TY - JOUR
T1 - Measuring DNA Copy Number Variation Using High-Density Methylation Microarrays
AU - Cho, Soonweng
AU - Kim, Hyun Seok
AU - Zeiger, Martha A.
AU - Umbricht, Christopher B.
AU - Cope, Leslie M.
N1 - Funding Information:
The authors would like to thank Rob Scharpf for advice in the early development of the Epicopy method and insight into DNA copy number analysis. They would also like to thank Jean-Philippe Fortin and Kasper D. Hansen for sharing the developer’s version of the funnorm code and advice on normalization methods. They are grateful to Elana J. Fertig for testing early versions of the Epicopy software. Data used for the study were obtained from the TCGA effort. Funding: This work was supported in part by grants from the Susan G. Komen Foundation (KG 110094) and the NIH (R01CA140331) awarded to C.B.U.
Publisher Copyright:
© Copyright 2019, Mary Ann Liebert, Inc., publishers 2019.
PY - 2019/4
Y1 - 2019/4
N2 - 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.
AB - 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.
KW - CNV
KW - TCGA.
KW - copy number variation
KW - methylation microarray
KW - microarray
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U2 - 10.1089/cmb.2018.0143
DO - 10.1089/cmb.2018.0143
M3 - Article
C2 - 30789293
AN - SCOPUS:85064072335
SN - 1066-5277
VL - 26
SP - 295
EP - 304
JO - Journal of Computational Biology
JF - Journal of Computational Biology
IS - 4
ER -