Major Copy Proportion Analysis of Tumor Samples Using SNP Arrays

DSpace/Manakin Repository

Major Copy Proportion Analysis of Tumor Samples Using SNP Arrays

Citable link to this page


Title: Major Copy Proportion Analysis of Tumor Samples Using SNP Arrays
Author: Winckler, Wendy; Sellers, William R; Li, Cheng; Beroukhim, Rameen; Weir, Barbara Ann; Garraway, Levi Alexander; Meyerson, Matthew Langer

Note: Order does not necessarily reflect citation order of authors.

Citation: Li, Cheng, Rameen Beroukhim, Barbara A. Weir, Wendy Winckler, Levi A. Garraway, William R. Sellers, and Matthew Meyerson. 2008. Major copy proportion analysis of tumor samples using SNP arrays. BMC Bioinformatics 9: 204.
Full Text & Related Files:
Abstract: Background: Single nucleotide polymorphisms (SNPs) are the most common genetic variations in the human genome and are useful as genomic markers. Oligonucleotide SNP microarrays have been developed for high-throughput genotyping of up to 900,000 human SNPs and have been used widely in linkage and cancer genomics studies. We have previously used Hidden Markov Models (HMM) to analyze SNP array data for inferring copy numbers and loss-of-heterozygosity (LOH) from paired normal and tumor samples and unpaired tumor samples. Results: We proposed and implemented major copy proportion (MCP) analysis of oligonucleotide SNP array data. A HMM was constructed to infer unobserved MCP states from observed allele-specific signals through emission and transition distributions. We used 10 K, 100 K and 250 K SNP array datasets to compare MCP analysis with LOH and copy number analysis, and showed that MCP performs better than LOH analysis for allelic-imbalanced chromosome regions and normal contaminated samples. The major and minor copy alleles can also be inferred from allelic-imbalanced regions by MCP analysis. Conclusion: MCP extends tumor LOH analysis to allelic imbalance analysis and supplies complementary information to total copy numbers. MCP analysis of mixing normal and tumor samples suggests the utility of MCP analysis of normal-contaminated tumor samples. The described analysis and visualization methods are readily available in the user-friendly dChip software.
Published Version: doi:10.1186/1471-2105-9-204
Other Sources:
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at
Citable link to this page:
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)


Search DASH

Advanced Search