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Recently I was looking at a 3-D PCA animation which Zack generated from the Harappa Ancestry Project data set. Click the link and come back. Notice the outlier clusters? The Burusho are straightforward, they seem to have low levels of Tibetan admixture. But what about the Gujarati cluster? Again, we see what we’ve seen before, the fractioning out of the Gujaratis in PCA into two groups, one a tight cluster, and the other relatively widely distributed. This prompted me to look more closely at the HapMap Gujarati sample. Today I was exploring the question with Plink’s identity-by-descent feature. First I’ll start out with a smaller data set, my family (father, mother, sibling 1, sibling 2, and myself), and an Indian (from Uttar Pradesh) and Pakistani as unrelated individuals. I merged out 23andMe derived genotypes, and with ~900,000 markers calculated pairwise IBD:

./plink --bfile IBDControl --genome

Here are the relevant results:

Individual 1 Individual 2 Z0 Z1 Z2 PI_HAT DST PPC RATIO
Indian Father 0.768 0.027 0.205 0.218 0.760 0.160 1.940
Indian Mother 0.782 0.010 0.209 0.214 0.759 0.026 1.886
Indian Razib 0.767 0.032 0.202 0.218 0.759 0.500 2.000
Indian Sibling1 0.769 0.025 0.206 0.219 0.760 0.198 1.949
Indian Sibling2 0.766 0.032 0.203 0.219 0.760 0.685 2.030
Indian Pakistani 0.781 0.017 0.203 0.211 0.758 0.533 2.005
Father Mother 0.776 0.018 0.207 0.215 0.759 0.284 1.965
Father Razib 0.002 0.777 0.221 0.610 0.851 1.000 450.800
Father Sibling1 0.001 0.785 0.214 0.606 0.850 1.000 898.800
Father Sibling2 0.002 0.779 0.220 0.609 0.851 1.000 643.143
Father Pakistani 0.778 0.019 0.203 0.213 0.758 0.201 1.950
Mother Razib 0.002 0.788 0.211 0.605 0.849 1.000 639.429
Mother Sibling1 0.002 0.781 0.218 0.608 0.850 1.000 639.857
Mother Sibling2 0.002 0.782 0.216 0.607 0.850 1.000 447.900
Mother Pakistani 0.779 0.020 0.201 0.211 0.758 0.052 1.904
Razib Sibling1 0.183 0.408 0.409 0.613 0.866 1.000 11.386
Razib Sibling2 0.194 0.432 0.374 0.590 0.858 1.000 11.491
Razib Pakistani 0.781 0.016 0.203 0.211 0.758 0.933 2.095
Sibling1 Sibling2 0.236 0.412 0.351 0.557 0.849 1.000 9.413
Sibling1 Pakistani 0.777 0.024 0.199 0.211 0.758 0.327 1.973
Sibling2 Pakistani 0.774 0.024 0.202 0.214 0.758 0.443 1.991

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:

Individual 1 Individual 2 Z0 Z1 Z2 PI_HAT DST PPC RATIO PopX PopY
NA20900 NA20891 0.003 0.974 0.023 0.510 0.842 1.000 188.250 Gujarati_A Gujarati_A
NA20909 NA20910 0.003 0.970 0.027 0.512 0.842 1.000 140.438 Gujarati_A Gujarati_A
NA20891 NA20907 0.412 0.557 0.032 0.310 0.803 1.000 5.730 Gujarati_A Gujarati_A
NA20900 NA20907 0.684 0.292 0.024 0.170 0.775 1.000 3.251 Gujarati_A Gujarati_A

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).

• Category: Science • Tags: Genetics, Genomics, Gujaratis 
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The figure to the left is a three dimensional representation of principal components 1, 2, and 3, generated from a sample of Gujaratis from Houston, and Chinese from Denver. When these two populations are pooled together the Chinese form a very homogeneous cluster. They don’t vary much across the three top explanatory dimensions of genetic variance. In contrast, the Gujaratis do vary. This is not surprising. In the supplements of Reconstructing Indian population history it was notable that the Gujaratis did tend to shake out into two distinct clusters in the PCAs. This is a finding you see over and over when you manipulate the HapMap Gujarati data set. In reality, there aren’t two equivalent clusters. Rather, there’s one “tight” cluster, which I will label “Gujarati_B” from now on in my data set, and another cluster, “Gujarati_A,” which really just consists of all the individuals who are outside of Gujarati_B cluster. Even when compared to other South Asian populations these two distinct categories persist in the HapMap Gujaratis.

Zack has already identified a major difference between the two clusters: Gujarat_A has some individuals with much more “West Eurasian” ancestry. To be more formal about this in the future I simply assigned individuals in my merged data set to one of the two Gujarati clusters based on their position in the first two PCs. Yesterday night I ran ADMIXTURE K = 2 to 10, with 75,000 SNPs. I also removed the Native American groups, and added more European and East Asian samples from the HapMap. Below are some populations at K = 4:

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.

Razib Khan
About Razib Khan

"I have degrees in biology and biochemistry, a passion for genetics, history, and philosophy, and shrimp is my favorite food. If you want to know more, see the links at"