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A practical approach to adjusting for population stratification in genome-wide association studies: principal components and propensity scores (PCAPS)

Zhao Huaqing (), Mitra Nandita, Kanetsky Peter A., Nathanson Katherine L. and Rebbeck Timothy R.
Additional contact information
Zhao Huaqing: Department of Clinical Sciences, Temple University School of Medicine, 3440 N. Broad Street, Kresge Hall East, Room 218, Philadelphia, PA 19140, USA, Phone: 215-707-6139, Fax: 215-707-3160
Mitra Nandita: Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104, USA
Kanetsky Peter A.: Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
Nathanson Katherine L.: Department of Medicine, University of Pennsylvania, South Pavilion, Perelman Center for Advanced Medicine, Philadelphia, PA 19104, USA
Rebbeck Timothy R.: Division of Population Sciences, Dana Farber Cancer Institute and Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA

Statistical Applications in Genetics and Molecular Biology, 2018, vol. 17, issue 6, 12

Abstract: Genome-wide association studies (GWAS) are susceptible to bias due to population stratification (PS). The most widely used method to correct bias due to PS is principal components (PCs) analysis (PCA), but there is no objective method to guide which PCs to include as covariates. Often, the ten PCs with the highest eigenvalues are included to adjust for PS. This selection is arbitrary, and patterns of local linkage disequilibrium may affect PCA corrections. To address these limitations, we estimate genomic propensity scores based on all statistically significant PCs selected by the Tracy-Widom (TW) statistic. We compare a principal components and propensity scores (PCAPS) approach to PCA and EMMAX using simulated GWAS data under no, moderate, and severe PS. PCAPS reduced spurious genetic associations regardless of the degree of PS, resulting in odds ratio (OR) estimates closer to the true OR. We illustrate our PCAPS method using GWAS data from a study of testicular germ cell tumors. PCAPS provided a more conservative adjustment than PCA. Advantages of the PCAPS approach include reduction of bias compared to PCA, consistent selection of propensity scores to adjust for PS, the potential ability to handle outliers, and ease of implementation using existing software packages.

Keywords: bias; principal components analysis; propensity score; testicular germ cell tumors; Tracy-Widom statistic (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1515/sagmb-2017-0054

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