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Choice of population structure informative principal components for adjustment in a case-control study
Gina M Peloso1, Kathryn L Lunetta
1Department of Biostatistics, Boston University School of Public Health, 801 Massachusetts Avenue, Boston MA 02118, USA.
BMC Genetics
|July 21, 2011
Summary
Choosing principal components (PCs) for population structure adjustment in genome-wide association studies (GWAS) impacts study power. The optimal PC selection strategy depends on genetic and phenotypic structure within samples.
Area of Science:
- Genetics
- Bioinformatics
- Statistical genomics
Background:
- Population structure is a key confounder in genome-wide association studies (GWAS).
- Principal components (PCs) are commonly used to adjust for population structure.
- Optimal selection of PCs for adjustment remains unclear and may depend on sample characteristics.
Purpose of the Study:
- To investigate optimal methods for selecting ancestry informative principal components (PCs) for adjustment in association models.
- To evaluate these methods across various population structure scenarios in genome-wide association studies (GWAS).
Main Methods:
- Simulations were used to explore different strategies for selecting PCs.
- The study examined scenarios with structured and unstructured SNPs and phenotypes.
- Association models were evaluated for power and Type I error rates.
Main Results:
- When SNP and phenotype frequencies are uniform across subpopulations, PC selection methods yield similar results.
- Phenotype structure with unstructured SNPs favors methods excluding PCs.
- SNP structure with unstructured phenotypes benefits from including PCs associated with the SNP.
Conclusions:
- Standard practice of including a fixed number of PCs in GWAS is not always optimal for power.
- For structured SNPs and unstructured phenotypes, including PCs associated with the SNP maximizes power.
- Selecting PCs based on association with the tested SNP is recommended for optimizing power in GWAS.
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