I'm interested in comparing the intra-regional genetic relatedness between two populations of mice. One population is native to Europe, while the other is native to North America.
I ran PCA to find which population has less genetic structure (i.e. less within population variations). In other words, I'm interested to find which of the two is the younger population (more recent ancestor means more related to each other). The result of PC1 vs. PC2 can be seen below.
It is clear that PC1 describes the continental scale, which separates the two mouse populations. The eigenvalue of PC1 is 72.5. Here, the left population is the European mice and the right is the N. American mice. The variations along PC1 for both seemed to be quite similar, except for a few outliers among the right population.
What I'm interested is the PC2 variation. From the plot, European mice exhibited much greater variation compared to the cluster formed by N. American mice. From my knowledge, PC2 is orthogonal to PC1, and it is always the axis with the greatest variation. From this, I concluded that the within population variations among European mice (left) is greater (i.e. more genetic structure) than among N. American mice (right).
I investigated higher components as well. For PC3, the opposite is true. The right population (N. America) exhibited the greatest variation relative to the left population. However, the eigenvalue of PC2 is 3.5, while for PC3 it is 2.2. Which means that PC2 still wins in explaining which of the two populations has the greatest genetic variations. PC4 and beyond doesn't add any new information.
Does my approach seem reasonable to you?