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

包括全外显子组测序(WES)和全基因组测序(WGS)在内的下一代测序技术已经彻底改变了单基因疾病的分子基础。WES和WGS对检测单核苷酸变异(SNV)和小插入和/或缺失(INDELs)高度敏感。Trio WES的诊断率为~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，这一比率可以提高2-10%。与WES相比，WGS有许多优点，例如，整个基因组的数据质量更统一，可以检测WES看不到的变异，如蛋白质编码基因启动子和非翻译区的变异，RNA基因和非编码区的变异，以及结构变异(SVS)，如倒位和易位。然而，在系统地确定每个基因组400-500万个遗传变异的优先顺序方面有很大的障碍。RNA测序转录分析(RNA-seq)是补充WGS的首选方法。RNA-SEQ可以检测异常转录本和差异和/或单等位基因表达。最近的研究表明，RNA-SEQ是WGS成功区分和解释数百万个遗传变异的关键伙伴。在这项应用中，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个体受到单基因疾病的影响，并且在WES后没有基因诊断。我们将使用几种生物信息学算法在42名患者的WES数据中识别可能的致病CNV。下一步，我们将对25名没有基因诊断的临床特征良好的患者及其父母进行WGS。我们将遵循一种逐步变化的过滤和优先排序协议。首先，我们将对蛋白质编码以及SNV和INDELs的剪接进行优先排序和解释。其次，我们将研究位于或靠近外显子、蛋白质编码和保守剪接区的CNV和SVS。第三，假定的调控元件中的从头开始的非编码变体将被优先考虑。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，我们将使用常规的分子遗传学技术。借助上述25例患者成纤维细胞来源的RNA的转录分析，我们的目标是检测异常剪接的mRNAs和显示显著差异和/或单等位基因表达的基因。通过结合WGS和RNA-SEQ数据，我们希望确定与疾病相关的变种的优先顺序。在国际合作中，我们寻找更多具有疾病候选基因变异和相似表型的病例。将通过进行生化和细胞生物学分析来研究不同变体对基因产物和相关途径的功能影响。

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.