Major Copy Proportion Analysis of Tumor Samples Using SNP Arrays

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Major Copy Proportion Analysis of Tumor Samples Using SNP Arrays

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dc.contributor.author Winckler, Wendy
dc.contributor.author Sellers, William R
dc.contributor.author Li, Cheng
dc.contributor.author Beroukhim, Rameen
dc.contributor.author Weir, Barbara Ann
dc.contributor.author Garraway, Levi Alexander
dc.contributor.author Meyerson, Matthew Langer
dc.date.accessioned 2011-05-13T01:10:50Z
dc.date.issued 2008
dc.identifier.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. en_US
dc.identifier.issn 1471-2105 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:4889440
dc.description.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. en_US
dc.language.iso en_US en_US
dc.publisher BioMed Central en_US
dc.relation.isversionof doi:10.1186/1471-2105-9-204 en_US
dc.relation.hasversion http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2375907/pdf/ en_US
dash.license LAA
dc.title Major Copy Proportion Analysis of Tumor Samples Using SNP Arrays en_US
dc.type Journal Article en_US
dc.description.version Version of Record en_US
dc.relation.journal BMC Bioinformatics en_US
dash.depositing.author Garraway, Levi Alexander
dc.date.available 2011-05-13T01:10:50Z
dash.affiliation.other SPH^Biostatistics en_US
dash.affiliation.other HMS^Medicine-Brigham and Women's Hospital en_US
dash.affiliation.other HMS^Medicine-Brigham and Women's Hospital en_US
dash.affiliation.other HMS^Pathology en_US

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