Category Archives: Dataset - Page 2

Behar Bene Israel

Posted by on April 19, 2011 1 comment

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.

Reference 3 Fixed

Posted by on April 17, 2011 2 comments

I have fixed the problem with Reference 3 but if you notice any strange results, do let me know.

While the Reference 3 admixture results were generally good (and I have some nice surprises on the way I hope), the Reich et al populations had some weird behavior. From one K value to the next, their admixture would swing wildly especially among the minor components.

For example, for Chenchu, the 2nd component after South Asian was Southwest Asian (42%) at K=6, European (45%) at K=7 and American (32%) at K=8. That just didn't make any sense. It was similar for other Reich et al populations, but all the other reference populations seemed pretty stable.

The issue was that when I was creating Reference 3, I had to juggle lists of SNPs to figure out a way to include Reich et al with a large (>100,000) number of SNPs in the dataset since Reich doesn't have as many SNPs in common with the other datasets plus 23andme (v2 and v3) and FTDNA. In that effort where I was doing lots of SNP set intersections and unions I messed up. I used 217,000 SNPs. While these SNPs were present in all the other datasets, Reich et al had only 102,000 SNPs common with that set. Ouch! This was a royal mess as the high missing rate of Reich et al caused weird instability in its admixture results even though the rest of the results were mostly stable.

Now, I have pared down Reference 3 to 118,000 SNPs. These have a low missing rate in all the datasets. So I don't expect the same problems.

I am redoing the admixture runs with this new data and will have some of the results up soon.

Behar Paniya

Posted by on April 16, 2011 6 comments

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.

Introducing Reference 3

Posted by on April 13, 2011 34 comments

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.

Reich et al Duplicates

Posted by on April 12, 2011 Comments Off on Reich et al 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 Reich et al Indian dataset.

The dataset doesn't have any duplicate or likely relative samples itself. However, there are two Kharia samples that are the same as the Austroasiatic dataset. Since Austroasiatic dataset has more SNPs in common with 23andme, I removed these two samples from Reich et al.

The IBS/IBS analysis and the sample IDs are in a spreadsheet as usual.

1000genomes

Posted by on April 10, 2011 Comments Off on 1000genomes

I got the 1000genomes data a couple of weeks ago. Trying to convert it from VCF to PED format using vcftools was a complete disaster. Then Dienekes sent me a conversion script which was more than a hundred times faster.

1000genomes will have 100 Assamese Ahom, 100 Kayadtha from Calcutta, 100 Reddys from Hyderabad, 100 Maratha from Bombay and 100 Lahori Punjabis later this year. Right now, the new populations (other than HapMap) are British, Finns, Han Chinese South, Puerto Ricans, Colombians, and Spaniards.

I removed all the 660 samples which were common with the HapMap data. Also, there were 31 pairs with high IBD values. The list of IBD/IBS values and the samples I removed can be seen in the spreadsheet.

Latino Dataset

Posted by on April 9, 2011 1 comment

Razib mentioned a Latino/Hispanic dataset to me a few days ago.

The relevant paper is "Genome-wide patterns of population structure and admixture among Hispanic/Latino populations" by Katarzyna Bryca, Christopher Velezb, Tatiana Karafetc, Andres Moreno-Estradaa, Andy Reynoldsa, Adam Autona, Michael Hammerc, Carlos D. Bustamantea, and Harry Ostrer. And the data is available on the GEO Accession viewer.

The dataset has 100 samples from Colombia, Dominican Republic, Ecuador, and Puerto Rico.

It's in the same format and uses the same chip as Behar et al and Rasmussen et al. So it was really easy to download and convert it to Plink PED format.

Now what does a Hispanic dataset got to do with a South Asian genetics project? Nothing, for now. But I am collecting all genotyping data. And also I am hoping that we get more participants of South Asian origin from the Caribbean and other countries of the region where there has been a longer presence of South Asians. In that case, it would be interesting to compare them against other populations of the Americas.

In keeping with my effort to clean the data of any relatives, here are the IBD/IBS analysis results. The 2nd sheet shows the two samples I removed.

Pan-Asian Dataset Duplicates and Relatives

Posted by on April 9, 2011 2 comments

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.

Looking at the Pan-Asian dataset, I found 3 pairs of duplicate samples and 82 pairs that could be closely related. I have removed 64 samples from the dataset.

You can see the IBD results from plink as well as the list of sample IDs I removed in a spreadsheet.

UPDATE: I found 4 Melanesians in the Pan-Asian dataset who were the same as those in HGDP. So I have removed those as well and added them in the list in the spreadsheet.

Austroasiatic Dataset Duplicates

Posted by on April 8, 2011 Comments Off on 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.

Rasmussen Likely Relatives

Posted by on April 7, 2011 Comments Off on Rasmussen Likely Relatives

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 Rasmussen et al dataset, which you can download from here.

While there are no duplicates, 9 pairs of samples have high IBS values (85% similar or more) and seem to be related (Plink PI_HAT > 0.5). You can see the IBD results in a spreadsheet, along with the 8 samples I removed.