Hungarian origins

This quote, from a new paper at Nature, High-resolution genomic history of early medieval Europe by Speidel et al., is the most idiotic take on the ancestry of present-day Hungarians that I've ever read.

Present-day populations of Hungary do not appear to derive detectable ancestry from early medieval individuals from Longobard contexts, and are instead more similar to Scythian-related ancestry sources (Extended Data Fig. 6), consistent with the later impact of Avars, Magyars and other eastern groups.

In fact, present-day Hungarians are overwhelmingly derived from West Slavic and German peasants, showing only minor ancestry from early Magyars (or rather Hungarian Conquerors). So in terms of genetic ancestry they're basically typical East Central Europeans.

Scythians and Avars don't even deserve a mention in this context.

The reason that Speidel et al. found present-day Hungarians to be broadly similar to Scythians is because they used so called Hungarian Scythians in their analysis.

It's important to understand that these Hungarian Scythians are genetically fairly typical Central Europeans for their time, and, by and large, don't show any significant genetic relationship to Asian Scythians, Avars or early Magyars. So they're mostly either just acculturated Scythians or wrongly classified as Scythians by archeologists.

That is, the broad similarity that Speidel et al. found between present-day Hungarians and Hungarian Scythians derives from the fact that both of these populations are genetically Central Europeans, rather than the ridiculously false idea that they show strong genetic links to Avars, Hungarian Conquerors and other eastern groups.

Here's a Principal Component Analysis (PCA) of West Eurasian genetic variation, courtesy of the excellent Vahaduo:Global25 Views, that perfectly illustrates my point.

If Speidel et al. were correct about the genetic origin of present-day Hungarians, then the Hungarian_Modern and Hungary_Scythian samples would be shifted away from other Europeans, much like many of the Hungary_Avar and Hungary_Conqueror individuals. But that's obviously not the case, and instead they cluster strongly with, say, present-day Germans from Hamburg.

I emailed two of the authors of this paper, Leo Speidel and Pontus Skoglund, when they posted the preprint of the paper at bioRxiv to cordially discuss this issue (see here). But they totally ignored me.

Citation...

Speidel et al., High-resolution genomic history of early medieval Europe, Published online: 1 January 2025, https://doi.org/10.1038/s41586-024-08275-2

New Global25 interpretation tools

They're available at Ancestry Calculator and GENOPLOT. Unfortunately, I can't tell you exactly how to get the most out of them. All I can recommend is robust experimentation.

See also...

Getting the most out of the Global25

Genetic ancestry online store

Modeling your ancestry has never been easier

Viking Age Iceland

I finally managed to get some of the Icelandic ancients from Ebenesersdóttir et al. 2018 into the Global25 datasheets (see here). Better late than never. Look for the"ISL_Viking_Age" prefix. Below is a screen cap of a Principal Component Analysis (PCA) with the new samples. It was done with an online Global25 PCA runner freely available here.

The individuals classified as unadmixed Gaels and Norse by Ebenesersdóttir et al. generally also look like it based on their Global25 coordinates.

The mixture models below, using all of the populations from the Global25 "modern pop averages scaled" datasheet, were run with an online tool freely available here. Note that the ADD DIST COL option is set to 1X. This is a useful feature for modeling the fine scale ancestry of samples that are derived from very similar populations.

See also...

They came, they saw, and they mixed

Commoner or elite?

Who were the people of the Nordic Bronze Age?

Etruscans, Latins, Romans and others

I've just added coordinates for more than 100 ancient genomes from the recently published Antonio et al. ancient Rome paper to the Global25 datasheets. Look for the population and individual codes listed here. Same links as always:

Global25 datasheet ancient scaled

Global25 pop averages ancient scaled

Global25 datasheet ancient

Global25 pop averages ancient

Thus far I've only managed to check a handful of the coordinates, so please let me know if you spot any issues. Below is a Principal Component Analysis (PCA) featuring the Etruscan and Italic speakers. I ran the PCA with an online tool specifically designed for Global25 coordinates freely available here.

Can we say anything useful about the origins of the Etruscan and early Italic populations thanks to these new genomes? Also, to reiterate my question from the last blog post, what are the genetic differences exactly between the Etruscans, early Latins, Romans and present-day Italians? Feel free to let me know in the comments below.

Update 13/11/2019: Here's another, similar PCA. This one, however, is based on genotype data, and it also highlights many more of the samples from the Antonio et al. paper. Considering these results, I'm tempted to say that the present-day Italian gene pool largely formed in the Iron Age, and that it was only augmented by population movements during later periods. The relevant datasheet is available here.

Update 13/11/2019: It seems to me that the two Latini-associated outliers show significant ancestry from the Levant, which possibly means that they're in part of Phoenician origin. These qpAdm models speak for themselves:

ITA_Ardea_Latini_IA_o
ITA_Proto-Villanovan 0.547±0.081
Levant_ISR_Ashkelon_IA2 0.453±0.081
chisq 7.573
tail prob 0.87027
Full output

ITA_Prenestini_tribe_IA_o
ITA_Proto-Villanovan 0.679±0.068
Levant_ISR_Ashkelon_IA2 0.321±0.068
chisq 7.222
tail prob 0.89033
Full output

The Proto-Villanovan singleton is also a key part of the models. Dating to the Bronze Age/Iron Age transition, she appears to be of western Balkan origin. Moreover, her steppe ancestry is probably derived directly from the Yamnaya horizon.

ITA_Proto-Villanovan
HRV_Vucedol 0.677±0.031
Yamnaya_RUS_Samara 0.323±0.031
chisq 10.397
tail prob 0.661174
Full output

The cluster made up of four early Italic speakers can be modeled with minor Proto-Villanovan-related ancestry, but, perhaps crucially, it doesn't need to be. Indeed, judging by the qpAdm output below, it's possible that almost all of its steppe ancestry came from the Bell Beaker complex, and, thus, the Corded Ware culture complex before that.

ITA_Italic_IA
Bell_Beaker_Mittelelbe-Saale 0.480±0.055
ITA_Grotta_Continenza_CA 0.411±0.042
ITA_Proto-Villanovan 0.109±0.084
chisq 10.294
tail prob 0.590205
Full output

Two out of the three available Etruscans look very similar to the Italic speakers in the above PCA plots, and yet they show a lot more Proto-Villanovan-related ancestry in my qpAdm run. The statistical fit is also relatively poor, perhaps suggesting that something important is missing.

ITA_Etruscan
Bell_Beaker_Mittelelbe-Saale 0.186±0.081
ITA_Grotta_Continenza_CA 0.283±0.064
ITA_Proto-Villanovan 0.531±0.126
chisq 17.175
tail prob 0.143143
Full output

Interestingly, the Etruscan outlier with significant North African admixture (proxied in my run by MAR_LN) doesn't need to be modeled with any Bell Beaker ancestry.

ITA_Etruscan_o
ITA_Proto-Villanovan 0.675±0.057
MAR_LN 0.325±0.057
chisq 14.864
tail prob 0.315912
Full output

Update 17/11/2019: The spatial maps below show how three groups of ancient Romans (from the Imperial, Late Antiquity and Medieval periods) compare to present-day West Eurasian populations in terms of their Global25 coordinates. The hotter the color, the higher the similarity. More here.

See also...

Getting the most out of the Global25

Modeling your ancestry has never been easier

An exceedingly simple, yet feature-packed, online tool ideal for modeling ancestry with Global25 coordinates is freely available HERE. It works offline too, after downloading the web page onto your computer. 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.

Another free, easy to use online tool that works with Global25 coordinates is the Principal Component Analysis (PCA) runner HERE. Below is a screen cap of me checking out one of the many PCA that it offers.

See also...

Getting the most out of the Global25

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

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

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. The map below is from 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?

The Steppe Maykop enigma

Who were the Steppe Maykop people exactly? Their ancestry must surely rank as one of the biggest surprises served up by ancient DNA to date.

I always thought that they'd turn out roughly like a mixture between populations associated with the Kura-Araxes and Yamnaya cultures (mostly because their territory was located sort of in between them). Nope, that wasn't even close. This is where they cluster compared to Kura-Araxes and Yamnaya samples in my Principal Component Analysis (PCA) of world-wide genetic variation: the Global25.

To explore the ancestry of the Steppe Maykop people in more detail I ran a couple of unsupervised Global25/nMonte tests, using basically every ancient population in the (scaled) Global25 datasheet that seemed chronologically sensible and even remotely relevant. I narrowed things down to these two mixture models.

Steppe_Maykop
Geoksiur_Eneolithic,11.2
Piedmont_Eneolithic,44.4
West_Siberia_N,44.4
distance%=1.5161

Steppe_Maykop
Piedmont_Eneolithic,46.6
Sarazm_Eneolithic,10.4
West_Siberia_N,43
distance%=1.6408

But, you might say, Global25/nMonte isn't a published analytical method and it doesn't run on formal statistics, the meat and potatoes of ancient DNA papers. OK then, let's try the same models with the qpAdm software, which is a published method and does run on formal statistics, using exactly the same samples.

Steppe_Maykop
Geoksiur_Eneolithic 0.100±0.032
Piedmont_Eneolithic 0.433±0.053
West_Siberia_N 0.467±0.028
chisq 19.155
tail prob 0.159096
Full output

Steppe_Maykop
Piedmont_Eneolithic 0.429±0.051
Sarazm_Eneolithic 0.119±0.033
West_Siberia_N 0.452±0.026
chisq 18.090
tail prob 0.202699
Full output

They're basically identical. Importantly, my models must reflect reality at some level, because otherwise I wouldn't be able to produce a pair of essentially identical results using such vastly different statistical methods. So the pertinent question is what do these results actually mean?

It seems unlikely to me that we're dealing here with a highly complex three-way mixture process, involving populations from such far flung locations as western Siberia and southern Central Asia. Rather, I suspect that Steppe Maykop was the result of a two-way mixture between Piedmont_Eneolithic (the population that lived before it on the steppe north of the Caucasus) and someone just a little bit more easterly. I'm guessing that the latter was the (as yet unsampled) population associated with the Kelteminar archeological culture.

By the way, please note that Piedmont_Eneolithic is made up of samples from two different locations on the Piedmont steppe, and I occasionally treat them as separate populations labeled Progress_Eneolithic and Vonyuchka_Eneolithic (for instance, see here).

Update 28/02/2019: Below is a PCA focusing on West Eurasian genetic variation. Overall, the position of Steppe Maykop relative to Geoksiur_Eneolithic, Piedmont_Eneolithic and West_Siberia_N appears to reflect my nMonte and qpAdm models. However, as per our discussion in the comments, one of the Steppe Maykop individuals (the most southerly one in the PCA) probably also has recent ancestry from the Caucasus.

See also...

An exceptional burial indeed, but not that of an Indo-European

Maykop: a multi-ethnic layer cake?

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

Modeling genetic ancestry with Davidski: step by step

There are many different ways to model your genetic ancestry but I prefer the Global25/nMonte method. This is a step by step guide to modeling ancient ancestry proportions with this simple but powerful method using my own genome.

As far as I know, the vast majority of my recent ancestors came from the northern half of Europe. This may or may not be correct, but it gives me somewhere to start, so that I can come up with a coherent model. If you don't have this sort of information, because, perhaps, you were adopted, then just look in the mirror, and work from there. Like I say, it's not imperative that you know anything whatsoever about your ancestry, because your genetic data will do the talking, but you do need a model when modeling.

In scientific literature nowadays Northern Europeans are often described as a three-way mixture between Yamnaya-related pastoralists, Anatolian-derived early farmers, and Western European Hunter-Gatherers (WHG). So let's see if this model works for me. Obviously, if it does, then it'll confirm the information that I have about my origins, but it might also reveal finer details that I'm not aware of. The datasheet that I'm using for this model is available here.

[1] distance%=6.9025 / distance=0.069025

Davidski

Yamnaya_Samara 53.9
Barcin_N 30.75
Rochedane 15.35
Tepecik_Ciftlik_N 0

Yep, the model does work, with a fairly reasonable distance of almost 7%. The ancestry proportions more or less match those from scientific literature and the plethora of analyses that I've featured at this blog on the topic. Please note that I've kept things very simple, using only four reference populations and individuals as proxies for four distinct streams of ancestry. But I've put my own twist on this Neolithic/Bronze Age model by including two populations from Neolithic Anatolia (Barcin_N and Tepecik_Ciftlik_N), just to see what would happen. The WHG proxy is Rochedane.

Admittedly, though, my Yamnaya cut of ancestry appears somewhat bloated at over 53%, and the model's distance is a little higher than what I normally see for really strong models. So let's check if I can get a better fitting and more sensible result by adding a slightly more easterly forager proxy than Rochedane: Narva_Lithuania.

[1] distance%=5.9331 / distance=0.059331

Davidski

Yamnaya_Samara 45.75
Barcin_N 31.45
Narva_Lithuania 22.8
Rochedane 0
Tepecik_Ciftlik_N 0

The statistical fit does improve, and when given a choice between Rochedane and Narva_Lithuania, the algorithm picks the latter as the only source of extra forager input in my genome.

What could this mean? It might mean that a large part of my ancestry derives from the Baltic region. Actually, I know for a fact that this is true. But even if I had no idea about my genealogy, this result would be a very strong hint about my genetic origins. Indeed, let's follow this trail and try to further improve the fit of the model by adding a more relevant Yamnaya-related proxy, such as early Baltic Corded Ware (CWC_Baltic_early).

[1] distance%=5.444 / distance=0.05444

Davidski

CWC_Baltic_early 54.95
Barcin_N 26.7
Narva_Lithuania 18.35
Rochedane 0
Tepecik_Ciftlik_N 0
Yamnaya_Samara 0

Holy shit! To be honest, I wasn't expecting this sort of resolution and accuracy, and I can't promise that everyone using the Global25/nMonte method will see such incredibly nuanced outcomes, but this isn't a fluke. It can't be, because it gels so well with everything that I know about my ancestry. Please note also that I belong to Y-chromosome haplogroup R1a-M417, which is a lineage intimately associated with the Corded Ware expansion across Northern Europe (for instance, see here).

But of course, the Baltic and nearby regions haven't been isolated from migrations and invasions since the Corded Ware times. For instance, at some point, probably during the Bronze Age, Uralic-speaking groups moved west across the forest zone of Northeastern Europe and into the East Baltic and northern Scandinavia. It's generally accepted that they brought Siberian admixture with them (see here). Moreover, from the Iron Age to the Middle Ages, East Central Europe was under intense pressure from a wide range of nomadic steppe groups with complex ancestry, such as the Sarmatians, Avars, Huns, and Mongolians. Did any of these peoples leave their mark on my genome? At the risk of overfitting the model, let's explore this possibility by adding a few more reference populations.

[1] distance%=5.444 / distance=0.05444

Davidski

CWC_Baltic_early 54.95
Barcin_N 26.7
Narva_Lithuania 18.35
Han 0
Mongolian 0
Nganassan 0
Rochedane 0
Sarmatian_Pokrovka 0
Tepecik_Ciftlik_N 0
Yamnaya_Samara 0

Nothing changes when I add the Han Chinese, Mongolians, Nganassans (a Uralic group from Siberia), and Sarmatians to the model. But what about if I throw in the only ancient Slav in my datasheet?

[1] distance%=2.9904 / distance=0.029904

Davidski

Slav_Bohemia 85.9
CWC_Baltic_early 7.7
Narva_Lithuania 6.4
Barcin_N 0
Rochedane 0
Tepecik_Ciftlik_N 0
Yamnaya_Samara 0

Considering that the vast majority of my recent ancestors were Poles, thus a Slavic-speaking people from near the Baltic, this outcome makes perfect sense. And check out the new distance! But the problem now is that I'm overfitting the model by using two very similar and probably very closely related references, CWC_Baltic_early and Slav_Bohemia. And overfitting should be avoided at all costs. So it might be useful to break up this effort into two models: one focusing on the Neolithic and Bronze Age, and the other on the Iron Age and Middle Ages. I'll do that soon, but not just yet, because there are still too few Iron Age and Medieval samples available from the Baltic region and surrounds for meaningful analyses of this type.

See also...

Genetic ancestry online store (to be updated regularly)

Genetic ancestry online store

Update 05/02/2025: To get your Global25 coords, please use the app HERE. The whole process usually takes a couple of days. Feel free to spread the word.

Please don't order the Global25 unless you have experience in modeling Global25 data with the Vahaduo analysis tools.

Note that the conversion of VCF, BAM, CRAM and/or fastq files is 30 to 50€ extra depending on the case. For enquiries please email teepean47 on g25requests@gmail.com.

...


Following a rigorous testing phase, the awesome Global 25 analysis is now available at the store for $12 USD. What's so awesome about this test, you might ask? See here and here.

Please send your request and autosomal genotype data (from AncestryDNA, FTDNA, LivingDNA, MyHeritage or 23andMe) to eurogenesblog at gmail dot com.

However, note that this test is free for anyone who already has Global 10 coordinates (see here). That's right, if you already have Global 10 coordinates, all you have to do is to send me your data and say what it's for. Simple as that.

...

My Celtic vs Germanic Principal Component Analysis (PCA) is now available via the store for $6 USD (see here). Please note that this test is only really useful for people of Central, Northern and/or Western European origin, and indeed geared for those of overwhelmingly Northwestern European ancestry.

Please send your request and autosomal genotype data (from AncestryDNA, FTDNA, LivingDNA, MyHeritage or 23andMe) to eurogenesblog at gmail dot com.

...

The popular Basal-rich K7 admixture test is now available via the store for $6 USD. It's suitable for everyone, except people with significant (>10%) Sub-Saharan ancestry. For more information about this test and some ideas about what to do with the output see here and here.

Please send your request and autosomal genotype data (from AncestryDNA, FTDNA, LivingDNA, MyHeritage or 23andMe) to eurogenesblog at gmail dot com.

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

Getting the most out of the Global25

Modeling genetic ancestry with Davidski: step by step