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在孟德尔疾病队列中从外显子组测序数据检测纯合和半合子拷贝数变异

Homozygous and hemizygous CNV detection from exome sequencing data in a Mendelian disease cohort.

作者信息

Gambin Tomasz, Akdemir Zeynep C, Yuan Bo, Gu Shen, Chiang Theodore, Carvalho Claudia M B, Shaw Chad, Jhangiani Shalini, Boone Philip M, Eldomery Mohammad K, Karaca Ender, Bayram Yavuz, Stray-Pedersen Asbjørg, Muzny Donna, Charng Wu-Lin, Bahrambeigi Vahid, Belmont John W, Boerwinkle Eric, Beaudet Arthur L, Gibbs Richard A, Lupski James R

机构信息

Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.

Institute of Computer Science, Warsaw University of Technology, Warsaw, 00-665 Warsaw, Poland.

出版信息

Nucleic Acids Res. 2017 Feb 28;45(4):1633-1648. doi: 10.1093/nar/gkw1237.

Abstract

We developed an algorithm, HMZDelFinder, that uses whole exome sequencing (WES) data to identify rare and intragenic homozygous and hemizygous (HMZ) deletions that may represent complete loss-of-function of the indicated gene. HMZDelFinder was applied to 4866 samples in the Baylor-Hopkins Center for Mendelian Genomics (BHCMG) cohort and detected 773 HMZ deletion calls (567 homozygous or 206 hemizygous) with an estimated sensitivity of 86.5% (82% for single-exonic and 88% for multi-exonic calls) and precision of 78% (53% single-exonic and 96% for multi-exonic calls). Out of 773 HMZDelFinder-detected deletion calls, 82 were subjected to array comparative genomic hybridization (aCGH) and/or breakpoint PCR and 64 were confirmed. These include 18 single-exon deletions out of which 8 were exclusively detected by HMZDelFinder and not by any of seven other CNV detection tools examined. Further investigation of the 64 validated deletion calls revealed at least 15 pathogenic HMZ deletions. Of those, 7 accounted for 17-50% of pathogenic CNVs in different disease cohorts where 7.1-11% of the molecular diagnosis solved rate was attributed to CNVs. In summary, we present an algorithm to detect rare, intragenic, single-exon deletion CNVs using WES data; this tool can be useful for disease gene discovery efforts and clinical WES analyses.

摘要

我们开发了一种名为HMZDelFinder的算法,该算法利用全外显子组测序(WES)数据来识别罕见的基因内纯合和半合子(HMZ)缺失,这些缺失可能代表所指示基因的完全功能丧失。HMZDelFinder应用于贝勒-霍普金斯孟德尔基因组学中心(BHCMG)队列中的4866个样本,检测到773个HMZ缺失调用(567个纯合或206个半合子),估计灵敏度为86.5%(单外显子调用为82%,多外显子调用为88%),精确率为78%(单外显子调用为53%,多外显子调用为96%)。在773个由HMZDelFinder检测到的缺失调用中,82个进行了阵列比较基因组杂交(aCGH)和/或断点PCR,64个得到了确认。其中包括18个单外显子缺失,其中8个是HMZDelFinder独家检测到的,而其他七种CNV检测工具均未检测到。对64个经过验证的缺失调用的进一步研究发现至少有15个致病性HMZ缺失。其中,7个在不同疾病队列中占致病性CNV的17 - 50%,其中7.1 - 11%的分子诊断解决率归因于CNV。总之,我们提出了一种利用WES数据检测罕见的、基因内的、单外显子缺失CNV的算法;该工具可用于疾病基因发现工作和临床WES分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d4f/5389578/d7c6a0a59b7f/gkw1237fig1.jpg

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