Tag Archives: behar

Behar Bene Israel

As Razib and I were discussing, the four Bnei Menashe Jewish samples from Behar et al didn't look right since Bnei Menashe are from Mizoram in the northeast of India and thus should be expected to have some East Asian admixture.

When I tried to confirm the admixture/PCA results for Bnei Menashe in the Behar et al paper, I didn't find any mention of the group. Instead, the South Asian Jewish group they mentioned was Bene Israel. According to their admixture and PCA results, Bene Israel looked more like Pakistani populations than their Indian host populations. This is consistent with what my admixture runs show.

So I suspected that the four Bene Israel samples mentioned in the Behar et al paper were accidently labeled as Bnei Menashe in the dataset. I sent an email to the authors and they have confirmed that this was the case.

I have corrected all my spreadsheets so you should see Bene Israel instead of Bnei Menashe now. If you spot Bnei Menashe anywhere, please let me know.

PS. Also, it has been confirmed that three Paniya samples were mislabeled when the data was submitted to the GEO database. They are working on fixing it soon.

UPDATE: Mait Metspalu tells me that the database has been updated with the fixed version of the Behar et al dataset.

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Behar Paniya

Behar as in the Behar et al paper/dataset and not the Indian state of Bihar. The Behar dataset contains 4 samples of Paniya, which apparently is a Dravidian language of some Scheduled Tribes in Kerala.

I had always been suspicious of those four samples since one of them had admixture proportions similar to other South Indians but the other three were like Southeast Asians.

When I got the Austroasiatic dataset, I found out that they had the four Paniyas from Behar et al in their data. However, only one of those four was the same as Behar. The other three were different. So I now had 7 Paniya samples.

Let's look at the K=12 admixture results for these Paniyas.

Behar's GSM536916 was the one which was the same as Austroasiatic's D36 and it has regular South Indian results. The other three Behar Paniyas are very Southeast Asian (yellow in the plot) while the three Paniyas from Austroasiatic data are similar to GSM536916/D36.

Since the Austroasiatic Paniya samples originated from Behar et al, I guess at some point before the Behar data being submitted to the GEO database the Paniyas got mislabeled.

I am now excluding the four Paniyas from Behar et al dataset and only using the Paniya samples from Austroasiatic dataset.

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Introducing Reference 3

Having collected 12 datasets, I have gone through them and finally selected the samples and SNPs I want to include in my new dataset, which I'll call Reference 3.

It has 3,889 individuals and 217,957 SNPs. Since this is a South Asia focused blog, there are a total of 558 South Asians in this reference set (compared to 398 in my Reference I).

You can see the number of SNPs of various datasets which are common to 23andme version 2, 23andme version 3 and FTDNA Family Finder (Illumina chip).

The following datasets had more than 280,000 SNPs common with all three platforms and hence were included in Reference 3:

  1. HapMap
  2. HGDP
  3. SGVP
  4. Behar
  5. Henn (Khoisan data)
  6. Rasmussen
  7. Austroasiatic
  8. Latino
  9. 1000genomes

Reich et al had about 100,000 SNPs in common with 23andme (v2 & v3 intersection) and 137,000 with FTDNA, but there was not a great overlap. Only 59,000 Reich et al SNPs were present in all three platforms. Since I really wanted Reich et al data in Reference 3, I included it but the SNPs used for FTDNA comparisons won't be the same as for the 23andme comparisons.

Of the datasets I could not include, I am most disappointed about the Pan-Asian dataset since it has a good coverage of South and Southeast Asia. Unfortunately, it has only 19,000 SNPs in common with 23andme v2 and 23,000 with 23andme v3. I am going to have to do some analyses with the Pan-Asian data but it just can't be included in my Reference 3.

I am also interested in doing some analysis with the Henn et al African data with about 52,000 SNPs for personal reasons.

Xing et al has about 71,000 SNPs in common with 23andme v3, so some good work could be done with that, though I'll have to use only 23andme version 3 participants.

The information about the populations included in Reference 3 is in a spreadsheet as usual.

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Austroasiatic Dataset Duplicates

As part of my effort to create one big reference dataset for my use, I have been going over all the datasets I have and make sure there's no duplicates or relatives or any other strange things that could cause issues with my analysis.

So I went back to the Chaubey et al Austroasiatic Indians dataset.

The dataset doesn't have any duplicate or likely relative samples itself. Of course, I had to remove the 632 HGDP samples it had, but that's easy to do since they have the same IDs (starting with HGDP).

As their paper mentions, the dataset also has 19 Dravidian speaking Indian samples from Behar et al. Since I got Behar et al data from the GEO site, I had different IDs for them than what they use in this dataset. So I had to figure out which samples were the same in both. The IBS/IBD results of duplicates as well as the list of sample IDs I removed is given in a spreadsheet.

Checking this out resolved an issue I had with Behar et al. Behar et al has 4 Paniya samples from South India. One of those four has admixture proportions similar to Indians but three seem very East Asian. I had always suspected that those three samples were mislabeled. Now the Austroasiatic dataset also has those four Paniya samples. However, only one of them is identical to the Behar et al one. The other three are different. I haven't checked yet which one of the Behar samples matches Austroasiatic, but my guess is that it is the more Indian admixture one. So I am keeping the other three Paniya samples from the Austroasiatic dataset and hoping that they are the correct ones.

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Behar Redo

As part of my effort to create one big reference dataset for my use, I have been going over all the datasets I have and make sure there's no duplicates or relatives or any other strange things that could cause issues with my analysis.

So I went back to the Behar et al dataset, which you can download from the GEO Accession website.

I found three set of duplicates and two pairs with very high identity-by-descent values, which I calculated using Plink. You can see the samples with PI_HAT greater than 0.5 in this spreadsheet. PI_HAT is the proportion IBD estimated by plink. Notice also that all these pairs also have high IBS similarity (the DSC column), more than 83% similar.

The five samples I have removed as a result of this are listed in this spreadsheet.

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Iranians

Since we have 7 Iranians in the project, it's time to look at them as a group. We also have 19 Iranians from the Behar et al dataset.

Let's look at their admixture results at K=12.

The big difference between Harappa Project Iranians and Behar et al Iranians is African admixture. Only one Harappa Iranian (HRP0046) has 1% African admixture while three Behar Iranians have more than 10%.

Let's do hierarchical clustering with complete linkage using the Euclidean distance between admixture components. First a caveat or two. This is not a phylogeny. Also, the Euclidean distance measure is not a good one for measuring differences in admixture but I am not sure what would be better.

HRP0010 who is an Assyrian actually clusters better with Caucasian, Iranian and Iraqi Jews than with Iranians.

I'll run an MDS or PCA of the whole region from Punjab/Kashmir to the Levant and Caucasus soon which should be more interesting for clustering.

UPDATE: Since Palisto wondered, I checked and found out that he, an Iraqi Kurd, is very like the Iranians in his admixture result. So I have included him (HRP0059).

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One PED File to Rule Them All

I am interested in North African populations due to my own heritage, so when Razib alerted me that Henn et al had a paper out about South African origins of humans and their African dataset was publicly available and included populations from all over Africa, I immediately downloaded it.

I have also been considering looking into the East Asian admixture in South Asians and Iranians in some detail to see where it originates from: Southeast Asia, Chinese/Japanese/Koreans, or the Turkic/Mongolian/Siberian populations of interior northeastern Asia. At a quick glance, Razib is correct:

The eastern Asian components are enriched among Bengalis, as you’d expect, but they’re found in different proportions among many individuals who hail from the northern fringe of South Asia more generally. It seems clear that the further west you go, the more likely the “eastern” element is going to be Turk, while the further east (and to some extent south) the more likely it is to be more southernly in provenance.

To do a better job though, it would be better to have more than the Yakut as an examplar of the Siberian component as I have done till now. Therefore, I downloaded the arctic populations dataset from Rasmussen et al.

Combining Henn et al and Rasmussen et al with my previous datasets (HapMap, HGDP, SGVP, Behar et al and Xing et al), I got 3,970 samples with a total of 1,716,031 SNPs represented, though at 99% genotyping rate it gets reduced to about 27,000 SNPs.

I did not remove any populations or individuals except for any duplicates and non-founders.

Here's the information on the populations represented in this dataset.

Now I am on the lookout for more datasets that are public, have enough SNPs in common with this set and can easily be converted into the Plink PED format. So if you know of any, let me know. May be I will have the biggest and most diverse dataset with your help.

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Reference Dataset II

Combining my reference population with Xing et al data gets me 3,222 3,161 samples but with only about 23,000 SNPs after LD-pruning.

The good thing is that this dataset has 544 South Asian samples from 24 ethnic groups. So it'll be useful for some analyses despite the low number of SNPs. I'll try to run parallel analyses on my reference population and this dataset so we can compare the pros and cons of both.

UPDATE: I removed 61 pygmy and San samples.

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Admixture: Reference Population

For regular admixture analysis, I am using HapMap, HGDP, SGVP and Behar datasets with some samples removed as I wrote earlier.

For each of these datasets,

  1. I first filtered to keep only the list of SNPs present in 23andme v2 chip.
    plink --bfile data --extract 23andmev2.snplist
  2. I also filtered for founders:
    plink --bfile data --filter-founders
  3. And excluded SNPs with missing rates greater than 1%:
    plink --bfile data --geno 0.01

Then, I merged the datasets one by one. The reason for doing it one by one was that there were conflicts of strand orientation (forward or reverse) between the different datasets. If the merge operation gave an error, I had to flip those strands in one dataset and try the merge again.

plink --bfile data1 --bmerge data2.bed data2.bim data2.fam --make-bed
plink --bfile data2 --flip plink.missnp --make-bed --out data2flip
plink --bfile data1 --bmerge data2flip.bed data2flip.bim data2flip.fam --make-bed

Once all the four datasets were merged, I processed the combined data file:

  1. Removed SNPs with a missing rate of more than 1% in the combined dataset
    plink --bfile data --geno 0.01
  2. Then i performed linkage disequilibrium based pruning using a window size of 50, a step of 5 and r^2 threshold of 0.3:
    plink --bfile data --indep-pairwise 50 5 0.3
    plink --bfile data --extract plink.prune.in --make-bed

This gave me a reference population of 2,693 2,654 individuals with each sample having about 186,000 SNPs. Out of these 2,693 2,654 individuals, we have a total of 398 South Asians belonging to 16 ethnic groups.

Finally, it's time to start having some fun!

UPDATE: I removed 39 Pygmy and San samples because they were causing some trouble with African ancestral components. Since we are not interested in detailed African ancestry and African admixture among South Asians is not likely to be pygmy or San, I decided it would be best to remove them.

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Behar et al Data

In their paper "The genome-wide structure of the Jewish people", Behar et al analyzed the genomes of some Jewish groups. More important than the Jewish samples (which include two South Asian Jewish groups) for us are the different South Asian, Middle Eastern, and European groups they sampled:

Ethnic group Count
Saudis 20
Jordanians 20
Georgians 20
Turks 19
Iranians 19
Hungarians 19
Ethiopians 19
Armenians 19
Lezgins 18
Chuvashs 17
Syrians 16
Romanians 16
Uzbeks 15
Spaniards 12
Egyptians 12
Cypriots 12
Moroccans 10
Lithuanians 10
North Kannadi 9
Belorussian 9
Yemenese 8
Lebanese 7
Sakilli 4
Paniya 4
Cochin Jews 4
Bene Israel 4
Samaritians 2
Russian 2
Malayan 2

Of the 466 samples, I excluded 8 because they were either duplicates or too similar in their genomes to others.

The series matrix files that I downloaded were in a somewhat different format. To convert them to Plink format, I had to look up the platform file for the Illumina genotyping BeadChip they used. Also, Illumina used an A/B alleles and Top/Bot strands system instead of the regular ACGT alleles and forward/reverse strands. This Illumina Technote explained it and I found a Perl script to convert between the two.

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