Whole-genome and RNA sequencing in 25 families with individual(s) affected by a Mendelian disorder

对 25 个患有孟德尔疾病的家庭进行全基因组和 RNA 测序

• 批准号: 443716202
• 负责人: Professorin Dr. Kerstin Kutsche
• 金额: --
• 依托单位: Institut für Humangenetik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/443716202?language=en
• 关键词: Whole; genome; RNA; sequencing; 25

Next-generation sequencing, including whole-exome sequencing (WES) and whole-genome sequencing (WGS), has revolutionized identification of the molecular basis of monogenic disorders. WES and WGS are highly sensitive for detection of single nucleotide variants (SNVs) und small insertions and/or deletions (indels). Trio WES has a diagnostic yield of ~40%; this rate can be increased by 2-10% by computerized analysis of copy number variants (CNVs) using exome data. WGS has many advantages over WES, for example a more uniform data quality over the whole genome and detection of variants that are not seen by WES, such as variants in promoter and untranslated regions of protein-coding genes, variants in RNA genes and non-coding regions as well as structural variants (SVs) such as inversions and translocations. However, there are substantial barriers to the systematic prioritization of the 4-5 millions of genetic variants per genome. Transcriptomic analysis by RNA sequencing (RNA-seq) is the method of choice to complement WGS. RNA-seq allows detection of aberrant transcripts and differentially and/or mono-allelically expressed genes. Recent studies have demonstrated that RNA-seq is an essential companion of WGS to successfully prioritize and interpret the millions of genetic variants.In this application we will identify the genetic cause and novel disease genes in 25 families with one or more individuals affected by a monogenic disorder and without a genetic diagnosis after WES. We will use several bioinformatics algorithms to identify possible pathogenic CNVs in available WES data from 42 patients. As a next step, we will perform WGS in 25 clinically well-characterized patients without a genetic diagnosis and their parents. We will follow a stepwise variant filtering and prioritization protocol. First, we will prioritize and interpret protein-coding and splicing SNVs and indels. Second, CNVs and SVs located in or near exonic, protein-coding and conserved splice regions will be investigated. And third, de novo non-coding variants in putative regulatory elements will be prioritized. For this purpose, various bioinformatics algorithms will be evaluated and established. For validation and segregation analysis of genetic variants we will use routine molecular genetic techniques. With the help of transcriptomic analysis using fibroblast-derived RNA of the 25 aforementioned patients, we aim to detect aberrantly spliced mRNAs and genes showing significant differential and/or mono-allelic expression. By combining WGS and RNA-seq data we hope to prioritize and identify disease-relevant variants. Within international collaborations we search for additional cases with variants in disease gene candidates and similar phenotype. Functional impact of different variants on the gene product and associated pathways will be studied by performing biochemical and cell biological assays.

下一代测序，包括全外显子组测序（WES）和全基因组测序（WGS），已经彻底改变了单基因疾病分子基础的鉴定。WES和WGS对于检测单核苷酸变异（SNV）和小插入和/或缺失（indel）高度敏感。Trio WES的诊断率约为40%;通过使用外显子组数据对拷贝数变异（CNV）进行计算机分析，该诊断率可提高2-10%。与WES相比，WGS具有许多优点，例如在整个基因组上更均匀的数据质量和检测WES看不到的变体，例如蛋白质编码基因的启动子和非翻译区中的变体，RNA基因和非编码区中的变体以及结构变体（SV），例如倒位和易位。然而，对每个基因组400万至500万个遗传变异进行系统性优先排序存在重大障碍。通过RNA测序进行的转录组学分析（RNA-seq）是补充WGS的首选方法。RNA-seq允许检测异常转录物和差异和/或单等位基因表达的基因。最近的研究表明，RNA-seq是WGS的重要伴侣，可以成功地优先考虑和解释数百万的遗传变异。在这个应用程序中，我们将在25个家庭中识别遗传原因和新的疾病基因，这些家庭中有一个或多个受单基因疾病影响的个体，并且在WES后没有遗传诊断。我们将使用几种生物信息学算法，以确定可能的致病CNVs在现有的WES数据从42例患者。作为下一步，我们将在25名临床特征良好的患者及其父母中进行WGS，这些患者没有遗传诊断。我们将遵循逐步的变体过滤和优先级方案。首先，我们将优先考虑和解释蛋白质编码和剪接SNV和indel。第二，将研究位于外显子、蛋白质编码区和保守剪接区中或附近的CNVs和SV。第三，将优先考虑推定调控元件中的从头非编码变体。为此，将评估和建立各种生物信息学算法。对于遗传变异的验证和分离分析，我们将使用常规分子遗传学技术。借助转录组学分析，使用成纤维细胞来源的RNA的25例上述患者，我们的目标是检测异常剪接的mRNA和基因显示显着的差异和/或单等位基因表达。通过结合WGS和RNA-seq数据，我们希望优先考虑和识别疾病相关的变体。在国际合作中，我们寻找具有疾病候选基因变异和相似表型的其他病例。将通过进行生物化学和细胞生物学测定来研究不同变体对基因产物和相关途径的功能影响。