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',])

fastIBD analysis of Balkans/West Asia

Now that I've discovered a way to boost Clusters Galore analysis even further by using fastIBD, I will start experimenting with different regional populations. This analysis took about 5 hours to complete, so it appears to be quite practical.

For my first experiment, I carry out an analysis of various populations from the Balkans and West Asia.

Clusters Galore

27 different clusters were inferred with 17 MDS dimensions. Some interesting findings:

• For the first time there emerge a couple of clusters that appear to be quite specific to Armenians (#2 and #3). 
• Similarly, Assyrians are broken to a few clusters that appear fairly specific to them  (#9-11)
• Georgians are split into three clusters, one of which (#14) is linked with the neighboring Abkhasians, who in turn have their own exclusive cluster (#25)
• The cluster modal in Greeks (#6) includes 14 of 19 Greek participants, and a few Greeks are also in the Balkan cluster (#8) and an Iranian-Turkish cluster (#4)
• The Behar Cypriot sample also splits into two, and the few Turkish Cypriot participants link to one of them (#13)
• The Ossetian project participant links to one of the three North_Ossetian clusters
• The major Balkan cluster (#8) still defies resolution. I am certain, however, that structure in this cluster will be uncovered with more participation. MCLUST adapts the cluster size and shape, and a "big", inclusive cluster spanning the Balkans appears more parsimonious than smaller clusters centered on the different groups. With larger participation, I anticipate that regional structure will be uncovered in the Balkans as well.

I cannot stress the importance of participation strongly enough. When groups have more participants, it is possible to both:

1. Discover group-specific clusters, by identifying what is common between members of groups
2. Discover within-group clusters, by identifying what is different between members of groups

For example, the great participation of Armenians in the Project has now allowed me to discover structure within the Armenian population. It appears, that cluster #2 corresponds to a more "western" Armenian group, and #3 to a more "eastern" one, with some overlap between the two.

Inter-population IBD


You can also see a visual representation of inter-population IBD:

I have only included populations with 5+ participants in this representation. Reddish shades express high IBD sharing; bluish ones low one. The heatmap has been scaled by row.

As you might expect, values across the diagonal are "reddish", since individuals within populations tend to have high IBD sharing with each other.

A few features "pop out" of the screen. Going from top to bottom:

• Intra-Iranic sharing
• Intra-Armenian sharing
• Intra-Balkan sharing
• Georgian-Abkhaz sharing

You can probably get more out of the figure, but these appear to be the most salient features.

Results for Project Participants


The results can be found in the spreadsheet, and include:

• Probabilities of assignment in each of the 27 clusters of the Clusters Galore analysis
• Z-scores of IBD between each individual and each of the 20 populations with 5+ participants. Higher values mean more IBD sharing. Note that Z-scores have been calculated for each row, hence each participant must scan his own row to find populations with an excess (+) or deficiency (-) of IBD sharing, and people should not compare across different rows.

Last but not least, I want to remind new project participants to leave a message in the Information about Project samples thread. Your comment will not appear immediately, since comment moderation is on, and also note that there are multiple pages of comments. 


If you haven't joined the Project yet, I encourage you to do so if you are eligible.

The Dodecad Oracle v1

Here is a little fun tool that tests the Dodecad v3 admixture proportions of an individual against all the reference populations, but also against the best pairwise combinations of these populations.

You need to install R to use it, and then download the program and double click on the file DodecadOracleV1.RData that can be found within the rar file. You will then be faced with a command prompt where you can enter the following commands:

Examining which populations are available

Just enter

X[,1]

You will see a list of 227 populations. You can use these population IDs in the next section.

Which populations are closest to a particular population?

Enter:

DodecadOracle("British_D")
[,1] [,2]
[1,] "British_D" "0"
[2,] "British_Isles_D" "0.9798"
[3,] "Cornwall_1KG" "1.1533"
[4,] "Kent_1KG" "2.265"
[5,] "Irish_D" "3.7643"
[6,] "Dutch_D" "4.5354"
[7,] "Mixed_Germanic_D" "6.8971"
[8,] "Norwegian_D" "11.3111"
[9,] "Orkney_1KG" "12.4652"
[10,] "Orcadian" "12.8195"

If you want to find e.g., the top-30 populations, rather than just the top-10, enter:

DodecadOracle("British_D", k=30)

Which populations are closer to a particular individual?

Enter the admixture proportions of the individual (from the "Individual results" tab of the spreadsheet) as follows:

DodecadOracle(c(4.6, 16.7, 33.6, 0, 23.2, 0.4, 0.6, 1.6, 0.7, 14.1, 4.5, 0.2))
[,1] [,2]
[1,] "Ashkenazi_D" "3.7908"
[2,] "Ashkenazy_Jews" "4.1473"
[3,] "Morocco_Jews" "6.338"
[4,] "S_Italian_Sicilian_D" "12.5443"
[5,] "Sephardic_Jews" "13.5067"
[6,] "C_Italian_D" "14.4554"
[7,] "Sicilian_D" "14.7469"
[8,] "S_Italian_D" "15.748"
[9,] "Tuscan_X" "15.9981"
[10,] "O_Italian_D" "16.1474"

Once again, you can specify k=30, if you desire the 30 top matching populations instead of the default 10.

Mixed Mode

You use mixed mode by adding mixedmode=T in any of the commands. The program then considers all pairs of populations, and for each one of them calculates the minimum distance to the sample in consideration, and the admixture proportions that produce it; population pairs where the distance to one of the two populations is smaller than to any admixture of the two are ignored.

Example:

DodecadOracle("Pathan",mixedmode=T)
[,1] [,2]
[1,] "Pathan" "0"
[2,] "84.8% Pakistani + 15.2% Urkarah" "1.075"
[3,] "84% Pakistani + 16% Stalskoe" "1.1555"
[4,] "63.9% TN_Brahmin + 36.1% Urkarah" "1.6669"
[5,] "32.4% Urkarah + 67.6% Meghawal" "2.3516"
[6,] "56.3% INS + 43.7% Urkarah" "2.4901"
[7,] "11.5% Adygei + 88.5% Pakistani" "2.6245"
[8,] "82.4% Sindhi + 17.6% Stalskoe" "2.6318"
[9,] "62.9% AP_Brahmin + 37.1% Urkarah" "2.7322"
[10,] "11.2% Lezgins + 88.8% Pakistani" "2.7749"

The mixed mode should be used with caution, and it shows, more than anything else, how similar apparent "mixes" can be achieved by different combinations of ancestry. Nonetheless, it may prove somewhat useful. For example, there is a suggestion in the above results, that Pathans can be viewed as a mix of other South Asian populations and populations from the eastern Caucasus, a suggestion that was arrived at independently by the Project using different methods.

Here is another example:

DodecadOracle("Assyrian_D",mixedmode=T)

[,1] [,2]

[1,] "Assyrian_D" "0"

[2,] "83.9% Armenians_16 + 16.1% Yemen_Jews" "1.7829"

[3,] "89.1% Armenian_D + 10.9% Saudis" "2.1624"

[4,] "84.3% Armenians_16 + 15.7% Saudis" "2.2884"

[5,] "88.9% Armenian_D + 11.1% Yemen_Jews" "2.2983"

[6,] "83.8% Armenian_D + 16.2% Bedouin" "4.1579"

[7,] "72.2% Armenian_D + 27.8% Syrians" "4.1841"

[8,] "23.4% Georgians + 76.6% Iraq_Jews" "4.2418"

[9,] "76.2% Armenians_16 + 23.8% Bedouin" "4.332"

[10,] "61.5% Armenians_16 + 38.5% Syrians" "4.4019"

This reaffirms the close relationship of Assyrians to Armenians that has been noticed in the project and by others, and it also shows that Assyrians differ from Armenians in a Southwestern Asian direction, consistent with their Semitic language.

Or, African Americans:

DodecadOracle("ASW",mixedmode=T)

[,1] [,2]

[1,] "ASW" "0"

[2,] "81.3% Hausa + 18.7% N._European" "2.3891"

[3,] "18.4% Orkney_1KG + 81.6% Hausa" "2.4031"

[4,] "18.5% Argyll_1KG + 81.5% Hausa" "2.4268"

[5,] "18.4% Orcadian + 81.6% Hausa" "2.4657"

[6,] "80.5% Igbo + 19.5% N._European" "2.5031"

[7,] "80.6% Brong + 19.4% N._European" "2.523"

[8,] "18.6% CEU + 81.4% Hausa" "2.5938"

[9,] "19.1% Argyll_1KG + 80.9% Brong" "2.6197"

[10,] "19% Orkney_1KG + 81% Brong" "2.6274"

I don't know that much about the slave trade, but I believe that Ghana was an important part of it?

Another thing to watch, is that some populations tend to have more than one sample available, so they appear to be mixtures of themselves, which is not really very informative, e.g., Spanish_D

DodecadOracle("Spanish_D",mixedmode=T)

[,1] [,2]

[1,] "Spanish_D" "0"

[2,] "7.9% French_Basque + 92.1% IBS" "0.8713"

[3,] "68.9% IBS + 31.1% Spaniards" "1.0377"

[4,] "98.8% IBS + 1.2% Irish_D" "1.2959"

[5,] "1.2% British_Isles_D + 98.8% IBS" "1.3018"

[6,] "1.2% British_D + 98.8% IBS" "1.3019"

[7,] "99% IBS + 1% Norwegian_D" "1.3046"

[8,] "1.2% Cornwall_1KG + 98.8% IBS" "1.3048"

[9,] "98.8% IBS + 1.2% Kent_1KG" "1.3142"

[10,] "2.2% French_D + 97.8% IBS" "1.3179"

To deal with these problems, you must "edit" the X matrix if you want to exclude some populations. For example, if you want to exclude "Spaniards" and "IBS", you must enter:

X <- X[setdiff(1:227,which(X[,1]=="IBS" | X[,1]=="Spaniards")),]

but notice, that you must relaunch the program, if you want to get the original matrix, or alternatively save it like this:

Z<-X

and then retrieve it like this:

X<-Z

Structure in West Asian Indo-European groups (part 2)

I will occasionally revisit old posts such as Structure in West Asian Indo-European groups to take advantage of new population samples from project submitters. This time around, I included our first Kurdish and Azeri participants, and limited the analysis to populations for which I had large numbers of markers (the final total is a ~132k pruned set of markers).

I also included Greeks, Caucasian populations (Georgians and Lezgins), and Levantine Arabs (Syrians-Lebanese) who frame this region from the West, North, and South respectively, as well as Assyrians who are interspersed in West Asia as an ethno-religious minority.

Here are dimensions 1 and 2 of the multidimensional scaling plot:

The two Caucasian groups (South and Northeast Caucasian Georgians and Lezgins respectively) form their own clusters. So do the Iranians and the Syro-Lebanese.

(As always population labels are placed on population averages, and _D denoted Dodecad Project populations)

Curiously, many linguists assert a close relationship of Greek and Armenian within the Indo-European language family. Turks speak an Altaic language due to migration of a numerically small population element, but their pre-Turkish genetic ancestors were Anatolian speakers, Greeks, Armenians, and Iranians, i.e., primarily Indo-Europeans.

Dimensions 1 and 3:

Dimension 3 contrasts Greeks from West Asian groups. Notice also the presence of 3 Armenians at the bottom of the plot, these are outliers of the Behar et al. Armenian sample.

Notice that the Azeri_D sample clustered with Iranians in dimension 2 and with Turks in dimension 3. This is not very surprising, as Azeris speak a Turkic language, but also have clear Iranian antescendants. The Kurd_D sample, on the other hand, clusters consistently with Iranians.

The variability of the Greek_D population sample along dimension 3 is also quite interesting. This could reflect variable levels of influence of extra-Greek European/Anatolian population elements on the basic Greek stock. Greeks who possess 23andMe or FTDNA Illumina population data are strongly encouraged to join the Project to help us better determine regional variation within the ethnic Greek population.

The Clusters Galore analysis results are as follows (9 clusters/3 MDS dimensions retained) :

In brief, the modal populations in each cluster are:

1. Greeks
2. Iranians
3. Turks
4. Syrians and Lebanese
5. Armenians and Georgians
6. Armenians and Assyrians
7. Syrians and Iranians
8. Lezgins
9. Georgians

I will be happy to provide to all Dodecad Project members from the _D populations with their individual results. If you send me e-mail at dodecad@gmail.com, I will send you a line with your probabilities of assignment in each of the 9 clusters, as well as the 3 co-ordinates in the first 3 MDS dimensions plotted above.

I strongly encourage individuals from West Asia, the Balkans, and Italy to contact me for inclusion in the Project (send e-mail first, not data). While submission to the Project is currently closed, I usually accept data from these regions.

ADMIXTURE analysis with Dodecad Populations (update #2)

Thanks to all the participants of the Project, the number of populations has increased, and so have sample sizes within pre-existing populations in the Project. There are now 17 populations with at least 5 individuals in the Project:

Assyrian, Scandinavian, Greek, Finnish, S_Italian_Sicilian, Ashkenazi, German, Indian, Portuguese, Armenian, Russian, Spanish, British, Irish, Turkish, N_Italian, Balkans

Below are the K=10 ADMIXTURE results with these populations:

Admixture proportions can be found in the spreadsheet.

The fact that the addition of 17 populations and 143 individuals to the core set of 36 populations and 692 individuals results in the same 10 ancestral components testifies to the stability of this solution. Hopefully, within 2011 I will develop an even better comparison set to work with.

Another test of the validity of the analysis is comparison of independent samples of the same populations:

Ashkenazi, Armenian, Spanish, Turkish, N_Italian

I have a sample of Dodecad Project members for each of the above, as well as a published population. A way to measure the concordance between the two is to calculate the correlation coefficient (rounded to the 3rd decimal point):

• Ashkenazi Jews: 0.999
• Armenians: 0.988
• Spanish: 0.998
• Turkish: 0.995
• N_Italian: 0.996

The concordance is remarkable.

I have also made a RAR of "population portraits". It is important to do this to determine whether minor ancestral components represent population-wide phenomena or are limited to a few individuals.

For example, here are the Turks of the Dodecad project:

The sample is a bit more varied than the sample included in Behar et al:

This probably underscores the importance of broad coverage of large countries and ethnic groups, as I have discovered recently in my analysis of 9 different populations of Pakistan.

Another new population are the Irish, presenting a picture of remarkable homogeneity:

Here is the population portrait for the Balkans, which consists of non-Greek, non-Roma inhabitants of the Balkans:

This appears quite varied; hopefully more Balkan project participants will allow me to split this into additional sample populations.

Finally, here is a portrait of the Ashkenazi population, which appears quite similar to the Behar et al. one:

A very interesting thing about this population is the existence of small slices of "East Asian" and "Northeast Asian" components totalling about 1.5% in almost all individuals. In my opinion this testifies to some type of old minor absorption, as it is fairly evenly spread in the population.

If you haven't joined the project yet, feel free to submit your sample during this opportunity.

Splitting populations: how many markers needed?

An interesting question in population genetics has to do with the number of markers necessary to infer population-of-origin of an individual.

In the Dodecad Project, we have seen that even very close populations such as Armenians and Assyrians can be classified accurately using the MDS/MCLUST "Clusters Galore" combination that I have proposed. But, do we really need ~177 thousand markers to achieve this level of detail?

I decided to carry out a small experiment, to see how the number of markers used affects the ability to correctly classify samples into two different populations.

Case A: Maximal differentiation (Papuans vs. Mbuti Pygmies)

I begin by considering the case of the two most differentiated human populations in my database, Papuans (17 individuals) and Mbuti Pygmies (13 individuals). For each step I reduce the number of markers by an order of magnitude, using PLINK's --thin 0.1 argument.

• With 176,598 markers, classification is 100% correct, i.e., all 13 Pygmies are assigned to one cluster and all 17 Papuans are assigned to another
• With 17,752 markers, classification is again 100% correct.
• With 1,725 markers, ditto
• With 152 markers, ditto
• With 20 markers, 3 Pygmies are misclassified into the Papuan cluster, hence accuracy is 90%
• With 4 markers, 5 Pygmies are misclassified as Papuans, and 6 Papuans as Pygmies, hence accuracy is 63%

Notice that these are not ancestry informative markers (AIMs) but randomly selected SNPs.

Case B: Small differentiation (Armenians vs. Assyrians)

I have previously shown that a sample of 7 Armenians and 8 Assyrians can be classified correctly, with a single Assyrian misclassified as Armenian, hence 93% accuracy.

• With 17,714 markers, 1 Armenian is misclassified as Assyrian, hence 87%
• With 1,808 markers, accuracy is 80%
• With 188 markers, it is 67%
• With 17 markers, it is 60%

Implications

I carried out this little experiment because I thought it would be interesting, but also for its implications. I can identify at least two major ones:

Ancient DNA: Due to poor preservation we are unlikely to get full genome sequences from ancient human DNA except in highly favorable conditions. In many instances, it may be possible to get only a limited number of markers tested. These results suggest that it is possible to get fairly decent assignment of individuals into populations without hundreds of thousands of SNPs. Thus, it may be possible to study genetic structure in ancient necropoleis, or the relationship of ancient remains to modern populations.

Data integration: Different genotyping platforms (e.g., those of Affymetrix and Illumina) often possess a very small subset of common SNPs. Imputation of genotypes is possible, but these results suggest that even when the overlap between markers is not substantial, it is possible to carry out fairly sophisticated genetic analyses on them.

Conclusion

Naturally, there are issues not addressed here: what happens when we are dealing with more than 2 populations? What happens if we want to study admixture rather than classification? In both cases, I expect the number of markers needed to be higher.

Nonetheless, I am fairly convinced, both from this, and a previous experiment that more markers than are used in current genotyping platforms will only add very little value to anthropological investigations of ethnic genetic differences.

Assyrians vs. Armenians

Assyrians and Armenians have invariably been assigned to the same cluster in Clusters Galore analysis thus far. It is very important to understand the following two axioms:

• Populations that are separated by clustering analysis have enough genetic differences between them to allow such separation
• Populations that are not separated may, or may not have enough genetic differences between them to allow separation

The second axiom is extremely important. Let's elaborate:

• The clustering algorithm may have its limitations
• The number of markers may be insufficient
• The number of individuals may be insufficient

In the Galore approach, I use MCLUST, as good a clustering algorithm as any. I also have samples of 8 Assyrians and 7 Armenians, and use about 177k SNPs, which are enough to squeeze out differences even between related populations. So, what gives? Are Assyrians and Armenians really genetically indistinguishable?

What constitutes a cluster? MCLUST can divide a dataset into as many clusters as you want, but it also chooses the number of clusters to optimize the Bayes Information Criterion. But, it's important to note that the BIC is not some god-given arbiter of what a cluster is; it is best viewed as a guide to choose a good number of clusters, and not as a guarantee that this is the true number of clusters that a population can be subdivided in.

Clustering Assyrians and Armenians, assuming 2 clusters

To that end, I decided to cluster Assyrians and Armenians, forcing MCLUST to infer 2 clusters. I only retained 2 MDS dimensions, as these are enough to distinguish between 2 groups. Here is the MDS plot:

We can observe that Assyrians can be distinguished from Armenians along Dimension 1, and there is also some structure in Assyrians, with 3 of them forming a mini-cluster on top, 4 of them forming another mini-cluster at the bottom, and 1 of them being closer to the Armenians.

By applying MCLUST with K=2, all 7 Armenians and 1 Assyrian are assigned to a cluster, and the remaining 7 Assyrians to another. Thus, the two groups can be separated from each other, although their differences (due to the factors I mentioned) are not large enough to lead to an improvement of the BIC.

With more individuals., it's possible that the BIC too will be able to track the improvement in likelihood that adding two clusters will produce.

And, indeed, it may be possible that some of these populations will be further subdivided. If more Assyrians join the project, for example, it may be the case that the two apparent Assyrian clusters will emerge, or it will be the case that the space between them will be filled by currently unsampled individuals.

As more individuals join the Project, ever-finer distinctions can be uncovered.

NB: I make the case for Assyrians and Armenians, but I have also tried this for other closely related populations who were assigned to the same cluster by Galore analysis, namely Spaniards and Portuguese. In that case, however, I was not able to find a clean solution with K=2. Once again, this may mean that Spaniards and Portuguese are not genetically that different, or it may mean that their difference will be revealed with more individuals joining the project.

ADMIXTURE analysis with Dodecad Populations (update #1)

Repeating the previous analysis with additonal populations of Dodecad Project members and/or modified sample sizes for pre-existing ones:

Assyrian, Scandinavian, Greek, Finnish, S_Italian_Sicilian, Ashkenazi, German, Indian, Portuguese, Armenian

Admixture proportions can be found in the spreadsheet. Dodecad Project populations in italics.

Populations portraits can be found in the RAR. For example, here are the ones for Dodecad Project Ashkenazi and Behar et al. (2010) Ashkenazi Jews:

and here is a Portrait of the Portuguese:

ADMIXTURE analysis with Dodecad Populations

Thanks to our project members, I am now able to repeat the ADMIXTURE analysis which launched this product, with 5 new populations, composed entirely of Project members. I have made a spreadsheet where you can find admixture proportions for the original 36 populations, as well as the 5 new ones:

The new populations are: Assyrians, Scandinavians, Greeks, Finns, and South Italians/Sicilians. As more people sign up to the project, more populations will pass the 5-person threshold, and will be included in the admixture analysis, and the admixture proportions of existing ones will be fine-tuned. Moreover, some of the populations, such as Scandinavians, may be split into e.g., Swedes, Danes, and Norwegians.

Stay tuned for a notice about future opportunities to submit your data.