Tag Archives: paintmychromosomes

Eurasian ChromoPainter Chunk Counts

Continuing with the Eurasian ChromoPainter analysis, here is the zip file containing the chunk counts that were donated by an individual in a column to an individual in a row. Please note that this is an all-against-all analysis, so it does not directly show the direction of gene flow. Also, the IDs I used here are based on ethnicity (except for harappann which are mixed Harappa Project participants). If you want to find out your ethnicID, take a look at this spreadsheet which has the appropriate mapping.

Since fineStructure classified these 2,001 individuals into 203 populations, it's easier to look at the chunk counts averaged over these populations.

From top to bottom (recipient) and left to right (donor), the five major branches are South Asian, European, Near Eastern & Western Asian, Inner Asian/Siberian, and East Asian respectively.

This population chunk count data is available in a spreadsheet.

Now, let's look at some specific recipient clusters/populations.

Here's the top 50 populations that have donated chunks to the Kalash (Pop133).

The three bars at the bottom are for the 3 different (closely related) Kalash clusters. The clusters donating the most after that are the Burusho, Sindhi, Pathan, etc. The top non-South Asian donor (Tajik Pop116) is at #21 and the next one is also Tajik (Pop95) at #38.

Now here are the top donors for the Pathans (Pop148).

Interestingly, the number of chunks donated to Pathans from Balochi, Brahui and Sindhi seems to be a bit more than from Punjabis. Again, Tajiks are the closest non South Asian group at #55 and #59, followed by Kurds at #62 and Iranians/Kurds cluster (Pop172) at #63.

Now let's look at the donors for Pop134 which includes 2 Bhatia, 2 Gujarati-B, 3 Haryana Jatt, 1 Kashmiri, 4 Pathan, 5 Punjabi, 1 Punjabi Brahmin, 5 Punjabi Jatt, 2 Punjabi Ramgarhia, 1 Rajasthani Brahmin, 1 Sindhi and 3 Singapore Indians.

The top donors (other than Punjabis, of course) are Sindhis and Gujarati-B. The top non South Asian donors are Tajiks at #65 & #67, Iranians/Kurds at #69, Turkmen at #70, Kurds at #73 and Lezgin at #75.

Now for Pop181 (2 Baloch and 9 Brahui).

The Baloch/Brahui are more inbred compared to Punjabis and Pathans. After teh top donors from Baloch, Brahui and Makrani, we get Sindhis, Pathans, Velama and Punjabis. The top non South Asian donor populations are Iranian/Kurd at #28, Turkmen at #33, Turk/Kurd (Pop162) at #35, Iranian Jews at #39, Kurd at #41, a lone Saudi at #42, Iraqi Jews at #43, Tajik at #44, Drue at #47, Armenians at #48, and Samaritian at #50. So it seems like Baloch and Brahui are a lot more West Asian than other groups in Pakistan/NW India.

Let's look at the donors for Pop129 (1 Tamil Nadu Brahmin, 4 Iyengar Brahmin, 8 Iyer Brahmin, and 9 Singapore Indians).

The top donors, after Pop129, are Iyengar Brahmins and a group consisting of other South Indian Brahmins, Kerala Christiand and Nairs, and then Velama. The Dusadh are the top north Indian donor, followed by Gujarati-B and Chamar. Top non South Asian donor is Tajik at #73.

Now for the top donors for Pop188 which includes 33 Singapore Indians, 4 Tamil Vellalar, 3 Andhra Pradesh Reddy, 2 Andhra Pradesh, 2 Dusadh, 2 Karnataka, 2 Sinhalese, 2 Tamil Nadar, 2 Tamil Nadu Scheduled Caste, 1 Chenchu, 1 Kerala Christian, 1 Kerala Muslim, 1 North Kannadi, 1 Tamil Muslim, 1 Tamil Vishwakarma and 1 Velama.

The top donors are Sakilli, Piramalaikallar, and Velama. Their top non South Asian donor is a group of 5 Singapore Malays at #72, followed by Romanian and Serbian Romany at #73.

Finally, let's see which clusters are the top donors for Paniya (Pop65) who get the most South Indian component in my HarappaWorld Admixture runs.

Their top donors are Paniya, Malayan, Pulliyar and Kurumba. Their top non South Asian donors are Singapore Malays at #55, Burmanese at #59, and Cambodian/Singapore Malay at #64.

Related Reading:

Eurasian fineStructure Dendrograms

The dendrogram in the last post about Eurasian ChromoPainter/fineStructure analysis is a little hard to make sense of, so here is the same info in a better format.

First, the upper portion showing the relationship of the five branches:

Now, let's take a look at Branch1 which consists of South Asians:

Branch2 is European.

Branch3 is mostly the Near East and western Asia.

Branch4 is Inner Asia/Siberia.

And Branch5 is East Asian.

Note that the leaf labels consist of ethnicity followed by the number of that group who belong to that particular cluster. However, some of the labels are cut off in the images since they were long.

Related Reading:

Eurasian ChromoPainter Analysis

Some months ago, I decided to run a big ChromoPainter analysis of the Eurasian samples I have. I removed from my dataset not only all Sub-Saharan Africans, but also North Africans and anyone else with more than 2% African admixture (which unfortunately included me).

Since the number of samples was still too large, I picked 25 random individuals from each non-South-Asian ethnicity while keeping all South Asians. I also tried to remove all close relatives and those with a high missing genotyping rate.

In the end, I had 254,576 SNPs for 2,001 samples belonging to 197 ethnic groups.

I ran ShapeIT to phase their genomes and then ChromoPainter and fineStructure. The whole process took about 2 months.

Then I got busy and the results sat on my computer for more than a month.

Now let's look at the ChromoPainter/fineStructure analysis. Due to my time constraints, I am going to present them in several posts.

Today, let's look at the fineStructure clustering run on the chunkcount output of ChromoPainter. It divided the individuals into 203 populations. Here's the spreadsheet containing the group and individual population clustering.

And here is the dendrogram showing the relationship of the clusters/populations computed by fineStructure.

UPDATE: Better dendrograms

Related Reading:

South Asian fineStructure Ref3 Admixture

I was wondering what the admixture patterns of the clusters fineSTRUCTURE computed were for my South Asian run. So I computed the average admixture for each cluster (total: 89) using reference 3 admixture results.

The default order of the clusters is to keep the closer clusters together.

Related Reading:

Dodecad South Asian ChromoPainter

Dienekes ran ChromoPainter/fineSTRUCTURE analysis of South Asians along with some West Eurasian populations, something I had neglected to do in my own South Asian run.

Using Dienekes' data, I was trying to figure out which South Asian populations had more DNA chunks in common with other groups when I ran into something strange. Looking at the chunkcount spreadsheet, if we focus on a recipient population (i.e., one row), we can see which populations contributed more "chunks". For most populations, the results are expected. It's either the same population or some close population. For example, let's look at top 5 matches for Velamas_M,

Velamas_M Pulliyar_M North_Kannadi Chamar_M Piramalai_Kallars_M
Velamas_M 1265.77 1259.38 1256.06 1255.6 1254.74

However, when we do the same for Pathans, Sindhis, Uttar Pradesh Brahmins, Kshatriyas and Muslims, we get strange results.

Chamar_M Velamas_M UP_Scheduled_Caste_M Piramalai_Kallars_M Muslim_M
Pathan 1229.91 1229.56 1229.53 1229.32 1229.27

Do Pathans match Chamar the best? Pathans don't show up as a donor till #11.

Chamar_M Piramalai_Kallars_M Pulliyar_M Velamas_M North_Kannadi
Sindhi 1234.09 1234.08 1233.85 1233.6 1233.55

Again, Sindhis as donors are #12.

Pulliyar_M Chamar_M North_Kannadi Kol_M Piramalai_Kallars_M
Brahmins_UP_M 1244.6 1244.53 1243.44 1242.88 1241.94

The same Brahmins_UP_M are #13 as donors.

Pulliyar_M Chamar_M North_Kannadi Kol_M Piramalai_Kallars_M
Kshatriya_M 1247.72 1247.36 1246.42 1244.98 1244.56

And #12.

Pulliyar_M Chamar_M North_Kannadi Kol_M Piramalai_Kallars_M
Muslim_M 1255.96 1255.36 1253.96 1251.74 1250.86

Muslim_M are #8 as donors.

There is a pattern here among the top donors for these populations. The same populations show up time and again.

Compare to my results (with a larger South Asian dataset) now. The top 10 matches for Pathans are:

  1. pathan
  2. punjabi-jatt
  3. bhatia
  4. haryana-jatt
  5. rajasthani-brahmin
  6. punjabi
  7. balochi
  8. kashmiri
  9. punjabi-brahmin
  10. sindhi

For Sindhis,

  1. sindhi
  2. bhatia
  3. balochi
  4. makrani
  5. brahui
  6. punjabi-jatt
  7. haryana-jatt
  8. meghawal
  9. pathan
  10. punjabi

For Brahmins from Uttar Pradesh,

  1. bihari-brahmin
  2. haryana-jatt
  3. brahmin-uttar-pradesh
  4. punjabi-jatt
  5. kurmi
  6. sourastrian
  7. bengali-brahmin
  8. bihari-kayastha
  9. bhatia
  10. up-brahmin

For Kshatriyas,

  1. bihari-brahmin
  2. kurmi
  3. meena
  4. kshatriya
  5. rajasthani-brahmin
  6. haryana-jatt
  7. punjabi-jatt
  8. bengali-brahmin
  9. kerala-muslim
  10. sourastrian

For Muslims,

  1. muslim
  2. chamar
  3. kol
  4. oriya
  5. uttar-pradesh-scheduled-caste
  6. bihari-muslim
  7. sourastrian
  8. brahmin-uttaranchal
  9. dusadh
  10. bihari-brahmin

If Dienekes can post a chunkcount file for the clusters computed by fineSTRUCTURE, may be we can try to figure out what happened.

Related Reading:

Dense South Asian ChromoPainter

I had run ChromoPainter/fineSTRUCTURE for 715 South Asians using only about 90,000 SNPs. I thought it would be a useful exercise to use more SNPs, so I had to drop the Reich et al dataset. That left me with 615 individuals and 418,854 SNPs.

The "chunkcounts" file has the donors in columns and recipients in rows. Here's a heat map of the same.

fineSTRUCTURE classified these 615 individuals into 89 clusters. I have named these clusters for convenience, however, the names do not imply that anyone in the Punjab cluster is Punjabi.

While I created the cluster tree at the top of the spreadsheet, here's how the clusters are related.

The most interesting thing is how Gujarati A (likely Patels) are an out-group to everyone else. Another major grouping is that of the Baloch, Brahui and Makrani, along with 4 Sindhis (might be one of the Baloch tribe of Sindh?).

The Punjabis, Sindhis and Pathan get better classification here than they did last time.

The Punjab cluster includes 3 Gujarati B, 4 Pathans, 2 Singapore Indians, Punjabis, Haryanvis, Kashmiris, and a Rajasthani Brahmin. Even using this method, HRP0036, who is half-Sri Lankan and half-German/Polish was classified in the same cluster.

The Dharkar and Kanjar could not be separated at all here. According to Metspalu:

There are three second degree relatives groups in our sample: ..snip.. [Kanjar evo_37 and Dharkar HA023]. Again the last pair needs further explanation. The Dharkar and Kanjar practice a nomadic lifestyle and were living side by side at the time of sampling. As the ethnic border between the two is permeable we cannot rule out neither our error during sample collection and/or subsequent labelling nor shifted self-identity.

The inter-cluster heat map:

And you can see the chunkcounts donated from each cluster to recipient individuals in a spreadsheet.

The pairwise coincidence:

And the PCA plots:

Related Reading:

ChromoPainter/fineStructure South Asians

You have probably heard of ChromoPainter/fineSTRUCTURE by now (Eurogenes, Dienekes, MDLP and Razib).

So I decided to run the South Asian samples data which I had earlier done PCA/MClust on through ChromoPainter and fineSTRUCTURE.

Here is the coancestry matrix among the 715 participants visualized as a heat map.

UPDATE: Here's a huge image showing the same.

fineSTRUCTURE can use this coancestry matrix to classify individuals into clusters, 52 in this case (compared to 38 using PCA and MClust). You can check the cluster assignments in a spreadsheet.

Note that I have named the clusters. That's just a shorthand so we don't have to refer to them by cluster number. Instead I used the population with the largest number of individuals in a cluster to label that cluster.

Here's the cluster-level coancestry heat map.

And the pairwise coincidence:

And finally PCA plots for the first 10 dimensions from fineSTRUCTURE.

UPDATE (Feb 9, 2012): New PCA plots with better markers for the clusters.

Related Reading: