PhD Candidate, Harvard University
First-ever systematic use of RNA sequencing provides rare disease families with a genetic diagnosis
Rare diseases exert a massive combined burden of suffering, accounting for around 10% of child hospital admissions and one-fifth of all child deaths. For many families affected by these diseases the underlying genetic cause is unknown. As a result, patients suffering from these disorders often experience frequent hospital visits and dozens of diagnostic tests. Without a diagnosis, rare disease families live in profound uncertainty, unable to predict the future course of the disease or to perform tests to ensure future children are healthy. Recently, sequencing the DNA of a person has become increasingly routine for pinpointing genetic changes that result in disease. While this approach has provided many patients with diagnoses, a substantial portion of patients remain unsolved even after this test and on average, clinicians are unable to provide a genetic diagnosis to over half of patients that present in the clinic. We explored the utility of RNA sequencing (RNA-seq) to improve the diagnosis rate for patients with rare genetic neuromuscular disorders. DNA sequence serves as a blueprint for producing functional molecules called RNA; measuring RNA thus gives us direct insight into whether changes to a patient’s DNA have resulted in major changes to the expression of their genes. We hypothesized that querying the RNA molecules would point us to pathogenic changes that seem benign when looking at the DNA alone. We assembled a cohort of 63 patients, 10 of whom had been diagnosed with DNA sequencing and 50 patients that had not received a diagnosis despite comprehensive clinical and genetic testing. We first verified that RNA-seq could identify known pathogenic variants in the 10 diagnosed patients. We then leveraged this knowledge to develop computational methods that efficiently identified these confirmed pathogenic lesions and then tested the tools in our undiagnosed patients. This led to the successful diagnosis of 17 patients, 35% of our undiagnosed patients, a remarkable increase in a rare disease cohort. In one example, we were able to use RNA-seq to diagnose 4 patients with a new genetic subtype of collagen VI-dystrophy, caused by a mutation in the COL6A1 gene. We reached out to collaborators who had patients with similar symptoms, and diagnosed another 27 of patients with exactly the same mutation. Based on this, it is now estimated that this mutation is a common cause of the disease, accounting for ~13% of all collagen VI-dystrophy cases in the US.
Abstract: The precise genetic cause remains elusive in nearly 50% of patients with presumed neurogenetic disease, representing a significant barrier for clinical care. This is despite significant advances in clinical genetic diagnostics, including the application of whole-exome sequencing and next-generation sequencing-based gene panels. In this study, we identify a deep intronic mutation in the DMD gene in a patient with muscular dystrophy using both conventional and RNAseq-based transcriptome analyses. The implications of our data are that noncoding mutations likely comprise an important source of unresolved genetic disease and that RNAseq is a powerful platform for detecting such mutations.
Pub.: 20 Jan '16, Pinned: 29 Jun '17
Abstract: Exome and whole-genome sequencing are becoming increasingly routine approaches in Mendelian disease diagnosis. Despite their success, the current diagnostic rate for genomic analyses across a variety of rare diseases is approximately 25 to 50%. We explore the utility of transcriptome sequencing [RNA sequencing (RNA-seq)] as a complementary diagnostic tool in a cohort of 50 patients with genetically undiagnosed rare muscle disorders. We describe an integrated approach to analyze patient muscle RNA-seq, leveraging an analysis framework focused on the detection of transcript-level changes that are unique to the patient compared to more than 180 control skeletal muscle samples. We demonstrate the power of RNA-seq to validate candidate splice-disrupting mutations and to identify splice-altering variants in both exonic and deep intronic regions, yielding an overall diagnosis rate of 35%. We also report the discovery of a highly recurrent de novo intronic mutation in COL6A1 that results in a dominantly acting splice-gain event, disrupting the critical glycine repeat motif of the triple helical domain. We identify this pathogenic variant in a total of 27 genetically unsolved patients in an external collagen VI-like dystrophy cohort, thus explaining approximately 25% of patients clinically suggestive of having collagen VI dystrophy in whom prior genetic analysis is negative. Overall, this study represents a large systematic application of transcriptome sequencing to rare disease diagnosis and highlights its utility for the detection and interpretation of variants missed by current standard diagnostic approaches.
Pub.: 21 Apr '17, Pinned: 29 Jun '17
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