Tag Archives: harappa

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.

Related Reading:

From Harappa to Hastinapura: A Study of the Earliest South Asian City and Civilization (American School of Prehistoric Research Monograph Series)
Indus Valley Painted Pottery - A Comparative Study Of The Designs On The Painted Wares Of The Harappa Culture
The Harappa Files
Understanding Harappa

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.

Related Reading:

Ancient Cities of the Indus Valley Civilization
The Foolish Dictionary An exhausting work of reference to un-certain English words, their origin, meaning, legitimate and illegitimate use, confused by a few pictures [not included]
Merriam-Webster's Everyday Language Reference Set
Script of Harappa & Mohenjodaro & Its Connection With Other Scripts

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.

Related Reading:

From Harappa to Hastinapura: A Study of the Earliest South Asian City and Civilization (American School of Prehistoric Research Monograph Series)
Oracle Core: Essential Internals for DBAs and Developers
Understanding Harappa
India Divided Religion 'Then' (1947) (East-West): 'Now' What Languages ( North-South ) ?....

Harappa Participant Admixture Group Averages

Posted by on February 21, 2012 38 comments

I have been reporting only individual admixture results for Harappa Project participants. I think it's way past time I posted some group averages too.

You can see the groups I have assigned participants and the current count for each group.

The average admixture results for each group are in a spreadsheet. This is using Reference 3. You can compare with the reference population results.

Here's the bar chart for participants group averages. Remember you can click on the legend or the table headers to sort.

Related Reading:

The Harappa Files
Deep Ancestry: Inside The Genographic Project
The Seven Daughters of Eve: The Science That Reveals Our Genetic Ancestry
How to Do Everything Genealogy

Admixture (Ref3 K=11) HRP0211-HRP0220

Posted by on February 13, 2012 13 comments

Here are the admixture results using Reference 3 for Harappa participants HRP0211 to HRP0220.

You can see the participant results in a spreadsheet as well as their ethnic breakdowns and the reference population results.

Here's our bar chart and table. Remember you can click on the legend or the table headers to sort.

If the above interactive charts are not working, here's a static bar graph.

Do note that small percentages for your results can be noise.

HRP0211 seems like a typical Tamil Brahmin.

HRP0212 is half-Fijian, half Indian/Pakistani/Afghan. It looks like his Fijian ancestry shows up as Papuan and East Asian mostly.

HRP0213 is a Gujarati Khoja whose results are not just different from the Gujarati Patels (Gujarati A) but also from HRP0130, a Gujarati Ganchi and HapMap Gujarati B.

HRP0216 is an Iraqi Assyrian and is a little more European than the other Assyrians. The Onge, Papuan and American are likely noise.

HRP0217 and HRP0218 are Kazakhs and fairly similar to the other Kazakhs in the project.

This will probably be the last admixture analysis using Reference 3.

Related Reading:

From Harappa to Hastinapura: A Study of the Earliest South Asian City and Civilization (American School of Prehistoric Research Monograph Series)
Ancient Cities of the Indus Valley Civilization
The Family Tree Problem Solver: Tried-and-True Tactics for Tracing Elusive Ancestors

Admixture (Ref3 K=11) HRP0201-HRP0210

Posted by on January 17, 2012 1 comment

Here are the admixture results using Reference 3 for Harappa participants HRP0201 to HRP0210.

You can see the participant results in a spreadsheet as well as their ethnic breakdowns and the reference population results.

Here's our bar chart and table. Remember you can click on the legend or the table headers to sort.

If the above interactive charts are not working, here's a static bar graph.

Do note that small percentages for your results can be noise.

Related Reading:

The Family Tree Problem Solver: Tried-and-True Tactics for Tracing Elusive Ancestors
Indus Valley Painted Pottery - A Comparative Study Of The Designs On The Painted Wares Of The Harappa Culture
Ancient Cities of the Indus Valley Civilization

South Asian PCA 3D Plot

Posted by on January 12, 2012 10 comments

Here's a 3-D plot of my South Asian PCA run, showing the first three principal components.

The principal components have been scaled according to their respective eigenvalues. The plot is rotating about the vertical 1st eigenvector.

You can find out your position on the plot by using the dropdown below the plot and selecting your Harappa ID.

Related Reading:

Ugly's Electrical References, 2011 Edition
Understanding Harappa
Ancient Cities of the Indus Valley Civilization
Pocket Ref 4th Edition

South Asian PCA Plots

Posted by on January 10, 2012 3 comments

I did a South Asian PCA + Mclust analysis last month. Here are the PCA plots from that analysis.

First, the eigenvectors are not scaled to the eigenvalues in the plots. So here's a table explaining how much each eigenvector is worth.

Eigenvector Percentage variation explained
1 1.134%
2 0.452%
3 0.351%
4 0.263%
5 0.254%
6 0.236%
7 0.228%
8 0.224%
9 0.215%
10 0.209%
11 0.207%
12 0.205%
13 0.203%
14 0.201%
15 0.198%
16 0.194%
17 0.191%
18 0.189%
19 0.189%
20 0.188%
21 0.188%
22 0.187%
23 0.186%
24 0.185%
25 0.184%
26 0.184%
27 0.183%
28 0.182%
29 0.180%
30 0.180%
31 0.179%
32 0.179%

Eigenvector 1 looks like the Indian cline but it's actually a West-East Eurasian cline. It's quite similar to Reich et al's Indian cline for their subset of populations (correlation between pc1 and ASI is 0.998869) but since East Asian is not separated out here due to the lack of any East Asian samples, we get a mix of East Asian and Ancestral South Indian towards the right of the plot.

Eigenvector 2 separates Kalash from everyone else.

Related Reading:

Script of Harappa & Mohenjodaro & Its Connection With Other Scripts
The Harappa Files
The New York Times Guide to Essential Knowledge: A Desk Reference for the Curious Mind
The Ultimate Guide to Teaching English in Thailand (Teaching English in Southeast Asia)

South Asian PCA + Mclust

Posted by on December 21, 2011 9 comments

I combined reference 3 with Metspalu et al data and Harappa Ancestry Project participants (up to HRP0200). Then I kept only those individuals whose combined proportion of South Asian and Onge components on my reference 3 admixture results was more than 50%.

I ran PCA on these South Asian samples and kept 31 dimensions. Running Mclust on the PCA results gave me 37 clusters.

The clustering results are in a spreadsheet.

For an individual, the value under a specific cluster shows the probability of that person belonging to that cluster. For example, HRP0152 has a 58% probability of belonging to cluster CL8 and 42% probability of being in cluster CL14.

For the populations in the first sheet, I added up the probabilities of all the samples in that population to get the expected number of individuals of that ethnicity belonging to a specific cluster.

In the second sheet, I have listed all the individual samples' clustering results.

There are some outliers who didn't belong in any cluster: HRP0001 (me, of course), 7 (out of 18) Makranis, 4 (out of 23) Sindhis, 3 (all) Great Andamanese, 1 (out of 20) Balochi, 1 (out of 4) Madiga, and 1 (only) Onge.

Related Reading:

Cluster Analysis (Wiley Series in Probability and Statistics)
Ancient Cities of the Indus Valley Civilization
Legends of the middle ages, narrated with special reference to literature and art
Southeast Asia: A Concise History
The Handy Cyclopedia of Things Worth Knowing A Manual of Ready Reference

Reference 3 + Yunusbayev + HAP PCA and Mclust

Posted by on December 19, 2011 8 comments

I ran Principal Component Analysis (PCA) on reference 3 along with Yunusbayev et al Caucasus dataset and Harappa Ancestry Project participants (up to HRP0200).

Then I ran mclust on the first 70 dimensions. The resulting 156 clusters can be seen in a spreadsheet.

For individuals belonging to Harappa Ancestry Project, the value in a column shows that person's probability of being in that cluster. So if there is a 1 in CL15 for example, then that person has a 100% probability of being in Cluster CL15.

For the reference population groups, I have added up the probabilities for all the individuals belonging to that group.

Related Reading:

The New York Times Guide to Essential Knowledge: A Desk Reference for the Curious Mind
Legends of the middle ages, narrated with special reference to literature and art
The Foolish Dictionary An exhausting work of reference to un-certain English words, their origin, meaning, legitimate and illegitimate use, confused by a few pictures [not included]
An Introduction to Applied Multivariate Analysis with R (Use R!)