From my Reference 3 dataset, I excluded the following populations for this set of admixture runs:

• Biaka Pygmy
• Mbuti Pymy
• San
• Bantu South Africa
• Hadza
• Chukchis
• Koryaks
• Colombian
• Dominican
• Ecuadorian
• Karitiana
• Maya
• Mexican
• Pima
• Puerto Rican
• Surui
• East Greenlanders
• West Greenlanders
• Australian aboriginals
• Melanesian
• Papuan

The San and Pygmy were removed since they are very distinct and take up clusters and the South African Bantu because they have significant admixture from the San. The Hadza seem to be a unique population too.

The Chukchis and Koryaks are Beringian populations from the Russian Far East which separate from the Siberian and Turco-Mongol groups at higher K's.

I also excluded all the American populations because our focus is on South Asia and environs. I have a few participants with Amerindian ancestry and I can always run their analyses with the full reference 3.

The Papuans and Melanesians take up 2 ancestral components in admixture at times and since admixture works well only for about K<12 or so, those are precious. Also, I originally thought that South Asians (specifically the ASI) might have some affinity with Papuans but that hasn't borne out. In addition to removing these populations, I reduced the number of samples of various groups (except South Asian ones) to 25 individuals so that admixture won't rely too heavily on any of those large groups (like the 161 Yoruba). In selecting individuals from these populations, I chose those closest to the median in terms of their admixture results. The admixture results of this dataset are in a spreadsheet as usual and the bar chart is below.

K=12 is the one with the lowest cross-validation error.

I am going to post another series of admixture runs tomorrow and then you guys can let me know which specific runs you like so we can switch to those for the project participants.

Another set of admixture runs. This one uses the South Asian, Middle Eastern, Caucasian, Central Asian, Southeast Asian and Oceanian samples from Reference 3.

Basically I consider these to be our target populations. The idea is to build out from here by adding a few samples from other populations to make the results better.

Right now, the absence of African, European, East Asian and Siberian populations makes some of the other populations substitute for them. For example, Siddi works as African substitute while Aonaga works as East Asian substitute.

Here are the admixture results. You can choose the number of ancestral components, K, from the dropdown below.

I find K=11 and K=14 to be the most interesting. They have the two lowest cross-validation errors too.

I removed all American populations and San and Pygmy (i.e., South and Central African) from Reference 3 for a better focus on our target populations.

Here are the admixture results. You can choose the number of ancestral components, K, from the dropdown below.

K=13, 14, 15 (in that order) have the lowest cross-validation error.

There's a bunch of interesting results in there. For example, the split into northern and southern European, and the split of Siberian into Siberian and Russian Far East (or Bering Strait). However, the Onge component as a proxy of the ASI does not appear. Also, we don't get much breakdown of the South Asian populations as we would like.