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

By Kai Wang, Mingyao Li, Dexter Hadley, Rui Liu, Joseph Glessner, Struan F. A. Grant, Hakon Hakonarson, Maja Bucan. Genome Research, 2007

1,861 citations recorded by the source corpus.

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 approximately 27 CNVs for each individual with a median size of approximately 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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9d3a22b070de6352d428b4b820502ce1fff3ce3a

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Citations in the corpus

Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. (2011, methodology): “CNV prediction was performed by PennCNV (PN) (Wang et al., 2007), QuantiSNP (QT) (Colella et al., 2007), and GNOSIS (GN), (www.CNVision.org) (Figure 1).”
Universal Patterns of Selection in Cancer and Somatic Tissues (2018, neutral): “CEL files provided by TCGA were processed using PennCNV libraries (Wang et al., 2007) to obtain logR and BAF values.”
Whole population, genome-wide mapping of hidden relatedness. (2009, neutral): “2008) and computational (Pinto et al. 2007; Wang et al. 2007).”
A contribution of novel CNVs to schizophrenia from a genome-wide study of 41,321 subjects: CNV Analysis Group and the Schizophrenia Working Group of the Psychiatric Genomics Consortium (2016, background): “Genetic associations were investigated by case-control tests of CNV burden at four levels: (1) genome-wide (2) pathways, (3) genes, and (4) probes.”
De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia (2011, methodology): “27 We then used a slight modification of the MeZOD algorithm 12 (Supplementary Section 3) to visualise outlier signals in probands potentially indicative of de novos (Figure 1).”