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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

机译:PECONPI:一种新型软件用于发现非综合征性感音神经性听力丧失和其他遗传异质性疾病的致病拷贝数变异

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摘要

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 heterogenous 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.
机译:本报告介绍了一种算法,可预测大样本群体中拷贝数变异(CNV)的致病性。在健康个体和有基因诊断的个体中发现CNV(基因组缺失和重复),区分这两类CNV可能具有挑战性,通常需要大量的手动治疗。我们已经开发了PECONPI,一种基于基因含量和CNV频率评估CNV致病性的算法。该软件已应用于大量具有遗传异质性非综合征性听力损失的患者,以根据其相对致病性对每个CNV进行评分和排名。在636名接受测试的个体中,我们确定了14名(2%)患者的听力损失的潜在病因(1名纯合子缺失,7名已知听力损失基因缺失和反式等位基因突变以及6删除大于1 Mb)。我们还确定了两个具有较小缺失的先证者,这些缺失包含可能与其听力损失在功能上相关的基因。在具有先天性心脏缺陷(CHD)的第二个遗传异质队列中测试了PECONPI确定CNV致病性的能力。它成功地在355个人中的6个人(2%)中确定了可能的病因。我们认为,该工具对于具有大量遗传异质性队列的研究人员很有用,有助于确定已知的病因和新的疾病基因。

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