./plink --bfile IBDControl --genome

Here are the relevant results:

You can infer some things without even knowing what the columns mean. Notice that there are differences between parent-child, sibling-sibling, and unrelated comparisons. The distance measure, DST, is basically exactly the same as the genome-wide comparison in 23andMe. Either the web app is running Plink, or, it’s using the exact same algorithm. Z0 = IBD 0, Z1 = IBD 1, and Z2 = IBD 2. Notice that with my siblings I have a fair amount of IBD 2, but far less with my parents. That’s because parents give you one copy, but you can share zero, one, or two, of a gene with your siblings. In contrast, with our parents there is hardly any IBD = 0, since they’re guaranteed to give you one copy. I assume that the IBD = 2 in that case is population wide fixation of a variant. Notice in the last column that there are different values for unrelated individuals (~2), siblings (~10), and parent-children (~500).

I ran a similar test among the Gujaratis. Remember that I’ve labeled them Gujarat_A and Gujarati_B based on PCA clusters, where the latter form a tight population cluster, and the former do not. Here are the mean pairwise DST values with the groups of pairs:

Mean of all: 0.746

Mean of Gujarati_A only: 0.744

Mean of Gujarati_B only: 0.749

Mean of Gujarati_A and Gujarati_B pairs only: 0.745

Gujarati_B are marginally closer to each other than Gujarati_A. I’m not sure these DST values are totally comparable to the ones from the 23andMe files. I’ll show you why. I constrained the pairs to those where the RATIO was > 2.5. Here’s what I found:

Notice that Z2 ~ 0, in contrast to the calculations above. I assume someone reading this knows that there’s a simple reason for this, so do tell. The IBD estimates for 23andMe always struck me as too high. In any case, to my surprise the definitely related individuals seem to be in the Gujarati_A cluster! What’s going on there? My first thought is that I messed up the data, or, I coded something incorrectly. I assume that this was double-checked before it got into the HapMap data set. Has anyone else seen this weird result? If not, I assume I made an error (that’s kind of my working model right now actually).

Let’s drill down to the level of individuals. Here are the Gujarati individuals, along with Sindhis, and my parents (Bengali). I’ve sorted by the “European” and then “South Asian” components (light blue and green respectively, while purple is modal in Papuans and red in East Asians):

The ADMIXTURE plots are in total alignment with the PCA. In the PCA Gujarati_A exhibit a spectrum of distance from the European cluster, and in the ADMIXTURE you see the same. In contrast, Gujarati_B is relatively uniform. So what’s going on? I will be posting something similar over at Sepia Mutiny soon. But my guess is that Gujarati_B are a subset of Patels. In other words, they’re a genetically distinct jati. I suspect that Gujarati_A are a more diverse bunch from a number of different jatis.

Does this matter? I believe it does. If Gujarati_B are a distinct ethno-social group which is a subset of Gujaratis, then they may not be as good a proxy for South Asian medical genetics as Gujarati_A. More concretely, Gujarati_B may have relatively high frequency rare disease alleles because they’re an inbred clan. In contrast, while Gujarati_A may exhibit all the hallmarks of South Asian endogamy, if they’re a larger number of different groups, then they’ll have all sorts of different rare alleles. The ones they have in common may be more generally South Asian.