Tag Archives: cline

HarappaWorld Ancestral South Indian

Using the same method as I used for reference 3 admixture, I decided to guesstimate the Ancestral South Indian proportions, as given by Reich et al, for my HarappaWorld admixture run.

Basically, I used the 92 (out of the 96 samples Reich et al used) to find population averages for the South Indian component. Then, I used linear regression between the South Indian component average and Reich et al's estimate of Ancestral South Indian (ASI) ancestry. Since Reich et al actually list Ancestral North Indian percentages in their paper but their model is a two-ancestry ANI+ASI one, I simply calculated the ASI percentages as 100% minus ANI.

The correlation between Reich et al ASI and my HarappaWorld South Indian component for the relevant populations turns out to be 0.99277086.

And the linear regression fit for the data is:

ASI = 2.5218942 + 0.8104836 * S_INDIAN

where both ASI (Reich et al) and S_INDIAN (HarappaWorld) are given in percentages.

Of the individuals in HarappaWorld, I kept only those who had a South Indian component of at least 20% for computing the ASI proportions.

The resulting ASI percentages can be seen in a spreadsheet.

Please note that in the Group sheet, the averages are based on the samples which met the 20% South Indian component threshold. Thus, the 20% ASI in the Romanians is the average of the two Romanians who met the threshold out of a total of 16 Romanian samples.

The individual results are available in the Individual sheet. These results are a little different from the estimates using reference 3. Thus, I would point out that these should be taken only as a rough estimate.

Indian Cline III

I have been working on creating 100% ASI (Ancestral South Indian) samples recently. So it was really interesting that Dienekes did similar experiments:

I am going about creating the "pure" allele frequencies somewhat differently, so that would be a useful exercise.

Anyway, I thought you guys would be itching for some new results. So here's a PCA plot:

This used the same Principal Component Analysis as the one here using the 96 Indian Cline samples, Utahn Whites and Onge. However, I projected three extra "populations" on this plot.

These three populations are simulated genetic data of 25 individuals using the allele frequencies from Reference 3 Admixture results.

  1. Onge11 is generated from the Onge (C2) component from K=11 admixture for Reference 3.
  2. SA11 is generated from the South Asian (C1) component from the same K=11 admixture.
  3. SA12 is generated from the South Asian (C1) component from the K=12 admixture.

As you can see, the SA12 population lies between 100% ASI and the Indian Cline samples.

The Onge11 generated samples are a bit beyond 100% ASI on the first principal component, but they are also shifted towards the real Onge on pc2.

Misuse of Correlation

I have been misusing correlation in computing Ancestral South Indian percentages from PCA/ADMIXTURE and Reich et al population-level averages.

I have tried to make it clear that just looking at the correlation is not enough, that an admixture component is not similar to ASI just because it correlates well with Reich et al's ASI averages for the 18 Indian cline populations. Even when the correlation is higher than 0.99. To illustrate what I mean, let's look at the Ref4C admixture runs.

I calculated the mean for each admixture component from the K=2 to K=12 runs for the 18 Indian cline populations and then computed the correlation between that and the Reich et results. Let's take a look:

K Component Correlation
2 C1 Euro-Afro -0.9941887
3 C2 East Asian 0.9955347
4 C3 European -0.993933
5 C3 European -0.993277
6 C1 South Asian 0.9675099
7 C1 South Asian 0.993081
8 C1 South Asian 0.9932762
9 C1 South Asian 0.9914145
10 C1 South Asian 0.9918095
11 C1 South Asian 0.9919097
12 C1 South Asian 0.9918594

Where do you see the highest correlation? At K=3 ancestral populations, the East Asian component is very highly correlated with ASI for the Indian cline populations. Does that mean that we could use that to compute ASI? No, not at all. While it is expected that at K=3, ASI would be a little closer to East Asian than to European, East Asian is not a good proxy for ASI at all since we cannot extrapolate to other individuals and populations.

Indian Cline II

One thing I forgot in the post yesterday about the Indian cline was to try to extrapolate from the PCA results to 100% ANI (Ancestral North Indian) and 100% ASI (Ancestral South Indian).

This is a simple linear extrapolation which should be okay since PCA is linear.Men's Club - Онлайн Журнал

The "N" denotes the extrapolated position of ANI and "S" denotes the ASI. The points to the left of "N" are all Utahn Whites while the Onge are on the bottom right of the graph.

As you can see, the ASI is about the same as Onge in terms of eigenvector 1 (which represents the Indian cline approximately), but ASI is far from Onge on the 2nd eigenvector. That is expected since the Onge have been separated from the mainland populations for a long time.

The more interesting thing is that the extrapolated position of ANI is a little to the right of all the Utahn Whites.

We'll need a similar analysis of the Indian cline with more populations to see which one the ANI is closest to.

PS. I should point out that I am using correlation between a limited number of population statistics to find a relationship between the 1st principal component and Reich et al's ASI estimate. This has a number of drawbacks. It would be much better to compute ASI directly.

Indian Cline

I had used linear regression to estimate Ancestral South Indian (ASI) component from Reference 3 K=11 admixture run. Now here are a couple more exercises along the same lines but much simpler.

Just using the 96 Indian cline samples from Reich et al to compute PCA or admixture doesn't work as the Chenchu separate out in both analyses from the rest. So I added the Utahn White (CEU) samples from HapMap and the Onge from Reich et al.

First, I ran supervised admixture with two ancestral components, Utahn Whites and Onge. Here's the Onge component plotted against Reich et al's ASI estimate along with a linear regression estimate. The correlation between the two is 0.9908.

Second, I ran Principal Component Analysis (PCA) on the Indian cline samples plus Utahn Whites and Onge. Here are the first two PCA dimensions plotted. The first eigenvector explains 4.04% of the total variation and the 2nd explains 1.94%.

The first principal component is mostly along the Indian cline while the second one basically separates the Onge from everyone else.

Using the 1st principal component to estimate ASI, here's the plot with Reich et al's ASI estimate along with a regression line. The correlation between pc1 and ASI is 0.9929.

Note that both these methods work only if the samples are on the Indian cline, i.e., they don't have any other admixture.

And now for comparison, here's the linear regression for the Reference 3 K=11 admixture Onge component and ASI. The correlation here is 0.9949. Note that this is a little different than my previous analysis since I calculated the population averages using only the 96 samples recommended by Reich et al.

Here's a spreadsheet containing the data for these three runs.

There are a couple more tricks I have to figure out some things regarding Ancestral South Indian admixture. Let's hope they provide us some insight.