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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zhao Huaqing & Mitra Nandita & Kanetsky Peter A. & Nathanson Katherine L. & Rebbeck Timothy R., 2018. "A practical approach to adjusting for population stratification in genome-wide association studies: principal components and propensity scores (PCAPS)," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 17(6), pages 1-12, December.
    • Handle: RePEc:bpj:sagmbi:v:17:y:2018:i:6:p:12:n:1
    DOI: 10.1515/sagmb-2017-0054
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    References listed on IDEAS

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    1. Nick Patterson & Alkes L Price & David Reich, 2006. "Population Structure and Eigenanalysis," PLOS Genetics, Public Library of Science, vol. 2(12), pages 1-20, December.
    2. Zhao Huaqing & Rebbeck Timothy R. & Mitra Nandita, 2012. "Analyzing Genetic Association Studies with an Extended Propensity Score Approach," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(5), pages 1-24, October.
    3. Matthieu Bouaziz & Christophe Ambroise & Mickael Guedj, 2011. "Accounting for Population Stratification in Practice: A Comparison of the Main Strategies Dedicated to Genome-Wide Association Studies," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-13, December.
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    5. Lin, D. Y. & Zeng, D., 2011. "Correcting for Population Stratification in Genomewide Association Studies," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 997-1008.
    6. B. Devlin & Kathryn Roeder, 1999. "Genomic Control for Association Studies," Biometrics, The International Biometric Society, vol. 55(4), pages 997-1004, December.
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