A Statistical Method for Excluding Non-Variable CpG Sites in High-Throughput DNA Methylation Profiling

Authors

Hailong Meng, Memorial Sloan-Kettering Cancer CenterFollow
Andrew R. Joyce, Venebio Group LLC
Daniel E. Adkins, Virginia Commonwealth UniversityFollow
Yankai Jia, Altria Client Services
Guoya Li, Bon Secours Virginia Health System
Tapas K. Sengupta, Altria Client Services
Barbara K. Zedler, Venebio Group LLC
E, Lenn Murrelle, Venebio Group LLC
Edwin JCG van den Oord, Virginia Commonwealth University

Document Type

Article

Original Publication Date

2010

Journal/Book/Conference Title

BMC Bioinformatics

Volume

11

Issue

227

DOI of Original Publication

10.1186/1471-2105-11-227

Comments

The electronic version of this article is the complete one and can be found online at:http://www.biomedcentral.com/1471-2105/11/227

or doi:10.1186/1471-2105-11-227

Date of Submission

August 2014

Abstract

Background

High-throughput DNA methylation arrays are likely to accelerate the pace of methylation biomarker discovery for a wide variety of diseases. A potential problem with a standard set of probes measuring the methylation status of CpG sites across the whole genome is that many sites may not show inter-individual methylation variation among the biosamples for the disease outcome being studied. Inclusion of these so-called "non-variable sites" will increase the risk of false discoveries and reduce statistical power to detect biologically relevant methylation markers.

Results

We propose a method to estimate the proportion of non-variable CpG sites and eliminate those sites from further analyses. Our method is illustrated using data obtained by hybridizing DNA extracted from the peripheral blood mononuclear cells of 311 samples to an array assaying 1505 CpG sites. Results showed that a large proportion of the CpG sites did not show inter-individual variation in methylation.

Conclusions

Our method resulted in a substantial improvement in association signals between methylation sites and outcome variables while controlling the false discovery rate at the same level.

Rights

© 2010 Meng et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Is Part Of

VCU Biomarker Research and Personalized Medicine Publications