Gradient Maps for African ADMIXTURE components

Here below are gradient maps for my last African ADMIXTURE run, Africa_V2b, courtesy of a demo download of Mapviewer7 . The Kriging method was used for Gridding and 'Grid Z limits' mode was used for color mapping.

Sampled Population's Index	Sampled Population's Location
PCA for the FST distances generated by ADMIXTURE	West-Africa Cluster Freq.
Nilo-Saharan Cluster Freq.	East-Africa-2 Cluster Freq.
North-Africa Cluster Freq.	Khoi-San Cluster Freq.
Omotic Cluster Freq.	Mbuti-Pygmy Cluster Freq.
Biaka-Pygmy Cluster Freq.	Hadza Cluster Freq.
East-Africa-1 Cluster Freq.	Isometric view of the MDS plot  for all Populations sampled

UPDATE (02/18/2013) : Below are gradient maps for the first African ADMIXTURE run, Africa_V1, courtesy of a demo download of Mapviewer7 . The same options as above were used both for gridding and color mapping.

Berber YDNA

Decent resolution composite Berber YDNA from The Berber and the Berbers, Genetic and linguistic diversities, Jean-Michel Dugoujon et. al (2009)

Phylogeny of the 29 biallelic MSY markers (in bold) tested

Update: With respect to R-P25 (x M269) found in the Siwa and Mozabite Berbers, there is an even more exact breakdown of the lineage in this table from another publication using the same samples as above. It shows for the Siwa Berbers, the 26.9% of R-P25 (x M269) being further resolved to 23.7 % R-V88* (x M18, V8, V35, V69) plus 3.2% R-V69 (a branch of R-V88), similarly for the Mozabite Berbers, the 3% of R-P25 (x M269) is all resolved to R-V88* (x M18, V8, V35, V69).

Intra African Genome-Wide Analysis, V2

See Also : Intra African Genome-Wide Analysis, V1


Population References and First Pass K10 Analysis

Finally got some more badly needed genome-wide data from East Africa. 12 sets of populations were added, 9 Afroasiatic (3 Omotic, 4 Cushitc, 2 Semitic) and 3 Nilo Saharan.

I updated my Africa reference map and table below where the newer populations are to be found indexed from 46-57,

 

In addition the data was merged with the older dataset, the bad news is that the genotyping rate for all the 26,129 SNPs dropped by about 7% to 92.4%, the good news off-course is that the data I was eagerly anticipating, especially Nilotic from South Sudan and Omotics from Ethiopia are now available.

When I re-run the model-based analysis with the same settings, i.e ADMIXTURE K10, the major shifts in the cluster allocations were that the Mbuti and Biaka Pygmy clusters combined and formed one Pygmy cluster, the West-Central African cluster disappeared, and in their place a Nilotic and an Omotic cluster were formed. There were quite major shifts in the ADMIXTURE proportions for all the populations except South AFRICA, including the FST distances where the previous major East African cluster (East Africa 2) is shifted much closer to the North African cluster:

 

This is also seen in the ADMIXTURE proportions where the East African proportion in North Africans is sgnificantly higher. I will look to update this post with more analysis but for now:

K2 - K10 Analysis

UPDATE:

Had a chance to rerun the exact same intra-African dataset as above, but this time for K=2-10, while at the same time checking for the Cross Validation Error values:

K, CV Error

1 0.58753

2 0.56519

3 0.55874

4 0.55554

5 0.55379

6 0.55315

7 0.55269

8 0.55239

9 0.55215

10 0.55201

As can be seen, the CV Error is still decreasing, meaning I still have some room to go in my K selection beyond K=10 for this Dataset.

I have uploaded the full set of results and processed output (mean, median, standard deviation) for anybody that may be interested here, but since I do not have time to plot out each K's results like I did for K10 earlier, I will post the peaking population breakdowns for each K run as my program tells me, as well as the Median Values for 3 selected populations: EtA-P (26), ARI-B (17) and South-Sudan (24):

Analyzing the North African cluster

Continuing with  the Intra-African genome-wide analysis, I wanted to further explore the 'North African' Cluster that appeared to be wide spread from East to North and West Africa, 408 individuals out of the 1065 total samples carried the North African cluster at a frequency greater than 5%. With some of these populations showing a relatively high Standard deviation (Normalized with N-1) for that particular cluster. 

The table below shows the Standard Deviation for each of the 10 clusters found in the Intra-African Genome-Wide Analysis.

Yellow; Moderate Standard Deviation, 5-10%

Green; High Standard Deviation, 10-20%

Red; Very High Standard Deviation, >20%

 

The North African cluster had a high standard deviation in the Sahara-OCC, Morrocans, SAN, Mozabite and Morroco-S populations. All of these populations however, excluding the SAN, carried the North African cluster, on Median, in very high proportions (> 69%), while the SAN had it on Median only at ~4%. 18 out of the 36 SAN samples did however carry the North African cluster anywhere between 5-56%. Therefore, I excluded these 18 samples from the 408 individuals who carried the North African cluster at greater than 5% and proceeded to create a Dataset with PLINK.

The North African Cluster Dataset thus included 390 individuals (plus a few private samples) typed at 26,129 SNPs (all other specifications held constant with the previous Dataset).

  

MDS Analysis

Here below are the MDS plots for the Dataset, the plots include a 3 Dimensional plot, C1 Vs. C2 plot and C1 vs. C3 Plot respectively.

 

The 1^St component separates North Africans from the rest, with Ethiopians and Fulanis located at an intermediate position in this separation. The  2^nd component separates West Africans from the rest, with Bantus (Kenya and South Africa) located at an intermediate position in this separation. The Last and 3^rd component separates the Sandawe from everybody else.

Model Based Analysis.

5 clusters were generated from this dataset using ADMIXTURE, K=5, Unsupervised. A cluster that peaked in the Fulani, one cluster that peaked in the Mozabites, another cluster that peaked in the Sandawe, a fourth cluster that peaked in the Maasai, which I named East African, and a Last cluster that peaked in the Egyptians, which I named North East African, were observed. A PCA for the Fst distances that were generated by ADMIXTURE for these clusters can be seen below.

  

The largest vectorized Fst distance is seen for the Fulani, both for components 1&2, while the East African and Sandawe clusters appear to be close, similar to how the Mozabite and North East African clusters are close.

A standard deviation table (Normalized with N-1) for the 5 clusters generated can be seen below.

The Highest Average Standard Deviation across populations for the five clusters was among the Southern Morrocans and Mozabites (10.61 and 11.7% respectively).

Above are the Median proportions for all five clusters in the dataset.

The Mozabite cluster tapers off in a direction going east from the Northwest of Africa, where it is found at moderate frequencies in Egypt (~10%), the same can be said of the Fulani cluster, i.e tapering off in an eastward direction from Western Africa and found at a moderate (~6%) frequency in the Sandawe. The Sandawe cluster seems to be restricted to East Africa, although relatively high frequencies of it can also be seen in Southern Africa. The East African cluster, which peaks in the Maasai, is observed throughout East, West and Southern Africa. Finally, the North East African cluster merges North Africa with East Africa, for which a major portion can be accounted for with bi-directional Nile Corridor migrations, in addition to populations that used to live in the Sahara at a time when the desert was habitable. Minor, but gradiently significant Extra African input in the formation of the Mozabite and North East African clusters can also not be ruled out.

Intra African Genome-Wide Analysis

The primary purpose of studying Haplogroups (NRY and mtDNA) is to describe population movements, AKA Phylogeography . Autosomal DNA on the other hand, gives a rather ambiguous indication of a certain populations Paternal and Maternal history, since the chromosomes used undergo genetic recombination and can not be traced back to a single common ancestor. But still, there are drawbacks in just using NRY or mtDNA to study the history of a given population, and that is that they constitute only of a single Loci, which thereby reduce the effective population size relative to the Autosomes.

To this end, I have utilised publicly available Genome-Wide SNP data to get further insight into the population structure of Africa which may not be fully understood only from the data of uni-parental markers that we have. Perhaps the best published work out there with respect to African Autosomal Genome-wide data is that from Tishkoff (2009), this important paper found 14 ancestral Clusters in the African continent using the most diverse African dataset to date, however, the paper used Autosomal Microsatellites and a handful of SNPs.

On a publicly available dataset, I carried out two of the most popular approaches to help investigate population structure in Africa using Autosomal genome-wide data; (1) The non-parametric approach known as Principal Components or Multi Dimensional Scaling, which uses a Matrix whose elements are the quantification of the genetic similarity between pairs of individuals, and on which such a Matrix is used in order to perform a Principal Component Analysis upon, and (2) An explicit model based population structure analysis using the software ADMIXTURE, where individuals are assumed to come from one of K discrete populations and where population membership and allele frequencies are estimated using a Bayesian modeling strategy.

DATASET

A super set of the Data I used can be downloaded from here :http://dl.dropbox.com/u/23271596/ref.zip

The global Data Set, compiled by this blog author, contains publicly available data from 3970 individuals from around the world typed for 27,022 Autosomal SNPs, which can be found all over the 22 pairs of chromosomes (but not uniformly). I then utilized PLINK to perform the following on the above Data Set:

1. Removed all Non-Continental African populations.
2. Removed 18 Tunisians from Henn (2011) as previous analysis had shown independent cluster formation by this group, perhaps a sign of inbreeding.
3. Removed 15 Morrocan Jews that came from Behar (2010) for the same reason as above.
4. Kept SNPs above 99.46% genotyping success rate.
5. Excluded SNPs in linkage disequilibrium (r2>0.5) with nearby markers in a window of 50 SNPs (advanced by 5 SNP).
6. Added a handful of private African samples that took their genetic test with the Personal Genomics Company, 23andME. (The results of which I can not unfortunately publish in this post)

The above procedures left me with a core (public) Dataset of 1,065 Individuals from Africa and 26,129 SNPs for analysis. The complete SNPs typed for these individuals can be retrieved from: Behar (2010), Hapmap III, Henn (2011), HGDP and Xing (2010). 
Furthermore, geographically, 362 were from East Africa, 304 from West Africa, 158 from North Africa, 142 from Central Africa and 99 from South Africa. Linguistically, the dataset contained 536 Niger Kordofanian speakers, 212 Nilo-Saharans , 211 AfroAsiatic speakers, 89 Khoisans and 17 Hadza.

Update: Reference Populations and Key:

 

MDS Analysis

The data for the MDS analysis was generated using PLINK, while the plots were generated using GNU OCTAVE. A 3 dimensional MDS plot for the dataset can be seen below, all populations are labelled according to their Median Co-ordinates.

Here, we can see that the first component, C1, separates East and North Africans from West/Central/South Africans, while the Second Component separates the divergent hunter gatherers (San,!kung, pygmies and Hadza from the rest), this may be more clearer on the two dimensional C1 vs C2 plot below,

 

The third Component C3, separates East Africans from all the rest, as more clearly seen on a C1 vs C3 plot below,

 

Model Based Analysis

The model based analysis was carried out for K=10 using ADMIXTURE, thus 10 clusters were generated from the Dataset, I took the liberty to name these clusters, some on a geographic basis, others on a linguistic basis and still others on a subsistence basis, there is obviously a lot of fluidity associated in naming a cluster, so it shouldn't be taken as something written in stone.

A PCA plot for the FST distances generated by ADMIXTURE for the 10 clusters can be seen below,

 

The extreme positioning of the 'Hadza' cluster is indeed striking, followed by the 'KhoiSan' and 'Pygmy' clusters. The 'West African', 'West-Central African' and 'Eastern Bantu' clusters are quite close to each other as can be expected. The divergence of the North African cluster from East Africa can be explained by the significant extra African Admixture North Africans have as evidenced by the amount of their direct maternal ancestries coming from Europe and the Near East, while a majority of their paternal Ancestry comes from East Africa (Namely, E1b1b).

Below are the Median proportions for the 10 clusters generated by ADMIXTURE for the 45 uniquely entered African populations categorised according to their 5 respective regions.

The unclear abbreviations above for the samples of EtA, EtO and EtT are respectively Ethiopian Amharas, Ethiopian Oromos and Ethiopian Tigrayans, these samples (as well as the Ethiopian Jews, AKA Beta Israel) come from Behar (2010), in addition, the EtO samples purportedly come from the southern most tip of Ethiopia close to the Kenyan border. The dominance of the North African cluster in Ethiopians is not much of a surprise, as it is well known that Ethiopia is a genetic conduit between East and North Africa.

Here, both the mbuti and biaka pygmies form completely independent clusters, which is not unexpected as they are some of the most divergent populations even on a global basis. Also to note, is the slight 'North African' Affinity of the Hema and the 'West African' affinity of the Bulala and Mada.

Many of the non-Khoisan South African populations in the Dataset show affinities to both the 'Eastern Bantu' and 'Central-West African' clusters in almost equal proportions, which is interesting.

As seen in the PCA plots of the FST distances, the 'Central-West African', the 'West African', as well as the 'Eastern Bantu' clusters are close. The Dogon population however shows the least amount of the 'Central-West African' cluster and is almost completely dominated by the 'West African' cluster, which the reverse is true for the Igbo and Yoruba. Similarly, the Fulani show almost none of the 'Central-West African' cluster but rather, are mostly dominated by the 'West African' cluster, with the difference from the Dogon being that the Fulani have a significant affinity with the 'North African' cluster rather than the 'Central-West African' one.

 

In the last graphic above, we can see a geographic affinity of North West Africans with West African based clusters and North East Africans with East African dominant clusters, as to be expected. As stated before however, the 'North African' cluster itself is likely a both ancient and recent synthesis of East African, European and Near Eastern Affinities.

Conclusion

I learned quite a bit on the population structure of Africa from this exercise but there is a lot more room left for improvement:

1. The SNPs that are typed using almost all genotyping arrays are Eurasian biased, as they were first found in Europeans, as time goes on, more African specific SNPs will be discovered and their use in genome-wide analysis will change these results.
2. More samples are needed, especially from both South and North Sudan, all along the Sahel belt, Tuaregs, different Omotic speakers from Ethiopia, populations from Mozambique and the South Eastern coast of Africa, as well as the South Western coast (Angola) and many many more. The inclusion of these samples will have an impact on these results.
3. More dense SNPs (~200k) may also give slightly different results, although Sikora (2010) notes the following: “We can conclude that the common set of 2841 SNPs genotyped is an appropriate tool to study population structure in African populations; in general, world-wide patterns are evident and robust when using a minimum of 1000 SNPs.”
4. Newer and more computer intensive methods for bridging the gap between model based and distance based Autosomal analysis have recently been published, it would be interesting to carry out an analysis of this dataset with these newer methods.