Here's the Principal Component Analysis (PCA) of Reference 3 data.
First the 3-D plot of the first three eigenvectors. The plot is rotating about the 1st eigenvector which is vertical. Also, I have stretched the principal components based on the corresponding eigenvalues.
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And now the plots of the first 24 principal components. Please note that the eigenvectors are not scaled by the corresponding eigenvalues in these plots (unlike the 3D plot).
Here are the first 24 eigenvalues (expressed as percentage of the sum of all eigenvalues):
Together, the first 24 eigenvectors explain 14.79% of the variation in the data.
According to the Tracy-Widom statistics from eigensoft, the number of significant principle components is 118.
UPDATE: I thought the eigenvectors 2 & 4 looked interesting for South Asians so I plotted them together.