PennCNV

PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Comprehensive identification and cataloging of copy number variations (CNVs) is required to provide a complete view of human genetic variation. The resolution of CNV detection in previous experimental designs has been limited to tens or hundreds of kilobases. Here we present PennCNV, a hidden Markov model (HMM) based approach, for kilobase-resolution detection of CNVs from Illumina high-density SNP genotyping data. This algorithm incorporates multiple sources of information, including total signal intensity and allelic intensity ratio at each SNP marker, the distance between neighboring SNPs, the allele frequency of SNPs, and the pedigree information where available. We applied PennCNV to genotyping data generated for 112 HapMap individuals; on average, we detected ∼27 CNVs for each individual with a median size of ∼12 kb. Excluding common rearrangements in lymphoblastoid cell lines, the fraction of CNVs in offspring not detected in parents (CNV-NDPs) was 3.3%. Our results demonstrate the feasibility of whole-genome fine-mapping of CNVs via high-density SNP genotyping. The PennCNV software is available from http://www.neurogenome.org/cnv/penncnv.]


References in zbMATH (referenced in 16 articles )

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  1. Cassese, Alberto; Guindani, Michele; Vannucci, Marina: iBATCGH: integrative Bayesian analysis of transcriptomic and CGH data (2016)
  2. Kuljus, Kristi; Lember, Jüri: On the accuracy of the MAP inference in HMMs (2016)
  3. Cassese, Alberto; Guindani, Michele; Tadesse, Mahlet G.; Falciani, Francesco; Vannucci, Marina: A hierarchical Bayesian model for inference of copy number variants and their association to gene expression (2014)
  4. Sampson, Joshua N.; Wheeler, Bill; Li, Peng; Shi, Jianxin: Leveraging local identity-by-descent increases the power of case/control GWAS with related individuals (2014)
  5. Luong, The Minh; Rozenholc, Yves; Nuel, Gregory: Fast estimation of posterior probabilities in change-point analysis through a constrained hidden Markov model (2013)
  6. Niu, Yue S.; Zhang, Heping: The screening and ranking algorithm to detect DNA copy number variations (2012)
  7. Nielsen, Jesper: A coarse-to-fine approach to computing the (k)-best Viterbi paths (2011)
  8. Siegmund, David; Yakir, Benjamin; Zhang, Nancy R.: Detecting simultaneous variant intervals in aligned sequences (2011)
  9. Greenman, Chris D.; Bignell, Graham; Butler, Adam; Edkins, Sarah; Hinton, Jon: PICNIC: an algorithm to predict absolute allelic copy number variation with microarray cancer data (2010)
  10. Perreault, Louis-Philippe Lemieux; Andelfinger, Gregor U.; Asselin, Géraldine; Dube, Marie-Pierre: Partitioning of copy-number genotypes in pedigrees (2010) ioport
  11. Rancoita, Paola M. V.; Hutter, Marcus; Bertoni, Francesco; Kwee, Ivo: An integrated Bayesian analysis of LOH and copy number data (2010) ioport
  12. Tai, Yu Chuan; Kvale, Mark N.; Witte, John S.: Segmentation and estimation for SNP microarrays: A Bayesian multiple change-point approach (2010)
  13. Zhang, Zhongyang; Lange, Kenneth; Ophoff, Roel; Sabatti, Chiara: Reconstructing DNA copy number by penalized estimation and imputation (2010)
  14. Li, Wentian; Lee, Annette; Gregersen, Peter K.: Copy-number-variation and copy-number-alteration region detection by cumulative plots (2009) ioport
  15. Rueda, Oscar M.; Díaz-Uriarte, Ramón: Detection of recurrent copy number alterations in the genome: taking among-subject heterogeneity seriously (2009) ioport
  16. Zöllner, Sebastian; Teslovich, Tanya M.: Using GWAS data to identify copy number variants contributing to common complex diseases (2009)