In PCA, which statement describes how population structure is typically visualized in sticklebacks?

In PCA, which statement describes how population structure is typically visualized in sticklebacks?

• A. PCA is used to estimate mutation rates.
• B. PCA can reveal population structure by plotting the first two principal components.
• C. Genotype many SNPs, compute principal components, and plot the first two components to reveal clustering by population or ecotype.
• D. PCA always shows no structure in population genetics.

Answer: (B)

In population genetics, PCA is used to visualize how individuals relate to each other by reducing thousands of SNP measurements into a few axes that capture the most variation. For sticklebacks, this often means computing the principal components from genotype data across many SNPs and then plotting the individuals using the first two components. The result is a 2D scatter where individuals from the same population or ecotype tend to cluster together, revealing underlying population structure such as shared ancestry or ecological differentiation. This is why the statement about revealing population structure by plotting the first two principal components is the best description: it captures the main idea that PCA provides a compact visualization of structure by showing how individuals group in PC space. PCA isn’t about estimating mutation rates, and PCA does typically reveal structure rather than nothing at all; while detailing the workflow (genotype many SNPs and plot PC1 vs PC2) is accurate, the core takeaway is that plotting the first two PCs exposes clustering by population or ecotype.

In population genetics, PCA is used to visualize how individuals relate to each other by reducing thousands of SNP measurements into a few axes that capture the most variation. For sticklebacks, this often means computing the principal components from genotype data across many SNPs and then plotting the individuals using the first two components. The result is a 2D scatter where individuals from the same population or ecotype tend to cluster together, revealing underlying population structure such as shared ancestry or ecological differentiation.

This is why the statement about revealing population structure by plotting the first two principal components is the best description: it captures the main idea that PCA provides a compact visualization of structure by showing how individuals group in PC space. PCA isn’t about estimating mutation rates, and PCA does typically reveal structure rather than nothing at all; while detailing the workflow (genotype many SNPs and plot PC1 vs PC2) is accurate, the core takeaway is that plotting the first two PCs exposes clustering by population or ecotype.