Analysis of High-throughput DNA Methylation Bead Arrays Utilizing Bayesian Genotyping Algorithms

Author

Yuanyuan Xiao ; Segal, M.R. ; Houseman, E.A. ; Wiemels, J. ; Wiencke, J. ; Shichun Zheng ; Wrensch, M. ; Christensen, Bjoern ; Marsit, C. ; Kelsey, K. ; Nelson, H. ; Karagas, Margaret

Author_Institution

Dept. of Epidemiology & Biostat., Univ. of California, San Francisco, CA

Volume

1

fYear

2008

fDate

27-30 May 2008

Firstpage

447

Lastpage

452

Abstract

We present a statistical framework, MAMS-M, for determining the methylation status of hundreds of cancer related CpG sites. MAMS-M extends and adapts our previous SNP genotyping algorithm, MAMS, to methylation bead array data, exploiting the similarities in data structure between the two platforms. MAMS-M employs a multi-site, multi-array model-based clustering approach to derive initial methylation calls, and then recalibrate these calls and associated confidence measures using site-specific adjustments. We demonstrate the performance of MAMS-M using a real-life data set with cancer applications.

Keywords

Bayes methods; DNA; biochemistry; cancer; genetics; medical computing; molecular biophysics; Bayesian genotyping algorithms; CpG sites; SNP genotyping algorithm; cancer; clustering approach; data structure; high-throughput DNA methylation bead arrays; Adaptive arrays; Algorithm design and analysis; Bayesian methods; Bioinformatics; Biomedical engineering; Cancer; Clustering algorithms; DNA; Genomics; Sequences; Illumina bead array; bayesian algorithm; methylation; model-based clustering;

fLanguage

English

Publisher

ieee

Conference_Titel

BioMedical Engineering and Informatics, 2008. BMEI 2008. International Conference on

Conference_Location

Sanya

Print_ISBN

978-0-7695-3118-2

Type

conf

DOI

10.1109/BMEI.2008.150

Filename

4548709