Statistics Papers

Document Type

Journal Article

Date of this Version

2011

Publication Source

PLoS Computational Biology

Volume

7

Issue

1

DOI

10.1371/journal.pcbi.1001060

Abstract

Chromosomal gains and losses comprise an important type of genetic change in tumors, and can now be assayed using microarray hybridization-based experiments. Most current statistical models for DNA copy number estimate total copy number, which do not distinguish between the underlying quantities of the two inherited chromosomes. This latter information, sometimes called parent specific copy number, is important for identifying allele-specific amplifications and deletions, for quantifying normal cell contamination, and for giving a more complete molecular portrait of the tumor. We propose a stochastic segmentation model for parent-specific DNA copy number in tumor samples, and give an estimation procedure that is computationally efficient and can be applied to data from the current high density genotyping platforms. The proposed method does not require matched normal samples, and can estimate the unknown genotypes simultaneously with the parent specific copy number. The new method is used to analyze 223 glioblastoma samples from the Cancer Genome Atlas (TCGA) project, giving a more comprehensive summary of the copy number events in these samples. Detailed case studies on these samples reveal the additional insights that can be gained from an allele-specific copy number analysis, such as the quantification of fractional gains and losses, the identification of copy neutral loss of heterozygosity, and the characterization of regions of simultaneous changes of both inherited chromosomes.

Comments

At the time of publication, author Nancy Zhang was affiliated with Stanford University. Currently, she is a faculty member at the Statistics Department at the University of Pennsylvania.

Keywords

chromosomes, genotyping, glioblastoma multiforme, Markov models, haplotypes, DNA, heterozygosity, hidden Markov models

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Date Posted: 27 November 2017

This document has been peer reviewed.