Measuring DNA Copy Number Variation Using High-Density Methylation Microarrays

Soonweng Cho, Hyun Seok Kim, Martha A Zeiger, Christopher B. Umbricht, Leslie M. Cope

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)295-304
Number of pages10
JournalJournal of Computational Biology
Volume26
Issue number4
DOIs
StatePublished - Apr 2019

Keywords

  • CNV
  • TCGA.
  • copy number variation
  • methylation microarray
  • microarray

ASJC Scopus subject areas

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

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