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

@article{Elhaik2022PrincipalCA,
  title={Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated},
  author={Eran Elhaik},
  journal={Scientific Reports},
  year={2022},
  volume={12}
}
  • E. Elhaik
  • Published 29 August 2022
  • Biology
  • Scientific Reports
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… 

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Be careful with your principal components

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