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

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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.