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Updated: Jun 5, 2026

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants
Published on: February 21, 2015
Bayesian estimation of genomic copy number with single nucleotide polymorphism genotyping arrays
Beibei Guo1, Alejandro Villagran, Marina Vannucci
1Department of Statistics, Rice University, 6100 Main, Houston, TX 77005-1827, USA. rguerra@rice.edu.
Background:
The identification of copy number aberration in the human genome is an important area in cancer research. We develop a model for determining genomic copy numbers using high-density single nucleotide polymorphism genotyping microarrays. The method is based on a Bayesian spatial normal mixture model with an unknown number of components corresponding to true copy numbers. A reversible jump Markov chain Monte Carlo algorithm is used to implement the model and perform posterior inference.
Results:
The performance of the algorithm is examined on both simulated and real cancer data, and it is compared with the popular CNAG algorithm for copy number detection.
Conclusions:
We demonstrate that our Bayesian mixture model performs at least as well as the hidden Markov model based CNAG algorithm and in certain cases does better. One of the added advantages of our method is the flexibility of modeling normal cell contamination in tumor samples.
Related Concept Videos
Comparing Copy Number Variations and SNPs
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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DNA Microarrays
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Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...

