Here are the admixture results using Reference 3 for Harappa participants HRP0121 to HRP0130.
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.
I had run PCA on the Reference 3 dataset before. Now I included Harappa participants in it as well.
Here's the dendrogram based on the Euclidean distance between Harappa participants in their PCA results.
Since no one liked the IBS nearest neighbors lists, I thought making a spreadsheet with every participants' closest 100 neighbors in PCA space might be more fruitful.
Note that for the Harappa participants, the median distance to their nearest neighbor is 0.2064. So if your nearest neighbor is more than let's say 0.3 away, then you are not close to anyone.
Here are the admixture results using Reference 3 for Harappa participants HRP0111 to HRP0120.
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.
Davidski created a Google map of Eurogenes participants and Reiver suggested something similar would be cool for Harappa Ancestry Project too.
So I have copied the idea and created a google map for Harappa Ancestry Project.
View Harappa Ancestry Project in a larger map
Now participants need to go and add themselves to the map at their ancestral location. Here are the instructions:
That's it!
Here are the admixture results using Reference 3 for Harappa participants HRP0101 to HRP0110.
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.
HRP0101 has 1/8th Gujarati Patel ancestry but has 0% Onge component while the expected value would be about 3%. Also, his/her being 1/2 Romany is not reflected in a 70% European percentage.
HRP0105, an Iranian Kurd, is similar to the Iraqi Kurd HRP0059.
HRP0108, a Halai Bhatia, looks mostly like Punjabis and Sindhis in the admixture results.
HRP0110, Mexican/Jewish, is half Native American and likely a quarter Jewish.
Here's my first admixture run using Reference 3 for Harappa participants with FTDNA data.
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.
Since this is my first analysis of FTDNA data, I asked HRP0006 to provide me with his FTDNA results (HRP0093) too so they can be compared. Let's see how that turned out.
| HRP0006 | HRP0093 | ||
| C1 | S Asian | 49.31% | 49.06% |
| C2 | Onge | 14.13% | 13.70% |
| C3 | E Asian | 1.12% | 0.00% |
| C4 | SW Asian | 14.65% | 12.52% |
| C5 | European | 18.88% | 22.44% |
| C6 | Siberian | 0.00% | 0.78% |
| C7 | W African | 0.00% | 0.00% |
| C8 | Papuan | 0.54% | 0.01% |
| C9 | American | 1.35% | 1.48% |
| C10 | San/Pygmy | 0.00% | 0.00% |
| C11 | E African | 0.00% | 0.00% |
There are differences of up to 3% but generally the results are reasonably close.
HRP0095 and HRP0100 thought they had possible South Asian ancestry. That seems fairly unlikely at least in the last few generations since their Onge component is zero or very low.
After a long tease, here is the spreadsheet containing the top 500 nearest neighbors (using IBS similarity percentages) for the Harappa participants from HRP0001 to HRP0089.
I am also providing an R data object with the same data (except it contains all the 3,975 individual from reference 3 and Harappa). To use this data,
load('harappa_ibs.RData')
to load the data
closest("HRP0001")
to find the 20 closest IBS neighbors of HRP0001. You can use any of the Harappa IDs here.
closest("HRP0010",50)
Enjoy!
Vasishta asked:
would it be possible to repeat the same exercise with the Reference II populations? These results seem to be far more plausible for every participant as compared to the previous ones.
Since it took only a few minutes, I calculated the scores as detailed in a previous post from the IBS measures between Harappa participants (1-80 only) and Reference 2.
The spreadsheet is here.
Now that we have the admixture results for project participants using Reference 3, let's take a look at a tree based on Euclidean distance of the admixture proportions for each participant.
Compare it to the earlier one with reference 1 admixture results.
And here is a dendrogram combining the average reference population results with the Harappa participants.
Here's my first admixture run using Reference 3 for Harappa participants. Since K=11 was the run with the Onge-ASI connection, I ran admixture at K=11 with all the 90 Harappa participants.
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.
Using the comparison between the Onge component here and Reich et al's Ancestral South Indian one, I get the following linear regression.
The correlation is 0.9949 which is probably as high as it can get. So let's calculate the ASI percentage for all the Harappa participants.
Note that I didn't calculate the ASI percentage for those who had a really low Onge component since the linear regression above would not be valid outside the range we have in our original data.
You can see the percentages in a spreadsheet too.
Let's compare with the Dodecad ANI-ASI results. I have 22.5% ASI here while it was 20.6% in the Dodecad analysis. Overall, it seems like my technique results in about 2% more ASI than Dodecad's, with a few exceptions: Like Razib who jumps from 34.3% to 43.3% (averaging his parents who are very close).
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