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

Minimal Corded Ware culture impact in Scandinavia – Bell Beakers the unifying maritime elite

copper-age-late-bell-beaker

Chapter The Sea and Bronze Age Transformations, by Christopher Prescott, Anette Sand-Eriksen, and Knut Ivar Austvoll, In: Water and Power in Past Societies (2018), Emily Holt, Proceedings of the IEMA Postdoctoral Visiting Scholar Conference on Theories and Methods in Archaeology, Vol. 6.

NOTE. You can download the chapter draft at Academia.edu.

Abstract (emphasis mine):

Along the western Norwegian coast, in the northwestern region of the Nordic Late Neolithic and Bronze Age (2350–500 BCE) there is cultural homogeneity but variable expressions of political hierarchy. Although new ideological institutions, technology (e.g., metallurgy and boat building), intensified agro‑pastoral farming, and maritime travel were introduced throughout the region as of 2350 BCE, concentrations of expressions of Bronze Age elites are intermittently found along the coast. Four regions—Lista, Jæren, Karmøy, and Sunnmøre—are examined in an exploration of the establishment and early role of maritime practices in this Nordic region. It is argued that the expressions of power and material wealth concentrated in these four regions is based on the control of bottlenecks, channels, portages, and harbors along important maritime routes of travel. As such, this article is a study of prehistoric travel, sources of power, and maritime landscapes in the Late Neolithic and Early Bronze Age of Norway.

Interesting excerpts:

(…)The [Corded Ware culture (CWC)] in Norway (or Battle Axe Culture, 2750–2400/2350 BCE) is primarily represented in Eastern Norway, with a patchy settlement pattern along the Oslo fjord’s coast through the inland valleys to Trøndelag in Central Norway (Hinsch 1956). The CWC represents an enigmatic period in Norwegian prehistory (Hinsch 1956; Østmo 1988:227–231; Prescott and Walderhaug 1995; Shetelig 1936); however the data at the moment suggests the following patterns:

  • Migration: The CWC was the result of a small‑scale immigration, but did not trigger substantial change.
  • Eastern and limited impact: The CWC was primarily located in small settlement patches in eastern Norway.
  • Terrestrial: In terms of maritime practices, the CWC does not represent a significant break from older traditions, though it seems to have a more pronounced terrestrial bearing. It is conceivable that pastures and hunting grounds were a more important political‑economic resource than waterways.

The mid‑third millennium in Norway, around 2400 BCE, represents a significant reorientation. Bell Beaker Culture (BBC) settlements in western Denmark and Norway archaeologically mark the instigation of the Nordic LN, though much of the historical process leading from the Bell Beaker to the Late Neolithic, 2500 to 2350 BCE, remains unclear (Prescott 2012; Prescott and Melheim 2009; Prieto‑Martinez 2008:116; Sarauw 2007:66; Vandkilde 2001, 2005). Still, the outcome is the establishment of the Nordic region of interaction in the Baltic, Northern Germany, Sweden, Denmark, and Norway. The distribution of artifact materials such as Bell Beakers and flint daggers attests to the far‑flung network of regular exchange and communication. This general region of interaction was reproduced through the Late Neolithic and Bronze Age.

nordic-late-neolithic
The Nordic region in the Late Neolithic and Bronze Age. Sites and regions discussed in the text are marked (ater Prescott and Glørstad 2015:fig. 1).

The transition from the preceding Neolithic period hunter‑gatherer societies was rapid and represents a dramatic termination of hunter‑gatherer traditions. It has been argued that the transformation is tied to initial migrations of people to the western coast of Norway from BBC areas, possibly from northern Jutland (Prescott 2011; Prescott and Walderhaug 1995:273). Bifacial tanged‑and‑barbed points, often referred to as “Bell Beaker points,” probably represent an early, short phase of the BBC‑transition around 2400 BCE. In Norway these points have a predominantly western and coastal distribution (Østmo 2012:64), underscoring the maritime nature of the initial BBC‑expansion.

late-neolithic-flint-daggers
Distribution routes for LN1 flint daggers type 1 suggesting communication routes and networks. (Redrawn after fig. 9, Apel 2001:17).

(…) In response to the question about what attracted people from Bell Beaker groups to western Norway, responses have hypothesized hunting products, political power, pastures, and metals. Particularly the latter has been emphasized by Lene Melheim (2012, 2015:37ff).

A recent study by Melheim and Prescott (2016) integrated maritime exploration with metal prospecting to explain initial excursions of BBC‑people along the western coast and into the fjords. Building on the archaeological concept of traveling metal prospectors as an element in the expansion of the Bell Beaker phenomenon, in combination with anthropological perspectives on prospecting, the article explores how prospecting for metal would have adjusted to the landscapes of western Scandinavia. Generally speaking, prospecting seldom leads to successful metal production, and it is difficult to study archaeologically. However, it will often create links between the prospectors’ society and indigenous groups, opening new territories, and have a significant transformative impact—on both the external and indigenous actors and societies.

While the text echoes the traditional idea that Corded Ware spread Indo-European languages, Prescott (since Prescott and Walderhaug 1995) is a supporter of the formation of a Nordic community and a Nordic (i.e. Pre-Germanic) language with the arrival of Bell Beakers.

An identification of the Corded Ware language as of a previous Proto-Indo-European stage is possible, as I have previously said (although my preference is Uralic-related languages).

This CWC language would thus still form the common substrate to both Germanic and Balto-Slavic, both being North-West Indo-European dialects, which spread with Bell Beakers over previous Corded Ware territory.

NOTE. This pre-LPIE nature could be in turn related to Kortlandt’s controversial proposal of an ealier PIE dative *-mus shared by both branches. However, that would paradoxically be against Kortlandt’s own assumption that the substrate was in fact of a non-Indo-European nature

See also:

Reproductive success among ancient Icelanders stratified by ancestry

iceland-pca

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.

icelanders
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).

icelander-admixture
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.

icelanders-y-dna
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).

Related:

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

yamna-indo-iranian-expansion

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.
fku

eurasian-steppe-samples
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.

steppe-migrations-pastoralists
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.

Related:

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

portugal-bronze-age-admixture

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.

Abstract

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.

yleaf-martiniano
Summary of NGS datasets used for automated NRY haplogrouping with Yleaf

Excerpt:

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).

Related:

The Indo-European demic diffusion model, and the “R1b – Indo-European” association

yamna_bell_beaker_cut

Beginning with the new year, I wanted to commit myself to some predictions, as I did last year, even though they constantly change with new data.

I recently read Proto-Indo-European homelands – ancient genetic clues at last?, by Edward Pegler, which is a good summary of the current state of the art in the Indo-European question for many geneticists – and thus a great example of how well Genetics can influence Indo-European studies, and how badly it can be used to interpret actual cultural events – although more time is necessary for some to realize it. Notice for example the distribution of ‘Yamnaya’ in 3000 BC, all the way to Latvia (based on the initial findings of Mathieson et al. 2017), and the map of 2000 BC with ‘Corded Ware’, both suggesting communities linked by admixture and unrelated to actual cultures.

Some people – especially those interested in keeping a simplistic picture of Europe, either divided into admixture groups or simplistic R1b-Vasconic / R1a-Indo-European / N1c-Uralic (or any combination thereof) – want (others) to believe that I am linking ‘Indo-Europeans’ with haplogroup R1b. That is simply not true. In fact, my model dismisses such simplistic identifications of the reconstructible proto-languages with any modern peoples, admixtures, or haplogroups.

vasconic-uralic
Simplistic Vasconic/R1b-Uralic/N1c distribution, and intruding Indo-European/R1a, according to Wiik.

The beauty of the model lies, therefore, precisely in that if you take any modern group speaking Indo-European languages, none can trace back their combination of language, admixture, and/or haplogroup to a common Indo-European-speaking people. All our ancestral lines have no doubt changed language families (and indeed cultures), they have admixed, and our European regions’ paternal lines have changed, so that any dreams of ‘purity’ or linguistic/cultural/regional continuity become absurd.

That conclusion, which should be obvious to all, has been denied for a long time in blogs and forums alike, and is behind the effort of many of those involved in amateur genetics.

Main linguistic aim

The main consequence of the model, as the title of the paper suggests, is that reconstructible Indo-European proto-languages expanded with people, i.e. with actual communities, which is what we can assert with the help of Genomics. From a personal (or ethnic, or political) point of view genomics is useless, but from an anthropological (and thus linguistic) point of view, genomics can be a very useful tool to decide between alternative models of language diffusion, which has given lots of headaches to those of us involved in Indo-European studies.

The demic diffusion theory for the three main stages of the proto-language expansion was originally, therefore, a dismissal of impossible-to-prove cultural diffusion models for the proto-language – e.g. the adoption of Late Proto-Indo-European by Corded Ware groups due to a patron-client relationship (as proposed by Anthony), or a long-lasting connection between cultures (as proposed by Kristiansen, and favoured by “constellation analogy” proponents like Clackson, who negated the existence of common proto-languages). It also means the acceptance of the easiest anthropological model for language change: migration and – consequently – replacement.

By the time of the famous 2015 papers, I had been dealing for some time with the idea that the shared features between Indo-Iranian and Balto-Slavic may have been due to a common substrate, and must have therefore had some reflection in genomic finds. The data on these papers, and the addition of a weak connection between Pre-Germanic and Balto-Slavic communities, together with their clearest genetic link – R1a-M417 subclades (especially European Z283) – made it still easier to propose a Corded Ware substrate, partially common to the three.

Allentoft Corded Ware
Allentoft et al. “Arrows indicate migrations — those from the Corded Ware reflect the evidence that people of this archaeological culture (or their relatives) were responsible for the spreading of Indo-European languages. All coloured boundaries are approximate.”

Before the famous 2015 papers (and even after them, if we followed their interpretation), we were left to wonder why the supposed vector of expansion of Indo-European languages, Corded Ware migrants – represented by R1a-Z645 subclades, and supposedly continued unchanged into modern populations in its ‘original’ ancestral territories, Balto-Slavic and Indo-Iranian – , were precisely the (phonetically) most divergent Indo-European languages – relative to the parent Late Indo-European proto-language.

My paper implied therefore the dismissal of an unlikely Indo-Slavonic group, as proposed by Kortlandt, and of a still less factible Germano-Slavonic, or Germano-Indo-Slavonic (?) group, as loosely implied by some in the past, and maybe supported in certain archaeological models (viz. Kristiansen or partially Anthony), and presently by some geneticists since their simplistic 2015 papers on “massive migrations from the steppe“, and amateur genetic fans with infinite pet theories, indeed.

A common Corded Ware substrate to Balto-Slavic and Indo-Iranian, and common also partially between Balto-Slavic and Germanic (as supported by Kortlandt, too, albeit with different linguistic connotations), would explain their common features. The Corded Ware culture (and Uralic, tentatively proposed by me as the group’s main language family) is a strong potential connection between them, further supported by phylogeography, too.

Other consequences

Interpretations in my paper help thus dismiss the simplistic Yamna -> Corded Ware -> Bell Beaker migration model implied with phylogeography in the 2000s, and revived again by geneticists and Kristiansen’s workgroup based on the famous 2015 papers, whereby – due to the “Yamnaya ancestral component” – the Yamna culture would have been composed of communities of R1a-M417 and R1b-M269 lineages which remained against all odds ‘related but separated’ for more than two thousand years, sharing a common unitary language (why? and how?), and which expanded from Yamna (mainly R1b-L23) into Corded Ware (mainly R1a-M417) and then into Bell Beaker (mainly R1b-L51), in imaginary migration waves whose traces Archaeology has not found, or Anthropology described, before.

While phylogeography (especially the distribution of ancient samples of certain R1b and R1a subclades) was the main genetic aspect I used in combination with Archaeology and Anthropology to challenge the reliability of the “Yamnaya ancestral component” in assessing migrations – and thus Kristiansen’s now-popular-again modified Kurgan model – , my main aim was to prove a recent expansion of Late Proto-Indo-European from the steppe, and a still more recent expansion of a common group of speakers of North-West Indo-European, the language ancestral to Italo-Celtic, Germanic, and probably Balto-Slavic (or ‘Temematic’, the NWIE substrate of Balto-Slavic, according to some linguists).

My arguments serve for this purpose, and modern distributions of haplogroups or admixture are fully irrelevant: I am ready to change my view at any time, regarding the role of any haplogroup, or ancestral component, archaeological data, or anthropological migration model, to the extent that it supports the soundest linguistic model.

proto-indo-european-stages
Stages of Proto-Indo-European evolution. IU: Indo-Uralic; PU: Proto-Uralic; PAn: Pre-Anatolian; PToch: Pre-Tocharian; Fin-Ugr: Finno-Ugric. The period between Balkan IE and Proto-Greek could be divided in two periods: an older one, called Proto-Greek (close to the time when NWIE was spoken), probably including Macedonian, and spoken somewhere in the Balkans; and a more recent one, called Mello-Greek, coinciding with the classically reconstructed Proto-Greek, already spoken in the Greek peninsula (West 2007). Similarly, the period between Northern Indo-European and North-West Indo-European could be divided, after the split of Pre-Tocharian, into a North-West Indo-European proper, during the expansion of Yamna to the west, and an Old European period, coinciding with the formation and expansion of the East Bell Beaker group.

Gimbutas’ old theory of sudden and recent expansion served well to support a real community of Proto-Indo-European speakers, as did later the Yamna -> Corded Ware -> Bell Beaker theory that circulated in the 2000s based on modern phylogeography, and as did later partially Anthony’s updated steppe theory (2007). On the other hand, Kristiansen’s long-lasting connections among north-west Pontic steppe cultures and Globular Amphorae and Trypillian cultures, did not fit well with a close community expanding rapidly – although recent genetic data on Trypillia and Globular Amphorae might be compelling him to improve his migration theory.

So, if data turns out to be not as I expect now, I will reflect that in future versions of the paper. I have no problem saying I am wrong. I have been wrong many times before, and something I am certain is that I am wrong now in many details, and I am going to be in the future.

If, for example, R1b-L23(xZ2105) is demonstrated to come from Hungary and not the steppe (as supported by Balanovsky) or R1a-M417 samples are proved to have expanded with West Yamna settlers (as recently proposed by Anthony, see below the Balto-Slavic question), I would support the same model from a linguistic point of view, but modified to reflect these facts. Or if a direct migration link is found in Archaeology from Yamna to Corded Ware, and from Corded Ware to Bell Beaker (as proposed in the 2015 papers), I will revise that too (again, see the image below). Or, if – as Lazaridis et al. (2017) paper on Minoans and Mycenaeans suggested – the Anatolian hypothesis (that is, one of the multiple ones proposed) turns out to be somehow right, I will support it.

calcolithic-expansion
My map of Late Proto-Indo-European expansion (A Grammar of Modern Indo-European, 2006), following Gimbutas and Mallory.

Haplogroups are the least important aspect of the whole model, they are just another data that has to be taken into account for a throrough explanation of migrations. It has become essential today because of the apparent lack of vision on the part of geneticists, who failed to use them to adjust their findings of admixture with findings of haplogroup expansions, favouring thus a marginal theory of long-lasting steppe expansion instead of the mainstream anthropological models.

Since many of these alternative scenarios seem less and less likely with each new paper, it is probably more efficient to talk about which developments are most likely to challenge my model.

Main points

My main predictions – based mostly on language guesstimates, archaeological cultures, and anthropological models of migration -, even with the scarce genomic data we had, have been proven right until know with new samples from Mathieson et al. (2017) and Olalde et al. (2017), among other papers of this past year. These were my original assumptions:

(1) A Middle Proto-Indo-European expansion defined by the appearance of steppe ancestry + reduction in haplogroup diversity and expansion of (mainly) R1b-M269 and R1b-L23 lineages;

(2) A Late Proto-Indo-European expansion defined by steppe ancestry + reduction in haplogroup diversity and expansion of (mainly) R1b-L23 subclades; and

(3) A North-West Indo-European expansion defined by steppe ancestry + reduction in haplogroup diversity and expansion of (mainly) R1b-L51 subclades.

The expansion of Corded Ware peoples, associated with steppe ancestry + reduction in haplogroup diversity and expansion of (mainly) R1a-Z645 subclades, represents thus a different migration, which is compatible with the different nature of the Corded Ware culture, unrelated to Yamna and without migration waves from one to the other (although there were certainly contacts in neighbouring regions).

As you can see, neither of the 3+1 expansion models imply that no other haplogroup can be found in the culture or regions involved (others have in fact been found, and still the models remain valid): these migrations imply a reduction of haplogroup diversity, and the expansion of certain subclades as is common in population expansions throughout history. While we all accept this general idea, some people have difficulties accepting just those cases not compatible with their dreams of autochthonous continuity.

Nevertheless, there are still voids in genetic investigation.

Controversial aspects

In my humble opinion, these are potential conflict periods and the most likely areas of change for the future of the theory:

1. When and how did R1b-M269 lineages become “chiefs” in the steppe?

Based on scarce data from Khvalynsk, it seems that during the Neolithic there were many haplogroups in the North Pontic and North Caspian steppes. A reduction to R1b-M269 subclades must have happened either just before or (as I support) during (the migrations that caused) the Suvorovo-Novodanilovka expansion among Sredni Stog, probably coinciding also with the expansion (or one of the expansions) of CHG ancestry (and thus the appearance of ‘Steppe component’ in the steppe). My theory was based initially on Anthony’s account and TMRCA of haplogroups of modern populations (both ca. 4200-4000 BC), but recent samples of the Balkans (R1b-M269 and steppe ancestry) seem to trace the population expansion some centuries back.

If my assessment is correct, then modern populations of haplogroup R1b-M269* and R1b-L23* in the Balkans probably reflect that ancient expansion, and samples related to Proto-Anatolian cultures in the Balkans will most likely be of R1b-M269 subclades and R1b-L23*. After admixture in the Balkans, posterior migrations of Anatolian languages into Anatolia might be associated with a different admixture component and haplogroups, we don’t have enough data yet.

If the haplogroup reduction and expansion in Khvalynsk happened later than the Suvorovo-Novodanilovka expansion, then we might find the expansion of Pre- or Proto-Anatolian associated with many different haplogroups, such as R1b (xM269), R1a, I, J, or G2, and more or less associated with steppe ancestry in the Balkans.

Another reason for finding such variety of haplogroups in ancient samples from the Balkans would be that this Khvalynsk group of “chiefs” traversed – and mixed with – the Sredni Stog population. Nevertheless, if we suppose homogeneity in haplogroups in Khvalynsk during the expansion, a high proportion of different haplogroups explained by admixture with the local population of Sredni Stog would challenge the whole “chief domination” explanation by Anthony, and we would have to return to the “different culture” theory by Rassamakin and potentially an older migration from Khvalynsk. In any case, both researchers show clear links of the Suvorovo-Novodanilovka phenomenon to Khvalynsk, and a differentiation with the surrounding Sredni Stog culture.

A less likely model would support the identification of the whole Eneolithic Pontic-Caspian steppe as a loose Indo-Hittite-speaking community, which would be in my opinion too big a territory and too loose a cultural bond to justify such a long-lasting close linguistic connection. This will probably be the refuge of certain people looking desperately for R1a-IE connections. However, the nature of the western steppe will remain distinct from Late Proto-Indo-European, which must have developed in the Yamna culture, so autochthonous continuity is not on the table anymore, in any case…

suvorovo-novodanilovka-region
Coexistence of the Varna-Gumelniţa culture and the Suvorovo phase of the sceptre-bearer communities. 1 — Fălciu; 2 — Fundeni-Lungoţi; 3 — Novoselskaja; 4 — Suvorovo; 5 — Casimcea; 6 — Kjulevča; 7 — Reka Devnja; 8 — Drama; 9 — Gonova mogila; 10 — Reževo; 11 — geographically separate Decea variant of the sceptre bearer group (after Govedarica, Manzura 2011: Abb. 5, adapted).

2. How did R1a-M417 (and especially R1a-Z645) haplogroups came to dominate over the Corded Ware cultures?

If I am right (again, based on TMRCA of modern populations), then it is precisely at the time of the potential expansion of Proto-Corded Ware from the Dnieper-Dniester forest, forest-steppe, and steppe regions, ca 3300-3000. Furholt’s recent radiocarbon analysis and suggestions of a Lesser Poland origin of the third or A-horizon, on which disparate archaeologists such as Anthony or Klejn rely now, seem to suggest also that Corded Ware was a cultural complex rather than a compact culture reflecting a migration of peoples – similar thus to the Bell Beaker complex.

This cultural complex interpretation of Corded Ware contrasts with the quite homogeneous late samples we have, suggesting clear migration waves in northern Europe, at least at some point in time, so Genomics will be a great tool to ascertain when and from where approximately did Corded Ware peoples expand. Right now, it seems that Eneolithic Ukraine populations are the closest to its origin, so the traditional interpretation of its regional origin by Kristiansen or Anthony remains valid.

3. How was Indo-Iranian adopted by Corded Ware invaders?

This is rather an anthropological question. We need reasonable models of founder effect/cultural diffusion necessary for that to happen – similar to the ones necessary to explain the arrival of N1c subclades into north-east Europe, or the arrival of R1b subclades in Basque/Iberian-speaking regions in south-west Europe. My description of potential events in the eastern steppe – based partially on Anthony – is merely a short sketch. Genomic data is unlikely to offer more than it does today (replacement of haplogroups, and gradually of some steppe component, by late Corded Ware groups in the steppe), but let’s see what new samples can contribute.

As for what some Indians – and other people willing to confront them – are looking for, regarding R1a-M417 and/or Indo-European origins in India, I don’t see the point, we already know a) that the origin of the expansion is in the steppe and b) that Hindu nationalist biggots will not accept results from research that oppose their views. I don’t expect huge surprises there, just more fruitless discussions (fomented by those who live from trolling or conspiracies)…

4. Yamna settlers from Hungary

Anthony’s new theory – and the nature of Balto-Slavic – hinges on the presence of R1a-M417 subclades (associated with later Corded Ware samples) in Yamna settlers of Hungary, potentially originally from the North Pontic area, where the oldest sample has been found.

My ‘modified’ version of Anthony’s new model (the only I deem just remotely factible) includes the expansion of a Proto-Corded Ware from Lesser Poland, but (given the overwhelming R1b found in East Bell Beaker), with R1a-M417 being associated with the region. How to explain this language change with objective data? Well, we have Bell Beaker expanding to these areas at a later time, so we would need to find R1b-L23 settlers in Lesser Poland, and then a resurge of R1a-M417 haplogroup. If not, resorting yet again to cultural diffusion Yamna “patrons” to Corded Ware “clients” of Lesser Poland would bring us to square one, now with the ‘steppe ancestry’ controversy included…

Since some Eastern Europeans are (for no obvious reason whatsoever) putting their hopes on that IE-R1a-CWC association, let’s hope some samples of R1a-M417 in Yamna or Hungary give them a break, so that they can begin accepting something closer to mainstream anthropological models. We could then work from there a Yamna-> Bell Beaker / North-West Indo-European association truce, and from there keep accepting that no single haplogroup from Yamna settlers is linked with modern languages, cultures or ethnic groups.

yamna-region
localization of Central-European funerary monuments with elements of the Pit Grave culture (after Bátora 2006);

5. How and when was Balto-Slavic associated with haplogroup R1a?

If we accept the Southern or Graeco-Aryan nature of Balto-Slavic with influence from an absorbed North-West Indo-European dialect, “Temematic” (as Kortlandt does), then Indo-Slavonic adopted in the steppe from Potapovka by Sintashta and Poltavka populations divided ca. 2000 BC into Indo-Iranian (migrating to the east with Andronovo), and Balto-Slavic (migrating westward with the Srubna culture). History from there is not straightforward, and it should follow Srubna, Thraco-Cimmerian, or other late expansions from cultures of the steppe.

On the other hand, if it is a Northern dialect related closely to Germanic and Italo-Celtic (in a North-West Indo-European group), then its origin has to be found in the initial expansion of East Bell Beakers, and its development into either the Únětice culture (of Balkan and thus potentially “Southern IE” influence), or the Mierzanowice-Nitra culture (of Corded Ware and thus potentially Uralic influence), or maybe from both, given the intermediate substrate found in Germanic and Balto-Slavic.

It is my opinion that the association of Balto-Slavic with haplogroup R1a is quite early after the East Bell Beaker expansion, probably initially with the subclade typically associated with West Slavic, R1a-M458. I have not much data to support this (apart from the most common linguistic model), just modern haplogroup distribution maps and common TMRCA, and highly hypothetical archaeological-anthropological models. Genetics will hopefully bring more data.

Let’s see also what information on ancient haplogroups we can obtain from the Tollense valley (already showing a close cluster with modern West Slavic populations) and steppe regions.

6. How did Germanic, Celtic, and Italic expand?

Germanic is probably the most interesting one. Following the expansion of R1b-L51 subclades (especially R1b-U106) and steppe ancestry (a confounding factor, with the previous expansion of R1a-Z284 subclades) in Scandinavia is going to be fascinating. Anthropological models already point to a linguistic and archaeological expansion of Pre-Germanic with Bell Beaker peoples.

The expansion of Celtic seems to be associated with chiefdoms, untraceable today in terms of haplogroups, and it seems thus different from previous expansions. New studies might tell how that happened, if it was actually in successive ways, as proposed, or maybe we don’t have enough data yet to reach conclusions.

We don’t know either how Italic expanded into the Italian Peninsula, or whether Latin expanded with peoples from Italy, if at all, or it was mostly a cultural diffusion event, as it seems.

Regarding Etruscan, while I think it is a controversy initiated based on fantastic accounts, and ignited with few finds of Middle Eastern ancestry (that seem logical from the point of view of regional contacts), it will be important for Italian linguists and archaeologists, also to accept the most likely scenario.

As for Palaeo-Hispanic languages, while steppe ancestry is found quite reduced in R1b-L51 subclades (after so many different expansions and admixture events since the departure from the steppe), their distribution from the Chalcolithic onwards and the resurgence of native haplogroups may serve to ascertain which Pre-Roman tribes were associated with the oldest regions where these subclades dominated. For that aim, a closer look at the developments in Aquitania and other pre-Roman Vasconic- and Iberian-speaking regions may shed some light on how founder effects might develop to leave the native language intact (in a case similar to the adoption of Indo-Iranian by post-Corded Ware Sinthastha and Potapovka in the eastern Pontic-Caspian steppe).

NOTE: Although mostly unrelated, linguistic questions may also be somehow altered with a change of migration models. For example, our current Corded Ware Substrate Hypothesis – strongly contested by Kortlandt and others – implies that Uralic was potentially the language spoken by Eneolithic Ukraine / Proto-Corded Ware peoples, therefore early Uralic languages were spoken by Corded Ware peoples, as a substrate for Germanic and Balto-Slavic, and Balto-Slavic and Indo-Iranian. If an Indo-Hittite branch different from Late PIE is accepted for Eneolithic Ukraine (thus suggesting a millennia-long cultural-historical community in the steppe), then the model still stands (e.g. Ger. and BSl. *-mos/-mus, as stated by Kortlandt, would correspond to the oldest morphological IE layer). As you can read in the different versions of our model, the different possibilities for the common substrate are stated, and the most likely one selected. But the most likely a priori option sometimes turns out to be wrong…

NOTE 2: You can comment whatever you want here, but I opened a specific thread in our forum if you want serious comments on the model to stuck and be further discussed.

Featured images: from the book Interactions, changes and meanings. Essays in honour of Igor Manzura on the occasion of his 60th birthday. Țerna S., Govedarica B. (eds.). 2016. Kishinev: Stratum Plus.

See also:

Migrations painted by Irish and Scottish genetic clusters, and their relationship with British and European ones

ireland-britain-cluster

Interesting and related publications, now appearing in pairs…

1. The Irish DNA Atlas: Revealing Fine-Scale Population Structure and History within Ireland, by Gilbert et al., in Scientific Reports (2017).

Abstract:

The extent of population structure within Ireland is largely unknown, as is the impact of historical migrations. Here we illustrate fine-scale genetic structure across Ireland that follows geographic boundaries and present evidence of admixture events into Ireland. Utilising the ‘Irish DNA Atlas’, a cohort (n = 194) of Irish individuals with four generations of ancestry linked to specific regions in Ireland, in combination with 2,039 individuals from the Peoples of the British Isles dataset, we show that the Irish population can be divided in 10 distinct geographically stratified genetic clusters; seven of ‘Gaelic’ Irish ancestry, and three of shared Irish-British ancestry. In addition we observe a major genetic barrier to the north of Ireland in Ulster. Using a reference of 6,760 European individuals and two ancient Irish genomes, we demonstrate high levels of North-West French-like and West Norwegian-like ancestry within Ireland. We show that that our ‘Gaelic’ Irish clusters present homogenous levels of ancient Irish ancestries. We additionally detect admixture events that provide evidence of Norse-Viking gene flow into Ireland, and reflect the Ulster Plantations. Our work informs both on Irish history, as well as the study of Mendelian and complex disease genetics involving populations of Irish ancestry.

european-ancestry-british-isles
The European ancestry profiles of 30 Irish and British clusters. (a) The total ancestry contribution summarised by majority European country of origin to each of the 30 Irish and British clusters. (b) (left) The ancestry contributions of 19 European clusters that donate at least 2.5% ancestry to any one Irish or British cluster. (right) The geographic distribution of the 19 European clusters, shown as the proportion of individuals in each European region belonging to each of the 19 European clusters. The proportion of individuals form each European region not a member of the 19 European clusters is shown in grey. Total numbers of individuals from each region are shown in white text. Not all Europeans included in the analysis were phenotyped geographically. The figure was generated in the statistical software language R46, version 3.4.1, using various packages. The map of Europe was sourced from the R software package “mapdata” (https://CRAN.R-project.org/package=mapdata).

2. New preprint on BioRxiv, Insular Celtic population structure and genomic footprints of migration, by Byrne, Martiniano et al. (2017).

Abstract:

Previous studies of the genetic landscape of Ireland have suggested homogeneity, with population substructure undetectable using single-marker methods. Here we have harnessed the haplotype-based method fineSTRUCTURE in an Irish genome-wide SNP dataset, identifying 23 discrete genetic clusters which segregate with geographical provenance. Cluster diversity is pronounced in the west of Ireland but reduced in the east where older structure has been eroded by historical migrations. Accordingly, when populations from the neighbouring island of Britain are included, a west-east cline of Celtic-British ancestry is revealed along with a particularly striking correlation between haplotypes and geography across both islands. A strong relationship is revealed between subsets of Northern Irish and Scottish populations, where discordant genetic and geographic affinities reflect major migrations in recent centuries. Additionally, Irish genetic proximity of all Scottish samples likely reflects older strata of communication across the narrowest inter-island crossing. Using GLOBETROTTER we detected Irish admixture signals from Britain and Europe and estimated dates for events consistent with the historical migrations of the Norse-Vikings, the Anglo-Normans and the British Plantations. The influence of the former is greater than previously estimated from Y chromosome haplotypes. In all, we paint a new picture of the genetic landscape of Ireland, revealing structure which should be considered in the design of studies examining rare genetic variation and its association with traits.

Here are some interesting excerpts (emphasis mine):

Population structure in Ireland

The geographical distribution of this deep subdivision of Leinster resembles pre-Norman territorial boundaries which divided Ireland into fifths (cúige), with north Leinster a kingdom of its own known as Meath (Mide) [15]. However interpreted, the firm implication of the observed clustering is that despite its previously reported homogeneity, the modern Irish population exhibits genetic structure that is subtly but detectably affected by ancestral population structure conferred by geographical distance and, possibly, ancestral social structure.

ChromoPainter PC1 demonstrated high diversity amongst clusters from the west coast, which may be attributed to longstanding residual ancient (possibly Celtic) structure in regions largely unaffected by historical migration. Alternatively, genetic clusters may also have diverged as a consequence of differential influence from outside populations. This diversity between western genetic clusters cannot be explained in terms of geographic distance alone.

In contrast to the west of Ireland, eastern individuals exhibited relative homogeneity; (…) The overall pattern of western diversity and eastern homogeneity in Ireland may be explained by increased gene flow and migration into and across the east coast of Ireland from geographically proximal regions, the closest of which is the neighbouring island of Britain.

Analysis of variance of the British admixture component in cluster groups showed a significant difference (p < 2×10-16), indicating a role for British Anglo-Saxon admixture in distinguishing clusters, and ChromoPainter PC2 was correlated with the British component (p < 2×10-16), explaining approximately 43% of the variance. PC2 therefore captures an east to west Anglo-Celtic cline in Irish ancestry. This may explain the relative eastern homogeneity observed in Ireland, which could be a result of the greater English influence in Leinster and the Pale during the period of British rule in Ireland following the Norman invasion, or simply geographic proximity of the Irish east coast to Britain. Notably, the Ulster cluster group harboured an exceptionally large proportion of the British component (Fig 1D and 1E), undoubtedly reflecting the strong influence of the Ulster Plantations in the 17th century and its residual effect on the ethnically British population that has remained.

ireland-population-structure
Fine-grained population structure in Ireland. (A) fineSTRUCTURE clustering dendrogram for 1,035 Irish individuals. Twenty-three clusters are defined, which are combined into cluster groups for clusters that are neighbouring in the dendrogram, overlapping in principal component space (B) and sampled from regions that are geographically contiguous. Details for each cluster in the dendrogram are provided in S1 Fig. (B) Principal components analysis (PCA) of haplotypic similarity, based on ChromoPainter coancestry matrix for Irish individuals. Points are coloured according to cluster groups defined in (A); the median location of each cluster group is plotted. (C) Map of Irelandshowing the sampling location for a subset of 588 individuals analysed in (A) and (B), coloured by cluster group. Points have been randomly jittered within a radius of 5 km to preserve anonymity. Precise sampling location for 44 Northern Irish individuals from the People of the British Isles dataset was unknown; these individuals are plotted geometrically in a circle. (D) “British admixture component” (ADMIXTURE estimates; k=2) for Irish cluster groups. This component has the largest contribution in ancient Anglo-Saxons and the SEE cluster. (E) Linear regression of principal component 2 (B) versus British admixture component (r2 = 0.43; p < 2×10-16). Points are coloured by cluster group. (Standard error for ADMIXTURE point estimates presented in S11 Fig.)

On the genetic structure of the British Isles

The genetic substructure observed in Ireland is consistent with long term geographic diversification of Celtic populations and the continuity shown between modern and Early Bronze Age Irish people

Clusters representing Celtic populations harbouring less Anglo-Saxon influence separate out above and below SEE on PC4. Notably, northern Irish clusters (NLU), Scottish (NISC, SSC and NSC), Cumbria (CUM) and North Wales (NWA) all separate out at a mutually similar level, representing northern Celtic populations. The southern Celtic populations Cornwall (COR), south Wales (SWA) and south Munster (SMN) also separate out on similar levels, indicating some shared haplotypic variation between geographically proximate Celtic populations across both Islands. It is notable that after the split of the ancestrally divergent Orkney, successive ChromoPainter PCs describe diversity in British populations where “Anglo-saxonization” was repelled [22]. PC3 is dominated by Welsh variation, while PC4 in turn splits North and South Wales significantly, placing south Wales adjacent to Cornwall and north Wales at the other extreme with Cumbria, all enclaves where Brittonic languages persisted.

In an interesting symmetry, many Northern Irish samples clustered strongly with southern Scottish and northern English samples, defining the Northern Irish/Cumbrian/Scottish (NICS) cluster group. More generally, by modelling Irish genomes as a linear mixture of haplotypes from British clusters, we found that Scottish and northern English samples donated more haplotypes to clusters in the north of Ireland than to the south, reflecting an overall correlation between Scottish/north English contribution and ChromoPainter PC1 position in Fig 1 (Linear regression: p < 2×10-16, r2 = 0.24).

North to south variation in Ireland and Britain are therefore not independent, reflecting major gene flow between the north of Ireland and Scotland (Fig 5) which resonates with three layers of historical contacts. First, the presence of individuals with strong Irish affinity among the third generation PoBI Scottish sample can be plausibly attributed to major economic migration from Ireland in the 19th and 20th centuries [6]. Second, the large proportion of Northern Irish who retain genomes indistinguishable from those sampled in Scotland accords with the major settlements (including the Ulster Plantation) of mainly Scottish farmers following the 16th Century Elizabethan conquest of Ireland which led to these forming the majority of the Ulster population. Third, the suspected Irish colonisation of Scotland through the Dál Riata maritime kingdom, which expanded across Ulster and the west coast of Scotland in the 6th and 7th centuries, linked to the introduction and spread of Gaelic languages [3]. Such a migratory event could work to homogenise older layers of Scottish population structure, in a similar manner as noted on the east coasts of Britain and Ireland. Earlier communications and movements across the Irish Sea are also likely, which at its narrowest point separates Ireland from Scotland by approximately 20 km.

ireland-britain-genetic-geography
Genes mirror geography in the British Isles. (A) fineSTRUCTURE clustering dendrogram for combined Irish and British data. Data principally split into Irish and British groups before subdividing into a total of 50 distinct clusters, which are combined into cluster groups for clusters that formed clades in the dendrogram, overlapped in principal component space (B) and were sampled from regions that are geographically contiguous. Names and labels follow the geographical provenance for the majority of data within the cluster group. Details for each cluster in the dendrogram are provided in S2 Fig. (B) Principal component analysis (PCA) of haplotypic similarity based on the ChromoPainter coancestry matrix, coloured by cluster group with their median locations labelled. We have chosen to present PC1 versus PC4 here as these components capture new information regarding correlation between haplotypic variation across Britain and Ireland and geography, while PC2 and PC3 (Fig 4) capture previously reported splitting for Orkney and Wales from Britain [7]. A map of Ireland and Britain is shown for comparison, coloured by sampling regions for cluster groups, the boundaries of which are defined by the Nomenclature of Territorial Units for Statistics (NUTS 2010), with some regions combined. Sampling regions are coloured by the cluster group with the majority presence in the sampling region; some sampling regions have significant minority cluster group representations as well, for example the Northern Ireland sampling region (UKN0; NUTS 2010) is majorly explained by the NICS cluster group but also has significant representation from the NLU cluster group. The PCA plot has been rotated clockwise by 5 degrees to highlight its similarity with the geographical map of the Ireland and Britain. NI, Northern Ireland; PC, principal component. Cluster groups that share names with groups from Fig 1 (NLU; SMN; CLN; CNN) have an average of 80% of their samples shared with the initial cluster groups. © EuroGeographics for the map and administrative boundaries, note some boundaries have been subsumed or modified to better reflect sampling regions.

Genomic footprints of migration into Ireland

Quite interesting is that it is haplogroups, and not admixture, that which defines the oldest migration layers into Ireland. Without evidence of paternal Y-DNA lineages we would probably not be able to ascertain the oldest migrations and languages broght by migrants, including Celtic languages:

Of all the European populations considered, ancestral influence in Irish genomes was best represented by modern Scandinavians and northern Europeans, with a significant single-date one-source admixture event overlapping the historical period of the Norse-Viking settlements in Ireland (p < 0.01; fit quality FQB > 0.985; Fig 6). (…) This suggests a contribution of historical Viking settlement to the contemporary Irish genome and contrasts with previous estimates of Viking ancestry in Ireland based on Y chromosome haplotypes, which have been very low [25]. The modern-day paucity of Norse-Viking Y chromosome haplotypes may be a consequence of drift with the small patrilineal effective population size, or could have social origins with Norse males having less influence after their military defeat and demise as an identifiable community in the 11th century, with persistence of the autosomal signal through recombination.

European admixture date estimates in northwest Ulster did not overlap the Viking age but did include the Norman period and the Plantations

The genetic legacies of the populations of Ireland and Britain are therefore extensively intertwined and, unlike admixture from northern Europe, too complex to model with GLOBETROTTER.

ireland-admixture-estimates
All-Ireland GLOBETROTTER admixture date estimates for European and British surrogate admixing populations. A summary of the date estimates and 95% confidence intervals for inferred admixture events into Ireland from European and British admixing sources is shown in (A), with ancestry proportion estimates for each historical source population for the two events and example coancestry curves shown in (B). In the coancestry curves Relative joint probability estimates the pairwise probability that two haplotype chunks separated by a given genetic distance come from the two modeled source populations respectively (ie FRA(8) and NOR-SG); if a single admixture event occurred, these curves are expected to decay exponentially at a rate corresponding to the number of generations since the event. The green fitted line describes this GLOBETROTTER fitted exponential decay for the coancestry curve. If the sources come from the same ancestral group the slope of this curve will be negative (as with FRA(8) vs FRA(8)), while a positive slope indicates that sources come from different admixing groups (as with FRA(8) vs NOR-SG). The adjacent bar plot shows the inferred genetic composition of the historical admixing sources modelled as a mixture of the sampled modern populations. A European admixture event was estimated by GLOBETROTTER corresponding to the historical record of the Viking age, with major contributions from sources similar to modern Scandinavians and northern Europeans and minor contributions from southern European-like sources. For admixture date estimates from British-like sources the influence of the Norman settlement and the Plantations could not be disentangled, with the point estimate date for admixture falling between these two eras and GLOBETROTTER unable to adequately resolve source and proportion details of admixture event (fit quality FQB< 0.985). The relative noise of the coancestry curves reflects the uncertainty of the British event. Cluster labels (for the European clustering dendrogram, see S4 Fig; for the PoBI clustering dendrogram, see S3 Fig): FRA(8), France cluster 8; NOR-SG, Norway, with significant minor representations from Sweden and Germany; SE_ENG, southeast England; N_SCOT(4) northern Scotland cluster 4.

Another study that strengthens the need to ascertain haplogroup-admixture differences between Yamna/Bell Beaker and Sredni Stog/Corded Ware.

Text and images from preprint article under a CC-BY-NC-ND 4.0 International license.

Featured image, from the article on Science Reports: The clustering of individuals with Irish and British ancestry based solely on genetics. Shown are 30 clusters identified by fineStructure from 2,103 Irish and British individuals. The dendrogram (left) shows the tree of clusters inferred by fineStructure and the map (right) shows the geographic origin of 192 Atlas Irish individuals and 1,611 British individuals from the Peoples of the British Isles (PoBI) cohort, labelled according to fineStructure cluster membership. Individuals are placed at the average latitude and longitude of either their great-grandparental (Atlas) or grandparental (PoBI) birthplaces. Great Britain is separated into England, Scotland, and Wales. The island of Ireland is split into the four Provinces; Ulster, Connacht, Leinster, and Munster. The outline of Britain was sourced from Global Administrative Areas (2012). GADM database of Global Administrative Areas, version 2.0. www.gadm.org. The outline of Ireland was sourced from Open Street Map Ireland, Copyright OpenStreetMap Contributors, (https://www.openstreetmap.ie/) – data available under the Open Database Licence. The figure was plotted in the statistical software language R46, version 3.4.1, with various packages.
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