Tag Archives: rasmussen

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.

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.

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.