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!

Zack Ajmal Phased Genome

A few months ago, I made my DNA genotyping results from 23andme public.

Since I got results for both my parents as well, I have now used BEAGLE to phase my genetic data. In simple words, I have been able to separate the contribution of my Dad and my Mom on my DNA.

I am making my phased genome public too. It’s in Plink format.

I haven’t made much use of the phased genome yet. So if you have any ideas about what can be done with a phased genome, please let me know.

I have also pledged to make my full sequenced genome public when genome sequencing becomes cheaper and I get it done.

Genome in the Wild

I tested with 23andme in April 2010 and then upgraded to their version 3 chip with almost a million SNPs last Christmas.

Now I am releasing my personal genome in the public domain.

CC0
To the extent possible under law, Zack Ajmal has waived all copyright and related or neighboring rights to Zack Ajmal 23andme v3 Genome. This work is published from: United States.

You can download my genome data in zipped files:

Razib has a list of people who have made their 23andme genomes public.

When Blaine Bettinger released his genome into the public domain, he issued a challenge:

So, I’m challenging everyone who reads this to download my data and analyze it to find the most interesting or surprising results. For example, you could use my most recent 23andMe V3 data.

I’ve already done a fair amount of analysis myself, including the Promethease reports above (and see here), and a recent blog post about my vastly increased Type 2 Diabetes risk. However, perhaps there’s a recent but relatively study that applies, or perhaps there’s a story you can weave with a handful of SNPs. Or, even better, what can you tell me about my ancestry other than mtDNA and Y-DNA haplogroups? Don’t worry about the strength of the study, reproducibility, etc. – I’m aware of the uncertainties associated with this type of research, and my goal here is to make people aware of possibilities.

Please post your findings in the comments below, and in two weeks I’ll pick the most surprising or interesting findings and make them the focus of a new blog post.

Can you surprise me with my own genome?

I have done a fair amount of analysis on my genome. For example, here’s my Promethease report. My ID is DOD128 in Dodecad, PKEG1 in Eurogenes and HRP0001 in Harappa.

My challenge for you would be to find interesting information about my chromosome 9 which is 93% homozygous.

If you analyze my genome, it would be great if you could let me know about what you found as I am always hungry for more information.

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).

Genetics and Health

When the doctor told me I had Ureterolithiasis, I logged into 23andme to check my genetic risk. There was only one SNP (rs4293393) listed there. The G allele increased the risk 14% but I have AA, so typical odds.

Next step was checking SNPedia where I found 8 SNPs, of which some are given below.

rs219780 (23andme): The high risk is CC but I wasn’t genotyped at this location. Also, CC is the most common, so it is quite likely that I have that.

rs219778 (23andme): Carriers of TT have a slightly increased risk and that’s what I have.

rs9310709 (23andme): Risk allele is C and I have CC.

rs10941694: I was not genotyped.

rs13070584: I was not genotyped.

More important than these though is the simple fact that my Dad had it too. Thus if there is a genetic association, I am likely to be higher than typical risk.

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.