Quantification of population structure using correlated SNPs by shrinkage principal components.
OBJECTIVE
Association studies using unrelated individuals have become the most popular design for mapping complex traits. One of the major challenges of association mapping is avoiding spurious association due to population stratification. Principal component analysis (PCA) on genome-wide marker genotypes is one of the most popular population stratification control methods. It implicitly assumes that the markers are in linkage equilibrium, a condition that is rarely satisfied and that we plan to relax.


METHODS
We carefully examined the impact of linkage disequilibrium (LD) on PCA, and proposed a simple modification of the standard PCA to automatically adjust for the correlations among markers.


RESULTS
We demonstrated that LD patterns in genome-wide association datasets can distort the techniques for stratification control, showing 'subpopulations' reflecting localized LD phenomena rather than plausible population structure. We showed that the proposed method effectively removes the artifactual effect of LD patterns, and successfully recovers underlying population structure that is not apparent from standard PCA.


CONCLUSIONS
PCA is highly influenced by sets of SNPs with high LD, obscuring the true population substructure. Our shrinkage PCA applies to all available markers, regardless of the LD patterns. The proposed method is easier to implement than most existing LD adjusted PCA methods.
Zou
Fei
F
Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. fzou @ bios.unc.edu
Lee
Seunggeun
S
Knowles
Michael R
MR
Wright
Fred A
FA
D005819:Genetic Markers; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D025341:Principal Component Analysis
D005819:Genetic Markers