Integrating Prior Knowledge in Multiple Testing Under Dependence with Applications to Detecting Differential DNA Methylation

• Activity Number: 242
• Type: Contributed
• Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Pei Fen Kuan*+
• Companies: The University of North Carolina at Chapel Hill
• Keywords: DNA Methylation ; False Discovery Rate ; Non-homogeneous Hidden Markov Model ; Kernel Density Estimation ; Microarray ; Semiparametric Model

Abstract

DNA methylation has emerged as an important hallmark of epigenetics. Numerous platforms including tiling arrays and next generation sequencing, and experimental protocols are available for profiling DNA methylation. Similar to other tiling array data, DNA methylation data shares the characteristics of inherent correlation structure among nearby probes. However, unlike gene expression or protein DNA binding data, the varying CpG density which gives rise to CpG island, shore and shelf definition provides exogenous information in detecting differential methylation. This paper aims to introduce a robust statistical testing and probe ranking procedure based on a non-homogeneous hidden Markov model that incorporates the above-mentioned features for detecting differential methylation. We show that this model improves probe ranking and is robust to model mispecification based on extensive simulation studies. We further illustrate that our proposed framework achieves good operating characteristics as compared to commonly used methods in real DNA methylation data that aims to detect differential methylation sites.