Title :
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
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;
Conference_Titel :
BioMedical Engineering and Informatics, 2008. BMEI 2008. International Conference on
Conference_Location :
Sanya
Print_ISBN :
978-0-7695-3118-2
DOI :
10.1109/BMEI.2008.150
