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Estimation of Parent Specific DNA Copy Number in Tumors using High-Density Genotyping Arrays

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  • Hao Chen
  • Haipeng Xing
  • Nancy R Zhang

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. Author Summary: Many genetic diseases are related to copy number aberrations of some regions of the genome. As we know, each chromosome normally has two copies. However, under some circumstances, for some regions, either one or both of the chromosomes change. Genotyping microarray data provides the copy number of the two alleles of polymorphic sites along the chromosomes, which make the inference of the copy number aberrations of the chromosome feasible. One difficulty is that genotyping microarray data cannot provide the haplotype of the two copies of a chromosome. In this paper, we model the copy number along the chromosome as a two-dimensional Markov Chain. Using the observed copy number of both alleles of all the sites, we can determine the parent specific copy number along the chromosome as well as infer the haplotypes of the two copies of the inherited chromosomes in regions where there is allelic imbalance. Simulation results show high sensitivity and specificity of the method. Applying this method to glioblastoma samples from the Cancer Genome Atlas data illustrate the insights gained from allele-specific copy number analysis.

Suggested Citation

  • Hao Chen & Haipeng Xing & Nancy R Zhang, 2011. "Estimation of Parent Specific DNA Copy Number in Tumors using High-Density Genotyping Arrays," PLOS Computational Biology, Public Library of Science, vol. 7(1), pages 1-15, January.
  • Handle: RePEc:plo:pcbi00:1001060
    DOI: 10.1371/journal.pcbi.1001060
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    References listed on IDEAS

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    1. Rameen Beroukhim & Ming Lin & Yuhyun Park & Ke Hao & Xiaojun Zhao & Levi A Garraway & Edward A Fox & Ephraim P Hochberg & Ingo K Mellinghoff & Matthias D Hofer & Aurelien Descazeaud & Mark A Rubin & M, 2006. "Inferring Loss-of-Heterozygosity from Unpaired Tumors Using High-Density Oligonucleotide SNP Arrays," PLOS Computational Biology, Public Library of Science, vol. 2(5), pages 1-10, May.
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    Cited by:

    1. Lai Yinglei & Gastwirth Joseph L., 2015. "Outlier reset CUSUM for the exploration of copy number alteration data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(4), pages 333-345, August.
    2. Anat Reiner-Benaim, 2016. "Scan Statistic Tail Probability Assessment Based on Process Covariance and Window Size," Methodology and Computing in Applied Probability, Springer, vol. 18(3), pages 717-745, September.
    3. Rui Xia & Selina Vattathil & Paul Scheet, 2014. "Identification of Allelic Imbalance with a Statistical Model for Subtle Genomic Mosaicism," PLOS Computational Biology, Public Library of Science, vol. 10(8), pages 1-11, August.

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