Whole genome and transcriptome sequencing for gene discovery in dystonia

肌张力障碍是一种多动症，通常会严重限制患者的健康和生活质量。它们是罕见的疾病，在病原学和表型表现上表现出广泛的异质性。它们的分子病因学知之甚少，限制了有效治疗的可用性。许多形式的肌张力障碍都有遗传基础；因此，确定致病基因是诊断和治疗方案亟需进展的极好起点。目前，全外显子组测序是单基因疾病基因发现的主要驱动力。我们已经对700多个索引病例进行了测序，并汇编了欧洲最大的肌张力障碍WES队列之一。这些努力导致了新的疾病基因的发现，并为肌张力障碍提出了新的病理生理学概念。然而，WES的检测范围有限，目前大多数病例尚未解决。因此，我们希望将全基因组测序(WGS)和转录组测序(RNA-SEQ)结合起来，以确定WES阴性个体肌张力障碍的遗传原因。WGS统一覆盖所有基因，检测结构变异，并能够分析非编码调控变异，这些变异也导致单基因疾病，但注释起来具有挑战性。我们通过将WGS与RNA-SEQ相结合来解决这个问题，以帮助解释WGS检测到的变异的后果。使用WGS和/或RNA-seq已经被证明成功地在其他类型的单基因疾病中发现了疾病基因。我们将在从我们的队列中选择的总共300个个体中应用WGS和RNA-seq：76个三联体，25个常染色体显性遗传的家系，11个常染色体隐性遗传模式的家系。样本是使用我们根据WES结果制定的优先顺序分数来选择的。它集成了几个基于表型的临床预测因子，并被证明可以识别具有很高可能性的肌张力障碍遗传原因的个体。我们将调用WGS中的遗传变量，并根据频率、功能和遗传模式对其进行过滤。功能指的是编码变异体(例如LoF)和非编码变异体(例如增强子中的调节变异体)。对于三联体，变异必须符合从头模型(对父母和受影响的后代进行测序)，而在家庭中，它们必须与观察到的遗传模式以及两个已测序的家庭成员之间的模式一致。我们将把这些变异与从RNA-seq(基因表达、单等位基因表达、剪接模式)的三个不同读数优先排序的基因整合起来，以确定基因作为肌张力障碍的候选原因基因的优先顺序。我们希望在我们的队列中识别至少四个新的疾病基因，并提供令人信服的证据。然后这些基因将被带到功能后续研究中进行验证。此外，我们将在独立的肌张力障碍队列中寻求进一步的确认。

Dystonias are hyperkinetic movement disorders, often imposing severe limitations on patients’ health and quality of life. They are rare diseases and show extensive heterogeneity in their etiology and phenotypic presentation. Their molecular etiology is poorly understood, limiting the availability of effective therapies. Many forms of dystonia have a genetic basis; therefore identification of the causal genes is an excellent starting point for the urgently needed progress in diagnostics and treatment options. At present, whole exome sequencing (WES) is the main driving force for gene discovery in monogenic disorders. We have sequenced more than 700 index cases and have compiled one of the largest WES cohorts for dystonia in Europe. These efforts have led to the discovery of novel disease genes and have suggested new pathophysiological concepts for dystonia. However, WES has a limited detection scope, leaving the majority of cases unsolved at present.Therefore, we want to combine whole genome sequencing (WGS) and transcriptome sequencing (RNA-seq) to identify the genetic causes of dystonia in WES-negative individuals.WGS has uniform coverage of all genes, detects structural variants, and enables the analysis of non-coding regulatory variants, which also contribute to monogenic disorders, but are challenging to annotate. We address this by combining WGS with RNA-seq to aid interpretation of the consequences of variation detected by WGS. Using WGS and/or RNA-seq has already proven successful for disease gene discovery in other types of monogenic disorders.We will apply WGS and RNA-seq in a total of 300 individuals selected from our cohort: 76 trios, 25 families with an autosomal-dominant, and 11 families with an autosomal-recessive mode of inheritance. Samples are selected using a prioritization score which we have developed based on our WES results. It integrates several phenotype-based clinical predictors and was shown to identify individuals with a high likelihood of a genetic cause of their dystonia. We will call genetic variants from WGS and filter these based on frequency, function, and mode of inheritance. Function refers to both coding variants (e.g. LoF) and non-coding variants (e.g. regulatory variant in an enhancer). For trios, variants have to be consistent with a de novo model (sequencing parents and affected offspring), whereas in families they have to be consistent with the observed mode of inheritance and between two sequenced family members. We will integrate these variants with genes prioritized by three different readouts from RNA-seq (gene expression, monoallelic expression, splicing patterns) to prioritize genes as candidate causal genes for dystonia. We expect to identify at least four novel disease genes with compelling evidence in our cohort. These genes will then be taken forward to validation in functional follow-up studies. In addition, we will seek additional confirmation in independent dystonia cohorts.