My friend Zack Ajmal has been running the Harappa Ancestry Project for several years now. This is a non-institutional complement to the genomic research which occurs in the academy. His motivation was in large part to fill in the gaps of population coverage within South Asia which one sees in the academic literature. Much of this is due to politics, as the government of India has traditionally been reluctant to allow sample collection (ergo, the HGDP data uses Pakistanis as their South Asian reference, while the HapMap collected DNA from Indian Americans in Houston). Of course this sort of project is not without its own blind spots. Zack must rely on public data sets to get a better picture of groups like tribal populations and Dalits, because they are so underrepresented in the Diaspora from which he draws many of the project participants.
Once Zack has the genotype one of the primary things he does is add it to his broader data set (which includes many public samples) and analyze it with the Admixture model-based clustering package. What Admixture does is take a specific number of populations (e.g. K = 12) and generate quantity assignments to individuals. So, for example individual A might be assigned 40% population 1 and 60% population 2 for K = 2. Individual B might be 45% population 1 and 55% population 2. These are not necessarily ‘real’ populations. Rather, the populations and their proportions are there to allow you to discern patterns of relationships across individuals.
Since Zack has put his results online, I thought it would be useful to review what patterns have emerged over the past two years, as his sample sizes for some regions are now moderately significant. Though he has K=16 populations, not all of them will concern us, because South Asians do not tend to exhibit many of the components. I will focus on seven: S Indian, Baloch, Caucasian, NE Euro, SE Asian, Siberian and NE Asian. These are not real populations, but the labels tell you which region these components are modal. So, for example, the “S Indian” component peaks in southern India. The “Baloch” in among the Baloch people of southeastern Iran and southwest Pakistan. The “NE Euro” among the eastern Baltic peoples. The last three are Asian components, running the latitude from south to north to center. They only concern the first population of interest, Bengalis. I will combine these last three together as “Asian.”
Below is a table, mostly individuals from Zack’s results (though there are some aggregate results from public data sets). Comments below.
I got my daughter a netbook, so now my computer is doing Harappa Prohect work 24×7.
Also, Simranjit was nice enough to offer me the use of a server. For privacy reasons, I am not going to upload any of the participants’ data there but it is much faster than my machine and hence very useful for running Admixture on the reference data (especially with crossvalidation).
As for steps back, I downloaded the current 1000genomes data (1,212 samples, 2.4 million SNPs). It’s in vcf format. Using vcftools to convert it to ped format will take about 3 weeks. Yes you heard that right. BTW, the good stuff from a South Asian point of view will come later this year with a 100 Assamese AhomF, 100 Kayadtha from Calcutta, 100 Reddys from Hyderabad, 100 Maratha from Bombay and 100 Lahori Punjabis.
Also, I spent most of Sunday evening and night in the ER and got a diagnosis of ureterolithiasis for my efforts. All I can say is: Three cheers for Percocet!!
First, wish Zack well. Second, he has over 70 individuals in the Harappa Ancestry Project data base (in addition to the public data sets). If you’re South Asian, Iranian, Burmese, or Tibetan, here are the details of participation.
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.
Zack has started to improve on static R plots with Google powered charts. Check it out. Alas, I can’t inject script tags into the body of my posts, so that’s not feasible for me. Notice on Zack’s plot that I’m more East Asian than either of my parents. The tendency first cropped up with 23andMe’s ancestry painting, and I have seen it in my own ADMIXTURE runs, so I don’t dismiss it as V2 vs. V3 chip anymore. Though I’ve ordered an upgrade myself, so we’ll see for sure. Also, though both my parents are about the same East Asian, they exhibit a different balance of East Asian subcomponents. I’ve seen this in my own ADMIXTURE runs, and I’m going to check for more fine-grained matches with the HGDP East Asian populations soon to ascertain whether their eastern ancestral mix is different. Good times.
Since I know plenty of friends are getting, or just got, their V3 results, I thought I’d pass this on, Open-ended submission opportunity for 23andMe data (#2):
Who is eligible
Everyone who is of European, Asian, or North African ancestry and all four of his/her grandparents are from the same European, Asian, or North African ethnic group or the same European, Asian, or North African country.
Also, Zack has more than 30 individuals in HAP. The “cow belt” is still way underrepresented. The only Bengalis in the data set are my parents.
As you have begun interpreting the reference results, let me make a friendly warning: you have to keep in mind that most of the reference populations of ethnic groups are extremely limited in sample size (with only between 2 and 25 individuals) and from very obscure sources, and you should keep away from drawing conclusions about millions of people based on such limited number of individuals.
This seems a rather reasonable caution. But I don’t think such a vague piece of advice really adds any value. These sorts of caveats are contingent upon:
– The scope of the question being asked (i.e., how fine a grain is the variation you are attempting to measure going to be)
– The sample size
– The representativeness
– The thickness of the marker set (10 autosomal markers vs. 500,000 SNPs)
Zack is going to post the first batch of results from HAP tomorrow. It looks like he’s going to be using mostly the merged HGDP, HapMap, SVGP, and Behar data set, supplemented by a second set which also merges the Xing et al. sample (the intersection of Xing et al. with the other results is a much smaller number of SNPs, but, it includes a better coverage of various South Asian groups). He’ll initially be posting ADMIXTURE estimates as you’ve seen on Dodecad. I’m especially interested in the Anglo-Indian and Roma individuals which have sent Zack their samples. I don’t know of any genomic investigation of the former community, while the published research on Roma genetics doesn’t include SNP-chip results (usually they’re mtDNA, Y, or only a few autosomal markers). I’d be curious for possible evidence of homozygosity or linkage disequilibrium in the Roma individual due to the population bottlenecks which other studies have detected (I assume that’ll be in the future). The Roma are to a good approximation an admixture of India, West Asia, and European (often Balkan) groups, but, their history of endogamy and small founding groups experience rapid demographic expansion, are also critical to remember.
Here is the regional breakdown so far:
Zack has been posting his data sources, as well as how he filtered and formatted them, all this week. I assume that the first wave of results will be online soon. As of yesterday, this is what he had (I know he got some more today):
– Punjab 7
– Bengal 1
– Bihar 1
– Tamil 5
– Karnataka 1
– Anglo-Indian 1
– Roma 1
– Iran 3
Whole swaths of north-central India are missing. I am hopeful that more people will join in after the first wave of results are put out there. But, from what I have discussed with Zack it looks plausible that the very first wave will have a richer set of results because of the necessity of preliminary steps. So there’s some benefit in getting early. It’s really ridiculous to have literally 1 sample representing the 300 million people of Uttar Pradesh and Bihar. That’s 25% of South Asians represented by one person. I’ve gotten a commitment from one friend who was born U.P. to give his data up once it comes in, but there have to be others out there. (the Bengali N should go up to 2 when I swap my parents in for me)
The public data sources have Gujaratis, Tamils, Pakistanis (Punjabis, Pathans, Sindhis), and some South Indian groups (Tamil and Telugu). This leaves a blank spot on the North Indian plain.
Here’s the brief for the project again.
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.
This is the feed:
If your ancestry is from these nations:
Read on! If not, “for entertainment purposes only”….