Principal Components Analysis (PCA) is a tool that has been used to infer population structure in genetic data for several decades, long before the GWAS era17–20. It should be noted that top PCs do not always reflect population structure: they may reflect family relatedness19, long-range LD (for example, due to inversion polymorphisms4), or assay artifacts10; these effects can often be eliminated by removing related samples, regions of long-range LD, or low-quality data, respectively, from the data used to compute PCs. In addition, PCA can highlight effects of differential bias that require additional quality control21.
