Tag Archives: duplicate

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.

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Pan-Asian Dataset Duplicates and 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.

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.

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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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Henn 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 Henn et al dataset, which you can download from their website.

There are 107 samples common from the HapMap (IDs start with NA) and 131 from HGDP (IDs start with HGDP).

Henn et al has two PED files. One for the Khoisan data and one for all Africa 55k SNP set. Unfortunately they have 31 San duplicated in both these PED files with same individual IDs but different family IDs (SAN and SAN_SA). So they do not get automatically merged per Plink procedures. Just remove all the ones with SAN_SA FID since they have fewer SNPs. All the IBD info etc is in this spreadsheet.

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Xing 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 Xing et al dataset, which you can download from their website.

I found no duplicates within the Xing et al data but there are 259 samples common from the HapMap. Since they are not assigned any family IDs they will pass through the ped files without being merged into HapMap samples. So you need to remove any samples with IDs starting with "NA".

Xing et al also contains 6 duplicates from HGDP with completely different IDs and two Xing samples look to be related to HGDP samples.

There are also three 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 85% similar.

The samples I have removed as a result of this (other than HapMap) are listed in this spreadsheet.

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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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HapMap 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 HapMap, which you can download from their website. I am using HapMap 3 public release #3 from May 28, 2010.

I found one set of duplicates, NA21344 is identical to NA21737. And a whole bunch of pairs with 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 85% similar in fact.

All the 41 samples I have removed as a result of this are listed in this spreadsheet.

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