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Investigating population stratification and admixture using eigenanalysis of dense genotypes.

¹Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, MD 20892-5635, USA. shrinerda@mail.nih.gov

|March 31, 2011

Summary

Velicer

Area of Science:

Genetics
Bioinformatics
Statistical Genetics

Background:

Principal components analysis (PCA) is crucial for genetic data analysis, addressing population stratification in association testing.
PCA helps estimate group membership, independent of self-reported ethnicity.
Dimension reduction in PCA relies on identifying significant principal components using various stopping rules.

Purpose of the Study:

To compare Velicer's minimum average partial (MAP) test with the Tracy-Widom (TW) distribution test for principal component selection in genome-wide association studies (GWAS).
To evaluate the performance of EIGENSOFT's TW-based method in estimating the number of significant principal components.
To assess the impact of overestimating principal components on association testing and group differentiation inference.

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Main Methods:

Computer simulations based on coalescent theory to model genetic data.
Comparison of Velicer's MAP test against the Tracy-Widom distribution test implemented in EIGENSOFT.
Evaluation of principal component estimation accuracy in both admixed and unadmixed populations.

Main Results:

EIGENSOFT systematically overestimates the number of significant principal components, particularly in admixed samples.
Overestimation leads to potential loss of statistical power in association testing and inaccurate group differentiation.
Velicer's MAP test demonstrates lower bias and variance, often achieving a mean squared error of 0.

Conclusions:

Velicer's MAP test is a more accurate and reliable method for determining the number of principal components to retain in GWAS.
The MAP test, implemented in R, is suitable for diverse genomic datasets, with or without population labels.
Accurate principal component selection is vital for robust genetic association studies and population structure inference.