Salpietro Vincenzo, Manole Andreea, Efthymiou Stephanie, Houlden Henry
Department of Molecular Neuroscience, Institute of Neurology, University College London, London WC1N 3BG, UK.
Curr Genomics. 2018 Sep;19(6):412-419. doi: 10.2174/1389202919666180330153316.
The rapid development in the last 10-15 years of microarray technologies, such as oligonucleotide array Comparative Genomic Hybridization (CGH) and Single Nucleotide Polymorphisms (SNP) genotyping array, has improved the identification of fine chromosomal structural variants, ranging in length from kilobases (kb) to megabases (Mb), as an important cause of genetic differences among healthy individuals and also as disease-susceptibility and/or disease-causing factors. Structural genomic variations due to unbalanced chromosomal rearrangements are known as Copy-Number Variants (CNVs) and these include variably sized deletions, duplications, triplications and translocations. CNVs can significantly contribute to human diseases and rearrangements in several dosage-sensitive genes have been identified as an important causative mechanism in the molecular aetiology of Charcot-Marie-Tooth (CMT) disease and of several CMT-related disorders, a group of inherited neuropathies with a broad range of clinical phenotypes, inheritance patterns and causative genes. Duplications or deletions of the dosage-sensitive gene PMP22 mapped to chromosome 17p12 represent the most frequent causes of CMT type 1A and Hereditary Neuropathy with liability to Pressure Palsies (HNPP), respectively. Additionally, CNVs have been identified in patients with other CMT types (e.g., CMT1X, CMT1B, CMT4D) and different hereditary poly- (e.g., giant axonal neuropathy) and focal- (e.g., hereditary neuralgic amyotrophy) neuropathies, supporting the notion of hereditary peripheral nerve diseases as possible genomic disorders and making crucial the identification of fine chromosomal rearrangements in the molecular assessment of such patients. Notably, the application of advanced computational tools in the analysis of Next-Generation Sequencing (NGS) data has emerged in recent years as a powerful technique for identifying a genome-wide scale complex structural variants (e.g., as the ones resulted from balanced rearrangements) and also smaller pathogenic (intragenic) CNVs that often remain beyond the detection limit of most conventional genomic microarray analyses; in the context of inherited neuropathies where more than 70 disease-causing genes have been identified to date, NGS and particularly Whole-Genome Sequencing (WGS) hold the potential to reduce the number of genomic assays required per patient to reach a diagnosis, analyzing with a single test all the Single Nucleotide Variants (SNVs) and CNVs in the genes possibly implicated in this heterogeneous group of disorders.
在过去10至15年中,微阵列技术迅速发展,如寡核苷酸阵列比较基因组杂交(CGH)和单核苷酸多态性(SNP)基因分型阵列,这提高了对精细染色体结构变异的识别能力。这些变异长度从千碱基(kb)到兆碱基(Mb)不等,是健康个体间遗传差异的重要原因,也是疾病易感性和/或致病因素。由不平衡染色体重排导致的结构基因组变异被称为拷贝数变异(CNV),包括大小可变的缺失、重复、三倍体和易位。CNV可显著导致人类疾病,已确定几个剂量敏感基因的重排是夏科-马里-图思(CMT)病及几种CMT相关疾病分子病因中的重要致病机制。CMT病是一组具有广泛临床表型、遗传模式和致病基因的遗传性神经病。定位于17p12的剂量敏感基因PMP22的重复或缺失分别是1A型CMT和遗传性压力易感性神经病(HNPP)最常见的病因。此外,在其他类型的CMT患者(如CMT1X、CMT1B、CMT4D)以及不同的遗传性多神经病(如巨大轴索性神经病)和局灶性神经病(如遗传性神经痛性肌萎缩)患者中也发现了CNV,这支持了遗传性周围神经病可能是基因组疾病的观点,并使得在对此类患者进行分子评估时识别精细染色体重排至关重要。值得注意的是,近年来,先进的计算工具在下一代测序(NGS)数据分析中的应用已成为一种强大的技术,可用于识别全基因组范围内的复杂结构变异(例如由平衡重排产生的变异)以及通常超出大多数传统基因组微阵列分析检测限的较小致病(基因内)CNV;在遗传性神经病领域,迄今已确定70多个致病基因,NGS尤其是全基因组测序(WGS)有潜力减少每个患者为确诊所需的基因组检测数量,通过一次检测分析可能与此类异质性疾病组相关基因中的所有单核苷酸变异(SNV)和CNV。