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Genetic background comparison using distance-based regression, with applications in population stratification
Qizhai Li1, Sholom Wacholder, David J Hunter
1Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.
Genetic Epidemiology
|January 14, 2009
Summary
Population stratification (PS) in genome-wide association studies (GWAS) can cause false positives. PC-Finder offers an efficient method to select principal components (PCs) for effective PS correction, improving GWAS power.
Area of Science:
- Genetics
- Statistical genomics
- Bioinformatics
Background:
- Population stratification (PS) is a major confounder in genome-wide association studies (GWAS), potentially inflating false-positive findings.
- Current methods adjusting for a fixed number of principal components (PCs) may reduce statistical power, especially when PCs are evenly distributed between cases and controls.
Purpose of the Study:
- To introduce PC-Finder, a computationally efficient procedure for identifying a minimal set of principal components (PCs) for effective population stratification (PS) correction in GWAS.
- To provide a method for assessing the existence and correction of PS using a pseudo F statistic.
Main Methods:
- Developed PC-Finder, a procedure utilizing a general pseudo F statistic derived from a non-parametric multivariate regression model.
- Applied the procedure to empirical data from two GWAS within the Cancer Genetic Markers of Susceptibility (CGEMS) project.
- Conducted simulation studies to compare the power of PC-Finder against existing PS correction strategies.
Main Results:
- PC-Finder efficiently identifies a minimal set of PCs for effective PS correction.
- The pseudo F statistic effectively assesses PS and the adequacy of its correction.
- Simulation studies demonstrate a power advantage for PC-Finder over current methods, particularly when genetic variation is similarly distributed in cases and controls.
Conclusions:
- PC-Finder offers a computationally efficient and powerful approach for correcting population stratification in GWAS.
- This method enhances the reliability and power of GWAS by optimizing the selection of principal components for controlling genetic heterogeneity.
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