ArticleScientific reports2022
Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers, 2 of them syntheses that pooled it.
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Who cites it
107 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Genome-wide DAPC-based genetic structure, linkage disequilibrium, and effective population size in indigenous buffaloes of Northeastern and Central India.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- Admixture-informed polygenic risk reporting using the ePRS framework.Nature communications · 2026Article
- Assessment of cardiac allograft vasculopathy in heart transplant patients using multidimensional dynamic CTA and principal components analysis.BMC medical imaging · 2026Article
- Optimizing image watermarking integrity and visual quality via DTPSO and hybrid transform methods.Scientific reports · 2026Article
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- Heterogeneity in the association of genetic risk for RA and resultant RA phenotypes.Rheumatology (Oxford, England) · 2026Article
- Autoencoders for genomic variation analysis.Genome research · 2026Article
- Genetic structure and conservation relevance in the narrowly distributed tree Catalpa huangxin revealed by RAD-Seq.Heredity · 2026Article
- GrafAnc: Reliable and reproducible inference of continental and regional population structure.HGG advances · 2026Article
- Targeted Chromosomal Sequencing of Wild Bonobos Identifies a Genetically Distinct Subpopulation East of the Lomami River.bioRxiv : the preprint server for biology · 2026Article
47 more citing papers are in PubMed but not listed here.
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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 common test cases using an intuitive color-based model alongside human population data. We demonstrate that PCA results can be artifacts of the data and can be easily manipulated to generate desired outcomes. PCA adjustment also yielded unfavorable outcomes in association studies. PCA results may not be reliable, robust, or replicable as the field assumes. Our findings raise concerns about the validity of results reported in the population genetics literature and related fields that place a disproportionate reliance upon PCA outcomes and the insights derived from them. We conclude that PCA may have a biasing role in genetic investigations and that 32,000-216,000 genetic studies should be reevaluated. An alternative mixed-admixture population genetic model is discussed.
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