Improving genetic diagnosis in Mendelian disease with transcriptome sequencing

By Beryl B. Cummings, Jamie L. Marshall, T. Tukiainen, M. Lek, S. Donkervoort, A. Foley, V. Bolduc, L. Waddell, S.A. Sandaradura, G. O'grady and 28 more. Science Translational Medicine, 2016

689 citations recorded by the source corpus.

Transcriptome sequencing improves the diagnostic rate for Mendelian disease in patients for whom genetic analysis has not returned a diagnosis. RNA analysis for patients Although genome and exome sequencing are becoming increasingly common and are often useful in diagnosing unexplained genetic disease, more than half of all patients remain undiagnosed by these methods. Cummings et al. have now gone one step further, using RNA sequencing to evaluate patients with undiagnosed muscle disorders. With this approach, the authors were able to provide a diagnosis for another 35% of their patients, suggesting its potential utility for clinical genetic evaluation. The authors also identified a new disease-causing mutation in collagen VI and validated it in an additional cohort of patients with undiagnosed collagen dystrophy, again successfully diagnosing a sizeable percentage of patients. 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.

Subfields: Genetics, Molecular Biology, Pathology and Forensic Medicine

View Paper: https://www.semanticscholar.org/paper/c52d43bbc7f00869c479599f50d84c7694433f5c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

  1. Xin Gao

In Molecular Biology

  1. Xin Gao
  2. Ramzan Umarov

Citations in the corpus