Participation Changes

Posted by on May 16, 2012 2 comments

Now that I have DIY HarappaWorld out, I am changing the participation requirements a little bit with somewhat different requirements for South Asians compared to other regions.

If you have any real ancestry from a South Asian origin, you are eligible to participate. Partial South Asian ancestry is okay. The list of countries of origin I count as South Asian are as follows:

  • Afghanistan
  • Bangladesh
  • Bhutan
  • India
  • Maldives
  • Nepal
  • Pakistan
  • Sri Lanka

Note that 2-3% South Asian from Dr. McDonald's BGA or Dodecad Project does not count as South Asian ancestry.

If you have all four of your grandparents from one of the following countries or regions, you can also send me your data.

  • Burma
  • Tibet
  • Uyghur from Xinjiang, China
  • Tajikistan
  • Kyrgyzstan
  • Kazakhstan
  • Uzbekistan
  • Turkmenistan
  • Iran
  • Turkey
  • Azerbaijan
  • Armenia
  • Georgia
  • North Caucasian Federal District, Russia
  • Iraq
  • Syria
  • Lebanon
  • Jordan

Relatives will only be accepted when they are a better replacement for current participants. For example, replacing a participant by his/her parents or his maternal uncle and paternal aunt gets us two unrelated participants (assuming, of course, that the two sides of the family are not related by blood). Another example could be if a participant is of partial South Asian ancestry and they get replaced by a relative who has more South Asian ancestry.

Everyone else can use DIY HarappaWorld. It's fairly easy to use on both Windows and Linux. The only hard part right now is that you have to install R to standardize your genome file. I might look into creating an executable for that to make it easier.

Finally, please be honest.

HarappaWorld Oracle

Posted by on May 11, 2012 11 comments

Here's the HarappaWorld Oracle to go with the HarappaWorld admixture results and DIYHarappaWorld.

It works similar to the old Ref3 Harappa Oracle, with a couple of differences. One, there is no panasian switch since the Pan-Asian dataset is not included in this calculator.

I have added an optional mincount argument. It picks only those groups where the number of individuals is equal to or more than mincount for the Oracle calculation. By default mincount is 2, so only those groups which have 2 or more samples are used to compute your Oracle results.

Let's look at my top 20 Oracle results in mixed mode excluding population groups with less than 4 individuals.

HarappaOracle(c(26.46,36.82,14.22,4.78,0.00,1.32,0.86,0.04,0.19,0.06,3.63,8.07,0.00,2.44,0.43,0.67),k=20,mincount=4,mixedmode=T)

[,1] [,2]
[1,] "18.1% egyptian_behar_12 + 81.9% punjabi-arain_xing_25" "2.3361"
[2,] "18.1% egypt_henn2012_19 + 81.9% punjabi-arain_xing_25" "2.5615"
[3,] "80.7% punjabi-arain_xing_25 + 19.3% yemenese_behar_8" "2.8388"
[4,] "18.4% palestinian_hgdp_46 + 81.6% punjabi-arain_xing_25" "2.9944"
[5,] "84.7% punjabi-arain_xing_25 + 15.3% yemen-jew_behar_15" "3.0923"
[6,] "19.1% jordanian_behar_20 + 80.9% punjabi-arain_xing_25" "3.1877"
[7,] "18% egypt_henn2012_19 + 82% sindhi_hgdp_24" "3.4814"
[8,] "17.9% egyptian_behar_12 + 82.1% sindhi_hgdp_24" "3.5554"
[9,] "20.3% jordanian_behar_20 + 79.7% punjabi_harappa_7" "3.6161"
[10,] "18.9% egyptian_behar_12 + 81.1% punjabi_harappa_7" "3.6587"
[11,] "19.5% palestinian_hgdp_46 + 80.5% punjabi_harappa_7" "3.7079"
[12,] "19% egypt_henn2012_19 + 81% punjabi_harappa_7" "3.8303"
[13,] "18.3% palestinian_hgdp_46 + 81.7% sindhi_hgdp_24" "3.8762"
[14,] "80.4% punjabi-arain_xing_25 + 19.6% syrian_behar_16" "3.8908"
[15,] "19% lebanese_behar_7 + 81% punjabi-arain_xing_25" "4.0494"
[16,] "18.9% jordanian_behar_20 + 81.1% sindhi_hgdp_24" "4.078"
[17,] "79.9% punjabi_harappa_7 + 20.1% yemenese_behar_8" "4.1222"
[18,] "15.1% bedouin_hgdp_46 + 84.9% punjabi-arain_xing_25" "4.1522"
[19,] "85.3% punjabi-arain_xing_25 + 14.7% saudi_behar_20" "4.2014"
[20,] "79.1% punjabi_harappa_7 + 20.9% syrian_behar_16" "4.2191"

These results are closer to my actual reported ancestry than the ones from reference 3 oracle.

DIY HarappaWorld

Posted by on May 7, 2012 69 comments

Based on Dienekes' instructions, I have created DIYHarappaWorld for anyone to compute their admixture results for my HarappaWorld calculator.

Here's what you need to do:

  1. Download DIYHarappaWorld files and unzip them.
  2. Download DIYDodecad v2.1 (File->Download).
  3. Unpack DIYDodecad2.1.rar by using 7-zip, WinRAR, or Linux rar/unrar command.
  4. Start R and change the working directory to where you have the DIY files.
  5. Enter the following command in R: 
    source('standardize.r')
  6. If you have your 23andme raw data, run the following command in R: 
    standardize('genome_john_doe.txt', company='23andMe')

    where genome_john_doe.txt is the filename for your raw data file.

  7. If you have your FTDNA Family Finder data in a file named johndoe.csv, run the following in R: 
    standardize('johndoe.csv', company='ftdna')
  8. From your operating system command prompt, run the appropriate command: 
    DIYDodecadWin harappaworld.par
./DIYDodecadLinux32 harappaworld.par
./DIYDodecadLinux64 harappaworld.par
  9. The program will start computing the admixture percentages. It took about 5-10 minutes on my computer.
  10. The best way to understand your results is to compare them with other populations and individuals. Do not take the component names seriously. They do not represent true ancestral populations.

You can also edit the harappaworld.par file's last line to one of genomewide/bychr/byseg/target to calculate the admixture percentages for the whole genome, by chromosome, by segment or target region respectively. Do note that the last three will have larger noise.

UPDATE: I should also point out that this DIY calculator will work better for those individuals whose genetic variation was included in computing the admixture model. Those belonging to a group not included at all in the set of samples I used might get somewhat odd results.

HarappaWorld Admixture

Posted by on May 4, 2012 6 comments

Here is a new admixture calculator. This uses populations all over the world and I got the best results (i.e., lowest crossvalidation error) at K=16.

You can see the admixture results for different ethnic groups as well as results for individual (founder-only) project participants.

UPDATE: The population results have been calculated using weighted means.

The group results are also shown in the usual interactive bar chart below. You can click on the component labels to sort by that ancestral component.

Do note that the admixture components do not necessarily represent real ancestral populations. Also, the names I have chosen for the components should be thought of as mnemonics to ease discussion. I chose them based on which populations in my data these components peaked in. They do not tell anything directly about ancestral populations. The best way to look at these admixture results is by comparing individuals and populations.

I used about 188,173 SNPs for this run. The results for Henn2011 (181,223 SNPs for Hadza, Sandawe and San, 26,494 SNPs for other groups), Henn2012 (26,494 SNPs), Reich (48,967 SNPs) and Xing (18,986 SNPs) datasets reported above were however calculated using lower number of common SNPs. Hence caution should be exercised in interpreting those results.

You can also see the Fst distances between the ancestral components.

I should have HarappaWorldOracle and DIYHarappaWorld calculators out in the next few days.

Also, I am working on another calculator which will focus more closely on South Asia.

ADMIXTURE Seed and Cross-Validation

Posted by on April 17, 2012 9 comments

I have been running some ADMIXTURE experiments recently. This is with my world dataset and about 180,000 SNPs.

I ran ADMIXTURE at K=15 ancestral components with different random seeds. Let's take a look at the final log likelihood and cross-validation errors I got for 11 runs.

As you can see, as the log likelihood increases, the cross-validation error decreases, though there is a fair bit of variation there.

For different runs, I got fairly different ancestral components. Remember that the only difference between the different runs was the random seed used to initialize the algorithm. Some ancestral components stayed very similar across the runs but others appeared and disappeared or switched subtly between different populations in a broad region.

The cross-validation (CV) error is important in my opinion since it gives you an idea of which run has results that generalize better. Basically, it is calculated by removing a portion of the individuals in the dataset.

At K=15, the minimum CV error I got was 0.52200 and the median was 0.52206. The maximum CV error was 0.52241, which is pretty large for this data. Let's superimpose this maximum CV value on a graph showing how CV error varies for different values of K (number of ancestral components).

The set of runs in this graph (other than the red line for the maximum CV error at K=15) used the default random seed for ADMIXTURE.

What this shows is that running ADMIXTURE only once using the default random seed (or any other seed) is fraught with problems. A better approach is to run it multiple times with different seeds so you can be sure that you have arrived at a computationally optimum solution.

Pan-Asian Admixture Results

Posted by on April 3, 2012 1 comment

I ran Reference 3 based supervised ADMIXTURE on the HUGO Pan-Asian dataset. While it used only 5,400 SNPs, it did get me curious about any relationship between Onge and Jehai and Kensiu. Unfortunately, Pan-Asian data doesn't have a good overlap even with Reich et al. So as a first exercise, I decided to run unsupervised ADMIXTURE on the Pan-Asian dataset by itself.

Here are the bar charts for the admixture results. K=12 ancestral components had the lowest cross-validation error.

You can see these results in a spreadsheet too.

South Asian fineStructure Ref3 Admixture

Posted by on March 27, 2012 1 comment

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.

Harappa Oracle

Posted by on March 23, 2012 13 comments

Based on the Dodecad Oracle, here is Harappa Oracle using reference 3 admixture results.

I am using Dienekes' code with a couple of changes. One of them is using weighted distance based on Fst divergences between ancestral components. Because of that it is several times slower than DodecadOracle. I plan to offer an option soon to switch between Euclidean distance and Fst-weighted distance.

You need to install R to use it. Then unzip the Oracle zip file. Double-click on the file or use the following in R:

load('HarappaOracleR3fst.RData')

In R, you can look at the 385 populations included by typing:

X[,1]

To use it to find your closest populations, you need your Harappa Reference 3 admixture results. Use them separated by commas like this (for me):

HarappaOracle(c(44,12,0,24,14,1,2,0,0,1,2))

You will get a result, with the first column showing the closest populations and the 2nd column their distance to you.

[,1] [,2]
[1,] "balochi" "8.0242"
[2,] "bene-israel" "9.2843"
[3,] "brahui" "9.5158"
[4,] "pathan" "9.7034"
[5,] "makrani" "10.1014"
[6,] "sindhi" "10.9236"
[7,] "Bhatia" "11.8441"
[8,] "Sindhi" "12.1704"
[9,] "Kashmiri" "13.4229"
[10,] "punjabi-arain" "13.9192"

You can also find out the closest populations to one of the reference populations:

HarappaOracle("punjabi-arain")

By default, the Oracle shows the 10 closest populations. You can change that:

HarappaOracle("punjabi-arain",k=20)

Also, by default, the Oracle excludes the Pan-Asian dataset since the overlap is only 5,400 SNPs. You can include Pan-Asian populations:

HarappaOracle("punjabi-arain",panasian=T)

There is also a mixed mode where the individual (or mean reference population) is compared against all pairs of populations as ancestors.

HarappaOracle("Haryana Jatt",mixedmode=T)

which has the following output:

[1,] "Haryana Jatt" "0"
[2,] "15.4% lithuanians + 84.6% Punjabi Brahmin" "1.9553"
[3,] "10.6% russian + 89.4% Rajasthani Brahmin" "2.0626"
[4,] "14.7% finnish + 85.3% Punjabi Brahmin" "2.0863"
[5,] "9.2% finnish + 90.8% Rajasthani Brahmin" "2.1142"
[6,] "89.4% Rajasthani Brahmin + 10.6% mordovians" "2.1727"
[7,] "9.6% lithuanians + 90.4% Rajasthani Brahmin" "2.1989"
[8,] "10.1% belorussian + 89.9% Rajasthani Brahmin" "2.2938"
[9,] "16.8% russian + 83.2% Punjabi Brahmin" "2.3015"
[10,] "16.2% belorussian + 83.8% Punjabi Brahmin" "2.3656"

You can of course combine any or all of the options.

Think of Harappa Oracle as a tool to help you interpret your admixture results by comparing who you are closest to. Do not think of it as giving you your real ancestry.

Ref3 Admixture Dendrograms

Posted by on March 19, 2012 3 comments

I have posted the reference 3 K=11 admixture results for all populations and datasets. Here are the relevant links:

So let's try a dendrogram of all these populations' average admixture results. Instead of using regular Euclidean distance, I used some weighting based on Fst distances between admixture components, very similar to what Palisto did.

Here's a dendrogram of all datasets using complete linkage.

Since the Pan-Asian dataset had only 5,400 SNPs common with reference 3, we need to be careful interpreting the tree above. Just to make sure, here's the dendrogram excluding Pan-Asian populations.

Henn Ref3 K=11 Admixture

Posted by on March 16, 2012 5 comments

There have been two Henn et al papers since I started this project.

  1. Hunter-gatherer genomic diversity suggests a southern African origin for modern humans by Brenna M. Henn, Christopher R. Gignoux, Matthew Jobin, Julie M. Granka, J. M. Macpherson, Jeffrey M. Kidd, Laura Rodríguez-Botigué, Sohini Ramachandran, Lawrence Hon, Abra Brisbin, Alice A. Lin, Peter A. Underhill, David Comas, Kenneth K. Kidd, Paul J. Norman, Peter Parham, Carlos D. Bustamante, Joanna L. Mountain, and Marcus W. Feldman
  2. Genomic Ancestry of North Africans Supports Back-to-Africa Migrations by Brenna M. Henn, Laura R. Botigué, Simon Gravel, Wei Wang, Abra Brisbin, Jake K. Byrnes, Karima Fadhlaoui-Zid, Pierre A. Zalloua, Andres Moreno-Estrada, Jaume Bertranpetit, Carlos D. Bustamante, David Comas

The data for both is available online:

I ran reference 3 K=11 admixture on these datasets using about 48,000 SNPs.

Here is the spreadsheet with the Henn group averages for reference 3 admixture at K=11 ancestral components.

Note that the Sandawe, Hadza and San from Henn2011 were already included in Reference 3 and are not listed here.