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Improving genetic diagnosis in Mendelian disease with transcriptome sequencing
Beryl B Cummings,
Jamie L Marshall,
A. Reghan Foley,
Hemakumar M Reddy,
Konrad J. Karczewski,
Anne H. O’Donnell-Luria,
Emily C. Oates,
Peter B Kang,
Alan H Beggs,
Kathryn N North,
Nigel F Clarke,
Sandra T Cooper,
Carsten G Bonnemann,
Daniel G. MacArthur
Posted 08 Sep 2016
bioRxiv DOI: 10.1101/074153 (published DOI: 10.1126/scitranslmed.aal5209)
Posted 08 Sep 2016
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 25-50% [1-4]. Here, we explore the utility of transcriptome sequencing (RNA-seq) as a complementary diagnostic tool in a cohort of 50 patients with genetically undiagnosed rare neuromuscular 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 over 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 collagen VI dystrophy in whom prior genetic analysis is negative. Overall, this study represents the largest systematic application of transcriptome sequencing to rare disease diagnosis to date and highlights its utility for the detection and interpretation of variants missed by current standard diagnostic approaches.
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