Finally, a proto-Uralic genome

Obviously, genes don't speak languages, people do. But sometimes it's possible to associate a linguistic group with a very specific genetic signature.

A while ago many of us in the blogosphere spotted an uncanny connection between the Uralic language family, Y-haplogroup N-L1026 and Nganasan-like genome-wide genetic ancestry.

As a result, we expected a Nganasan-like population rich in N-L1026 to eventually appear in the ancient DNA record, probably somewhere in Siberia and in burials from a likely proto-Uralic archeological culture. This hasn't happened yet, but we now have direct evidence that such a population must have existed somewhere deep in Siberia as early as the Bronze Age.

Kra001, whose genome was published recently along with Kilinc et al., belongs to a pre-N-L1026 lineage and, at least in terms of genome-wide genetic structure, could well be from a population directly ancestral to present-day Nganasans. Of course, the Nganasan language is part of the Samoyedic branch of Uralic.

Below is a series of Principal Component Analyses (PCA) featuring kra001. He's labeled RUS_Krasnoyarsk_BA, after the location and age of his burial. Note the obvious Uralic cline running across the plots. That is, from west to east. Kra001 is positioned at the end of this cline very close to a small cluster of Nganasans. To see interactive versions of the plots, paste the Global25 coordinates here into the relevant field here.

Admittedly, there's no way of knowing whether this individual spoke proto-Uralic or not. Indeed, he may have spoken something totally unrelated. The important point is that the very specific genetic signature shared by almost all present-day Uralic speakers, except perhaps Hungarians, is now finally represented in the ancient DNA record. And I can reveal to you that we'll soon be seeing many more ancients very similar to kra001 in upcoming papers.

See also...

The Uralic cline with kra001 - no projection this time

Fresh off the sledge

That old chestnut: Northeast vs Northwest Euros

In the last comment thread reader Greg put forth this question:

David, when are you going to explain the genetic discrepancy between Northeastern and Northwestern Europeans? You know, the one that people believe is due to Baltic Hunter-Gatherer admixture, whereas you believe it is due to genetic drift? You ought to make a post about this issue at some point, because a lot of people are wondering what's causing the differences.

Well, Greg, this issue has been discussed to the proverbial death here and elsewhere. In fact, there were two posts and rather lengthy comment threads on the same topic at this blog just a few months ago. See here and here.

Nevertheless, it seems that a fair number of people are still befuddled, so I'm going to try to explain this one last time, as briefly as a I can using just a handful of f4-stats.

Admittedly, Northeast Europeans generally do pack higher levels of indigenous European hunter-gatherer ancestry than Northwest Europeans. This is especially true of Balts, who show more of this type of ancestry than even Scandinavians in practically every type of analysis.

The f4-stats below back this up unambiguously. Note the significantly positive (>3) Z scores, which suggest that Latvians and Lithuanians harbor more Baltic hunter-gatherer-related ancestry than Norwegians and Swedes.

Chimp Baltic_HG Norwegian Latvian 0.001301 7.114
Chimp Baltic_HG Swedish Latvian 0.001017 4.205
Chimp Baltic_HG Norwegian Lithuanian 0.001023 7.341
Chimp Baltic_HG Swedish Lithuanian 0.000763 3.408

Greg, I know what you're thinking: the naysayers are right! But wait, because there's a twist to this tale. Check out these f4-stats:

Chimp Baltic_HG Norwegian Belarusian 0.000265 1.934
Chimp Baltic_HG Swedish Belarusian 0.000152 0.7
Chimp Baltic_HG Norwegian Polish 6.4E-05 0.519
Chimp Baltic_HG Swedish Polish -0.000235 -1.074

Please note, Greg, that none of the Z scores reach significance, which means that these Northwest Europeans and Slavs are symmetrically related to Baltic_HG. They're also symmetrically related to other relevant ancient groups such as the Yamnaya steppe herders. This, of course, suggests that they harbor very similar levels of basically the same ancient genetic components.

Chimp Karelia_HG Norwegian Belarusian 0.000136 0.844
Chimp Karelia_HG Swedish Belarusian 7.9E-05 0.32
Chimp Karelia_HG Norwegian Polish -4.7E-05 -0.304
Chimp Karelia_HG Swedish Polish -0.000134 -0.54

Chimp Yamnaya_Samara Norwegian Belarusian -0.000134 -1.085
Chimp Yamnaya_Samara Swedish Belarusian -6.6E-05 -0.34
Chimp Yamnaya_Samara Norwegian Polish -0.000225 -1.995
Chimp Yamnaya_Samara Swedish Polish -0.000311 -1.574

Chimp Barcin_N Norwegian Belarusian -0.000335 -2.809
Chimp Barcin_N Swedish Belarusian -0.000284 -1.491
Chimp Barcin_N Norwegian Polish -0.000222 -2.057
Chimp Barcin_N Swedish Polish -0.000318 -1.662

Chimp Baikal_N Norwegian Belarusian 0.000186 1.3
Chimp Baikal_N Swedish Belarusian -7E-05 -0.33
Chimp Baikal_N Norwegian Polish -4.6E-05 -0.351
Chimp Baikal_N Swedish Polish -0.000477 -2.277

Interestingly, pairing up Ukrainians with English samples from Cornwall and Kent produces similar outcomes. But that's because most ancient ancestry proportions in Europe show a closer correlation with latitude than longitude.

Chimp Baltic_HG English_Cornwall Ukrainian 0.000282 2.242
Chimp Baltic_HG English_Kent Ukrainian 0.000225 1.748

Chimp Karelia_HG English_Cornwall Ukrainian 0.000323 2.175
Chimp Karelia_HG English_Kent Ukrainian 0.000239 1.634

Chimp Yamnaya_Samara English_Cornwall Ukrainian -6.6E-05 -0.569
Chimp Yamnaya_Samara English_Kent Ukrainian -0.000112 -0.977

Chimp Barcin_N English_Cornwall Ukrainian -0.000519 -4.641
Chimp Barcin_N English_Kent Ukrainian -0.000598 -5.232

Chimp Baikal_N English_Cornwall Ukrainian 0.000385 2.874
Chimp Baikal_N English_Kent Ukrainian 0.00036 2.836

Now, Greg, if at least in terms of genetic ancestry, Latvians, Lithuanians, Belarusians, Poles and Ukrainians all qualify as Northeast Europeans, then what makes them different, as a group, from Northwest Europeans? Do you believe that the key factor is admixture from Baltic hunter-gatherers? Or is it genetic drift?

Of course, considering all of the f4-stats above, logic dictates that it must be relatively recent genetic drift.

Keep in mind, however, that this only applies to Balto-Slavic speaking Northeast Europeans without significant Uralian ancestry. Overall, Uralic speakers have a more complex population history, and indeed genetic differences between them and Northwest Europeans are in large part due to somewhat different ancestry proportions and also Siberian admixture.

See also...

So who's the most (indigenous) European of us all?

David Anthony on Y-haplogroup R1a

Archeologist David Anthony has a new theory which attempts to explain why Y-haplogroup R1a hasn't yet been found in any Yamnaya graves. Basically, he thinks that it was carried by Yamnaya men who weren't buried in kurgans, because they were part of a social underclass, and so their remains are now difficult to locate. See here.

This is an interesting attempt to find a socio-archeological solution to a genetic question, but it's unnecessarily complicated and, in fact, also unnecessary.

The important thing to understand about R1a is that it's rarely seen in the ancient DNA record before the rise of the Corded Ware culture (CWC). Moreover, the vast majority of the R1a lineages in the world today belong to the R1a-M417 subclade, which is a relatively young (Eneolithic era?) marker and closely associated with the CWC population and its rapid expansion.

Indeed, modern R1a lineages show a very strong star-like phylogeny indicative of a series of rapid and massive expansions starting from a handful of lineages only a few thousand years ago.

So if R1a was actually present in the Yamnaya population, then the obvious reason why it hasn't yet been found in any Yamnaya remains is because it was only carried by a very small group of Yamnaya men. Simple as that.

Its expansions from the Pontic-Caspian (PC) steppe, predominantly via the highly successful R1a-M417, may have coincidentally and rather ironically started in a socially disenfranchised Yamnaya clan.

But my view is that R1a-M417 just happened to be present in a small group of early Yamnaya or Yamnaya-related males who came up with an economic package that allowed them to expand out of the PC steppe like no one else before them, and so they did just that.

Anthony is currently collaborating on a new paper about the Eneolithic era on the PC steppe with scientists from Harvard's David Reich Lab (see here). I'm really hoping that they get this right.

See also...

Fatyanovo as part of the wider Corded Ware family

Fully automated graph exploration

Scientists at Broad MIT are working on a new feature-packed and "lightning fast" version of Admixtools that runs in R. It's already available via this link...

uqrmaie1.github.io/admixtools

I don't have access to a Linux machine right now, but since this thing runs in R then it also runs in Windows, and I do have a Windows computer here.

One of the most interesting and useful features in the new R package is arguably the find_graphs function, which automatically searches for admixture graphs that reflect the observed f-statistics. That is, once the user chooses the samples and settings, find_graphs runs an unsupervised admixture graph analysis.

Here are a couple of graphs that I knocked out with find_graphs in about five minutes each. The commands and settings that I used are listed in a text file here.

The two topologies above were among the most commonly seen in a series of about 50 runs with the same sample set. A couple of basic inferences based on the output:

- RUS_Progress-Vonyuchka_En harbors GEO_Kotias-Satsurblia_HG-related ancestry, not IRN_Ganj_Dareh_N-related ancestry

- IRN_Ganj_Dareh_N and TKM_Geoksyur_En form a clade to the exclusion of GEO_Kotias-Satsurblia_HG.

The results are certainly in line with those from other types of analyses that I've done on this blog (for instance, see here and here).

Update 05/01/21: Robert Maier, one of the creators of Admixtools2, has left this message in the comments below.

I'm glad to see that there is so much interest in Admixtools2! I very much appreciate any comments and suggestions on how to improve it and how to make it more user friendly.

Because it's still under active development, some things are likely to change in the future. For example, there is a faster successor to "find_graphs", called "find_graphs2", but in the future they will probably be merged into one.

I'm in David Reich’s group at Harvard and Broad and we are hoping to publish a paper describing Admixtools2 where we illustrate its value by using it to test how robust several previously published results are by exploring a large number of alternative models for each of them. If any of you use Admixtools2 to find graphs that are significantly better fits than published graphs and are also historically plausible - or if you find families of graphs that are equally good fits to the published ones but provide qualitatively different conclusions about population relationships - please contact us. That would be a meaningful contribution to the paper we write about this and we’d be open to including someone as a co-author based on identifying case studies like this.

Looking forward to a post-Covid world

I was hoping that the Covid-19 pandemic wouldn't have an immediate impact on the publication of ancient DNA papers and new data, but considering how much things have slowed down in this respect, it seems that I was fooling myself.

So let's take a break until early next year, and then see what happens.

Trust me, we've got a lot to look forward to in the post-Covid-19 world. Based on what I've heard from various sources, here are some predictions about what we might see:

- the search for the Proto-Indo-European homeland will shift west to the North Pontic steppe

- on the other hand, the search for the Proto-Uralic homeland will move deep into Siberia

- the key role of the Single Grave (westernmost Corded Ware) culture in the population history of Western Europe will finally get some attention

- following on from the above, Y-haplogroup R1b-L51 will be revealed as a Single Grave marker

- the idea that the Pontic-Caspian steppe was colonized by migrants from Mesopotamia during the Bronze Age will be forgotten, and, ironically, we'll instead learn that there was a significant influx of steppe ancestry into ancient Mesopotamia

- Old Kingdom Egyptians will come out less Sub-Saharan African than present-day Egyptians.

I probably shouldn't blab everything out, so that's all you're getting from me for now. You'll just have to wait for the rest until next year, or perhaps even the year after that.

See also...

Slavic-like Medieval Germans

The samples labeled DEU_MA_Krakauer_Berg in the Principal Component Analysis (PCA) plot below are from a recent paper by Parker et al. at Scientific Reports. Their remains were excavated from a Medieval cemetery in the now abandoned village of Krakauer Berg in eastern Germany.

Krakauer sounds sort of like Kraków, doesn't it? That's probably not a coincidence, especially considering how these people behave in my analysis. To see an interactive version of the plot, paste the coordinates from the text file here into the relevant field here.

See also...

Yamnaya-related ancestry proportions in present-day Poles

Warriors from at least two different populations fought in the Tollense Valley battle

Viking world open analysis and discussion thread

Viking world open analysis and discussion thread

Global25 and Celtic vs Germanic coordinates for most of the samples from the recent Margaryan et al. Viking paper are now available HERE and HERE, respectively. Look for the VK2020 prefix.

Feel free to put them through their paces and let me know what you find. Below are a couple of examples of what can be done with these coordinates using Vahaduo Global25 Views.

See also...

Viking invasion at bioRxiv

Commoner or elite?

Who were the people of the Nordic Bronze Age?

Warriors from at least two different populations fought in the Tollense Valley battle

I can't get the genotype data from the Burger et al. paper. The lead authors, Joachim Burger and Daniel Wegmann, aren't replying to my emails.

But they were gracious enough to release the BAM files for each of their samples, and these files can be converted to genotype data. So I've included ten of the Tollense Valley warriors (DEU_Tollense_BA) in the Global25 datasheets (see here).

The claim in the paper that these warriors "represent an unstructured population" is absolutely false and extremely naive.

Below are a couple of Principal Component Analysis (PCA) plots produced with Vahaduo Global25 views. The samples are labeled according to their Y-chromosome haplogroups. To see interactive versions of the same plots, paste the Global25 coordinates from the text file here into the relevant fields here.

These warriors are not a single unstructured population, because they cover too much ground in the above plots for that to be possible. It's clear to me that they represent at least two different groups from Central Europe and surrounds.

Of course, this would be a lot easier to work out if Burger et al. cared to supply more information about each of the warriors, such as their attire, weapons, circumstances of death, and so on. It's a complete mystery to me why this wasn't included in the paper, and the authors are refusing to talk to me, so it's unlikely that I'll ever be able to get it from them.

In the absence of such crucial archeological and anthropological data, I don't want to speculate too much, and get overly creative, but here are a couple of possible scenarios to explain the ancient DNA results:

- this may have been a battle between two Central European armies, one rich in Y-haplogroup R1b and the other rich in Y-haplogroup I2a, as well as their allies or hired help, including warriors from Eastern Europe belonging to Y-haplogroup R1a

- or perhaps it was an invasion from the east by warriors rich in Y-haplogroup R1a, and it was a success, with the local armies, rich in Y-haplogroups R1b and I2a, losing the battle and suffering most of the casualties.

I'm sure that one day someone will attempt to undertake a decent multidisciplinary study of this epic battle, and we'll at least have a rough idea about what happened. Or not.

Citation...

Burger et al., Low Prevalence of Lactase Persistence in Bronze Age Europe Indicates Ongoing Strong Selection over the Last 3,000 Years, Current Biology, Available online 3 September 2020, https://doi.org/10.1016/j.cub.2020.08.033

See also...

Genetic and linguistic structure across space and time in Northern Europe

Yamnaya-related ancestry proportions in present-day Poles

Modeling ancient ancestry proportions in present-day Europeans with the qpAdm software is now a lot more difficult. The reasons for this are updates to qpAdm as well as the availabiity of more useuful outgroups or right pops.

This isn't necessarily a bad thing, because users are forced to work harder to find successful models, which is likely to lead to some interesting discoveries. But it can be very frustrating.

I don't think that settling for poor statistical fits or using a small number of outrgoups are acceptable short cuts. Perhaps sequencing modern-day samples in exactly the same way as the ancient samples, and thus increasing the compatability between them, might help?

Limiting qpAdm runs to higher quality SNPs from transversion sites does help, but perhaps largely because of the significant reduction in markers?

In any case, I've now given up on running such analyses, at least until I see some serious pointers on the topic from Harvard's qpAdm experts. But before I put this project to bed for the time being, I'd like to share some new results for Poles from eastern and western Poland, respectively.

right pops:

CMR_Shum_Laka_8000BP
MAR_Taforalt
IRN_Ganj_Dareh_N
Levant_PPNB
GEO_CHG
TUR_Barcin_N
RUS_Piedmont_En
SRB_Iron_Gates_HG
WHG
RUS_Karelia_HG
MNG_North_N
RUS_Ust_Kyakhta

left pops:

Polish_East
CWC_Baltic_early 0.572±0.024
SWE_TRB 0.428±0.024
chisq 11.776
tail prob 0.300296
Full output

Polish_West
CWC_Baltic_early 0.587±0.021
SWE_TRB 0.413±0.021
chisq 11.165
tail prob 0.34478
Full output

Even using transversion sites, this is one of the very few combinations of ancient reference samples that works for the Poles with these right pops. That is, the combination of early Corded Ware samples from the East Baltic (CWC_Baltic_early) and Funnel Beaker samples from Scandinavia (SWE_TRB). The former are obviously the proxy here for Yamnaya-related ancestry.

Adding any sort of hunter-gatherer population to this model doesn't help or even makes things worse (for instance, see here and here). It is possible to add Baltic hunter-gatherers to a similar model after dropping CWC_Baltic_early in favor of closely related samples from the Early to Middle Bronze Age Pontic-Caspian steppe. Note, however, that the statistical fits are somewhat poorer.

Polish_East
Baltic_LTU_Narva 0.032±0.014
PC_steppe_EMBA 0.483±0.019
SWE_TRB 0.485±0.019
chisq 17.143
tail prob 0.0465198
Full output

Polish_West
Baltic_LTU_Narva 0.031±0.011
PC_steppe_EMBA 0.491±0.015
SWE_TRB 0.477±0.016
chisq 22.444
tail prob 0.00757421
Full output

Interestingly, but not surprisingly, the ancestry of many present-day Northwestern European populations can be modeled in basically the same way. That's because ancient ancestry proportions are more closely correlated with latitude than longitude across much of the European continent.

English_Kent
CWC_Baltic_early 0.527±0.024
SWE_TRB 0.473±0.024
chisq 13.042
tail prob 0.221357
Full output

Icelandic
CWC_Baltic_early 0.586±0.023
SWE_TRB 0.414±0.023
chisq 16.517
tail prob 0.085751
Full output

Scottish
CWC_Baltic_early 0.583±0.021
SWE_TRB 0.417±0.021
chisq 12.144
tail prob 0.275536
Full output

A zip file with the qpAdm output from this analysis and a list of the most relevant ancients is available here. I might try to run a few more populations over the next few days, but probably only from the northern half of Europe, so please check the zip file in a week or so to see what else is in there.

If anyone wants to challenge my results, note that these and very similar samples are freely available to the public via Harvard University here and here.

Update 22/08/2020: From Nick Patterson (Broad) in the comments:

My general advice for qpAdm is 1) Work on the right hand set. Don't include irrelevant population (except for one population as an outgroup); picking the best RHS can dramatically reduce s. errors on the admixture weights. 2) If qpAdm gives a very low p-value try and understand why, sometimes it is telling you that the target is not a mixture of the sources but sometimes the assumptions are violated, for example recent gene-flow from left pops -> right.

See also...

Ancient ancestry proportions in present-day Europeans

Awesome new toys from Vahaduo

Vahaduo now offers a 3D PCA experience. Check it out HERE and HERE. Below are a couple of screen caps of me messing around with the new tools.

Vahaduo says:

Hi everyone!

New tool - PCA 3D Viewer.

Global 25 version:

https://vahaduo.github.io/3d/g25

West Eurasia version:

https://vahaduo.github.io/3d/we


Usage:

Dots - ancients, circles - moderns.

Click X, Y, Z or COLOR tab and then click one of the PCx buttons to switch dimensions.

Click already active X, Y, Z or COLOR tab to temporarily reverse selected dimension. It will be restored to a default state when any of the dimensions will be switched to another one.

ADD CUSTOM POINTS - self-explanatory. Points will be added as "+". IMPORTANT - G25 version takes NON-SCALED coordinates. This will be true for any new tool dedicated to G25 and coordinates will be scaled automatically when needed or desired.

"Type parts of names." + TAG button - type parts of names to tag certain samples (try for example "KK1 Afon Pinar"). Search is Case Sensitive. Points will be redrawn as "x".

Next row - Labels and Annotations. Click the right button to cycle trough:

CLEAR LABELS + LABELS:AUTO
CLEAR ANNOTATIONS + ANNOTATIONS:CLICK
CLEAR ANNOTATIONS + ANNOTATIONS:AUTO
CLEAR ALL + LABELS AND ANNOTATION OFF
CLEAR LABELS + LABELS:CLICK

CLICK - click to add/delete labels/annotations.
AUTO - same as CLICK plus labels/annotations will be automatically added to newly plotted or tagged samples. Unfortunately adding new labels and annotations becomes very slow when there is too much of them, so there is a limit for the AUTO setting - 250 labels or 20 annotations at once.

Annotations are editable. They can be dragged to another place and text can be changed. Text can be also wrapped into an HTML SPAN element and some styles can be used, like "font-size" or "color". BR element (new line) works too.

HIGHLIGHT CLICK/OFF/HOVER - highlight all samples that belong to a single population. Set to HOVER to ignore clicks. Set to OFF to disable this feature and to remove highlight triggered by hover (highlights triggered by clicks will stay until they will be cleared or removed by a click). Click white dot to cycle trough available highlight colors.

Plotly buttons:

Default download is set to 1600x1200px PNG.

Custom Plotly buttons:
"Toggle projection: orthographic / perspective" - self-explanatory.
"Toggle background color" - cycles trough dark grey, black, white and light grey. Text color and white highlight will be switched to black when background will be set to white or light grey.
"Toggle color scheme" - cycles trough several gradients.
"Reverse color scheme" - reverses all gradients permanently.
"Download plot as png (custom size)" - default size is the size of the currently displayed plot.

See also...

New Global25 interpretation tools