Major copy proportion analysis of tumor samples using SNP arrays

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 allelespecific 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 s...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Cheng Li, Rameen Beroukhim, Barbara A. Weir, Wendy Winckler, Levi A. Garraway, William R. Sellers, Matthew Meyerson
• Keywords: BMCBI 2008, MCP Analysis, SNP Array, Tumor Samples