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Principal Component Analysis (PCA)

Definition
AI-generated

Principal component analysis finds orthogonal directions of maximum variance; in GWAS, PCA of genotypes (often after LD pruning and relatedness filtering) yields ancestry axes used as covariates or for visualizing population structure.

Topics
Population genetics
Related
Ancestry Genetic Relationship Matrix (GRM) LD Pruning

Why it matters in GWAS

Including leading principal components in regression reduces confounding from population stratification; PC-based QC also flags outliers and admixture.

Example usage

"Cross-cohort analyses modeled Principal Component Analysis (PCA) to improve transferability and reduce population-structure confounding."

Ancestry Genetic Relationship Matrix (GRM) LD Pruning Multidimensional Scaling (MDS) Population Stratification Relatedness and Sample Structure UMAP Genome-Wide Association Study (GWAS)
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Price AL, et al. (2006). Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet.
  • GWASTutorial: Sample PCA.
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