利用互作组驱动的优先级排序，通过单体全外显子组和全基因组测序对遗传性脑白质病进行诊断。
Diagnosis of Genetic White Matter Disorders by Singleton Whole-Exome and Genome Sequencing Using Interactome-Driven Prioritization.
机构信息
From the Neurometabolic Diseases Laboratory (A.S., A.R.-P., E. Verdura, V.V.-S., M.R., S.F., L.P.-S., J.J.M., C.G., C.C., A.P.), Bellvitge Biomedical Research Institute (IDIBELL); Instituto de Salud Carlos III (ISCIII) (A.S., A.R.-P., E. Verdura, M.R., S.F., L.P.-S., J.J.M., C.G., R.A., M.O., A.G.-C., J.A., M.d.T., L.A.P.-J., A.M., A.P.) and Secció d'Errors Congènits del Metabolisme-IBC, Servei de Bioquímica i Genètica Molecular, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) (M. Girós), Center for Biomedical Research on Rare Diseases (CIBERER); Pediatric Neurology Unit, Department of Pediatrics, Hospital Universitari Germans Trias i Pujol (A.R.-P.), and Pediatric Neurology Research Group, Vall d'Hebron Research Institute (A.M.), and Pediatric Neurology Department, Vall d'Hebron University Hospital (M.d.T., A.M.), Universitat Autònoma de Barcelona; Neuromuscular Unit, Neurology Department (V.V.-S., C.C.), Hospital Universitari de Bellvitge and Hospitalet de Llobregat, Universitat de Barcelona; Institut de Recerca Pediàtrica (R.A., M.O., A.G.-C.) and Molecular and Genetics Medicine Section (J.A.), Hospital Sant Joan de Déu (IRP-HSJD), Barcelona; Pediatric Neurology Unit, Department of Pediatrics (M.E.Y., S.A.-A.), Navarra Health Service, Navarrabiomed Research Foundation; Departments of Neuropediatrics (I.M.) and Neurology (E.M.R., A.L.d.M.), Hospital Universitario Donostia; Biodonostia Health Research Institute (Biodonostia HRI) (I.M., E.M.R., A.L.d.M.); University of the Basque Country (UPV-EHU) (I.M., A.L.d.M.), San Sebastian; Centro de Investigación Biomédica en Red para Enfermedades Neurodegenerativas (CIBERNED) (I.M., E.M.R., A.L.d.M.), Carlos III Health Institute, Madrid, Spain; Département de Médecine Translationnelle et Neurogénétique (C.R., J.L.M.), IGBMC, CNRS UMR 7104/INSERM U964/Université de Strasbourg, Illkirch; Laboratoire de Diagnostic Génétique (J.L.M.), Hôpitaux Universitaires de Strasbourg; Chaire de Génétique Humaine (J.L.M.), Collège de France, Illkirch; Complejo Asistencial Universitario de Burgos (D.C.); Department of Paediatric Neurology (C.S.-C.), Complejo Hospitalario Jaén; CNAG-CRG, Centre for Genomic Regulation (CRG) (S.B., M. Gut), Barcelona Institute of Science and Technology (BIST); Department of Pediatric Radiology (E. Vázquez), Hospital Materno-Infantil Vall d'Hebrón, Barcelona, Spain; Pediatric Neurology (M.T.), Hospital Clínico San Borja Arriarán, Central Campus Universidad de Chile; Genetics Service (L.A.P.-J.), Hospital del Mar Research Institute (IMIM); Department of Experimental and Health Sciences (L.A.P.-J.), Universitat Pompeu Fabra, Barcelona; Department of Paediatric Neurology (L.G.G.-S.), Children's University Hospital Niño Jesús, Madrid; and Catalan Institution of Research and Advanced Studies (ICREA) (A.P.), Barcelona, Spain.
出版信息
Neurology. 2022 Mar 1;98(9):e912-e923. doi: 10.1212/WNL.0000000000013278. Epub 2022 Jan 10.
BACKGROUND AND OBJECTIVES
Genetic white matter disorders (GWMD) are of heterogeneous origin, with >100 causal genes identified to date. Classic targeted approaches achieve a molecular diagnosis in only half of all patients. We aimed to determine the clinical utility of singleton whole-exome sequencing and whole-genome sequencing (sWES-WGS) interpreted with a phenotype- and interactome-driven prioritization algorithm to diagnose GWMD while identifying novel phenotypes and candidate genes.
METHODS
A case series of patients of all ages with undiagnosed GWMD despite extensive standard-of-care paraclinical studies were recruited between April 2017 and December 2019 in a collaborative study at the Bellvitge Biomedical Research Institute (IDIBELL) and neurology units of tertiary Spanish hospitals. We ran sWES and WGS and applied our interactome-prioritization algorithm based on the network expansion of a seed group of GWMD-related genes derived from the Human Phenotype Ontology terms of each patient.
RESULTS
We evaluated 126 patients (101 children and 25 adults) with ages ranging from 1 month to 74 years. We obtained a first molecular diagnosis by singleton WES in 59% of cases, which increased to 68% after annual reanalysis, and reached 72% after WGS was performed in 16 of the remaining negative cases. We identified variants in 57 different genes among 91 diagnosed cases, with the most frequent being , , , and , and a dual diagnosis underlying complex phenotypes in 6 families, underscoring the importance of genomic analysis to solve these cases. We discovered 9 candidate genes causing novel diseases and propose additional putative novel candidate genes for yet-to-be discovered GWMD.
DISCUSSION
Our strategy enables a high diagnostic yield and is a good alternative to trio WES/WGS for GWMD. It shortens the time to diagnosis compared to the classical targeted approach, thus optimizing appropriate management. Furthermore, the interactome-driven prioritization pipeline enables the discovery of novel disease-causing genes and phenotypes, and predicts novel putative candidate genes, shedding light on etiopathogenic mechanisms that are pivotal for myelin generation and maintenance.
背景与目的
遗传性脑白质病（genetic white matter disorders，GWMD）具有异质性起源，目前已确定超过 100 个致病基因。经典的靶向方法仅能在所有患者的一半中实现分子诊断。我们旨在确定单样本全外显子组测序（singleton whole-exome sequencing，sWES）和全基因组测序（whole-genome sequencing，WGS）的临床应用价值，同时识别新的表型和候选基因，我们采用表型和相互作用组驱动的优先级算法对 GWMD 进行解读。
方法
2017 年 4 月至 2019 年 12 月，在贝尔维奇生物医学研究所（Bellvitge Biomedical Research Institute，IDIBELL）和西班牙三级医院的神经病学单位的合作研究中，招募了所有年龄段的、尽管进行了广泛的标准临床检查仍未确诊的 GWMD 患者进行病例系列研究。我们进行了 sWES 和 WGS，并应用了我们的基于每个患者的人类表型本体论术语的 GWMD 相关基因种子组的网络扩展的相互作用组优先级算法。
结果
我们评估了 126 例患者（101 例儿童和 25 例成人），年龄从 1 个月至 74 岁。在 59%的病例中，通过单样本 WES 获得了首次分子诊断，经过每年的重新分析，该比例增加至 68%，对 16 例阴性病例进行 WGS 后，诊断率达到 72%。在 91 例确诊病例中，我们发现了 57 个不同基因中的变异，最常见的是 、 、 、 ，在 6 个家族中存在复杂表型的双重诊断，这突显了基因组分析对解决这些病例的重要性。我们发现了 9 个引起新疾病的候选基因，并提出了其他潜在的新 GWMD 候选基因。
讨论
我们的策略具有较高的诊断率，是 GWMD 的 trio WES/WGS 的良好替代方法。与经典的靶向方法相比，它缩短了诊断时间，从而优化了适当的管理。此外，相互作用组驱动的优先级分析管道可发现新的致病基因和表型，并预测新的潜在候选基因，揭示髓鞘生成和维持的发病机制，这对髓鞘生成和维持至关重要。
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