fastIBD analysis of Afroasiatic groups (Jews, Arabs, Assyrians, Berbers, Somalis, Amharas, etc.)

Please refer to the previous analysis on the Balkans/West Asia for more information about the interpretation of this type of analysis.

I am very pleased with the way this analysis of Afroasiatic groups has turned out, revealing an exceptional degree of resolution. I invite individuals from the Near East and Africa who are eligible, to submit their data, so that they can be included in future runs of this kind.

Clusters Galore


45 clusters were inferred with 29 dimensions.

I can't comment on all 45 clusters, so I'll just limit myself to the ones that are significantly represented among Project participants: 1. Ashkenazi, 4. Assyrian/Mandaean, 6. Somali, 7. Moroccan, 8. Algerian/Tunisian, 9. Sephardic, 10. Morocco Jews, 11. Iran/Iraq Jews, 12. Non-Jewish Ethiopians, 13. Saudi, 14. Arab #1, 15. Arab #2, 16. Egyptian

Inter-Population IBD

Results for Project Participants


The results can be found in the spreadsheet.

I have also added the full IBD sharing matrix which lists how many Morgans of sequence are estimated to be IBD with probability greater than 10^-6 between all pairs of individuals.

You can google any non-Project sample IDs to get some more information about their origin. For example, GSM536710 is an Iraqi Jew who shares about half his genome with GSM536714, also an Iraqi Jew. These two samples are almost certainly first-degree relatives. Or, GSM537032, a Samaritan shares 740-1,480cM with the other 2 Samaritans, an exceptional amount in this small and probably highly inbred population.

You can manipulate this matrix in R. After you download it and unzip it, you can load it into R as follows:

X<-read.table('afroasiatic_ibd_sharing.txt',row.names=1,header=T)

Then, you can, for example, sort the IBD sharing for a particular individual, as follows:

sort(X['DOD026',])

How to create Zombies from ADMIXTURE etc.

ADMIXTURE infers K ancestral populations, and estimates the admixture proportions of individuals from these K populations, as well as the allele frequencies for all SNPs for each ancestral population.

An interesting use of the allele frequencies is to generate synthetic "zombies" from the ancestral populations. These are artificial individuals whose genotypes are drawn randomly based on the allele frequencies. For example, there is a "West Asian" component in the Dodecad Project, but no individuals who have 100% membership in the "West Asian" component. A "West Asian" zombie is a synthetic individual who appears to be drawn from that "West Asian" component only, without any other (e.g., "South European", or "Southwest Asian") admixture at all.

"Zombies" may be viewed as either useful theoretical abstractions, or as reconstructed hypothetical ancient-like individuals, purged of centuries or millennia of admixture. Irrespective of how one views them, they are very useful as a tool.

Zombies of K=10 components

I generate 25 zombies for each of the 10 ancestral components of the Dodecad Project. Below, you can see an MDS plot of these 250 individuals, which is quite similar to the MDS plot generated using only the Fst divergences between the ancestral components.

Including real and "zombie" populations

I include the "West African", "North European", and "South European" zombie populations, together with 25 African Americans (ASW) from HapMap-3:

Notice the direction of the African American cline: slightly tilted towards North Europeans. This makes sense as the European ancestry of African Americans is derived mainly from Northwestern Europe and neither exclusively from the Mediterranean or Northern Europe where the "South European" and "North European" components peak.

Convert unsupervised ADMIXTURE runs to supervised ADMIXTURE

The most exciting use of "zombies" is to convert unsupervised ADMIXTURE runs into supervised ones. In unsupervised mode, ADMIXTURE treats all individuals alike, and tries to infer their ancestral proportions. In supervised mode, some individuals are treated as "fixed" (belonging 100% in one of K ancestral components), and the ancestry of the rest is inferred.

The idea is fairly simple: run an unsupervised ADMIXTURE analysis once to generate allele frequencies for your K ancestral components; then generate zombie populations using these allele frequencies; whenever you want to estimate admixture proportions in new samples run supervised ADMIXTURE analysis using the zombie populations.

You can thus use the zombie populations to mimic a regular (unsupervised) ADMIXTURE run. This is useful for two reasons:

1. It can be much faster: the initial set (of the unsupervised run) can be huge, but the zombie populations need only be large enough to capture the allele frequencies of the inferred components.
2. It avoids the generation of spurious clusters, especially if you include individuals from highly-inbred populations, or a large number of test individuals

I re-estimated admixture proportions for the 9 individuals of the last run, using the "zombie" populations in a supervised ADMIXTURE run. This took less than 1/10 of the time, and achieved results that were highly concordant with the ones previously reported: correlation was +0.999729; the average difference in ancestral proportions was 0.3%, the maximum difference 2.1%.

The speedup is due to two reasons: first, I'm running ADMIXTURE on 250 "zombie"+9 real individuals, as opposed to 692+9 real individuals using the unsupervised method. Moreover, admixture proportions are only estimated for the 9 real individuals and are fixed for the 250 "zombie" ones. This idea seems to work like a charm.

More average K=10 results

I was also able to calculate admixture proportions for the 10 Dodecad components in Druze, Kalash, and Palestinians. These populations have a tendency of forming their own population-specific clusters, so they are very difficult to compare against other populations: you just can't get their breakup into ancestral components easily, because they become their own ancestral components at fairly low K.

Using the trick of "zombie" populations, we can determine their ancestral components and compare them with other Dodecad populations.

I have labored long to be able to compare these to the ones in the standard Dodecad set, and I am very pleased that I was finally able to achieve it:

• Both Druze and Palestinians have substantial "Southwest Asian" component as do most Semitic (Arab, Jewish, Ethiopio-Semitic) populations in my database
• Druze have more "West Asian" than "Southwest Asian", and the reverse is true for Palestinians
• Palestinians have more African admixture than Druze

By far, the most exciting thing about this analysis are the results for the Kalash, a population that speaks a language of the Dardic group of Indo-Iranian. Some linguists place Dardic languages in the Indo-Aryan subgroup (of which Sanskrit and Hindi are the most famous representatives), whereas others view Dardic as a third branch of Indo-Iranian together with Iranian (like Kurdish, Persian, or Pashto) and Indo-Aryan. In any case, the study of these mountaineers is extremely crucial to the study of Indo-Iranians in general.

The Kalash have been much mythologized as either long-lost Aryans or the descendants of Alexander the Great's soldiers.

The absence of the South European component among them agrees with Y-chromosome research about the absence of a Mediterranean or Greek influence in that population. The Kalash are completely split between the West Asian component (56%) and the South Asian one (43.5%). Indeed, their West Asian admixture is very high compared to my south Asian populations, exceeding even that of the Pathans (~40%) and reaching levels found only in West Asia proper. It is also perfectly consistent with my theory of Indo-Aryan origins in West Asia.

The way forward

I initially considered the idea of zombies as a way to include more Project participants in my detailed ADMIXTURE runs, such as the recent K=12 and K=11 ones. There are two problems with these runs:

• Each one takes 24+ hours to complete, so it is not exactly possible to replace the standard K=10 analysis with them just yet
• Including all project participants, especially those of mixed background, makes them completely impractical, in addition to making them very capricious: at high K different components begin to appear depending on sample composition, and the solution is not as robust as in the standard K=10 analysis.

With the use of zombie populations, these problems can be largely solved. I can spend many hours or even days in a very detailed ADMIXTURE run with a large sample, create "zombie" populations from the inferred results, and then run project participants fairly fast using these "zombie" populations and supervised ADMIXTURE mode. In fact, I am working on exactly this type of test at the moment, so project members of all backgrounds should expect good things to come in the next days or weeks.

ADMIXTURE analysis of Eurasian populations with K=15

Note: color coding in the initially uploaded RAR of individual variation was off; please get the new one. [working link, May 4, 2011]

I have added populations from several sources: HapMap-3, HGDP, Behar et al. (2010), Rasmussen et al. (2010), and the Dodecad Ancestry Project, and ran the most ambitious ADMIXTURE analysis of Eurasian variation yet: 69 populations, and 1,189 individuals in total.

K=15 ADMIXTURE plot:

Admixture proportions for the 69 populations can be found in the spreadsheet.

Population portraits, showing individual variation within populations, can be found in the RAR.

RELATIONSHIP BETWEEN 15 COMPONENTS

The table of Fst distances between the 14 components:

MDS representation:

Hierarchical clustering with complete linkage (this is not a phylogeny):

What has changed

In comparison to the K=10 analysis, the increased resolution allows us to:

1. South Asians belonged primarily to the South Asian and West Asian components; this South Asian component spilt over to Iran and Central Asia. Now, a new Central-South Asian component, corresponding to the Ancestral North Indian of a recent study is inferred, and a corresponding South Indian component.
2. HGDP Bedouins and Behar et al. (2010) Saudis take up their own component which I labeled Arabian. This appears to be a subset of the Southwest Asian component of the K=10 analysis
3. There are several components in Siberian and Central Asian populations, alread discovered in my regional analysis. These are Central Siberian, Nganasan, Koryak, Chukchi, and Altaic which replace the K=10 Northeast Asian component

A final note:

The K=14 analysis revealed a Palestinian-centered "Levantine" cluster; this folded at my K=15 run, and two additional splits occurred (Koryak/Chuckhi and Nganasan).

At this level of resolution, many alternative representations can occur for a given K, and the order in which splits occur can vary; they continue, however, to correlate well with populations. Noise levels seem to be slightly increased, especially for clusters associated with single populations of few individuals, but the broad patterns are quite evident.

Up to now, I have not encountered any nonsensical results, so I will continue this as far as it goes. Regional analyses indicate there is more structure to be discovered, so we'll see how far the data can be pushed.

UPDATE: Razib of Gene Expression worries that the South Indian and Central-South Asian components I have identified may not correspond to ASI/ANI, suggesting that ASI ought to be closer to East Asians.

However, Reich et al. state that:

“Many of the analyses in this study are based on modeling the history of Indo-European andDravidian speaking groups of the Indian subcontinent in terms of a two-way historical mixture ofan “Ancestral North Indian” (ANI) population that is genetically close to Central Asians, MiddleEasterners, and Europeans, and an “Ancestral South Indian” (ASI) population that is not close to any large modern group outside the Indian subcontinent.”

Indeed, this is what I discover, with SIN being about Fst=0.085 from Caucasoids and about Fst=0.1 from East Asians. As I couldn't find raw Fst's between the components in Reich et al.'s paper, I can turn to one of their figures (S2 Fig.1):

It is evident that South Asian groups (except a few with East Eurasian admixture) are arrayed along a cline toward Europeans from a third pole (top of the figure): thus, the ASI, which represents this pole is not particularly related to East Asians, it is about equi-distant to Europeans and East Asians.

It is possible, however, that the correspondence is not perfect, as the North Kannada group which forms the South Indian pole in my analysis may not be as "southern" as the tribals Reich et al. had access to.

Portraits of the populations

Averages give an overview of different populations' makeup, but they may mask important variation. Thus, I have decided to present the individual-variation of the various populations included in the Project. You can get a RAR file with all populations here.

Below, I will highlight a few ones. I apologize about the quality of the graphics, but these were generated by a script I wrote which doesn't seem to have the presentation controls of normal "Save As".

First, the Saudis:

You can see, for example, that the West African admixture in this population comes pretty much from 3 individuals, with one of them having a substantial chunk of it. Some individuals have South European or West Asian admixture, while many belong 100% to the Southwest Asian component.

Next, the Ashkenazi Jews:

You can see that some individuals have an excess of the North European component, while others have almost none.

Next, the Burusho:

These appear very homogeneous, with what variation we might expect from random hereditary processes or the limitations of admixture inference.

Next, the Romanians

I've commented on the presence of 2 probable Roma individuals in this population before, and this seems quite evident in this plot.

Finally, the Gujarati:

It is evident that the North European and East Asian element is limited to a few individuals, with most of them appearing to be a "South Asian" and "West Asian" blend. One might speculate that these represent individuals admixed with other subcontinental populations where these two components are more important.