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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
Published on: October 18, 2013
Major copy proportion analysis of tumor samples using SNP arrays
Cheng Li1, Rameen Beroukhim, Barbara A Weir
1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, 3 Blackfan Circle, Boston, MA 02115, USA. cli@hsph.harvard.edu
BMC Bioinformatics
|April 23, 2008
Summary
Major copy proportion (MCP) analysis improves upon loss-of-heterozygosity (LOH) analysis for SNP array data, offering better insights into allelic imbalance and copy numbers, especially in contaminated samples.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Single nucleotide polymorphisms (SNPs) are common genetic variations used as genomic markers.
- Oligonucleotide SNP microarrays enable high-throughput genotyping of numerous SNPs for various studies.
- Hidden Markov Models (HMM) have been previously applied to SNP array data for copy number and LOH inference.
Purpose of the Study:
- To introduce and implement Major Copy Proportion (MCP) analysis for oligonucleotide SNP array data.
- To enhance the analysis of allelic imbalance and copy number variations in tumor samples.
- To provide a method applicable to normal-contaminated tumor samples.
Main Methods:
- Construction of a Hidden Markov Model (HMM) to infer MCP states from allele-specific signals.
- Utilizing emission and transition distributions within the HMM framework.
- Comparison of MCP analysis with traditional LOH and copy number analysis using various SNP array datasets (10 K, 100 K, 250 K).
Main Results:
- MCP analysis demonstrates superior performance compared to LOH analysis, particularly in allelic-imbalanced regions and normal-contaminated samples.
- MCP analysis successfully infers major and minor copy alleles from allelic-imbalanced regions.
- The method provides complementary information to total copy number analysis.
Conclusions:
- MCP analysis extends tumor LOH analysis to encompass allelic imbalance, offering valuable insights.
- MCP analysis is effective for normal-contaminated tumor samples, as demonstrated with mixed samples.
- The MCP analysis and visualization methods are integrated into the user-friendly dChip software.

