Gene-panel and whole-exome analyses are now standard methodologies for mutation detection in Mendelian disease. However, the diagnostic yield achieved is at best 50%, leaving the genetic basis for disease unsolved in many individuals. New approaches are thus needed to narrow the diagnostic gap. Whole-genome sequencing is one potential strategy, but it currently has variant-interpretation challenges, particularly for non-coding changes.
In this study researchers from the Hospital for Sick Children focus on transcriptome analysis, specifically total RNA sequencing (RNA-seq), by using monogenetic neuromuscular disorders as proof of principle. They examined a cohort of 25 exome and/or panel “negative” cases and provided genetic resolution in 36% (9/25). Causative mutations were identified in coding and non-coding exons, as well as in intronic regions, and the mutational pathomechanisms included transcriptional repression, exon skipping, and intron inclusion. The researchers address a key barrier of transcriptome-based diagnostics: the need for source material with disease-representative expression patterns. They establish that blood-based RNA-seq is not adequate for neuromuscular diagnostics, whereas myotubes generated by transdifferentiation from an individual’s fibroblasts accurately reflect the muscle transcriptome and faithfully reveal disease-causing mutations. This work confirms that RNA-seq can greatly improve diagnostic yield in genetically unresolved cases of Mendelian disease, defines strengths and challenges of the technology, and demonstrates the suitability of cell models for RNA-based diagnostics. This data set the stage for development of RNA-seq as a powerful clinical diagnostic tool that can be applied to the large population of individuals with undiagnosed, rare diseases and provide a framework for establishing minimally invasive strategies for doing so.
70 samples were processed through the pipeline
Total RNA was extracted from muscle biopsies, fibroblasts, and t-myotubes, was polyA selected, and then sequenced at a depth of 50–100 paired-end reads. Our RNA-seq diagnostic algorithm compares undiagnosed individuals with our in-house database and with control transcriptome data obtained from GTEx. We worked first from a panel of genes known to be mutated in neuromuscular disorders (n = 132), and we focused our analysis in parallel on (1) novel splicing events (far left), (2) imbalances in allelic expression (middle left), (3) statistically significant differences in expression (middle right), and (4) rare sequence variants of clinical relevance (far right). Using this strategy, we were able to solve 36% of cases that were “negative” according to the gene panel and/or exome sequencing (n = 25 total unknown cases).