Tag Archives: genome - Page 2

Dodecad Project II

I talked about the Dodecad Project last time. Dienekes also did some cluster analysis using mclust.

When he classified everybody into 48 clusters, I showed up almost all alone in cluster 21. Only one other member who is a Bihari Brahmin had a 50% chance of belonging in my cluster.

With 56 clusters, I am classified with 9 Sindhis (out of a reference population total of 24) and the same Bihari guy (who now has 99% chance of belongign in this cluster).

It looked like I was an outlier and when Dienekes tested for outlier data samples he found me among them.

With 64 clusters, I am again an outlier, though I am classified with a few Punjabis and 20/24 reference Sindhis and 10/22 reference Pathans. I am likely making their cluster not a good tight fit.

For 63 cluster analysis, the outlier status remains and the story is about the same as with 64 clusters.

More interesting was when Dienekes analyzed just South Asians. In his cluster analysis, I was classified with the 3 Punjabis in his project as well as the following reference population samples: 2 out of 25 Singapore Indians, 1 out of 24 Balochi, 18 out of 24 Sindhi, and 9 out of 22 Pathan.

His admixture results for me in this South Asian analysis were:

Pakistan 39.8
Indian 22.4
West Asian 16.3
Dagestan 11.8
European 2.8
North Kannadi 2.2
Southeast Asian 1.9
Irula 1.8
Siberian 1.1

An interesting pattern I have noticed is that my European admixture percentage is generally lower than other Punjabis. When the European is divided into North and South, I have less North European admixture than a typical Sindhi, Punjabi or Pathan but more South European than those groups.

The final analysis from Dodecad is a fun one:

Using Pakistani Punjabis from Xing et al. (2010) and Behar et al. (2010) Egyptians as references requires me to drop the number of markers to ~38k, but the result of the supervised ADMIXTURE analysis is 77.4% Punjabi and 22.6% Egyptian, which seems compatible with what he expected.

Basically, Dienekes used only 25 Punjabis and 12 Egyptians as reference and then tried to estimate my proportion of these two populations. Of course, the assumption is that these two are my only ancestries. Interestingly, this is very close to what I expected. I plan to do this same analysis with several different reference populations and see what I get.

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Dodecad Ancestry Project

I asked Dienekes to include me in his Dodecad Ancestry Project and he gave me the following results:

Ancestral Component Percentage
South Asian 44.9%
West Asian 33.7%
Southwest Asian 5.7%
North European 5.5%
South European 3.7%
East African 3.4%
Northwest African 2.1%
West African 0.6%
East Asian 0.4%
Northeast Asian 0.1%

You can see the results of all the project participants in a spreadsheet. You can also check out the admixture results for the reference samples he used.

Below is a bar chart showing the ancestral population percentages for me (DOD128) along with some other Dodecad participants (those starting with DOD) and some reference populations. I selected those individuals and populations that were somewhat closer to me in their admixture results. Also, as initially sorted, the list goes from most similar to me to least similar from top to bottom.

You can sort the bar chart by the different ancestral components by clicking on the legend on the right.

A word about the ten ancestral components (South Asian, West Asian, Southwest Asian, North European, South European, etc): Admixture results in this case gave 10 ancestral components. These do not necessarily correspond to “pure” ancestral populations and they are not labeled, only defined by their allele frequencies. Dienekes looked at the admixture output for his reference populations and assigned the 10 components different names based on which region it is most common in. Thus calling an ancestral component “West Asian” just means that it is found at highest frequencies in the reference populations living in Western Asia nowadays.

I used hierarchical clustering on the Dodecad results to find out which participants are most similar to me. A tree below shows the section including me.

Closest to me are a Punjabi Brahmin and a half-Sindhi half-Balochi guy, then three Punjabi Jatts.

Through all these investigations, some things have cropped up again and again.

One is that I have a minor amount of African admixture (4% East + West African). Most of it seems to be East African, which is why it doesn’t show up in 23andme ancestry painting. This is consistent with a quarter Egyptian ancestry. An average Egyptian reference sample is 14.7% East African and 4.1% West African. A quarter of that would be 3.7% and 1.0% respectively. Compare that to my 3.4% and 0.6%.

Also, while I am not very similar to Punjabis, they are the group most similar to me. Since there are no Punjabis in the reference data, Sindhis are the next closest. I am in fact more similar to Gujaratis than I am to Turks or any Central or West Asian groups.

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McDonald Ancestry Analysis II

When my sister got her 23andme results, we sent them over to Doug McDonald. I was expecting something close to my results, but it was radically different:

This one is different it says 37% Druze, 4% Bushman or Pygmy, the rest North India. It is complicated enough that the program refuses to generate a spot on the map. The chromosome painting looks quite reasonable for that assignment.
I am including several plots .. these show just how odd this is.

Here are the PCA plots that Doug sent. My sister is shown by the crosshairs.

Think of this as two-dimensional projections of a multidimensional space and you’ll notice that my sister is not close to any of the reference groups.

You can see her 3-D position (“Test Person”) in the animation below (or by clicking on animation).

Her chromosome painting, a similar concept to 23andme’s ancestry painting, shows which chromosome segments are most like some population. As you can see, there are a few chromosomes that have almost no “South Asian” segments.

I was very surprised by my sister’s results, especially the 4% Bushman/Pygmy. I expected some East African admixture due to the Egyptian ancestry but no Pygmy. Also, I expected some (10-20%) Middle East contribution but Druze at 37% is just too high. So I asked Doug McDonald to redo my ancestry analysis with the new version of his software.

Here’s what he told me:

It says you are half North India, 3% Bushman or Pygmy, and the rest Iranian, OR 80% Sindhi, 2% Bushman or Pygmy, the rest being Bedouin.

The spot on the map is far SW Pakistan.

The Pygmy is clearly a mistake!

The Pygmy is definitely a mistake. Pygmies are a very distinctive population and because genetic diversity is very high in Africa, the continent of humanity’s origin, sometimes these reference populations can give weird results. These analyses basically try to fit your genetic data to reference populations’ data samples. That’s one reason why you see Sindhi or Pathan as a result for Punjabis because there are no Punjabis in the reference data of HapMap or HGDP.

Here are my PCA plots:

And here is my chromosome painting:

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Doug McDonald Ancestry Analysis

As I noted last time, I was in a situation where I needed some help into ascertaining my genetic ancestry. Fortunately, there are people willing to do that sort of analysis for you. One of these is Doug McDonald. So I sent him my data and within an hour I had an analysis.

The PCA plot below shows me as a large cross in relation to different reference populations (like Europeans, Africans, East Asians etc).

Doug McDonald Ancestry Plot for me

Here’s what he said:

We also do quantitative tests. These come in three flavors, first without South Asia (represented by Pakistan) and the Mideast, second with South Asia, and finally with all three, as comparison panels.

The typical random error in the data (standard deviation) is 1%, meaning that numbers less than about 2% are not highly significant. There are also systematic errors. In particular, there is cross-coupling of values for Europe, the Mideast and S. Asia. For example, on the middle panel, a pure, northwestern European measures about 9% S. Asian, and on the third panel they typically measure 4.5% Mideastern and 8% S. Asian. Actual people from South Asia or the Mideast always test at least 15% European.

His first panel:

Europe 71.1%
East Asia 12.3%
Africa 8.2%
Oceania 4.7%
America 3.3%

When South Asia is added:

South Asia 48.7%
Europe 36.8%
Africa 5.8%
East Asia 5.0%
Oceania 2.4%
America 1.2%

And finally when Middle East is added to the list:

South Asia 46.9%
Europe 29.0%
Mideast 11.0%
East Asia 5.1%
Africa 4.3%
Oceania 2.3%
America 1.4%

And here is his analysis of these results:

This is basically a person from somewhere in region from say Iraq to Pakistan, with a substantial African contribution. The East Asian is probably not real. The African could be a few percent direct recent admixture, or it could be in input from a previously mixed population like the Makrani of Pakistan. My techniques can’t tell them apart.

The most interesting thing here for me was the African percentage.

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Genome Similarity

23andme has a feature where you can find out how similar your genes are to your friends and family (who you are sharing with at the site). The result is a bar list with percentages showing similarity.

The number next to each person in the bar list is a measure of similarity. Specifically, it is the percentage of matching genotypes for all of the SNPs on our chip that are located in the genes or regions of interest.

If person 1 has AA and person 2 has AA at a particular position, they are 100% similar. If person 1 has AA and person 2 has AG, they are 50% similar at that position. If person 1 has AA and person 2 has GG, they are 0% similar at that position. We then average the percent similarity over all positions included in that comparison.

You can also calculate these similarity measures (IBS, Identical by State, distances) using plink if you have the genetic data for someone.

Discussing the expected similarity percentages, I figured that siblings and parents generally have similarity measures around the mid-80s. Usually for South Asians, it seems like their similarity percentage with other unrelated South Asians is close to 74%, especially for similar ethnic or geographic groups. Please note that this specific number 74% is valid for the specific set of SNPs included in the 23andme v2 chip. For v3 chip, people are getting higher numbers.

However, I looked far and wide and shared data with 81 people. My highest similarity percentage came out to be 73.22% with Amber, followed closely by a Bihari guy at 73.2% and a couple of other Punjabis. While most of my top matches are South Asian, with a large number of Punjabis, there is no particular pattern with several South Indians and Biharis matching highly too. My top non-South Asian matches are Iranians.

I expected my similarity percentages to be lower like they turned out to be due to my quarter non-South Asian ancestry. So that wasn’t a surprise.

I asked my parents and uncles and aunts about my great-grandmother’s ancestry. I knew she was from Egypt, but I found out that her ancestors had arrived in Egypt with Muhammad Ali Pasha. Since Muhammad Ali Pasha was an Albanian who worked for the Ottoman Sultanate, my relatives deduced that we have some Turk and/or Balkan ancestry.

So I asked a number of Turks, Southern Europeans and North Africans to share. I expected my similarity with them to be less than my similarity with South Asians, since 3/4 ancestry is more than 1/4. But I found something strange.

Let’s take any random person I am comparing my genes to who is not South Asian. If I compare how similar that person is to me against how similar he is to any South Asian, it turns out he’s more similar to the South Asian. This turns out to be true for similarity measures between me and all non-South Asians vs similarity measures between that non-South Asian and all South Asians. In brief, all East Africans, North Africans, Southern Europeans, Northern Europeans, Turks and Iranians (that are in my friends list) are more similar to all the South Asians among my friends list than they are to me.

I found this weird. If I had someone in my friends list who belonged to my great grandmother’s ethnicity or a closely related one, then I should be more similar to that friend than any random South Asian, instead of being the least similar like I was in all cases.

Clearly there was a need for better ancestry analysis in my case.

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DNA & Genealogy

Harappa Ancestry Project Update

I have got 25 participants to the Harappa Ancestry Project now. But we still need more especially from the Hindi belt.

I have been detailing the datasets I am using:

I have also started admixture analysis of the reference populations and first batch of project participants.

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I have found out I am actually from West Virginia. Ok, I am just joking.

I knew that my family had a history of marriages among relatives. After all I have only 10 great-great-grandparents instead of the usual 16. With my genome in hand, I set about to quantify the inbreeding.

First, I used David Pike’s Homozygosity tool. It analyzes your genome to find significant runs where the same haplotype is inherited from both parents. Large portions of the human genome are like that. The length of these homozygous regions, however, varies depending on the relation of your parents. If your parents are closely related (first cousins in my case), then you will have longer runs. If your parents are distantly related, then over the generations those genes have had a chance to recombine and so you will have shorter runs that are homozygous.

Overall, the percentage of my autosomal (i.e. on chromosomes 1-22) SNPs that are homozygous is 71.767 and I have 41 runs of homozygosity (ROH) of length at least 200. Here are some of my longest runs:

  • Chr 1 has a ROH of length 6009 (30.95 Mb)
  • Chr 8 has a ROH of length 5819 (33.00 Mb)
  • Chr 9 has a ROH of length 5877 (57.81 Mb)
  • Chr 9 has a ROH of length 5941 (24.38 Mb)

Let’s look at my homozygosity percentage by chromosome.

Chr 1: 71.734 %
Chr 2: 69.952 %
Chr 3: 65.741 %
Chr 4: 71.563 %
Chr 5: 69.270 %
Chr 6: 76.025 %
Chr 7: 69.445 %
Chr 8: 72.690 %
Chr 9: 93.323 %
Chr 10: 69.765 %
Chr 11: 71.866 %
Chr 12: 68.443 %
Chr 13: 74.184 %
Chr 14: 68.571 %
Chr 15: 73.087 %
Chr 16: 66.541 %
Chr 17: 77.555 %
Chr 18: 67.763 %
Chr 19: 66.267 %
Chr 20: 66.228 %
Chr 21: 79.902 %
Chr 22: 69.896 %

A majority of chromosomes seem to have reasonable percentages while chromosomes 4, 6, 8, 11, 13, 15, 17 and 21 are high. However, chromosome 9 is really weird: It is 93.323% homozygous.

David Pike writes that:

So far the largest ROHs in 23andMe V2 data that I am aware of consist of:

  • 9191 consecutive tested SNPs, corresponding to a DNA segment of length 49.99 Mb.
  • 6129 consecutive tested SNPs, corresponding to a DNA segment of length 39.05 Mb.
  • 5594 consecutive tested SNPs, corresponding to a DNA segment of length 28.95 Mb.
  • 4644 consecutive tested SNPs, corresponding to a DNA segment of length 27.71 Mb.

The highest percentage for overall autosomal homozygosity that I have so far seen from 23andMe V2 data is 71.763%.

As you can see, I am an extreme case.

A number of members at DNA Forums reported their homozygous percentage. Of all those listed, mine is the 2nd highest.

According to the paper Genomic Runs of Homozygosity Record Population History and Consanguinity:

South/Central Asians and West Asians have more than three times as many ROH in all categories over 4 Mb long than sub-Saharan Africans and other Eurasians. 19% of individuals from these populations have ROH over 16 Mb in length, consistent with the high prevalence of consanguineous marriage (marriage between individuals who are second cousins or closer) in these populations.

My total ROH length (segments > 0.5Mb) is about 282Mb which is about 1.2 standard deviations above the Central/South Asian sample mean in that paper. But I am more than 1.7 standard deviations above the mean for longer segments (>5Mb).

Let’s take a look at a graph from the paper’s supplemental material which plots total ROH length versus number of homozygous segments:

My inbreeding coefficient based on the length of long (>5Mb) runs of homozygosity in my genome (fROH5) is about 0.11 while the average in the Central and South Asian sample for the HGDP dataset is 0.015 (not directly comparable due to different number of SNPs used to calculate).

Finally, I used Plink to calculate my inbreeding coefficient F using all the South Asians from my reference datasets. That coefficient comes out to be 0.1184.

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Harappa Project New Site

As several people had asked, I have set up a separate website for the Harappa Ancestry Project at http://www.harappadna.org/.

I am keeping a link to the new site on the top menu bar here titled Harappa DNA.

I might also crosspost some items from the project here.

I have also set up a Facebook page for the Harappa Ancestry Project. Please like it on Facebook so I can get a nice short name for the Facebook page URL.

I have received several samples and will be reporting some analysis results soon. However, I do need lots of participants, so please spread the word.

Cross-posted at Harappa Ancestry Project.

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