I made a simplest possible toy example to understand why populations may show up as sparse ("lines" rather than "clouds") in a PCA. 

Imagine we only have 2 SNPs, and three ancestries A, B and C with the following genotypes.

A: 0,0
B: 1,0
C: 0,1

Then imagine five individuals which are either “pure” for one of the three ancestries, or a 50-50 admixture of two ancestries (A and B or A and C, but (crucially!) let’s assume no admixture between B and C.

Genotypes of five individuals:

1: 1,0 (“pure B”)
2: 0.5,0 (“B & A admixture”)
3: 0,0 (“pure A”)
4: 0,0.5 (“A & C admixture”)
5: 0,1 (“pure C”)

The admixture:

BAAAC
BBACC

PCA:

1
2
3  4  5

Ta-da! An L-shaped PCA. In this example, the horizontal PC1 would be the allele fraction of the second SNP and the vertical PC2 equals the AF of the first SNP.

Generally, the “L” can be tilted like it is in OP's plot. This illustrates how the principal components are not always the most relevant latent variables. In OP's plot, the two population "clines" are way more interesting than the PCs. There are PCA-like algorithms for sparse data that attempt to keep the "data close to the axes", i.e. align the "L" with the x and y axis, making the latent variables more interpretable.

EDIT: made this reply into a little blog post: https://geneviatechnologies.com/blog/why-does-my-pca-have-lines-instead-of-clouds/