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Covariate adjusted differential variability analysis of DNA methylation with propensity score method

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  • Kuan Pei Fen

    (Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-3600, USA)

Abstract

It has been proposed recently that differentially variable CpG methylation (DVC) may contribute to transcriptional aberrations in human diseases. In large scale epigenetic studies, potential confounders could affect the observed methylation variabilities and need to be accounted for. In this paper, we develop a robust statistical model for differential variability DVC analysis that accounts for potential confounding covariates by utilizing the propensity score method. Our method is based on a weighted score test on strata generated propensity score stratification. To the best of our knowledge, this is the first proposed statistical method for detecting DVCs that adjusts for confounding covariates. We show that this method is robust against model misspecification and achieves good operating characteristics based on extensive simulations and a case study.

Suggested Citation

  • Kuan Pei Fen, 2014. "Covariate adjusted differential variability analysis of DNA methylation with propensity score method," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(6), pages 1-14, December.
    • Handle: RePEc:bpj:sagmbi:v:13:y:2014:i:6:p:14:n:2
    DOI: 10.1515/sagmb-2013-0072
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    References listed on IDEAS

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    1. Pei Fen Kuan & Derek Y. Chiang, 2012. "Integrating Prior Knowledge in Multiple Testing under Dependence with Applications to Detecting Differential DNA Methylation," Biometrics, The International Biometric Society, vol. 68(3), pages 774-783, September.
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