Asiatic East Germanics

Around a third of the ancient individuals in my dataset associated with East Germanic-speaking cultures show obvious ancestry from Central and/or West Asia.

This shouldn't be too surprising, considering, for instance, the well documented contacts between East Germanic tribes and the Avars, Huns, Sarmatians and other nomadic groups that streamed into Europe from the Asian steppes during the Migration Period. It's a topic that I've raised before at this blog (see here).

But the curious thing is that very little, if any, of this ancestry has percolated down to present-day Europeans.

The easiest way to show this is with a Principal Component Analysis (PCA) based on my Global25 data. The relevant PCA datasheet can be downloaded here. Basic details about the ancient samples in the analysis are available here.

Some of the Northeastern European populations, particularly the Uralic speakers, appear to be attracted to the Hunnic cluster. However, this is mostly an artifact of pre-Migration Period east to west population expansions in the far north of Europe, probably including those of the Proto-Uralians (see here).

So how is it that, despite ruling over vast areas of Europe for hundreds of years, the East Germanics appear not to have contributed significantly to the present-day European gene pool? My theory is that, much like the Avars and Huns, they were militarily and demographically overwhelmed by the ascending groups around them, such as the Slavs, and they simply went extinct.

To wrap things up, here's a basic qpAdm mixture model designed to test for Hunnic-related ancestry in a few Eastern and Northern European populations of interest. Note the significant slice of this type of ancestry in the likely early Goths of the Chernyakhiv culture. Is it real? Feel free to share your thoughts in the comments below.

UKR_Chernyakhiv
DEU_MA 0.863±0.038
Hun_Tian_Shan 0.137±0.038
chisq 12.525
tail prob 0.325466
Full output

Swedish
Baltic_EST_IA 0.126±0.078
DEU_MA 0.849±0.073
Hun_Tian_Shan 0.025±0.020
chisq 8.338
tail prob 0.595877
Full output

Ukrainian
Baltic_EST_IA 0.121±0.064
DEU_MA 0.857±0.060
Hun_Tian_Shan 0.022±0.017
chisq 11.458
tail prob 0.322956
Full output

Estonian
Baltic_EST_IA 0.597±0.069
DEU_MA 0.373±0.064
Hun_Tian_Shan 0.030±0.017
chisq 15.739
tail prob 0.107361
Full output

See also...

Conan the Barbarian probably belonged to Y-haplogroup R1a

More on the association between Uralic expansions and Y-haplogroup N

Uralic-specific genome-wide ancestry did make a signifcant impact in the East Baltic

Getting the most out of the Global25

The first thing you need to know about the Global25 is that I update the relevant datasheets regularly, usually every few weeks, but they're always at these links:

Global25 datasheet ancient scaled

Global25 pop averages ancient scaled

Global25 datasheet ancient

Global25 pop averages ancient

...

Global25 datasheet modern scaled

Global25 pop averages modern scaled

Global25 datasheet modern

Global25 pop averages modern

Global25 data for samples from a variety of papers that have been published recently will eventually be incorporated into the main datasheets linked above, but the process might take several weeks or even months. In the meantime, feel free to use the temporary datasheets below. Thanks for your patience.

Allentoft 2023

Chylenski 2023

Jeong 2024

Koptekin 2022

Olalde 2023

Peltola 2022

Penske 2023

Posth 2023

Sirak 2024

Skourtanioti 2023

Stolarek 2023

Varela 2023

Wang 2023

Yu 2023

Each sample has a population code and an individual code. The population codes represent the countries, ethnic groups and/or archeological affinities of the samples, and I often modify these codes to suit my needs. On the other hand, the individual codes are unique to most of the samples and I usually don't change them.

So if you'd like to know more details about the samples try searching for their individual codes via a decent online search engine. Basic information about many of the samples is also available in the "anno" files here.

The main purpose of the Global25 is to provide data for mixture modeling. In other words, for estimating ancestry proportions, both ancient and modern (see here). This can be done on your computer with the R program and the nMonte R script, or online with a couple of different tools, which I discuss below.

If you don't have R installed on your computer, you can get it here, while nMonte is available here. For this tutorial please download nMonte and nMonte3, and store them in your main working folder (usually My Documents).

Once you have R set up, make sure its working directory is the same place where you stored nMonte. You can check this in R by clicking on "File" and then "Change dir". Additionally, you'll need two nMonte input files in the working directory titled "data" and "target". Examples of these files are available here. We'll be using them to test the ancient ancestry proportions of a sample set from present-day England.

Before you can begin the analysis you need to first call the nMonte script by typing or copy pasting source('nMonte.R') into the R console window, and then hitting "enter" on your keyboard. This is what you should see in the R console window afterwards.

To start the mixture modeling process, type or copy paste getMonte('data.txt', 'target.txt') into the R console window, hit "enter", and wait for the results. After a short time, probably less than a minute or two, you should see this output.

The data and target files contain population averages. And, as you can see, the results that these population averages have produced are in line with what one would expect from such a model focusing on the genetic shifts in Northern Europe during the Late Neolithic. Very similar ancient ancestry proportions have been reported for the English and other Northern Europeans recently in scientific literature.

However, when focusing on exceptionally fine-scale genetic variation that isn't reflected too well in the Global25 population averages, a more effective strategy might be to use multiple individuals from each reference population and let nMonte3 aggregate and average the inferred ancestry proportions.

This is often the case when attempting to model ancestry proportions for more recent periods, such as the Middle Ages. So let's try this with the English sample set using a modified data file, which is available here.

Replace the old data file with the new one in your working directory, and, like before, copy paste into the R console window the following two commands, hitting "enter" after each one: source('nMonte3.R') and getMonte('data.txt', 'target.txt'). This is what you should eventually see.

It's difficult to say how accurate these estimates are. But they look more or less correct considering the limited and less than ideal reference samples. For instance, the individuals labeled SWE_Viking_Age_Sigtuna are supposed to be stand ins for Danish and Norwegian Vikings, but they're a relatively heterogeneous group from Sweden, possibly with some British or Irish ancestry, so they might be skewing the results.

However, I'll be adding many more ancient samples to the Global25 datasheets as they become available, including lots of new Vikings, which should greatly improve the accuracy of these sorts of fine-scale mixture models.

An exceedingly simple, yet feature-packed, online tool ideal for modeling ancestry with Global25 coordinates is the VahaduoJS. It's freely available HERE, and it also works offline after downloading the web page. Just copy paste the coordinates of your choice under the "source" and "target" tabs, and then mess around with the buttons to see what happens. The screen caps below show me doing just that.

However, it's important to note that the Global25 is a Principal Component Analysis (PCA), so it makes good sense to also use it for producing PCA graphs. To do this just plot any combination of two or three of its Principal Components (PCs) to create 2D or 3D graphs, respectively. This can be done with a wide variety of programs, including PAST, which is freely available here.

To produce a 2D graph, open a Global25 datasheet in PAST, choose comma as the separator, highlight any two columns of data, click on the "Plot" tab and, from the drop down list, pick "XY graph". Below is a series of graphs that I created in exactly this way. I also color coded the samples according to their geographic origins. This was done by ticking the "Row attributes" tab.

PAST can also be used to run PCA on subsets of the Global25 scaled data to produce remarkably accurate plots of fine-scale population structure. For instance, here's a plot based on present-day populations from north of the Alps, Balkans and Pyrenees.

To try this create a new text file with your choice of populations from the Global25 scaled datasheet, open it with PAST and choose Multivariate > Ordination > Principal Components Analysis. I've already put together several datasheets limited to European, Northern European, West Eurasian and South Asian populations. They're available at the links below along with more details on how to run them with PAST.

Global25 workshop 1: that classic West Eurasian plot

Global25 workshop 2: intra-European variation

Global25 workshop 3: genes vs geography in Northern Europe

The South Asian cline that no longer exists

Another free, easy to use online tool that works with Global25 coordinates is the Vahaduo Global25 Views [LINK]. Below is a screen cap of me checking out one of the many PCA that it offers.

And if you're fond of tree-like structures as a means to describe fine-scale genetic variation, please see this blog post...

Global25 workshop 4: a neighbour joining tree

See also...

New Global25 interpretation tools

Global25 workshop 4: a neighbour joining tree

Phylogenetic trees are easy to produce, but there's an infinite number of ways to run them, and, depending on the input data you're using, some methods are a lot more effective than others. In this tutorial I'm going to demonstrate one method that has worked well for me when looking at the fine scale genetic relationships between ancient and present-day human populations with my Global25 data.

To get started download this datasheet, plug it into the PAST program, which is freely available here, then select all of the columns by clicking on the empty tab above the labels, and choose Multivariate > Clustering > Neighbour joining. Here's a screen cap of me doing just that...

Then, from the tabs on the right, choose Chord as the similarity index and MAR_Iberomaurusian, the most distinct unit in the datasheet, as the root. PAST offers an exceptionally large range of similarity indices and they generally produce similar results, but, in my experience, Chord creates among the most visually pleasing outcomes when dealing with fine scale genetic substructures.

This is the tree you should see after exporting the image via the graph settings tab in PAST, and, if you like, rotating it 90 degrees with an image editing software of your choice. Note the fairly substantial differences between the populations from Northwestern Europe, which are often difficult to tease apart in such analyses.

If you have your own Global25 coordinates you can add them to my PAST-compatible datasheet to see where you cluster in this tree. And, of course, you can design your own PAST-compatible datasheets and trees with any combination of populations and/or individuals from the Global25 text files at the links below. It's easy; just copy paste the coordinates of your choice into an empty text file, open it with PAST and then save it with the dat extension to create a new PAST datasheet. But make sure never to mix up the scaled and non-scaled coordinates.

Global25 datasheet ancient scaled

Global25 pop averages ancient scaled

Global25 datasheet ancient

Global25 pop averages ancient

...

Global25 datasheet modern scaled

Global25 pop averages modern scaled

Global25 datasheet modern

Global25 pop averages modern

An important point to keep in mind when running these sorts of analyses is that PAST and other such programs need enough genetic differentiation to latch onto in order to produce meaningful results. Thus, even when studying the relationships between very closely related populations, it's not just useful to include a root population or individual, but also some near and far related groups to help the analysis algorithm flesh out the key genetic substructures.

To be honest, I don't really know whether using the Chord index and rooting the tree with MAR_Iberomaurusian is the best way to run a neighbour joining tree analysis of ancient and present-day West Eurasian genetic variation. What do you think? Feel free to let me know in the comments.

See also...

Global25 workshop 1: that classic West Eurasian plot

Global25 workshop 2: intra-European variation

Global25 workshop 3: genes vs geography in Northern Europe

The South Asian cline that no longer exists

Getting the most out of the Global25

Genetic ancestry online store (to be updated regularly)

They came, they saw, and they mixed

Y-chromosome haplogroup N is strongly associated with Uralic-speaking populations. That's probably because it was a salient feature of the gene pool of the earliest Uralic speakers, and it went with them as they migrated across northern Eurasia. However, some of its younger subclades appear to have spread with the speakers of Indo-European and Turkic languages.

For instance, N-Y10931 seems to be a marker of the Rurikids, a Varangian dynasty that, according to most sources, ruled the Kievan Rus in what are now Russia and Ukraine. And the Kievan Rus was a lose medieval political federation in which Slavic, Finnic (west Uralic) and Germanic languages were probably spoken. The latest on the genetic genealogy of the Rurikids was presented a couple of days ago at the Centenary of Human Population Genetics conference in Moscow, and there's an abstract of the talk available here (download the PDF and scroll down to page 84).

I'm not aware of any Rurikids among the thousands of ancients in my dataset, or even of any samples belonging to N-Y10931. But I do have the genome of someone who belongs to N-Y4339, which, as per the abstract linked to above, is proximally ancestral to N-Y10931. Not only does this person come from Viking Age Scandinavia, but he was buried in a crouched position typical of Slavic funerary customs of the time.

The individual in question is vik_84001. His genome was published recently along with a paper on the population structure of the Swedish town of Sigtuna way back when it was a Viking stronghold (see here). This is where his Y-chromosome sequence, labeled ERS2540883, is positioned on the YFull Y-chromosome phylogenetic tree (see here).

However, the result is likely to be compromised to some extent by missing data. If so, it's possible that vik_84001 does indeed belong to N-Y10931 and ought to be sitting near or even among that cluster of Russian samples (Rurik descendants?) at the bottom of the page.

In any case, vik_84001 seems to be the closest individual in the ancient DNA record to a Rurikid. The Principal Component Analysis (PCA) below is based on my Global25 data. It features 18 other Viking Age individuals from Sigtuna alongside vik_84001 (look for the black dots). The relevant datasheet is available here. Interestingly, despite his eastern Y-haplogroup, vik_84001 is one of the few Sigtuna ancients who clusters strongly with present-day Swedes.

But here's what happens when I model his ancestry proportions with the Global25/nMonte method using a wide range of reference populations from Northern and Eastern Europe. The Swedes in this model are the same as those in the PCA.

vik_84001
Swedish,84.6
Ingrian,9.2
Russian_Tver,6.2
Belarusian,0
Estonian,0
Finnish,0
Finnish_East,0
Karelian,0
Latvian,0
Mordovian,0
Russian_Kostroma,0
Russian_Kursk,0
Russian_Orel,0
Russian_Pinega,0
Russian_Smolensk,0
Russian_Voronez,0
Ukrainian,0
Vepsian,0

[1] "distance%=2.3778"

Yep, despite his position in the PCA, vik_84001 shows a strong signal of ancestry related to the present-day populations of northwestern Russia. I'm not sure what this means exactly, but it's certainly fascinating stuff. And, by the way, I usually wouldn't use so many similar reference populations in a single Global25/nMonte model because of the problem of "overfitting", but in some cases it's OK to do so if the nMonte algorithm has enough recent genetic drift to latch onto.

See also...

More on the association between Uralic expansions and Y-haplogroup N

Fresh off the sledge

Uralic-specific genome-wide ancestry did make a signifcant impact in the East Baltic

It was always going to be this way

Conan the Barbarian probably belonged to Y-haplogroup R1a

Fresh off the sledge

As things stand, the closest individual to a Proto-Uralic speaker in the ancient DNA record is arguably 0LS10 from an Iron Age tarand grave in what is now Estonia. I say that because:

- isotopic data suggest that 0LS10 wasn't born where he died, and considering his elevated Siberian ancestry relative to earlier and most contemporaneous Baltic ancients, he was very likely a migrant to the Baltic region from the east

- the tarand grave tradition appears to be specifically a Finnic (west Uralic) phenomenon that probably spread from the Volga-Oka region, which is just west of where most people place the Proto-Uralic homeland

- 0LS10 belongs to Y-chromosome haplogroup N-L1026, a paternal marker that is especially closely associated with Uralic-speaking populations and probably only appeared in the East Baltic region during the transition from the Bronze Age to the Iron Age

You can find more background info about 0LS10 and other relevant samples in Saag et al. 2019 (see here). This is where he sits in my Principal Component Analyses (PCA) focusing on fine scale Northern European genetic diversity. The relevant datasheets are available here and here, respectively.

Note that 0LS10 doesn't cluster strongly with any ancient or modern populations. To investigate this in more detail I ran a series of two-way qpAdm analyses, testing tens of ancient individuals and populations as potential admixture sources. These two models stood out above the rest in terms of their statistical fits, chronology and overall plausibility.

Baltic_EST_IA_0LS10
Baltic_EST_BA 0.826±0.045
RUS_Sintashta_MLBA_o1 0.174±0.045
chisq 12.527
tail prob 0.564048
Full output

Baltic_EST_IA_0LS10
Baltic_EST_BA 0.683±0.102
RUS_Mezhovskaya 0.317±0.102
chisq 13.811
tail prob 0.463864
Full output

Please note that RUS_Sintashta_MLBA_o1 isn't representative of the Sintashta culture population as a whole. It's a group of the most extreme genetic outliers among the Sintashta samples, and they may or may not have been Uralic speakers (see here). Interestingly, the Mezhovskaya culture population is generally associated with the Ugric branch of the Uralic language family.

I was also able to closely replicate these results with the Global25/nMonte method; down to almost one per cent. However, the statistical fits (distances) are poor, probably because the reference populations aren't the real mixture sources. This is in line with the fact that their Y-haplogroups are Q1a, R1a and R1b, rather than any type of N.

Baltic_EST_IA:0LS10
Baltic_EST_BA,83.8
RUS_Sintashta_MLBA_o1,16.2
distance%=4.7955

Baltic_EST_IA:0LS10
Baltic_EST_BA,69.8
RUS_Mezhovskaya,30.2
distance%=3.5783

I do realize that two Bronze Age samples from Bolshoy Oleni Ostrov, Kola Peninsula, belong to N-L1026, but adding them to my mixture models doesn't help. Little wonder, because the Kola Peninsula lies within the Arctic Circle, and I'm pretty sure that 0LS10 and his N-L1026 came from somewhere just north of the mixture cline marked on the map below. Unfortunately, I can't test this directly yet due to the scarcity of ancient samples from this region.

See also...

It was always going to be this way

On the association between Uralic expansions and Y-haplogroup N

Uralic-specific genome-wide ancestry did make a signifcant impact in the East Baltic

Uralic-specific genome-wide ancestry did make a signifcant impact in the East Baltic

I've started analyzing the ancient genotype data from the recent Saag et al. paper on the expansion of Uralic languages and associated spread of Siberian ancestry into the East Baltic region. The paper is freely available here and the data are here.

I really like the paper, but I don't agree with the authors' claim that the appearance of Y-chromosome haplogroup N in what is now Estonia and surrounds during the Iron Age is "not matched by a clear shift in autosomal profiles". In my opinion it certainly is, and, as one would expect, it's a shift towards a genetic profile typical of western Uralic speakers.

I'd say that the easiest way to find this signal is with a Principal Component Analysis (PCA) focusing on fine scale genetic substructures within Northern Europe, like the one below. The relevant datasheet is available here.

Note that the East Baltic Iron Age samples, all from burial sites in what is now Estonia, appear to be peeling away from their Bronze Age predecessors and overlapping strongly with present-day Estonians, who are Uralic speakers. Indeed, the PCA suggests to me that the formation of the greater part of the present-day Estonian gene pool took place in the East Baltic during the transition from the Bronze Age to the Iron Age. That is, when Uralic languages are generally accepted to have arrived in the region from near the Ural Mountains in the east.

I was also able to closely replicate these outcomes with my Global25 data using the method described here. However, in this effort, present-day Estonians are clearly more western than the Estonian Iron Age samples (EST_IA), which might be due to the presence of low level Germanic ancestry in Estonia dating to the medieval period. The relevant datasheet is available here.

Interestingly, the Estonian Bronze Age samples (EST_BA) come from stone-cist graves which are widely hypothesized to have been introduced to the East Baltic from the Nordic Bronze Age civilization. I even recall reading a paper on the topic which claimed that the remains buried in such graves were those of Proto-Germanic-speaking Scandinavian migrants. Well, I haven't had a chance to study these samples in any great detail yet, but considering that in both of the PCA above they're overlapping strongly with Latvian Bronze Age samples (LVA_BA) and sitting far away from the nearest Scandinavians, I'd say they're probably of local stock from way back.

See also...

It was always going to be this way

On the association between Uralic expansions and Y-haplogroup N

Inferring the linguistic affinity of long dead and non-literate peoples: a multidisciplinary approach

It was always going to be this way

The native peoples of the East Baltic - Estonians, Latvians and Lithuanians - are genetically alike and their paternal gene pools are dominated by the same two Y-chromosome haplogroups: R1a and N3a.

Linguistically, however, Estonians are a world apart from Latvians and Lithuanians. That's because the Estonian language belongs to the Uralic language family, which has an obvious North Eurasian character. On the other hand, Latvian and Lithuanian are both classified as Indo-European languages, along with the vast majority of other European languages.

The Uralic and Indo-European language families may or may not descend from the same ancestral tongue, but even if they do, their relationship is very distant.

So how is it that Estonians came to speak a Uralic language? As far back as I can remember, the basic explanation accepted by most people was that Uralic speech arrived in what is now Estonia and neighboring Finland during the Bronze Age with migrants, or perhaps invaders, rich in N3a from somewhere around the Ural Mountains. Conversely, Latvians and Lithuanians were generally assumed to have retained the Indo-European speech of their R1a-rich forefathers from the Pontic-Caspian steppe, who colonized much of Eastern Europe north of the steppe during the Late Neolithic.

Ancient DNA has now uncannily corroborated these theories (for instance, see Mittnik et al. 2018 and, published today, Saag et al. 2019). All it took was a handful of samples from a few relevant sites. I think that's awesome; I love it when sensible, long-standing hypotheses are validated by cutting edge science.

I'll have a lot more to say about the spread of Uralic languages and Uralian genes to the East Baltic when I get my hands on the genotype data from the new Saag et al. paper. I also have a post coming soon about the Nordic Bronze Age. Stay tuned.

Update 10/05/2019: Uralic-specific genome-wide ancestry did make a signifcant impact in the East Baltic

See also...

Late PIE ground zero now obvious; location of PIE homeland still uncertain, but...

Corded Ware people =/= Proto-Uralics (Tambets et al. 2018)

Inferring the linguistic affinity of long dead and non-literate peoples: a multidisciplinary approach

Inferring the linguistic affinity of long dead and non-literate peoples: a multidisciplinary approach

Ancient DNA has treated us to many surprises in recent years. But it has also uncannily corroborated some well established hypotheses that were formulated decades ago from historical linguistics and archeological data. One such hypothesis is that the population associated with the Late Neolithic Corded Ware culture (CWC), and its myriad offshoots, spoke early Indo-European languages and spread them across much of Europe and into the Indian subcontinent.

Below is a series of figures in which I explain why the CWC and its likely close relative, the Sintashta culture, are widely regarded as early Indo-European-speaking cultures, even though their languages aren't attested. To view the images at their maximum size, right click on the thumbs and choose "open link in a new tab".

It's a damn shame that we still don't know where the modern domesticated horse lineage ultimately came from. I'm pretty sure that it came from the Pontic-Caspian steppe, but I was hoping this would be confirmed in the latest paper on horse genomics published today at Current Biology: Tracking Five Millennia of Horse Management with Extensive Ancient Genome Time Series. Nope, the topic wasn't even covered, and no wonder, because the sampling strategy in the paper didn't allow it to be. What we desperately need are samples associated with such archeological cultures as Khvalynsk, Repin, Sredny Stog and Yamnaya. Maybe next time, eh?

See also...

Some myths die hard

Ancient DNA tells us that the Bronze Age wasn't kind to the indigenous populations of Central Asia. It seems to have wiped them out totally. Indeed, Central Asia might well be the only major world region in which native hunter-gatherers failed to make a perceptible impact on the genetics of any extant populations.

Before the Neolithic transition, much of Central Asia was home to hunter-gatherers closely related to those of nearby western Siberia. During the Neolithic, agriculturalists and pastoralists from the Near East gradually moved into the more arable parts of southern and eastern Central Asia, eventually giving rise to the Bactria Margiana Archaeological Complex, or BMAC, and other similar communities.

It's not clear what their relationship was like with the native hunter-gatherers in these areas. But they did mix with them in varying degrees. This is obvious because genome-wide genetic ancestry characteristic of the Botai people, who hunted and eventually domesticated horses on the Kazakh steppe during the 4th millennium BCE, and were probably the archetypal Central Asians for their time, is found at significant levels in a number of later samples from Central Asian farmer and pastoralist sites, such as Dali, Gonur Tepe and Sarazm.

Thus, even though the Neolithic transition did have a big impact on Central Asia, and clearly led to large scale population replacements in some parts of the region, this was just the beginning of these population shifts. Moreover, in some cases the expanding farmer and pastoralist populations seem to have acquired significant indigenous Central Asian ancestry and spread it with them.

The precise geographic extent of the relatively unique Botai-related ancestry in prehistoric Eurasia is still something of a mystery. But to give you a general picture of where it was found from around 6,000 BCE to 2,000 BCE, here's a map with info about samples with significant levels of this type of ancestry from a wide range of sites in space and time.

Going by this map, I'd say it's safe to infer that the Botai-related ancestry was a major feature of practically all forager populations living between the Caspian Sea and the Altai Mountains. It was also present in the Early Bronze Age (EBA) pastoralist population associated with the Steppe Maykop archeological culture of Eastern Europe, so it may have already been in Europe as early as 3,800 BCE, because that's when the Steppe Maykop culture first appeared.

It's an interesting question where the ancestors of the Steppe Maykop herders came from. I once simply assumed that they were closely related to the Maykop people who lived in the Caucasus Mountains. But it's now clear that the populations associated with these two similar cultures were starkly different, with the Maykop people being basically of Near Eastern origin and lacking any discernible Botai-like ancestry. My guess for now is that the Steppe Maykop herders were in large part the descendants of the Kelteminar culture population from just east of the Caspian Sea, but we'll see about that when more ancient DNA comes in.

The other great mystery is what eventually happened to the Steppe Maykop people. Around 3,000 BCE, their culture vanished from the archeological record and their particular genetic signature disappeared from the steppe ancient DNA record. Where did they go? Did they migrate back east?

I don't know, but at about that time other Eastern European steppe herders, those associated with the Yamnaya and Corded Ware archeological cultures, began to stir and migrate in big numbers in basically all directions, including into Steppe Maykop territory. Indeed, unlike the Steppe Maykop population, these groups weren't closely related to any contemporaneous or earlier Central Asians. But they ended up moving into Central Asia, and in a big way too.

Their impact all the way from the Ural Mountains to what are now China and India was profound. For instance, not only did they end up totally replacing the Botai people, but also their horses. For more details on this topic check out the Youtube clip here. I have a strong suspicion that the same sort of thing happened to the aforementioned Steppe Maykop people. In other words, they may have been forced out from the Eastern European steppe, and perhaps sought shelter in the Caucasus Mountains?

Admittedly, I'm not offering anything new here. I just wanted to emphasize a few key points, because I'm still seeing some confusion online about the population history of Central Asia, and especially how it relates to the population history of Europe, and also the Proto-Indo-European homeland question. Make no mistake, thanks to the ancient DNA already available from Central Asia, we can confidently infer the following:

- the chance that the ancient European populations associated with the Yamnaya, Corded Ware and other closely related archeological cultures formed as a result of migrations from Central Asia is zero

- the chance that the Proto-Indo-European homeland was located in Central Asia is zero

- the chance that present-day Europeans, by and large, derive from any ancient Central Asian populations is zero

See also...

Central Asia as the PIE urheimat? Forget it

The Steppe Maykop enigma

Late PIE ground zero now obvious; location of PIE homeland still uncertain, but...

R1b-M269 in the Bronze Age Levant

The new Harvard genotype datasets that I blogged about recently include a couple of potentially very useful samples from the Levant dated to 1400-1100 BCE. Search for IDs I2062 and I1934 in the anno files here. They're both from an archeological paper about a Late Bronze Age (LBA) burial site in what is now Israel that was published back in 2017 (see here).

Surprisingly, individual I2062 is listed in the anno files as belonging to Y-haplogroup R1b1a1a2, which is also known as R1b-M269. The reason that this is a surprise to me is because R1b-M269 is closely associated with the Bronze Age expansions of pastoralists from the Pontic-Caspian steppe in Eastern Europe, and these expansions didn't impact the Levant in any direct or significant way.

The Y-haplogroup assignment may or may not be correct. Sometimes the Y-haplogroups in these sorts of datasheets are indeed wrong. Unfortunately, as far as I know, the BAM file for I2062 isn't available anywhere online, so I can't check whether he does really belong to R1b-M269. But, intriguingly, his autosomes do show a subtle signal of Yamnaya-related ancestry from the Pontic-Caspian steppe that is missing in earlier ancients from the Levant.

To characterize his genome-wide ancestry, I first ran a series of unsupervised and supervised analyses with the Global25/nMonte3 method (using this datasheet). For the sake of simplicity, I narrowed things down to the mixture models below based on three reference populations each. Levant_ISR_C is made up of Chalcolithic samples from Israel. The identities of the other reference sets should be obvious to most readers. If confused, feel free to ask for more details in the comments below.

Levant_ISR_MLBA:I2062
Levant_ISR_C,66.8
IRN_Seh_Gabi_C,27
Yamnaya_RUS_Samara,6.2
[1] distance%=1.8905

Levant_ISR_MLBA:I2062
Levant_ISR_C,66.2
Kura-Araxes_ARM_Kaps,30.2
Yamnaya_RUS_Samara,3.6
[1] distance%=2.0856

Levant_ISR_MLBA:I2062
Levant_ISR_C,67.8
Kura-Araxes_RUS_Velikent,31.8
Yamnaya_RUS_Samara,0.4
[1] distance%=2.1738

To further confirm the reliability of my models, I tested them with the formal statistics-based qpAdm software. As far as I can tell, the output from qpAdm looks very solid across the board.

Levant_ISR_MLBA_I2062
IRN_Seh_Gabi_C 0.193±0.052
Levant_ISR_C 0.710±0.038
Yamnaya_RUS_Samara 0.098±0.026
chisq 9.304
tail prob 0.67676
Full output

Levant_ISR_MLBA_I2062
Kura-Araxes_ARM_Kaps 0.249±0.076
Levant_ISR_C 0.681±0.051
Yamnaya_RUS_Samara 0.071±0.035
chisq 11.101
tail prob 0.52032
Full output

Levant_ISR_MLBA_I2062
Levant_ISR_C 0.661±0.042
Kura-Araxes_RUS_Velikent 0.339±0.042
chisq 7.979
tail prob 0.844942
Full output

Admittedly, even though I2062 can be modeled with Yamnaya-related admixture, he doesn't need to be. Indeed, his ratio of this type of ancestry varies significantly between the models, from around 10% to nothing. This appears to be dependent on the geography of the non-Levant and non-Yamnaya reference populations; the closer they are to the Pontic-Caspian steppe, the smaller the ratio of Yamnaya-related ancestry in I2062. I'd describe this as an artifact of the isolation-by-distance phenomenon, and it totally makese sense, but it prevents me from confirming beyond any doubt that I2062 does harbor genome-wide steppe ancestry. Unfortunately, individual I1934 doesn't offer enough data to be analyzed with the same methods.

Samples associated with the Kura-Araxes or Early Transcaucasian culture are particularly strong references for the eastern ancestry in I2062. This probably isn't a coincidence, and it might also explain his Y-haplogroup, because, at its maximum extent, the territory occupied by the Kura-Araxes culture stretched all the way from the Pontic-Caspian steppe to the southern Levant (see Wilkinson 2014).

See also...

Downloadable genotypes of present-day and ancient DNA data

Early chariot riders of Transcaucasia came from...

R-V1636: Eneolithic steppe > Kura-Araxes?