Outbreeding Works in a Single Generation

As I have mentioned before, cousin marriages were fairly common among my family. My parents are first cousins. So are my father’s parents. My mother’s parents are second cousins once removed. So instead of 32 great-great-great grandparents, I have only about 18.

Since my wife and I are not related, I wondered how my inbred genome had transmitted to our daughter.

Using David Pike’s ROH utility, I computed the regions of homozygosity for my parents, me, my wife, and my daughter, all tested by 23andme.

I used the default settings for the utility. The total Mb gives the total size in megabases of the long autosomal regions where both alleles are the same. The longest ROH gives the size of the longest such region. Percent Homozygous is the percentage of the genome where the two alleles are the same.

I included the worst chromosome column because of my chromosome 9, which is beyond crazy. This column gives the percent homozygosity of the worst chromosome.

Person Total Mb Longest ROH (Mb) % Homozygous Worst chromosome (%)
Dad 297.45 57.4 72.498% 76.921%
Mom 112.13 22.99 70.662% 79.802%
Me 402.78 71.38 73.588% 93.542%
Wife 37.33 9.64 70.003% 72.411%
Daughter 42.40 8.82 69.936% 71.759%

As you can see, my Dad has higher levels of homozygosity than my Mom as expected and I have the highest levels. My wife is not inbred at all and our daughter has ROH results about the same as my wife. So one generation of marrying someone unrelated, even if from the same/similar ethnicity, has removed all the long runs of homozygosity bred over generations. Good news!

Family DNA Results

I posted my genetic ancestry results. Now, we’ve got my parents, my sister and my wife tested with 23andme. So I thought a comparison would be interesting.

Here’s the ancestry painting from 23andme which uses three reference populations: Yoruba from Nigeria, Chinese and Japanese, and Utahns of Northwestern European descent.

Dad Mom Sister Me Wife
African 0.56% 0.95% 0.96% 0.34% 0.00%
Asian 8.68% 6.63% 8.00% 6.58% 10.18%
European 90.76% 92.42% 91.04% 93.09% 89.82%

You can basically use my wife as a sort of reference for Punjabi ancestry here (which is 3/4th of our ancestry too). Also, my wife and I are unrelated.

As you can see, while our results are close, my mom and sister have more African and I have the least.

And here are the similarity numbers for us with different reference populations.

Dad Mom Sister Me Wife
Central & South Asians 67.13 67.09 67.05 67.12 67.12
Northern Europeans 66.97 66.92 66.92 66.91 66.94
Southern Europeans 66.97 66.88 66.92 66.90 66.85
Near Easterners 66.85 66.76 66.81 66.79 66.72
Siberians 66.59 66.50 66.48 66.52 66.77
Eastern Asians 66.52 66.41 66.42 66.45 66.70
North Americans 66.48 66.40 66.38 66.44 66.69
South Americans 66.46 66.37 66.40 66.40 66.76
Oceanians 66.39 66.41 66.39 66.35 66.62
Northern Africans 66.17 66.10 66.15 66.13 65.94
Eastern Africans 64.08 64.06 64.11 64.10 63.89
Southern Africans 63.96 64.00 64.06 64.00 63.77
Central Africans 63.93 63.93 64.00 63.97 63.74
Western Africans 63.91 63.91 63.97 63.94 63.70

As compared to my wife, we are closer to Africans and farther from Eastern Asians, Native Americans (who are really a branch of East Asians) and Oceanians.That’s expected because of the 25% Egyptian ancestry we have.

Finally, here are our Dodecad Project results.

Dad Mom Sister Me Wife
East_European 4.96% 5.71% 4.59% 4.19% 6.28%
West_European 7.43% 9.59% 8.98% 8.97% 11.10%
Mediterranean 11.10% 9.28% 10.99% 9.24% 5.77%
Neo_African 1.36% 1.12% 1.45% 1.15% 0.26%
West_Asian 23.86% 22.41% 22.88% 23.88% 19.81%
South_Asian 33.94% 37.24% 33.15% 36.57% 45.64%
Northeast_Asian 2.53% 1.64% 1.79% 1.95% 3.22%
Southeast_Asian 3.04% 2.85% 3.95% 2.61% 3.35%
East_African 1.86% 2.18% 3.06% 2.30% 0.00%
Southwest_Asian 7.49% 5.57% 5.75% 6.57% 4.56%
Northwest_African 1.90% 1.49% 2.32% 1.57% 0.00%
Palaeo_African 0.53% 0.92% 1.10% 1.01% 0.00%

Similar results but interesting differences.

Dodecad Oracle

Dodecad has come up with a new version (v3) of its admixture results. Here are my results:

South Asian 37.4%
West Asian 23.3%
Mediterranean 9.8%
West European 9.6%
Southwest Asian 6.2%
East European 3.5%
Southeast Asian 2.4%
East African 2.2%
Northeast Asian 1.9%
Northwest African 1.5%
Neo African 1.1%
Palaeo African 1.0%

Dodecad also has a fun tool to check one’s results against different population averages. My closest populations are:

Population Distance
1 Pathan 7.2021
2 Bene Israel Jews 8.6822
3 Sindhi 10.0479
4 Punjabi Arain 10.0926
5 Kashmiri Pandit 10.5778
6 Burusho 11.179
7 Balochi 11.6705
8 Brahui 13.0208
9 Makrani 15.6735
10 Cochin Jews 18.1403

If I make use of mixed mode, the tool tries to find a combination of two ethnic groups with differing percentages that fits my results best.

Two Population Mix Distance
1 17.3% Palestinian + 82.7% Sindhi 3.0122
2 17% Morocco Jews + 83% Sindhi 3.1181
3 17.3% Palestinian + 82.7% Punjabi Arain 3.1228
4 17.2% Egypt + 82.8% Punjabi Arain 3.1846
5 82.9% Sindhi + 17.1% Egypt 3.288
6 17% Lebanese + 83% Sindhi 3.4994
7 16.7% Jordanians + 83.3% Sindhi 3.5238
8 16.7% Jordanians + 83.3% Punjabi Arain 3.5608
9 15.8% Samaritians + 84.2% Sindhi 3.6356
10 16.9% Ashkenazi + 83.1% Sindhi 3.7077

This actually fits reasonably well with my actual ancestry (75% Punjabi + 25% Egyptian).

Punjabi and What?

Looking at the admixture analysis for my Harappa Ancestry Project, I count 6 Punjabis (not including my sister and I). Let’s average those six and compare them to me and my sister.

Ancestral Pop Average Punjabi Me My sister NSA1 NSA2
South Asian 46% 38% 35% 7% 7%
Balochistan/Caucasus 35% 35% 34% 32% 30%
Kalash 5% 5% 2% -1% 0%
Southeast Asian 1% 0% 1% 1% 1%
Southwest Asian 0% 10% 12% 43% 40%
European 11% 6% 7% -7% 0%
Papuan 0% 0% 1% 2% 2%
Northeast Asian 0% 0% 0% 0% 0%
Siberian 2% 2% 2% 4% 3%
Eastern Bantu 0% 0% 0% 0% 0%
West African 0% 1% 2% 6% 6%
East African 0% 3% 3% 12% 11%

As you can see, the major difference between the Punjabis and me is my Southwest Asian and African percentages.

Now, I know that a quarter of my ancestry is not South Asian. So let’s try to estimate the admixture percentages for that ancestry. Being three-quarters Punjabi (I think), let’s average my sister’s and my results and then subtract 3/4th of the average Punjabi results from them. Then, multiply by 4 and you get NSA1 in the table above, which is supposedly my non-South-Asian ancestor.

Since some of the percentages are negative, I set those to zero and rescaled all the others so that they summed to 100%. That is NSA2.

I computed the average admixture results for a bunch of reference populations. Let’s compare NSA1 and NSA2 to those populations.

The top five populations most similar to NSA1 are:

  1. Yemenese
  2. Jordanians
  3. Palestinian
  4. Egyptians
  5. Syrians

For NSA2, we get the same five populations but the Egyptians and Palestinians exchange places.

This shows roughly that my quarter ancestry is most likely from the Middle East region of Egypt, Arabia or the Levant.

Let’s look at it another way. If I know that I have Punjabi and Egyptian ancestry, I can use the average Punjabi and average Egyptian admixture percentages to calculate my percentage of both ancestries since that has to sum to 100%. So:

Zack = p * Punjabi + (1-p) * Egyptian

And we solve for p using least squares.

I got 81.3% Punjabi for myself and 75.8% for my sister. On average, that’s 78.5% Punjabi and 21.5% Egyptian, which is pretty close to our genealogical information.

Harappa Clustering

As I had computed the admixture percentages for myself, my sister and other participants in my Harappa Ancestry Project, I decided to do some clustering analysis on them to see which persons clustered together. The resulting tree for a hierarchical clustering is as follows. It shows which persons are the most similar.

I am HRP0001 and my sister is HRP0035. As expected, we cluster together and then with a half-Sindhi half-Balochi guy and finally with all the Punjabis.

Since I have a lot of reference populations in my data, I did the same cluster analysis using the average admixture results for each reference analysis. Here’s the section of the tree containing my sister and me.

This time we cluster with the Bene Israel, a Jewish tribe from Bombay, India, though our similarity with them is not that great. Then with the Punjabis, Sindhis and Pathan.

Doing the same analysis with individual samples from my references,

A weak clustering with Burusho!

If I use PCA (Principal Component Analysis) results to compute hierarchical clusters, you can see that I am an outlier among the South Asian participants.

If you look at my PCA coordinates, you’ll realize that among the 365 South Asians I used in the analysis, I am one of the five complete outliers.

However, when I use model-based clustering on the PCA results, I end up in a really weird, loose cluster (CL9) with a Kashmiri, 5/21 Balochis, 2 Bene Israel Jews, 3/23 Brahui, 1/25 Burusho, 8/17 Makranis, 2/21 Pathans and 3/22 Sindhis. This is mostly a group of outliers and those who have some African admixture.

My Harappa Project Results

I have been blogging up a storm on Harappa Ancestry Project with more than 50 posts since I last linked to it.

Let’s see what I have found out about myself there. Here are the admixture results for me and my sister:

Me My sister
South Asian 37.9% 34.8%
Balochistan/Caucasus 34.7% 34.2%
Southwest Asian 9.8% 12.3%
European 5.9% 7.2%
Kalash 4.5% 2.3%
East African 3.1% 3.0%
West African 1.4% 1.8%
Siberian 2.4% 1.8%
Papuan 0.2% 1.0%
Northeast Asian 0.2% 0.3%
Southeast Asian 0.0% 1.3%
Eastern Bantu 0.0% 0.0%

You can see the results of all the participants in a spreadsheet or in a nice interactive bar chart. I am HRP0001 and my sister is HRP0035.

Interestingly, both McDonald and 23andme ancestry painting show my sister to have more African admixture than me, but here I have about the same East African as her and even her West African percentage is only a tiny bit higher.

To figure out what these ancestral populations mean, do read the post about the reference population analysis.

Eurogenes

Davidski of Eurogenes is also a genome blogger. In his admixture analysis for West, South and Central Asians, I am PKEG1 and my results are as follows:

European 4%
Siberian 1%
Caucasus 32%
Sub-Saharan African 4%
Middle Eastern 9%
East Asian 1%
South Asian 50%

Here’s a chart showing some of the reference samples and Eurogenes participants closest to me. 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.

As you can see, Pathans and Punjabi Jatts are most similar to me in their admixture results.

Eurogenes also did a supervised admixture analysis by choosing 11 reference populations as the ancestral populations. Here are my results:

Pathan + Sindhi 86.39%
Middle Eastern (Jordanian + Palestinian) 10.78%
Sub-Saharan African (Mandenka + Yoruba) 2.82%
Anatolian + Caucasus (Armenian + Georgian) 0.00%
North Slavic (Polish + Belorussian) 0.00%
Western/Southern European (French) 0.00%
Balochi + Brahui + Makrani 0.00%
Burusho 0.00%
North Kannadi + Sakilli + Selected Gujarati 0.00%
East Asian (Han Chinese) 0.00%
Koryak + Nganassan + Yakut 0.00%

From these results, it doesn’t look like there is any Turkic, Turkish or Balkan ancestry in my past. I was also surprised at the really high Pathan + Sindhi percentage and the lack of so many of the others.

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.

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.

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: