Interpreting principal component analyses of spatial population genetic variation

Abstract

Nearly 30 years ago, Cavalli-Sforza et al. pioneered the use of principal component analysis (PCA) in population genetics and used PCA to produce maps summarizing human genetic variation across continental regions¹. They interpreted gradient and wave patterns in these maps as signatures of specific migration events^1,2,3. These interpretations have been controversial^4,5,6,7, but influential⁸, and the use of PCA has become widespread in analysis of population genetics data^{9,10,11,12,13}. However, the behavior of PCA for genetic data showing continuous spatial variation, such as might exist within human continental groups, has been less well characterized. Here, we find that gradients and waves observed in Cavalli-Sforza et al.'s maps resemble sinusoidal mathematical artifacts that arise generally when PCA is applied to spatial data, implying that the patterns do not necessarily reflect specific migration events. Our findings aid interpretation of PCA results and suggest how PCA can help correct for continuous population structure in association studies.