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Efficient toolkit implementing best practices for principal component analysis of population genetic data
[article]
2019
bioRxiv
pre-print
Principal Component Analysis (PCA) of genetic data is routinely used to infer ancestry and control for population structure in various genetic analyses. However, conducting PCA analyses can be complicated and has several potential pitfalls. These pitfalls include (1) capturing Linkage Disequilibrium (LD) structure instead of population structure, (2) projected PCs that suffer from shrinkage bias when projecting PCA from a reference dataset to another independent dataset, (3) detecting sample
doi:10.1101/841452
fatcat:wrjh5qwnqngvjihvzfwi77lasy