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PCAmatchR: a flexible R package for optimal case-control matching using weighted principal components.
Derek W Brown1,2, Timothy A Myers1, Mitchell J Machiela1
1Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA.
Bioinformatics (Oxford, England)
|September 14, 2020
Summary
PCAmatchR is a new R package that helps researchers match controls to cases based on ancestry using principal component analysis (PCA). This tool minimizes population stratification bias in genome-wide association studies (GWAS).
Area of Science:
- Genetics
- Bioinformatics
- Statistical Genomics
Background:
- Population stratification, or ancestry differences between cases and controls, can bias results in genome-wide association studies (GWAS).
- Accurate control selection is crucial for mitigating this bias and ensuring reliable genetic association findings.
Purpose of the Study:
- To introduce PCAmatchR, an open-source R package designed for optimal case-control matching.
- To facilitate the selection of controls with similar ancestry profiles to cases using principal component analysis (PCA).
Main Methods:
- PCAmatchR utilizes user-provided PCA outputs to perform ancestry-based matching.
- A weighted Mahalanobis distance metric, weighting principal components by explained genetic variation, is employed for control selection.
- The package was evaluated using data from the 1000 Genomes Project.
Main Results:
- PCAmatchR successfully selected ancestry-matched controls for case populations.
- The package demonstrated effectiveness in reducing inflation of association test statistics.
- Genomic similarity between matched cases and controls was improved, minimizing population stratification effects.
Conclusions:
- PCAmatchR provides a robust solution for addressing population stratification in GWAS.
- The package enhances the reliability of genetic association studies by improving control matching.
- PCAmatchR is freely available on GitHub and CRAN.
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