Most people in South Asia speak one of two varieties of language, Indo-Aryan and Dravidian. These two are not particularly closely related. Indo-Aryan is an Indo-European language, as is evident in the plethora of obvious cognates with other Indo-European dialects. I have a minimal fluency in Bengali, the easternmost of the Indo-European languages, and quite a bit more fluency with English, one of the most westernmost, and it was evident to me rather early on (e.g., grass vs. gash, man vs. manush, nose vs. nak). In contrast to me Dravidian languages are peculiar because the accent and cadence are clearly South Asian, but they are utterly impenetrable (though there are many loan words into Indo-Aryan from Dravidian).
A Cape Coloured family
I’ve mentioned the Cape Coloureds of South Africa on this weblog before. Culturally they’re Afrikaans in language and Dutch Reformed in religion (the possibly related Cape Malay group is Muslim, though also Afrikaans speaking traditionally). But racially they’re a very diverse lot. In this way they can be analogized to black Americans, who are about ~75% West African and ~25% Northern European, with the variance in ancestral proportions being such that ~10% are ~50% or more European in ancestry. The Cape Coloureds though are much more complex. Some of their ancestry is almost certainly Bantu African. This element is related to the West African affinities of black Americans. And, they have a Northern European element, which likely came in via the Dutch, German, and Huguenot settlers (mostly males). But the Cape Coloureds also have other contributions to their genetic heritage. Firstly, they have Khoisan ancestry, whether from Bushmen or Khoi. This is well known in their oral memory. The the hinterlands of the Cape of Good Hope are beyond the ecological range of the Bantu agricultural toolkit, so the region was still dominated by the Khoisan when the Europeans arrived. But there are also other suggestions of ancestry from Asia. The existence of the Cape Malays, whose adherence to Islam derives from the Muslims slaves brought by the Dutch, hints at likely relationships to the populations of maritime Southeast Asia. Finally, there are the Indians. This element is not too well recalled in cultural memory. But the Dutch brought many slaves from India as well as Southeast Asia. The Dutch first governor of the Cape Colony had a maternal grandmother who was an Indian slave, by various accounts Goan or Bengali (the town of Stellensbosch is named for him). No doubt it was far more likely that the usual lot of the descendants of Indian slaves during the Dutch era would be to be absorbed into the melange of the Coloured population than assimilated into what later became the Afrikaners.
Why is this aspect of Cape Coloured ancestry forgotten? I think part of the reason is that there is a large South African Indian community present today, but that community post-dates the Dutch period, and arrived with the British. When South Africans think of Indians they think of these people. Interestingly when the new genetic studies confirming Indian ancestry came on the scene I was “corrected” several times by Indians themselves when reporting this part of the Coloured heritage. They were under the impression I must be mistaken, as no one was familiar with the Cape Coloureds having Indian ancestry. Unfortunately pointing to PCA and STRUCTURE plots did not clear up the confusion.
In any case, thanks to the African Ancestry Project I now have three unrelated Coloured samples (I have more, but they are related). Since AAP is Afrocentric I thought it would be appropriate to run the Coloured samples separate first. So that’s what I did.
Two years ago Reconstructing Indian Genetic History reframed how we should view South Asian historical genomics. In short, Indians can be viewed as a hybrid between a West Eurasian group, “Ancestral North Indians” (ANI) and a very different group, “Ancestral South Indians” (ASI), which had distant connections to West and East Eurasians. At least to a first approximation. Last fall I posted on a new paper which surveyed the Austro-Asiatic speaking peoples of India, and concluded that they were exogenous to the subcontinent. This is an interesting point. Prehistoric treatments of South Asia often use linguistic terms to denote putative ancient populations. One model is that first it was the Munda, the most ancient Austro-Asiatics. Then the Dravidians. And finally the Indo-Aryans. These genetic data imply that the Munda arrived after the initial ANI-ASI synthesis. The Munda people of India can be thought of as ANI-ASI, with an overlay of East Eurasian ancestry.
Zack Ajmal’s K = 11 ADMIXTURE run has highlighted some further issues. He has a set of Austro-Asiatic samples, as well as a host of Indo-Aryan and Dravidian speaking populations. I now believe we can now further clarify and refine our model of the peopling of India. Here it is:
1) ASI, circa ~10,000 years BP
2) ANI enters the subcontinent from the northwest, synthesis with ASI
3) The ancestors of the Munda enter from the northeast, synthesis with ANI + ASI in their region
4) A subsequent group of West Eurasians, related to the ANI, so I will term them ANI2, enters from the northwest and overlays the ANI + ASI synthesis. In the northeast quadrant of the subcontinent this group marginalizes the Munda people, who are either assimilated or escape to more remote locations. I believe that ANI2 is likely the Indo-Europeans, but it may be Dravidians as well
5) A second group of Austro-Asiatic peoples enters from the northeast, and synthesizes with the AN2 + ANI + ASI. In some regions they are absorbed (Assam), but in other regions they are culturally dominant (Meghalaya)
Below are two plots which illustrate where I’m coming from. The “S Asian” component from K = 11 above seems to overlap, but is not identical to, ANI. The “Onge” component plays a similar role with ASI. The “SW Asian” and “European” elements are pretty straightforward. They’re very closely related to the “S Asian” one, but they do separate from it. Their relationship to distant non-Indian groups as well as a gradient toward the northwest suggests to me a more recent arrival of this element.
Zack Ajmal now has over 50 participants in the Harappa Ancestry Project. This does not include the Pakistani populations in the HGDP, the HapMap Gujaratis, the Indians from the SVGP. Nevertheless, all these samples still barely cover vast heart of South Asia, the Indo-Gangetic plain. Here is the provenance of the submitted samples Zack has so far:
Again, note the underrepresentation of two of India’s most populous states, Uttar Pradesh, ~200 million, and Bihar, ~100 million. Nevertheless, there are already some interesting yields from the project. Below I’ve reedited Zack’s static images (though go to his website for something more dynamic) with the labels of individuals. I’ve highlighted myself and my parents with the red pointers.
Last week I announced the Harappa Ancestry Project. It now has its own dedicate website, http://www.harappadna.org. Additionally, it has its own Facebook page. For Zack to get his own URL he needs about 10 more “likes,” so please like it! (if you are so disposed) Finally, from what I’ve heard the first wave of the 23andMe holiday sale results are coming online this week. Actually, one of the relatives who I purchased the kit for is in processing currently, so I know that we should have a bunch of new people in the system very, very, soon.
Speaking of people, last I heard Zack had gotten about a dozen responses. That’s enough to start an initial round of runs, but obviously he needs more people. More importantly, the goal here is to get better population coverage. One of the things we know intuitively and also from the most current research is the existence of a lot of within-region population variation in South Asia which is structured by community. In other words, a sample of 30 people, where you have 3 from 10 different communities exhibiting geographical and caste diversity is going to be far more useful right now than 300 Jatts from Indian Haryana. Getting 300 Jatts for Haryana would be interesting in that it would give you a window into intra-communal variance, but there’s diminishing returns on the inferences you could make about South Asians as a whole.
If you know someone who has done the 23andMe testing and has preponderant ancestry from South Asia, Iran, Burma, or Tibet, please forward the the URL for the Harappa Ancestry Project. If you are a 23andMe member, and involved in the forums, it might be useful to post a comment thread on this project, as the people you share genes with would see it.
I have put up a few posts warning readers to be careful of confusing PCA plots with real genetic variation. PCA plots are just ways to capture variation in large data sets and extract out the independent dimensions. Its great at detecting population substructure because the largest components of variation often track between population differences, which consist of sets of correlated allele frequencies. Remeber that PCA plots usually are constructed from the two largest dimensions of variation, so they will be drawn from just these correlated allele frequency differences between populations which emerge from historical separation and evolutionary events. Observe that African Americans are distributed along an axis between Europeans and West Africans. Since we know that these are the two parental populations this makes total sense; the between population differences (e.g., SLC24A5 and Duffy) are the raw material from which independent dimensions can pop out. But on a finer scale one has to be cautious because the distribution of elements on the plot as a function of principal components is sensitive to the variation you input to generate the dimensions in the first place.
I can give you a concrete example: me. I showed you my 23andMe ancestry painting yesterday. I didn’t show you my position on the HGDP data set because I’ve shared genes with others and I don’t want to take the step of displaying other peoples’ genetic data, even if at a remove. But, I have reedited some “demo” screenshots and placed where I am on the plot to illustrate what I’m talking about above. The first shot is my position on the two-dimensional plot of first and second principal components of genetic variation from the HGDP data set.
Dienekes has a post up where he highlights the fact that the recent paper on South Asian metabolic diseases has a figure which elucidates population structure within the region. Accounting for structure is important for genome-wide associations since you might get a spurious correlations if trait value/disease frequency is simply tracking cryptic population variation. Dienekes says:
The existence of two clusters is kind of obvious, while their interpretation is not as dots of the same color appear in both clusters: a placement of these individuals in a global context might have been useful here. Things are clearer at the top cluster which shows a clear gradient anchored by Punjabi Sikh and Hindu Tamils on either end.
Also of interest is the group of isolated Muslim/Christian individuals on the left which deviate strongly from the mainstream; these probably represent exogenous elements that don’t resembe the bulk of the Indian population.
The second issue is easily addressed. The Christian outliers are both give English as their native language. That suggests to me that they’re Anglo-Indian, a community of mixed South Asian and European origin. South Asian Muslims are overwhelmingly of indigenous origin. But, a minority of the Muslim elite are West Asian, or have substantial West Asian ancestry, as is evident by the fact that they look white. Benazir Bhutto’s mother was of Kurdish and Persian ethnic background (her family was from Esfahan in Iran). I’ve reedited the religious & linguistic PC plots to fit onto the screen.
Despite the reality that I’ve cautioned against taking PCA plots too literally as Truth, unvarnished and without any interpretive juice needed, papers which rely on them are almost magnetically attractive to me. They transform complex patterns of variation which you are not privy to via your gestalt psychology into a two or at most three dimensional representation which can you can grok immediately. That is why History and Geography of Genes was so engrossing. You recognize patterns which were otherwise unrecognizable. But how you interpret those patterns, that’s a wholly different matter. And how those patterns arise is also not something one can ignore.
First, let’s start with an easy case. To the left is a PCA plot with four populations. Nigerians, East Asians (Chinese + Japanese), Europeans (whites from Utah), and finally, African Americans. The x-axis is the first principal component of variation, and the y-axis the second. That means that the x-axis is the independent dimension of variation within the patterns of genetic data which explains the largest fraction of the total amount of genetic variation. The sum totality of the variation can be decomposed into an large set of independent dimensions which can be rank ordered from the largest explanatory components to the smaller ones, successively by number. In a human genetic context the first principal component invariably separates Africans from non-Africans, and the second principal component often maps onto a west-east axis from Europe to the New World. Subsequent principal components can often be useful in smoking out fine scale distinctions, or relationships which are confused by the existence of similar but different signals in admixed populations.
The interpretation of this plot is rather easy. You see that African Americans lay along a continuum between Nigerians and Europeans, skewed toward Nigerians, with some outliers toward East Asians. We know from other genetic findings that ~20% of the African American ancestral quanta is European, but, that quanta is not equally distributed across the population. ~10% of the African American population is more than 50% European in ancestry, while 90% is less than 50% European. And so you have a distribution which reflects this variation. As for the outliers, I will speculate and suggest that these are indications of Native American ancestry among some African Americans.
The story I presented above is probably plausible as an explanation of the visual because we have a wealth of historical data to corroborate the plausibility of that narrative. The fit between the results from the technique of analysis of genetic variation and what scholars have long inferred from textual sources is relatively easy. It is far more difficult to look at a PCA plot, and generate a plausible narrative that you yourself accept with a high degree of confidence with little external support. It is with that caveat in mind that I present Toward a more uniform sampling of human genetic diversity: A survey of worldwide populations by high-density genotyping: