Dataset Information

PECONPI: a novel software for uncovering pathogenic copy number variations in non-syndromic sensorineural hearing loss and other genetically heterogeneous disorders.

ABSTRACT: This report describes an algorithm developed to predict the pathogenicity of copy number variants (CNVs) in large sample cohorts. CNVs (genomic deletions and duplications) are found in healthy individuals and in individuals with genetic diagnoses, and differentiation of these two classes of CNVs can be challenging and usually requires extensive manual curation. We have developed PECONPI, an algorithm to assess the pathogenicity of CNVs based on gene content and CNV frequency. This software was applied to a large cohort of patients with genetically heterogeneous non-syndromic hearing loss to score and rank each CNV based on its relative pathogenicity. Of 636 individuals tested, we identified the likely underlying etiology of the hearing loss in 14 (2%) of the patients (1 with a homozygous deletion, 7 with a deletion of a known hearing loss gene and a point mutation on the trans allele and 6 with a deletion larger than 1 Mb). We also identified two probands with smaller deletions encompassing genes that may be functionally related to their hearing loss. The ability of PECONPI to determine the pathogenicity of CNVs was tested on a second genetically heterogeneous cohort with congenital heart defects (CHDs). It successfully identified a likely etiology in 6 of 355 individuals (2%). We believe this tool is useful for researchers with large genetically heterogeneous cohorts to help identify known pathogenic causes and novel disease genes.

SUBMITTER: Tsai EA

PROVIDER: S-EPMC3745548 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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PECONPI: a novel software for uncovering pathogenic copy number variations in non-syndromic sensorineural hearing loss and other genetically heterogeneous disorders.

Tsai Ellen A EA Berman Micah A MA Conlin Laura K LK Rehm Heidi L HL Francey Lauren J LJ Deardorff Matthew A MA Holst Jenelle J Kaur Maninder M Gallant Emily E Clark Dinah M DM Glessner Joseph T JT Jensen Shane T ST Grant Struan F A SF Gruber Peter J PJ Hakonarson Hakon H Spinner Nancy B NB Krantz Ian D ID

American journal of medical genetics. Part A 20130729 9

This report describes an algorithm developed to predict the pathogenicity of copy number variants (CNVs) in large sample cohorts. CNVs (genomic deletions and duplications) are found in healthy individuals and in individuals with genetic diagnoses, and differentiation of these two classes of CNVs can be challenging and usually requires extensive manual curation. We have developed PECONPI, an algorithm to assess the pathogenicity of CNVs based on gene content and CNV frequency. This software was a ...[more]

PMID: 23897863

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Project description:BackgroundGenomic aberrations can be used to determine cancer diagnosis and prognosis. Clinically relevant novel aberrations can be discovered using high-throughput assays such as Single Nucleotide Polymorphism (SNP) arrays and next-generation sequencing, which typically provide aggregate signals of many cells at once. However, heterogeneity of tumor subclones dramatically complicates the task of detecting aberrations.ResultsThe aggregate signal of a population of subclones can be described as a linear system of equations. We employed a measure of allelic imbalance and total amount of DNA to characterize each locus by the copy number status (gain, loss or neither) of the strongest subclonal component. We designed simulated data to compare our measure to existing approaches and we analyzed SNP-arrays from 30 melanoma samples and transcriptome sequencing (RNA-Seq) from one melanoma sample.We showed that any system describing aggregate subclonal signals is underdetermined, leading to non-unique solutions for the exact copy number profile of subclones. For this reason, our illustrative measure was more robust than existing Hidden Markov Model (HMM) based tools in inferring the aberration status, as indicated by tests on simulated data. This higher robustness contributed in identifying numerous aberrations in several loci of melanoma samples. We validated the heterogeneity and aberration status within single biopsies by fluorescent in situ hybridization of four affected and transcriptionally up-regulated genes E2F8, ETV4, EZH2 and FAM84B in 11 melanoma cell lines. Heterogeneity was further demonstrated in the analysis of allelic imbalance changes along single exons from melanoma RNA-Seq.ConclusionsThese studies demonstrate how subclonal heterogeneity, prevalent in tumor samples, is reflected in aggregate signals measured by high-throughput techniques. Our proposed approach yields high robustness in detecting copy number alterations using high-throughput technologies and has the potential to identify specific subclonal markers from next-generation sequencing data.

Dataset Information

PECONPI: a novel software for uncovering pathogenic copy number variations in non-syndromic sensorineural hearing loss and other genetically heterogeneous disorders.

Publications

PECONPI: a novel software for uncovering pathogenic copy number variations in non-syndromic sensorineural hearing loss and other genetically heterogeneous disorders.

Similar Datasets

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