Here are the top ten eigenvalues:
While the first two eigenvalues are much bigger than the rest, the first explains 6.82% of the variation and the second 4.54%, the Tracy-Widom stats show that about 70-something eeigenvectors are significant.
Here are the plots for the first 10 principal components. Remember that the 1st eigenvector is 1.5 times the 2nd.
Here is a 3-D PCA plot (hat tip: Doug McDonald) showing 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.
Your browser does not support frames. Go here to see the animation.
I also ran MClust on the PCA data and got 16 clusters. The results are in a spreadsheet. I am sure with more principal components than the 10 I used, I would be able to deduce finer population structure.
Note that African Americans cluster with East Africans in CL1. That's because African Americans have some European ancestry (20% on average) and that pulls them away from West Africans and towards Europeans. East Africans also lie in that direction, so they cluster together in a PCA. However, that doesn't mean that African Americans have East African ancestry. If you look at the Admixture results for African Americans, you see that their East African ancestry is negligible.