Measuring DNA Copy Number Variation Using High-Density Methylation Microarrays

Journal of computational biology: A journal of computational molecular cell biology

Christopher B.Umbricht^4,5¹  Hyun-SeokKim^2²  SoonwengCho^1⁴  Leslie M.Cope^6,7⁵  Martha A.Zeiger^3⁶ 

[1] Address correspondence to: Prof. Christopher B. Umbricht, Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD 21287^4;Department of Medicine, Rutgers New Jersey Medical School, New Brunswick, New Jersey^2;Department of Oncology Bioinformatics, The Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, Maryland^7;Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, Maryland^1;Department of Surgery, The Johns Hopkins University School of Medicine, Baltimore, Maryland^5;Department of Surgery, The University of Virginia School of Medicine, Charlottesville, Virginia^3;Prof. Leslie M. Cope, Department of Oncology Bioinformatics, The Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD 21287^6

关键词: CNV;    copy number variation;    methylation microarray;    microarray;    TCGA;   

DOI  :  10.1089/cmb.2018.0143

学科分类：生物科学（综合）

来源: Mary Ann Liebert, Inc. Publishers

【 摘 要 】

Genetic and epigenetic changes drive carcinogenesis, and their integrated analysis provides insights into mechanisms of cancer development. Computational methods have been developed to measure copy number variation (CNV) from methylation array data, including ChAMP-CNV, CN450K, and, introduced here, Epicopy. Using paired single nucleotide polymorphism (SNP) and methylation array data from the public The Cancer Genome Atlas repository, we optimized CNV calling and benchmarked the performance of these methods. We optimized the thresholds of all three methods and showed comparable performance across methods. Using Epicopy as a representative analysis of Illumina450K array, we show that Illumina450K-derived CNV methods achieve a sensitivity of 0.7 and a positive predictive value of 0.75 in identifying CNVs, which is similar to results achieved when comparing competing SNP microarray platforms with each other.

【 授权许可】

Unknown