QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data

By Stefano Colella, Christopher Yau, Jennifer M. Taylor, G. Mirza, H. Butler, P. Clouston, Anne S. Bassett, A. Seller, Christopher C. Holmes, Jiannis Ragoussis. Nucleic Acids Research, 2007

662 citations recorded by the source corpus.

Array-based technologies have been used to detect chromosomal copy number changes (aneuploidies) in the human genome. Recent studies identified numerous copy number variants (CNV) and some are common polymorphisms that may contribute to disease susceptibility. We developed, and experimentally validated, a novel computational framework (QuantiSNP) for detecting regions of copy number variation from BeadArray™ SNP genotyping data using an Objective Bayes Hidden-Markov Model (OB-HMM). Objective Bayes measures are used to set certain hyperparameters in the priors using a novel re-sampling framework to calibrate the model to a fixed Type I (false positive) error rate. Other parameters are set via maximum marginal likelihood to prior training data of known structure. QuantiSNP provides probabilistic quantification of state classifications and significantly improves the accuracy of segmental aneuploidy identification and mapping, relative to existing analytical tools (Beadstudio, Illumina), as demonstrated by validation of breakpoint boundaries. QuantiSNP identified both novel and validated CNVs. QuantiSNP was developed using BeadArray™ SNP data but it can be adapted to other platforms and we believe that the OB-HMM framework has widespread applicability in genomic research. In conclusion, QuantiSNP is a novel algorithm for high-resolution CNV/aneuploidy detection with application to clinical genetics, cancer and disease association studies.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/415cdd6924a9524cb42018c147f7120716bf9e8f

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Citations in the corpus

Sensitive and accurate detection of copy number variants using read depth of coverage. (2009, methodology): “Therefore, events such as these can be detected using the same types of segmentation algorithms that are used for microarray data (Colella et al. 2007; Wang et al. 2007, 2009; Cahan et al. 2008; Korn et al. 2008).”
PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. (2007, methodology): “Compared to the BeadStudio LOH-plus algorithm (Il-lumina) and the QuantiSNP algorithm (Colella et al. 2007), there are several advantages in the implementation of PennCNV.”
Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci (2015, methodology): “This metric outperformed our prior approach of selecting the intersection of PennCNV and QuantiSNP calls (Sanders et al., 2011).”
Runs of homozygosity in European populations. (2008, methodology): “, 2007.(53) A custom Perl script was developed for comparison of the identified heterozygous deletions and ROHs.”
The Diploid Genome Sequence of an Individual Human (2007, methodology): “The results from the BeadChip were analyzed for CNV content using QuantiSNP as previously described [101].”
Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. (2011, methodology): “, 2007), QuantiSNP (QT) (Colella et al., 2007), and GNOSIS (GN), (www.”