Bayesian hidden Markov models for detecting regions of deletion and duplication in the human genome using Illumina BeadChip arrays

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Bayesian hidden Markov models for detecting regions of deletion and duplication in the human genome using Illumina BeadChip arrays

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  • DOI:  10.1049/ic:20060374
  • ISBN: 0-86341-716-7
  • Location: Cambridge, UK
  • Conference date: 9 Nov. 2006
  • Format: PDF
The association of CNVs with disease is of interest in medical genetics. This paper makes use single nucleotide polymorphism (SNP) to infer copy number variation (CNV). A microrray technology known as SNP-CGH (SNP-comparative genomic hybridization) is used to detect DNA CNV. Current SNP-CGH arrays contain > 100, 000 probes. The Illumina BeadChip has been designed for whole genome SNP genotyping applications. It can probe thousands of SNPs on one array and up to 30 replicates per SNP. The study shows that fluoresescence intensity signals are proportional to the amount of gDNA. SNPs in deleted or duplicated regions show decreased/elevated signals relative to normal regions. Signal processing using Bayesian hidden Markov model has been applied to BeadChip data to find CNVs.

Inspec keywords: hidden Markov models; molecular biophysics; Bayes methods; fluorescence; polymorphism; bio-optics; diseases; genetics; DNA; medical signal processing

Subjects: Signal processing and detection; Probability theory, stochastic processes, and statistics; Patient diagnostic methods and instrumentation; Markov processes; Biomolecular structure, configuration, conformation, and active sites; Markov processes; Biology and medical computing; Optical and laser radiation (medical uses); Optical and laser radiation (biomedical imaging/measurement); Digital signal processing; Fluctuation phenomena, random processes, and Brownian motion

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