Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated.

ABSTRACT: Principal Component Analysis (PCA) is a multivariate analysis that reduces the complexity of datasets while preserving data covariance. The outcome can be visualized on colorful scatterplots, ideally with only a minimal loss of information. PCA applications, implemented in well-cited packages like EIGENSOFT and PLINK, are extensively used as the foremost analyses in population genetics and related fields (e.g., animal and plant or medical genetics). PCA outcomes are used to shape study design, identify, and characterize individuals and populations, and draw historical and ethnobiological conclusions on origins, evolution, dispersion, and relatedness. The replicability crisis in science has prompted us to evaluate whether PCA results are reliable, robust, and replicable. We analyzed twelve

SUBMITTER: Elhaik E 

PROVIDER: S-EPMC9424212 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature