This Article Full Text Full Text (PDF) Supplemental Research Data All Versions of this Article: gr.6861907v1 17/11/1665    most recent Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Wang, K. Articles by Bucan, M. Search for Related Content PubMed PubMed Citation Articles by Wang, K. Articles by Bucan, M. Social Bookmarking                   What's this?

Methods

PennCNV: An integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data

¹ Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA; ² Department of Biostatistics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA; ³ Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA; ⁴ Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA

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.

E-mail bucan{at}pobox.upenn.edu; fax (215) 573-2041.

[Supplemental material is available online at www.genome.org. The PennCNV software is available from http://www.neurogenome.org/cnv/penncnv.]

Article published online before print. Article and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.6861907

⁶ The AGRE Consortium: Dan Geschwind, UCLA, Los Angeles, CA; Maja Bucan, University of Pennsylvania, Philadelphia, PA; W. Ted Brown, N.Y.S. Institute for Basic Research in Developmental Disabilities, Staten Island, NY; Rita M. Cantor, UCLA School of Medicine, Los Angeles, CA; John N. Constantino, Washington University School of Medicine, St. Louis, MO; T. Conrad Gilliam, University of Chicago, Chicago, IL; Martha Herbert, Harvard Medical School, Boston, MA; Clara Lajonchere, Cure Autism Now/Autism Speaks, Los Angeles, CA; David H. Ledbetter, Emory University, Atlanta, GA; Christa Lese-Martin, Emory University, Atlanta, GA; Janet Miller, Cure Autism Now/Autism Speaks, Los Angeles, CA; Stanley F. Nelson, UCLA School of Medicine, Los Angeles, CA; Gerard D. Schellenberg, University of Washington, Seattle, WA; Carol A. Samango-Sprouse, George Washington University, Washington, DC; Sarah Spence, UCLA, Los Angeles, CA; Matthew State, Yale University, New Haven, CT; Rudolph E. Tanzi, Massachusetts General Hospital, Boston, MA.

CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?