by ancestry, as genetic drift alone is unlikely to systematically alter ancestry at thousands of independent loci (fig. S10). We note that many settlers of Gaelic ancestry came to Iceland as slaves, whose survival and freedom to reproduce is likely to have been constrained (17). Some shift in ancestry must also be due to later immigration from Denmark, which maintained colonial control over Iceland from 1380 to 1944 (for example, in 1930 there were 745 Danes out of a total population of 108,629 in Iceland) (18).

Shared drift of ancient and contemporary Icelanders. (B) Estimated Norse,
Gaelic, and Icelandic ancestry for ancient Icelanders using ADMIXTURE
in supervised mode.

Five pre-Christian Icelanders (VDP-A5, DAVA9, NNM-A1, SVK-A1 and TGS-A1) fall just outside the space occupied by contemporary Norse in Fig. 3A. That these individuals show a stronger signal of drift shared with contemporary Icelanders is also apparent in the results of ADMIXTURE, run in supervised mode with three contemporary reference populations (Norse, Gaelic, and Icelandic) (Fig. 3B). The correlation between the proportion of Icelandic ancestry from this analysis and PC1 in Fig. 2A is |r| = 0.913.(…)

(…) as the five ancient Icelanders fall well within the cluster of contemporary Scandinavians (Fig. 3C), we conclude that they, or close relatives, likely contributed more to the contemporary Icelandic gene pool than the other pre-Christians. We note that this observation is consistent with the inference that settlers of Norse ancestry had greater reproductive success than those of Gaelic ancestry.

Haplogroup data, from the paper. Image modified by me, with those close to Gaelic and British/Irish samples (see above Scatterplot of D-statistics and ADMIXTURE data) marked in fluorescent: yellow closer to Gaelic, green less close.

Ancient Icelanders show a clear relation with the typically Norse Y-DNA distribution: I1 / R1a-Z284 / R1b-U106.

  • Among R1a, the picture is uniformly of R1a-Z284 (at least five of the seven reported).
  • There are six samples of I1, with great variation in subclades.
  • Among R1b-L51 subclades (ten samples), there are U106 (at least one sample), L21 (three samples), and another P312 (L238); see above the relationship with those clustering closely with Gaelic samples, marked in fluorescent, which is compatible with Gaelic settlers (predominantly of R1b-L21 lineages) coming to Iceland as slaves.

Probably not much of a surprise, coming from Norse speakers, but they are another relevant reference for comparison with samples of East Germanic tribes, when they appear.

Also, the first reported Klinefelter (XXY) in ancient DNA (sample ID is YGS-B2).


Post-Neolithic Y-chromosome bottleneck explained by cultural hitchhiking and competition between patrilineal clans

Open access study Cultural hitchhiking and competition between patrilineal kin groups explain the post-Neolithic Y-chromosome bottleneck, by Zeng, Aw, and Feldman, Nature Communications (2018).

Abstract (emphasis mine):

In human populations, changes in genetic variation are driven not only by genetic processes, but can also arise from cultural or social changes. An abrupt population bottleneck specific to human males has been inferred across several Old World (Africa, Europe, Asia) populations 5000–7000 BP. Here, bringing together anthropological theory, recent population genomic studies and mathematical models, we propose a sociocultural hypothesis, involving the formation of patrilineal kin groups and intergroup competition among these groups. Our analysis shows that this sociocultural hypothesis can explain the inference of a population bottleneck. We also show that our hypothesis is consistent with current findings from the archaeogenetics of Old World Eurasia, and is important for conceptions of cultural and social evolution in prehistory.

Relevant excerpts:

Tree of Y-chromosome genotypes from samples found among cultures with hunter-gatherer subsistence, and agropastoralist subsistence. The blue background represents hunter-gatherer subsistence while the green background represents agropastoralist subsistence. Letters in red circles match individuals from sites with their archaeological context. Note that R1b-P321 is synonymous with R1b-S116. Adapted from Figs. 3, 4, 5 and 6 of Kivisild67, with addition of information from Olalde et al.64. The vertical axis represents time; the position of branch points represent the ages of branch-defining mutations, with nomenclature and age from yfull (https://www.yfull.com/tree/)

Our hypothesis explains the bottleneck as a consequence of intergroup competition between patrilineal kin groups, which caused cultural hitchhiking between Y-chromosomes and cultural groups and reduction in Y-chromosomal diversity. Competition between demes can dramatically reduce genetic diversity within a population1, especially if the population is structured such that variation is greater between demes than within demes. Culturally transmitted kinship ideals and norms can cause homophilous sorting and limit interdemic gene flow, creating homogeneous demes that differ strongly from one another. Patrilineal corporate kin groups, with coresiding male group members descending from a common male ancestor, would produce such an effect on Y-chromosomes only, as patrilineal corporate kin groups generally coexist with female exogamy40, which would homogenize the mitochondrial gene pools of different groups41,42.

With intergroup competition between patrilineal corporate kin groups, two mechanisms would operate to reduce Y-chromosomal diversity. First, patrilineal corporate kin groups produce high levels of Y-chromosomal homogeneity within each social group due to common descent, as well as high levels of between-group variation. Second, the presence of such groups results in violent intergroup competition preferentially taking place between members of male descent groups, instead of between unrelated individuals. Casualties from intergroup competition then tend to cluster among related males, and group extinction is effectively the extinction of lineages.

There is evidence that other analogous situations involving gene-culture hitchhiking in culturally-defined social groups may have affected genetic diversity. Central Asian pastoralists, who are organized into patriclans, have high levels of intergroup competition and demonstrate ethnolinguistic and population-genetic turnover down into the historical period59. They also have a markedly lower diversity in Y-chromosomal lineages than nearby agriculturalists42,60. In fact, Central Asians are the only population whose male effective population size has not recovered from the post-Neolithic bottleneck; it remains disproportionately reduced, compared to female estimates using mtDNA4. Central Asians are also the only population to have star-shaped expansions of Y-chromosomes within the historical period, which may be due to competitive processes that led to the disproportionate political success of certain patrilineal clans60.

The simulation offers an interesting graphic. I had been thinking for some time about developing an interactive image with waves of expansion showing how only few haplogroups expand and thus their variability is reduced in successive migration waves, because a lot of people seemed not to be willing to accept this:

Schematic of the steps in the simulation, according to the order described in the algorithm. a (i) Patrilineal (PT) starting conditions, where cultural groups strictly determine haplogroup type. a (ii) The non-patrilineal (NPT) condition where they are perfectly uncorrelated. b The killing step, with a more (PT) and less (NPT) patrilineal starting condition. The number of deaths in each group is inversely related to group size. The blue cultural group goes extinct in both cases. This causes the haplogroup represented by the diamonds to go extinct in PT, but no haplogroup extinction occurs in NPT. c The mutation step, where a small number of individuals in the largest haplogroup change their haplogroup. d The regeneration step, where (i) is a replica of (b) PT (iii), and (d) (ii) shows how the original number of individuals before the killing step is restored by proportionally increasing the number of individuals in all cells. e Group fission step. Where an empty row occurs, the largest cultural group splits, and half the individuals form a new cultural group in the empty row. The step in which we remove cultural groups that are too small—between (c, d) (see Methods)—is not shown

You only have to imagine this process happening in many successive waves of expansion (external as well as internal to each culture) since the first Neolithic expansions in the steppe in the late-6th millennium BC, even before the formation of the Khvalynsk-Sredni Stog cultural-historical community, to understand what happened in the next thousands of years with evolving patrilineal clans and their distinct cultures.

The whole paper is an interesting read. It’s great to see sociology and genetics finally catch up and interact to develop more complex anthropological hypotheses.

The fact that this paper appears in mid-2018 and geneticists are beginning to discuss this only now when their statistical methods fail to explain the obvious (see David Reich’s recent interview) seems anachronistic, though, because all this was quite clear already in 2015 – at least for those who were looking for mainstream Yamna – Bell Beaker connections, instead of inventing new migration pathways to justify the results of certain statistical analyses

Anyway, better late than never.

Also, they use YFull estimates, which vindicates my use of them in the Indo-European demic diffusion model (2017). On the other hand, their use of these estimates right now in 2018 for R1a-M417 and R1b-M269 – when we know of a R1a-Z93 case much older than YFull’s estimated 5,000 YBP for this subclade, and possibly for R1b-L23, too, is the biggest pitfall in their temporal assessment, although the bottlenecks seen in Chalcolithic expansions seem to have indeed began during the Mesolithic-Neolithic transition in the steppe.

So, say goodbye (if you haven’t already) to dat fantasy ‘steppe people’ of mixed R1a/R1b descent cooperating with the same mixed steppe language, all represented by the Yamnaya™ ancestral component, and say hello to distinct, competing ethnolinguistic steppe groups during the Neolithic.


The R1b-L23/Late PIE expansions, and the ‘R1a – Indo-European’ association


I wrote a series of posts at the end of 2017 / beginning of 2018, to answer the wrong assumptions I could read in forums and blogs since 2015.

I decided not to publish them then, seeing how many successive papers were confirming my Indo-European demic diffusion model in a (surprisingly) clear-cut way.

Nevertheless, because I keep reading the same comments no matter what gets published, even in mid-2018 – the latest ones in our Facebook page (“was haplogroup X Indo-European?”), and in this very blog (“I see it very difficult to link Bell Beaker with Balto-Slavic, when now Balto-Slavic people are strikingly R1a-dominated”); and because I see even more misunderstandings and personal attacks, I have decided to publish them.

This way I will be able to explain my “R1b-L23/Proto-Indo-Europeans” theory with simplistic maps (however badly I hate such maps when I find them on Google searches), and I will also have a page to redirect those who don’t want to dismiss the “R1a – Indo-European association”, instead of answering comments about this question each time they pop up…

Here you have the links to the posts – and also on the menu above (there is a lot of rambling, because they are from a period of less clear data on Yamna and Corded Ware; today I would have never written such long discussions, they are mostly unnecessary):

  1. Haplogroup is not language, but R1b-L23 expansion was associated with Proto-Indo-Europeans
  2. The history of the simplistic ‘haplogroup R1a — Indo-European’ association
  3. Tips for dialogue with those supporting the R1a/Indo-European association


Eurasian steppe dominated by Iranian peoples, Indo-Iranian expanded from East Yamna


The expected study of Eurasian samples is out (behind paywall): 137 ancient human genomes from across the Eurasian steppes, by de Barros Damgaard et al. Nature (2018).

Dicussion (emphasis mine):

Our findings fit well with current insights from the historical linguistics of this region (Supplementary Information section 2). The steppes were probably largely Iranian-speaking in the first and second millennia bc. This is supported by the split of the Indo-Iranian linguistic branch into Iranian and Indian33, the distribution of the Iranian languages, and the preservation of Old Iranian loanwords in Tocharian34. The wide distribution of the Turkic languages from Northwest China, Mongolia and Siberia in the east to Turkey and Bulgaria in the west implies large-scale migrations out of the homeland in Mongolia since about 2,000 years ago35. The diversification within the Turkic languages suggests that several waves of migration occurred36 and, on the basis of the effect of local languages, gradual assimilation to local populations had previously been assumed37. The East Asian migration starting with the Xiongnu accords well with the hypothesis that early Turkic was the major language of Xiongnu groups38. Further migrations of East Asians westwards find a good linguistic correlate in the influence of Mongolian on Turkic and Iranian in the last millennium39. As such, the genomic history of the Eurasian steppes is the story of a gradual transition from Bronze Age pastoralists of West Eurasian ancestry towards mounted warriors of increased East Asian ancestry—a process that continued well into historical times.

This paper will need a careful reading – better in combination with Narasimhan et al. (2018), when their tables are corrected – , to assess the actual ‘Iranian’ nature of the peoples studied. Their wide and long-term dominion over the steppe could also potentially explain some early samples from Hajji Firuz with steppe ancestry.

Principal component analyses. The principal components 1 and 2 were plotted for the ancient data analysed with the present-day data (no projection bias) using 502 individuals at 242,406 autosomal SNP positions. Dimension 1 explains 3% of the variance and represents a gradient stretching from Europe to East Asia. Dimension 2 explains 0.6% of the variance, and is a gradient mainly represented by ancient DNA starting from a ‘basal-rich’ cluster of Natufian hunter-gatherers and ending with EHGs. BA, Bronze Age; EMBA, Early-to-Middle Bronze Age; SHG, Scandinavian hunter-gatherers.

For the moment, at first sight, it seems that, in terms of Y-DNA lineages:

  • R1b-Z93 (especially Z2124 subclades) dominate the steppes in the studied periods.
  • R1b-P312 is found in Hallstatt ca. 810 BC, which is compatible with its role in the Celtic expansion.
  • R1b-U106 is found in a West Germanic chieftain in Poprad (Slovakia) ca. 400 AD, during the Migration Period, hence supporting once again the expansion of Germanic tribes especially with R1b-U106 lineages.
  • A new sample of N1c-L392 (L1025) lineage dated ca. 400 AD, now from Lithuania, points again to a quite late expansion of this lineage to the region, believed to have hosted Uralic speakers for more than 2,000 years before this.
  • A sample of haplogroup R1a-Z282 (Z92) dated ca. 1300 AD in the Golden Horde is probably not quite revealing, not even for the East Slavic expansion.
  • Also, interestingly, some R1b(xM269) lineages seem to be associated with Turkic expansions from the eastern steppe dated around 500 AD, which probably points to a wide Eurasian distribution of early R1b subclades in the Mesolithic.

NOTE. I have referenced not just the reported subclades from the paper, but also (and mainly) further Y-SNP calls studied by Open Genomes. See the spreadsheet here.

Interesting also to read in the supplementary materials the following, by Michaël Peyrot (emphasis mine):

1. Early Indo-Europeans on the steppe: Tocharians and Indo-Iranians

The Indo-European language family is spread over Eurasia and comprises such branches and languages as Greek, Latin, Germanic, Celtic, Sanskrit etc. The branches relevant for the Eurasian steppe are Indo-Aryan (= Indian) and Iranian, which together form the Indo-Iranian branch, and the extinct Tocharian branch. All Indo-European languages derive from a postulated protolanguage termed Proto-Indo-European. This language must have been spoken ca 4500–3500 BCE in the steppe of Eastern Europe21. The Tocharian languages were spoken in the Tarim Basin in present-day Northwest China, as shown by manuscripts from ca 500–1000 CE. The Indo-Aryan branch consists of Sanskrit and several languages of the Indian subcontinent, including Hindi. The Iranian branch is spread today from Kurdish in the west, through a.o. Persian and Pashto, to minority languages in western China, but was in the 2nd and 1st millennia BCE widespread also on the Eurasian steppe. Since despite their location Tocharian and Indo-Iranian show no closer relationship within Indo-European, the early Tocharians may have moved east before the Indo-Iranians. They are probably to be identified with the Afanasievo Culture of South Siberia (ca 2900 – 2500 BCE) and have possibly entered the Tarim Basin ca 2000 BCE103.

The Indo-Iranian branch is an extension of the Indo-European Yamnaya Culture (ca 3000–2400 BCE) towards the east. The rise of the Indo-Iranian language, of which no direct records exist, must be connected with the Abashevo / Sintashta Culture (ca 2100 – 1800 BCE) in the southern Urals and the subsequent rise and spread of Andronovo-related Culture (1700 – 1500 BCE). The most important linguistic evidence of the Indo-Iranian phase is formed by borrowings into Finno-Ugric languages104–106. Kuz’mina (2001) identifies the Finno-Ugrians with the Andronoid cultures in the pre-taiga zone east of the Urals107. Since some of the oldest words borrowed into Finno-Ugric are only found in Indo-Aryan, Indo-Aryan and Iranian apparently had already begun to diverge by the time of these contacts, and when both groups moved east, the Iranians followed the Indo-Aryans108. Being pushed by the expanding Iranians, the Indo-Aryans then moved south, one group surfacing in equestrian terminology of the Anatolian Mitanni kingdom, and the main group entering the Indian subcontinent from the northwest.

Summary map. Depictions of the five main migratory events associated with the genomic history of the steppe pastoralists from 3000 bc to the present. a, Depiction of Early Bronze Age migrations related to the expansion of Yamnaya and Afanasievo culture. b, Depiction of Late Bronze Age migrations related to the Sintashta and Andronovo horizons. c, Depiction of Iron Age migrations and sources of admixture. d, Depiction of Hun-period migrations and sources of admixture. e, Depiction of Medieval migrations across the steppes.

2. Andronovo Culture: Early Steppe Iranian

Initially, the Andronovo Culture may have encompassed speakers of Iranian as well as Indo-Aryan, but its large expansion over the Eurasian steppe is most probably to be interpreted as the spread of Iranians. Unfortunately, there is no direct linguistic evidence to prove to what extent the steppe was indeed Iranian speaking in the 2nd millennium BCE. An important piece of indirect evidence is formed by an archaic stratum of Iranian loanwords in Tocharian34,109. Since Tocharian was spoken beyond the eastern end of the steppe, this suggests that speakers of Iranian spread at least that far. In the west of the Tarim Basin the Iranian languages Khotanese and Tumshuqese were spoken. However, the Tocharian B word etswe ‘mule’, borrowed from Iranian *atswa- ‘horse’, cannot derive from these languages, since Khotanese has aśśa- ‘horse’ with śś instead of tsw. The archaic Iranian stratum in Tocharian is therefore rather to be connected with the presence of Andronovo people to the north and possibly to the east of the Tarim Basin from the middle of the 2nd millennium BCE onwards110.

Since Kristiansen and Allentoft sign the paper (and Peyrot is a colleague of Kroonen), it seems that they needed to expressly respond to the growing criticism about their recent Indo-European – Corded Ware Theory. That’s nice.

They are obviously trying to reject the Corded Ware – Uralic links that are on the rise lately among Uralicists, now that Comb Ware is not a suitable candidate for the expansion of the language family.

IECWT-proponents are apparently not prepared to let it go quietly, and instead of challenging the traditional Neolithic Uralic homeland in Eastern Europe with a recent paper on the subject, they selected an older one which partially fit, from Kuz’mina (2001), now shifting the Uralic homeland to the east of the Urals (when Kuz’mina asserts it was south of the Urals).

Different authors comment later in this same paper about East Uralic languages spreading quite late, so even their text is not consistent among collaborating authors.

Also interesting is the need to resort to the questionable argument of early Indo-Aryan loans – which may have evidently been Indo-Iranian instead, since there is no way to prove a difference between both stages in early Uralic borrowings from ca. 4,500-3,500 years ago…

EDIT (10/5/2018) The linguistic supplement of the Science paper deals with different Proto-Indo-Iranian stages in Uralic loans, so on the linguistic side at least this influence is clear to all involved.

A rejection of such proposals of a late, eastern homeland can be found in many recent writings of Finnic scholars; see e.g. my references to Parpola (2017), Kallio (2017), or Nordqvist (2018).

NOTE. I don’t mind repeating it again: Uralic is one possibility (the most likely one) for the substrate language that Corded Ware migrants spread, but it could have been e.g. another Middle PIE dialect, similar to Proto-Anatolian (after the expansion of Suvorovo-Novodanilovka chiefs). I expressly stated this in the Corded Ware substrate hypothesis, since the first edition. What was clear since 2015, and should be clear to anyone now, is that Corded Ware did not spread Late PIE languages to Europe, and that some east CWC groups only spread languages to Asia after admixing with East Yamna. If they did not spread Uralic, then it was a language or group of languages phonetically similar, which has not survived to this day.

Their description of Yamna migrations is already outdated after Olalde et al. & Mathieson et al. (2018), and Narasimhan et al. (2018), so they will need to update their model (yet again) for future papers. As I said before, Anthony seems to be one step behind the current genetic data, and the IECWT seems to be one step behind Anthony in their interpretations.

At least we won’t have the Yamna -> Corded Ware -> BBC nonsense anymore, and they expressly stated that LPIE is to be associated with Yamna, and in particular the “Indo-Iranian branch is an extension of the Indo-European Yamnaya Culture (ca 3000–2400 BCE) to the East” (which will evidently show an East Yamna / Poltavka society of R1b-L23 subclades), so that earlier Eneolithic cultures have to be excluded, and Balto-Slavic identification with East Europe is also out of the way.


Yleaf: software for human Y-chromosomal haplogroup inference from next generation sequencing data


Brief communication (behind paywall) Yleaf: software for human Y-chromosomal haplogroup inference from next generation sequencing data, by Arwin Ralf, Diego Montiel González, Kaiyin Zhong, and Manfred Kayser, Mol Biol Evol (2018), msy032.


Next generation sequencing (NGS) technologies offer immense possibilities given the large genomic data they simultaneously deliver. The human Y chromosome serves as good example how NGS benefits various applications in evolution, anthropology, genealogy and forensics. Prior to NGS, the Y-chromosome phylogenetic tree consisted of a few hundred branches, based on NGS data it now contains many thousands. The complexity of both, Y tree and NGS data provide challenges for haplogroup assignment. For effective analysis and interpretation of Y-chromosome NGS data, we present Yleaf, a publically available, automated, user-friendly software for high-resolution Y-chromosome haplogroup inference independently of library and sequencing methods.

Here is a link to the software Yleaf’s website, from the Department of Genetic Identification, at the University of Erasmus Medical Center.

Summary of NGS datasets used for automated NRY haplogrouping with Yleaf


In the time of NGS (or massively parallel sequencing, MPS), the amount of genomic data produced and made publically available is rapidly expanding, providing valuable resources for many areas of research and applications. Due to its haploid nature and male-specific inheritance, the non-recombining part of the human Y-chromosome (NRY) is highly suitable for phylogenetic studies and for addressing questions in evolution, anthropology, population history, genealogy and forensics (Jobling & Tyler-Smith, 2017). Over recent years, NGS data allowed the phylogenetic NRY tree to dramatically increase in size and complexity (Hallast et al. 2014; Poznik et al. 2016). The two most comprehensive tree versions ISOGG (http://www.isogg.org/tree) and Yfull (https://www.yfull.com/tree) currently contain thousands of branches. However, the complexity of both, Y tree and NGS data provide immense challenges for NRY haplogroup assignment, which reflects a key element in many NRY applications. Here we introduce Yleaf, a Phyton-based, easy-to-use, publically-available software tool for effective NRY single nucleotide polymorphism (SNP) calling and subsequent NRY haplogroup inference from NGS data. By comparative whole genome data analysis, we demonstrate high concordance of Yleaf in NRY-SNP calling compared to well-established tools such as SAMtools/BCFtools (Li et al. 2009), and GATK (McKenna, et al. 2010) as well as improved performance of Yleaf in NRY haplogroup assignment relative to previously developed tools such as clean_tree (Ralf et al. 2015), AMY-tree (Van Geystelen et al. 2015), and yHaplo (Poznik, 2016).

Yleaf allows analyzing NRY sequence data from many types of NGS libraries i.e., whole genomes, whole exomes, large genomic regions, and large numbers of targeted amplicons. Several modifications relative to our previously developed clean_tree tool (Ralf et al. 2015) were implemented to optimize the performance especially relevant for extremely large NGS datasets such as whole genomes. For instance, Yleaf extracts the Y-chromosomal reads prior to further processing and uses multi-threading, a batch option is included too. Importantly, Yleaf provides drastically increased haplogroup resolution i.e., from Downloaded from 530 positions defining 432 NRY haplogroups with clean_tree (Ralf et al. 2015) to over 41,000 positions defining 5353 haplogroups with Yleaf. For a detailed method description see the supplementary material.

Featured image: From Martiniano et al. (2017).