Consequences of PCA graphs, SNP codings, and PCA variants for elucidating population structure

Abstract

PCA is frequently used to display and discover patterns in SNP data from humans, animals, plants, and microbes--especially to elucidate population structure. Given the popularity of PCA, one might expect that PCA is understood well and applied effectively. However, our literature survey of 125 representative articles that apply PCA to SNP data shows that three choices have usually been made poorly: SNP coding, PCA variant, and PCA graph. Accordingly, we offer several simple recommendations for effective PCA analysis of SNP data. The ultimate benefit from informed and optimal choices of SNP coding, PCA variant, and PCA graph is expected to be discovery of more biology, and thereby acceleration of medical, agricultural, and other vital applications.