QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data
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 valida...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
Oxford University Press
2007
|
| Subjects: |
| _version_ | 1797071848840626176 |
|---|---|
| author | Colella, S Yau, C Taylor, J Mirza, G Butler, H Clouston, P Bassett, A Seller, A Holmes, C Ragoussis, J |
| author_facet | Colella, S Yau, C Taylor, J Mirza, G Butler, H Clouston, P Bassett, A Seller, A Holmes, C Ragoussis, J |
| author_sort | Colella, S |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-06T22:59:10Z |
| format | Journal article |
| id | oxford-uuid:617962f2-1779-4382-882e-f91a873fbd20 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T22:59:10Z |
| publishDate | 2007 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | oxford-uuid:617962f2-1779-4382-882e-f91a873fbd202022-03-26T18:00:18ZQuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping dataJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:617962f2-1779-4382-882e-f91a873fbd20Statistics (see also social sciences)Medical SciencesGenetics (medical sciences)EnglishOxford University Research Archive - ValetOxford University Press2007Colella, SYau, CTaylor, JMirza, GButler, HClouston, PBassett, ASeller, AHolmes, CRagoussis, JArray-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. |
| spellingShingle | Statistics (see also social sciences) Medical Sciences Genetics (medical sciences) Colella, S Yau, C Taylor, J Mirza, G Butler, H Clouston, P Bassett, A Seller, A Holmes, C Ragoussis, J QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data |
| title | QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data |
| title_full | QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data |
| title_fullStr | QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data |
| title_full_unstemmed | QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data |
| title_short | QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data |
| title_sort | quantisnp an objective bayes hidden markov model to detect and accurately map copy number variation using snp genotyping data |
| topic | Statistics (see also social sciences) Medical Sciences Genetics (medical sciences) |
| work_keys_str_mv | AT colellas quantisnpanobjectivebayeshiddenmarkovmodeltodetectandaccuratelymapcopynumbervariationusingsnpgenotypingdata AT yauc quantisnpanobjectivebayeshiddenmarkovmodeltodetectandaccuratelymapcopynumbervariationusingsnpgenotypingdata AT taylorj quantisnpanobjectivebayeshiddenmarkovmodeltodetectandaccuratelymapcopynumbervariationusingsnpgenotypingdata AT mirzag quantisnpanobjectivebayeshiddenmarkovmodeltodetectandaccuratelymapcopynumbervariationusingsnpgenotypingdata AT butlerh quantisnpanobjectivebayeshiddenmarkovmodeltodetectandaccuratelymapcopynumbervariationusingsnpgenotypingdata AT cloustonp quantisnpanobjectivebayeshiddenmarkovmodeltodetectandaccuratelymapcopynumbervariationusingsnpgenotypingdata AT bassetta quantisnpanobjectivebayeshiddenmarkovmodeltodetectandaccuratelymapcopynumbervariationusingsnpgenotypingdata AT sellera quantisnpanobjectivebayeshiddenmarkovmodeltodetectandaccuratelymapcopynumbervariationusingsnpgenotypingdata AT holmesc quantisnpanobjectivebayeshiddenmarkovmodeltodetectandaccuratelymapcopynumbervariationusingsnpgenotypingdata AT ragoussisj quantisnpanobjectivebayeshiddenmarkovmodeltodetectandaccuratelymapcopynumbervariationusingsnpgenotypingdata |