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

Surin Ahn, Tao Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

Abstract

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 commonly used 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.

Original languageEnglish (US)
Title of host publication18th Pacific Symposium on Biocomputing, PSB 2013
PublisherWorld Scientific Publishing Co. Pte Ltd
Pages69-79
Number of pages11
ISBN (Print)9781627480161
StatePublished - 2013
Event18th Pacific Symposium on Biocomputing, PSB 2013 - Kohala Coast, United States
Duration: Jan 3 2013Jan 7 2013

Other

Other18th Pacific Symposium on Biocomputing, PSB 2013
CountryUnited States
CityKohala Coast
Period1/3/131/7/13

Fingerprint

Methylation
Statistical methods
Tissue
DNA Methylation
Epigenomics
Reproducibility of Results
Ovarian Neoplasms
Genes
Genome
Phenotype

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Biomedical Engineering
  • Medicine(all)

Cite this

Ahn, S., & Wang, T. (2013). A powerful statistical method for identifying differentially methylated markers in complex diseases. In 18th Pacific Symposium on Biocomputing, PSB 2013 (pp. 69-79). World Scientific Publishing Co. Pte Ltd.

A powerful statistical method for identifying differentially methylated markers in complex diseases. / Ahn, Surin; Wang, Tao.

18th Pacific Symposium on Biocomputing, PSB 2013. World Scientific Publishing Co. Pte Ltd, 2013. p. 69-79.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ahn, S & Wang, T 2013, A powerful statistical method for identifying differentially methylated markers in complex diseases. in 18th Pacific Symposium on Biocomputing, PSB 2013. World Scientific Publishing Co. Pte Ltd, pp. 69-79, 18th Pacific Symposium on Biocomputing, PSB 2013, Kohala Coast, United States, 1/3/13.
Ahn S, Wang T. A powerful statistical method for identifying differentially methylated markers in complex diseases. In 18th Pacific Symposium on Biocomputing, PSB 2013. World Scientific Publishing Co. Pte Ltd. 2013. p. 69-79
Ahn, Surin ; Wang, Tao. / A powerful statistical method for identifying differentially methylated markers in complex diseases. 18th Pacific Symposium on Biocomputing, PSB 2013. World Scientific Publishing Co. Pte Ltd, 2013. pp. 69-79
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