Hidden Markov Model-Based CNV Detection Algorithms for Illumina Genotyping Microarrays

Abstract

Somatic alterations in DNA copy number have been well studied in numerous malignancies, yet the role of germline DNA copy number variation in cancer is still emerging. Genotyping microarrays generate allele-specific signal intensities to determine genotype, but may also be used to infer DNA copy number using additional computational approaches. Numerous tools have been developed to analyze Illumina genotype microarray data for copy number variant (CNV) discovery, although commonly utilized algorithms freely available to the public employ approaches based upon the use of hidden Markov models (HMMs). QuantiSNP, PennCNV, and GenoCN utilize HMMs with six copy number states but vary in how transition and emission probabilities are calculated. Performance of these CNV detection algorithms has been shown to be variable between both genotyping platforms and data sets, although HMM approaches generally outperform other current methods. Low sensitivity is prevalent with HMM-based algorithms, suggesting the need for continued improvement in CNV detection methodologies.

Keywords: copy number variation; genotyping microarray; hidden Markov model.

Figure 1

Size of CNVs detected in the HapMap samples using QuantiSNP, PennCNV, and GenoCN. The HMM-based CNV detection tools were applied to Illumina Human610-Quad BeadChip v1.0 data from three HapMap samples of European ancestry (NA06985, NA06991, and NA06993). For each sample, boxplots were generated for CNV sizes from each HMM-based detection method. Boxplots on the left are CNV sizes measured in genomic length (base pairs), and boxplots on the right are CNV sizes measured by the number of genotype microarray probes in the detected region.