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Simonson Tibet Dataset

Recently, I discovered that the paper Genetic Evidence for High-Altitude Adaptation in Tibet by Tatum S. Simonson, Yingzhong Yang, Chad D. Huff, Haixia Yun, Ga Qin, David J. Witherspoon, Zhenzhong Bai, Felipe R. Lorenzo, Jinchuan Xing, Lynn B. Jorde, Josef T. Prchal, RiLi Ge has its genotyping data online.

It contains 31 Tibetans from Madou county in Qinghai province. The chip is Affymetrix and there are 868,146 SNPs, which means it has a good overlap with Reich et al and Xing et al and also with my reference 3.

I ran reference 3 K=11 admixture on this dataset. Here are the individual results:

The average is as follows:

S Asian E Asian Siberian
1% 84% 14%

Dense South Asian ChromoPainter

I had run ChromoPainter/fineSTRUCTURE for 715 South Asians using only about 90,000 SNPs. I thought it would be a useful exercise to use more SNPs, so I had to drop the Reich et al dataset. That left me with 615 individuals and 418,854 SNPs.

The "chunkcounts" file has the donors in columns and recipients in rows. Here's a heat map of the same.

fineSTRUCTURE classified these 615 individuals into 89 clusters. I have named these clusters for convenience, however, the names do not imply that anyone in the Punjab cluster is Punjabi.

While I created the cluster tree at the top of the spreadsheet, here's how the clusters are related.

The most interesting thing is how Gujarati A (likely Patels) are an out-group to everyone else. Another major grouping is that of the Baloch, Brahui and Makrani, along with 4 Sindhis (might be one of the Baloch tribe of Sindh?).

The Punjabis, Sindhis and Pathan get better classification here than they did last time.

The Punjab cluster includes 3 Gujarati B, 4 Pathans, 2 Singapore Indians, Punjabis, Haryanvis, Kashmiris, and a Rajasthani Brahmin. Even using this method, HRP0036, who is half-Sri Lankan and half-German/Polish was classified in the same cluster.

The Dharkar and Kanjar could not be separated at all here. According to Metspalu:

There are three second degree relatives groups in our sample: ..snip.. [Kanjar evo_37 and Dharkar HA023]. Again the last pair needs further explanation. The Dharkar and Kanjar practice a nomadic lifestyle and were living side by side at the time of sampling. As the ethnic border between the two is permeable we cannot rule out neither our error during sample collection and/or subsequent labelling nor shifted self-identity.

The inter-cluster heat map:

And you can see the chunkcounts donated from each cluster to recipient individuals in a spreadsheet.

The pairwise coincidence:

And the PCA plots:

Relatives in Datasets

Recently, there was a paper Identification of Close Relatives in the HUGO Pan-Asian SNP Database by Xiong Yang, Shuhua Xu, and the HUGO Pan-Asian SNP Consortium.

three individuals involved in MZ pairs were excluded from the whole dataset to construct standardized subset PASNP1716; seventy-six individuals involved in first-degree relationships were excluded from PASNP1716 to construct standardized subset PASNP1640; and 57 individuals involved in second-degree relationships were excluded from PASNP1640 to construct standardized subset PASNP1583. The individuals excluded were summarized in Table S6, S7, S8.

Let me engage in some blog triumphalism by saying I wrote about the duplicates and relatives in the Pan-Asian dataset in April 2011.

Here are my blog posts about relatedness in datasets:

Early on, I was removing only first degree relatives from the reference datasets. Nowadays, I try to remove all second degree relatives too. I leave the third degree relatives in the data since it's sometimes hard to figure out how real the low IBD values are in Plink. There are a lot of 3rd degree relatives if Plink is to be believed, but I am a little skeptical.

Since Plink's IBD analysis requires homogenous samples, I am now using KING (paper) for the purpose. I am also looking at kcoeff (paper)

South Asian PCA Plots

I did a South Asian PCA + Mclust analysis last month. Here are the PCA plots from that analysis.

First, the eigenvectors are not scaled to the eigenvalues in the plots. So here's a table explaining how much each eigenvector is worth.

Eigenvector Percentage variation explained
1 1.134%
2 0.452%
3 0.351%
4 0.263%
5 0.254%
6 0.236%
7 0.228%
8 0.224%
9 0.215%
10 0.209%
11 0.207%
12 0.205%
13 0.203%
14 0.201%
15 0.198%
16 0.194%
17 0.191%
18 0.189%
19 0.189%
20 0.188%
21 0.188%
22 0.187%
23 0.186%
24 0.185%
25 0.184%
26 0.184%
27 0.183%
28 0.182%
29 0.180%
30 0.180%
31 0.179%
32 0.179%

Eigenvector 1 looks like the Indian cline but it's actually a West-East Eurasian cline. It's quite similar to Reich et al's Indian cline for their subset of populations (correlation between pc1 and ASI is 0.998869) but since East Asian is not separated out here due to the lack of any East Asian samples, we get a mix of East Asian and Ancestral South Indian towards the right of the plot.

Eigenvector 2 separates Kalash from everyone else.

Metspalu et al Data Relatedness

I performed IBD analysis on the Metspalu dataset using plink and found the relatedness of the following samples to be too high.

ID1 Source1 Population1 ID2 Source2 Population2 IBD Estimate
Mawasi1 Metspalu Mawasi Mawasi1 Chaubey Mawasi 100%
VELZ260 Metspalu Velama Velama_184_R2 Reich Velama 99%
VELZ260 Metspalu Velama VELZ265 Metspalu Velama 19%
VELZ265 Metspalu Velama Velama_184_R2 Reich Velama 19%
D254 Metspalu Tharu Tharu_107_R1 Reich Tharu 99%
D260 Metspalu Tharu Tharu_108_R1 Reich Tharu 98%
evo_32 Metspalu Kanjar 321e Metspalu Kol 53%
HA030 Metspalu Dharkar HA039 Metspalu Dharkar 52%
A387 Metspalu Dusadh A388 Metspalu Dusadh 52%
A394 Metspalu Dusadh A395 Metspalu Dusadh 52%
A395 Metspalu Dusadh A393 Metspalu Dusadh 46%
A394 Metspalu Dusadh A393 Metspalu Dusadh 45%
A392 Metspalu Dusadh A393 Metspalu Dusadh 32%
A392 Metspalu Dusadh A395 Metspalu Dusadh 31%
A392 Metspalu Dusadh A394 Metspalu Dusadh 28%
evo_37 Metspalu Kanjar HA023 Metspalu Dharkar 27%
HA039 Metspalu Dharkar HA041 Metspalu Dharkar 24%
HLKP245 Metspalu Hakkipikki Hallaki_137_R2 Reich Hallaki 22%
PULD160 Metspalu Pulliyar PULD162 Metspalu Pulliyar 20%

As you can see, three samples from Reich et al seem to be the same as Metspalu et al. In addition, two Reich samples seem to be related to Metspalu samples.

There are some Metspalu samples who are likely related to one another. A 50% indicates likely a parent-child or sibling-sibling relationship. A 45-46% relatedness is most likely siblings in my opinion. An 18-19% percentage could be a 1st cousin relationship in an endogamous community. it could also just be the background relatedness in a small, bottlenecked and endogamous community.

It looks like about half of the Dusadh in the Metspalu dataset are related.

I am surprised at the close relationship of a Kanjar and a Kol in the dataset, though both are from Uttar Pradesh.

ANI/ASI Admixture Dating

Via Razib, here's an interesting abstract from the International Congress of Human Genetics by David Reich's group:

Estimating a date of mixture of ancestral South Asian populations.

Linguistic and genetic studies have shown that most Indian groups have ancestry from two genetically divergent populations, Ancestral North Indians (ANI) and Ancestral South Indians (ASI). However, the date of mixture still remains unknown. We analyze genome-wide data from about 60 South Asian groups using a newly developed method that utilizes information related to admixture linkage disequilibrium to estimate mixture dates. Our analyses suggest that major ANI-ASI mixture occurred in the ancestors of both northern and southern Indians 1,200-3,500 years ago, overlapping the time when Indo-European languages first began to be spoken in the subcontinent. These results suggest that this formative period of Indian history was accompanied by mixtures between two highly diverged populations, although our results do not rule other, older ANI-ASI admixture events. A cultural shift subsequently led to widespread endogamy, which decreased the rate of additional population mixtures.

I would be very interested in reading that paper. Also, I wonder how many new samples did they genotype beyond the ones in Reich et al' Reconstructing Indian Population History and if I could get my hands on the new data.

I have a feeling that ANI (Ancestral North Indian) captures a bunch of different migrations and conquests etc, so I am not sure if it can be equated to Indo-European language movement.

I wonder if I can use HAPMIX or StepPCO to get similar admixture dating.

Misuse of Correlation

I have been misusing correlation in computing Ancestral South Indian percentages from PCA/ADMIXTURE and Reich et al population-level averages.

I have tried to make it clear that just looking at the correlation is not enough, that an admixture component is not similar to ASI just because it correlates well with Reich et al's ASI averages for the 18 Indian cline populations. Even when the correlation is higher than 0.99. To illustrate what I mean, let's look at the Ref4C admixture runs.

I calculated the mean for each admixture component from the K=2 to K=12 runs for the 18 Indian cline populations and then computed the correlation between that and the Reich et results. Let's take a look:

K Component Correlation
2 C1 Euro-Afro -0.9941887
3 C2 East Asian 0.9955347
4 C3 European -0.993933
5 C3 European -0.993277
6 C1 South Asian 0.9675099
7 C1 South Asian 0.993081
8 C1 South Asian 0.9932762
9 C1 South Asian 0.9914145
10 C1 South Asian 0.9918095
11 C1 South Asian 0.9919097
12 C1 South Asian 0.9918594

Where do you see the highest correlation? At K=3 ancestral populations, the East Asian component is very highly correlated with ASI for the Indian cline populations. Does that mean that we could use that to compute ASI? No, not at all. While it is expected that at K=3, ASI would be a little closer to East Asian than to European, East Asian is not a good proxy for ASI at all since we cannot extrapolate to other individuals and populations.

Indian Cline II

One thing I forgot in the post yesterday about the Indian cline was to try to extrapolate from the PCA results to 100% ANI (Ancestral North Indian) and 100% ASI (Ancestral South Indian).

This is a simple linear extrapolation which should be okay since PCA is linear.Men's Club - Онлайн Журнал

The "N" denotes the extrapolated position of ANI and "S" denotes the ASI. The points to the left of "N" are all Utahn Whites while the Onge are on the bottom right of the graph.

As you can see, the ASI is about the same as Onge in terms of eigenvector 1 (which represents the Indian cline approximately), but ASI is far from Onge on the 2nd eigenvector. That is expected since the Onge have been separated from the mainland populations for a long time.

The more interesting thing is that the extrapolated position of ANI is a little to the right of all the Utahn Whites.

We'll need a similar analysis of the Indian cline with more populations to see which one the ANI is closest to.

PS. I should point out that I am using correlation between a limited number of population statistics to find a relationship between the 1st principal component and Reich et al's ASI estimate. This has a number of drawbacks. It would be much better to compute ASI directly.

Indian Cline

I had used linear regression to estimate Ancestral South Indian (ASI) component from Reference 3 K=11 admixture run. Now here are a couple more exercises along the same lines but much simpler.

Just using the 96 Indian cline samples from Reich et al to compute PCA or admixture doesn't work as the Chenchu separate out in both analyses from the rest. So I added the Utahn White (CEU) samples from HapMap and the Onge from Reich et al.

First, I ran supervised admixture with two ancestral components, Utahn Whites and Onge. Here's the Onge component plotted against Reich et al's ASI estimate along with a linear regression estimate. The correlation between the two is 0.9908.

Second, I ran Principal Component Analysis (PCA) on the Indian cline samples plus Utahn Whites and Onge. Here are the first two PCA dimensions plotted. The first eigenvector explains 4.04% of the total variation and the 2nd explains 1.94%.

The first principal component is mostly along the Indian cline while the second one basically separates the Onge from everyone else.

Using the 1st principal component to estimate ASI, here's the plot with Reich et al's ASI estimate along with a regression line. The correlation between pc1 and ASI is 0.9929.

Note that both these methods work only if the samples are on the Indian cline, i.e., they don't have any other admixture.

And now for comparison, here's the linear regression for the Reference 3 K=11 admixture Onge component and ASI. The correlation here is 0.9949. Note that this is a little different than my previous analysis since I calculated the population averages using only the 96 samples recommended by Reich et al.

Here's a spreadsheet containing the data for these three runs.

There are a couple more tricks I have to figure out some things regarding Ancestral South Indian admixture. Let's hope they provide us some insight.

Harappa (1-90) K=11 Admixture Ref3

Here's my first admixture run using Reference 3 for Harappa participants. Since K=11 was the run with the Onge-ASI connection, I ran admixture at K=11 with all the 90 Harappa participants.

You can see the participant results in a spreadsheet as well as their ethnic breakdowns and the reference population results.

Here's our bar chart and table. Remember you can click on the legend or the table headers to sort.

Using the comparison between the Onge component here and Reich et al's Ancestral South Indian one, I get the following linear regression.

The correlation is 0.9949 which is probably as high as it can get. So let's calculate the ASI percentage for all the Harappa participants.

Note that I didn't calculate the ASI percentage for those who had a really low Onge component since the linear regression above would not be valid outside the range we have in our original data.

You can see the percentages in a spreadsheet too.

Let's compare with the Dodecad ANI-ASI results. I have 22.5% ASI here while it was 20.6% in the Dodecad analysis. Overall, it seems like my technique results in about 2% more ASI than Dodecad's, with a few exceptions: Like Razib who jumps from 34.3% to 43.3% (averaging his parents who are very close).