As you all know, there have been two aDNA papers released recently about Central Asia to North India. I didn’t dedicate a post to them (there are comments in the previous thread about them, though), mostly because the first one (The formation of human populations in South and Central Asia, Narasimhan el al. 2019) had already been extensively commented when the preprint was out, and while it did bring more samples these mostly add quantity to already sampled populations with few new ones (and not relevant enough to deserve a new post), while the second one (An Ancient Harappan Genome Lacks Ancestry from Steppe Pastoralists or Iranian Farmers, Shinde et al. 2019) finally brought the first ancient sample from within modern India, but it was only one low quality one that didn’t add much to the better quality “Indus periphery” ones already present in the former paper.
However, there’s still a bit of confusion regarding the ancestry to the people of the Indus Valley (and generally to the genetic structure of SC Asian populations), so here I’ll try to give some insights that might help to clarify the situation for further, better informed, analysis.
The basic premise here would be to split Iranian ancestry into West and East Iranian. The main difference would be the ratio of Basal Eurasian to ANE ancestry (higher in the west, lower in the east), but given the lack of Mesolithic samples we’re still unable to get the whole picture. However, some basic concepts can still help us to better understand the situation. So let’s start.
Vahaduo’s online modelling tool
And I’ll use his post to introduce a recently released online tool that deserves more attention, given its quality and usefulness. It’s been written by Vahaduo, with a similar purpose to my own Xmix, but more complete, faster and not requiring any local installation. So I’ll use this post to show how to use it for any of the readers to be able to try their own models and be able to test for themselves whatever they are interested in.
The first (and only) thing you’ll need is to get some datasheets that are valid to use with Vahaduo’s program. The best (and recommended) ones being the Global 25 scaled datasheets from Eurogenes. One will have all the individual samples and the other the averages of each population. Ones you have these, you can proceed to the site and start testing. Here we’ll go directly to test what I mentioned above: East vs. West Iranian ancestry.
For West Iranian ancestry, I’ll use the average of the Early Neolithic samples from the Ganj Dareh site in the Zagros mountains. And for east Iranian I’ll use the average of the easternmost samples we have so far: Sarazm_Eneolithic. So I’ll need to copy the coordinates of these in the “SOURCE” tab (one per line):
Now, two sources will probably not be enough to test the samples from SC Asia and Indus periphery, since there are more streams of ancestry in them (at least one related to ANF and the other to AASI). So I’ll go ahead and add the average of Barcin_N samples and the average of modern Onge and Naxi populations.
Then for the targets, I’ll use individuals instead. In this case I’ll start with the “Indus periphery” samples, which are labelled in the datasheets as IRN_Shahr_I_Sokhta_BA2 and TKM_Gonur2_BA, so again one per line I copy and paste them in the “TARGET” tab:
And now we’re ready to run the program and get the results. Since we’ve added multiple target samples, we should go to the “MULTI” tab and click on the “RUN” button, which will show us this:
As you see, Naxi doesn’t appear in the results, and that’s because all the samples got 0% ancestry from it. If we wanted to see all the sources in the output, we’d just have to click on the “PRINT ZEROES -NO” button (which would change to “PRINT ZEROES – YES”) and click “RUN”. The “AGGREGATE – YES” button is to aggregate the percentage of multiple sources with the same label (for example if instead of using the average of Ganj_Dareh_N we would have used all the individuals as sources, we would choose to either see the results with each individual specified or to aggregate them into a single column with the sum of them).
Then we can download a .CSV file to import it into a spreadsheet and make further calculations if needed (or for sharing purposes using Google Docs, for example). The “DISTANCE” tab is also useful to calculate the distance between a sample to all the sources (you could copy for example the whole datasheet, being careful not to copy the first row with the PCA labels) and get the top 25 closest samples/populations.
It just takes some minutes to get familiar with the program and the options so go ahead and try it. It’s definitely a very useful tool.
Some insights into SC Asian and Indus Valley ancestry
So let’s start with what we see in the above model of the Indus periphery samples. Leaving (for now) aside the fact that they may have some recent admixture from the places where they were found, one striking thing is the very variable ratios of West and East Iranian ancestry. In the following spreadsheet the above results can be seen (Sheet 1) together with a second run with the 100AHG simulation provided by Matt in the previous thread (Sheet 2), and in both the the calculated ratio of West to East Iranian ancestry. It’s easy to see that there is no correlation between that ratio and the amount of AASI in each sample, which makes it irrelevant for this matter whether they have any admixture from the local populations or not. Either way, we’re seeing a diverse population not just in terms of AASI to West Eurasian, but in the more sutle, but still important, West to East Iranian ancestry.
This pattern of significantly different ratios in West and East Iranian ancestry is equally seen in the regular Shahr-I-Sokhta BA samples (Sheet 3) and in the Turan Eneolithic samples (Sheet 4). The Iranian-like ancestry in the Indus periphery samples is therefor very similar to the one in those places. But they’re not all homogeneous, and point to mixed populations with probable input from West Iran. Modelling the samples with more proximate sources and using the 100AHG simulation again, it looks like this:
And using the sample with the highest amount of AASI as a source instead of the 100AHG simulation, something like this:
So what does this mean? First that things are a bit more complicated than getting the average of a population and building a tree estimating the divergence time from another one under an assumption that there is no admixture between them just because they’re not the same. In more simple words, we can’t really know with certainty if there was some migration from the Zagros Neolithic to North India or if there was none. Both options are possible. What we can say, though, is that we’re talking about a significantly different case to the Neolithic transition in Europe, since there must not have been a large replacement by outside farmers in any case.
All of this opens some interesting questions regarding the genetic history of South Asia. Unfortunately, we don’t have the data to give any answer to those questions, but it’s still worth knowing them and the different possible answers. For example:
- Who were the Mesolithic Hunter-Gatheres from North India?
- Who were the first farmers?
- Was there any subsequent migration before the Bronze Age?
Let’s break up the genetic structure of (putative) IVC samples into the 3 main streams of ancestry:
- West Iranian
- East Iranian
This does not mean necessarily three different populations. Two or more of these ancestries could have been already mixed since very early. But let’s examine the possibilities:
First, a basic look at the geography of India tells us that there are no major barriers within it, compared to the big barriers with the outside. This makes less likely he possibility of two extremely different populations during the Mesolithic living in South and North India, and the one in north India being almost identical to the ones outside (Iran and Turan). It could be (only aDNA can tell us), but it looks like the least parsimonious.
Together with the diversity in the ratios of those 3 streams of ancestries, it’s really unlikely that we could be talking about an isolated India-specific population. We have to think in terms of some degree of migration to India from outside before the Bronze Age.
The possibilities about who was where at each point in time are many, and I won’t argue for any of them. It’s speculative at this point. But as possible examples:
We could have a AASI-rich population, but with significant East Iranian ancestry too during the Mesolithic. Then we could have a moderate migration from the Zagros Neolithic and no more migrations up to the IVC time where we have samples. This would be somehow similar to the Neolithic transition in Turan, where presumably a mostly East Iranian population was there in the Mesolithic and received some migration from West Iran during the Neolithic transition. The difference (apart from the lack of AASI ancestry in Turan), is that the communication between West Iran and Turan is easier, and gene flow continued (both ways) throughout the Chalcolithic and Bronce Age.
The problem with this scenario is how to explain the presumed differences in levels of AASI in the IVC and their lack of correlation with the East Iranian ancestry that would have been associated with it.
Scenarios were we separate the three streams of ancestry could better explain the situation, though given that Turan Chacolithic had already a diversity in East and West Iranian ancestry ratios that could serve as a single migration too (note that neither West Iran Chalcolithic or Turan Bronze Age would fit well as admixing sources due to their excess of ANF-related ancestry). I’ll leave to the comments any further variations within these constraints.
The Steppe ancestry in Turan and North India
This subject has already been discussed everywhere in great detail, for a very long time. So I didn’t plan to look at it again. I don’t have much more to say, but I’ll go through it fast.
The post BMAC samples that we have hardly show any steppe admixture. In the same spreadsheet linked above (Sheet 5), I’ve added the samples with an average date in calBP of <3700 years in descending order (note that the Present is defined as 1950 CE, so you’d need to add 69 years to get the real BP as of today). There’s one Parkhai_LBA_outlier (1497-1413 calBCE) that shows 9.2% Sintashta_MLBA admixture. The rest until the last BA samples (3250 BP) are in the noise levels. It’s only the single Iron Age sample from Turkmenistan (912-799 calBCE) that has a big increase to 50%.
In the Swat Valley, we have the earliest samples from the period 1200-800 BCE. They have significantly more steppe admixture, ranging between 20% and 0% and an average of around 10%. The variability of the amount of steppe ancestry doesn’t seem very compatible with their estimate of admixture happening 26 generations before in that same place, in that same population. But the shortcomings of their observations that provide evidence of the arrival of steppe ancestry to South Asia in the first half of the second mill. should have been already evident without looking at individual variability with up to 0% levels.
Another of the inferences for supporting such evidence was their observation that after he MLBA the steppe got Siberian/East Asian admixture, which is not found in modern India. However, they could model modern population using the Kangju samples from Kazakhstan (II-V CE). Modern samples are never a good way to make inferences about prehistory (including modern frequencies of certain uniparental markers). It seems rather arbitrary why would populations choose Sintashta or Kangju (though maybe Kshatriya ones make sense),
or indeed why would they choose Sintashta_MLBA or the Kashkarchi_BA samples from 1200-1000 BCE which are almost identical,
or even adding the Turkmenistan_IA sample mentioned above too as a source (as suggested in the comments from the previous thread), which further splits the steppe ancestry in a relatively random way.
Overall not much to add about all of this steppe part. We’ll have to wait to see those samples from the first half of the second mill. BC around the Punjab before we can know with certainty how all this went.