A powerful statistical method for identifying differentially methylated markers in complex diseases

DNA methylation is an important epigenetic modification that regulates transcriptional expression and plays an important role in complex diseases, such as cancer. Genome-wide methylation patterns have unique features and hence require the development of new analytic approaches. One important feature...

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Bibliographic Details
Published inPacific Symposium on Biocomputing. Pacific Symposium on Biocomputing p. 69
Main Authors Ahn, Surin, Wang, Tao
Format Journal Article
LanguageEnglish
Published United States 2013
Subjects
Analysis of Variance
Case-Control Studies
Computational Biology
Computer Simulation
Databases, Nucleic Acid
Disease - genetics
DNA Methylation
DNA, Neoplasm - genetics
Female
Genetic Markers
Genetic Techniques
Humans
Middle Aged
Ovarian Neoplasms - genetics
Regression Analysis
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Summary:DNA methylation is an important epigenetic modification that regulates transcriptional expression and plays an important role in complex diseases, such as cancer. Genome-wide methylation patterns have unique features and hence require the development of new analytic approaches. One important feature is that methylation levels in disease tissues often differ from those in normal tissues with respect to both average and variability. In this paper, we propose a new score test to identify methylation markers of disease. This approach simultaneously utilizes information from the first and second moments of methylation distribution to improve statistical efficiency. Because the proposed score test is derived from a generalized regression model, it can be used for analyzing both categorical and continuous disease phenotypes, and for adjusting for covariates. We evaluate the performance of the proposed method and compare it to other tests including the most commonlyused t-test through simulations. The simulation results show that the validity of the proposed method is robust to departures from the normal assumption of methylation levels and can be substantially more powerful than the t-test in the presence of heterogeneity of methylation variability between disease and normal tissues. We demonstrate our approach by analyzing the methylation dataset of an ovarian cancer study and identify novel methylation loci not identified by the t-test.
ISSN:2335-6936