Bayesian Piecewise Constant Regression of LOH and CN data (gBPCR)



SNP-microarrays are able to measure simultaneously both genotype and copy number (CN) at several Single Nucleotide Polymorphism (SNP) positions. We call LOH data the homozygous status of the SNPs deduced from the genotyping data. Combining the two data, it is possible to better identify genomic aberrations. For example, a long sequence of homozygous SNPs might be shown due to either a uniparental disomy event (UPD), i.e. each SNP has two identical alleles both derived from only one parent, or the physical loss of one allele. In this situation, the knowledge of the copy number value can help in distinguishing between these two events.
The gBPCR algorithm is Bayesian piecewise constant regression which infers the type of aberration occurred (high amplification, gain, loss, homozygous deletion, IBD/UPD, normal state), taking into account all the possible influence in the microarray detection of the genotype, resulting from an altered copy number level.


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