TY - JOUR
T1 - Improving genetic diagnosis in Mendelian disease with transcriptome sequencing
AU - Genotype-Tissue Expression Consortium, National Institutes of Health (NIH) Common Fund, iospecimen Collection Source Site-National Disease Research Interchange, Biospecimen Collection Source Site-Roswell Park Cancer Institute, Biospecimen Core Resource-Va
AU - Brain Bank Repository-University of Miami, Leidos Biomedical Project Management, Ethical, Legal, and Social Implications Study, Genome Browser Data Integration, and Visualization-European Bioinformatics Institute, Genome Browser Data Integration and Visua
AU - LDACC-Analysis Working Group (AWG)
AU - Funded Statistical Methods groups-AWG
AU - Enhancing GTEx funded Group
AU - NIH/NHGRI
AU - NIH/NCI
AU - NIH/NIMH
AU - NIH/NIDA
AU - Other members of the AWG
AU - Cummings, Beryl B.
AU - Marshall, Jamie L.
AU - Tukiainen, Taru
AU - Lek, Monkol
AU - Donkervoort, Sandra
AU - Foley, A. Reghan
AU - Bolduc, Veronique
AU - Waddell, Leigh B.
AU - Sandaradura, Sarah A.
AU - O'Grady, Gina L.
AU - Estrella, Elicia
AU - Reddy, Hemakumar M.
AU - Zhao, Fengmei
AU - Weisburd, Ben
AU - Karczewski, Konrad J.
AU - O'Donnell-Luria, Anne H.
AU - Birnbaum, Daniel
AU - Sarkozy, Anna
AU - Hu, Ying
AU - Gonorazky, Hernan
AU - Claeys, Kristl
AU - Joshi, Himanshu
AU - Bournazos, Adam
AU - Oates, Emily C.
AU - Ghaoui, Roula
AU - Davis, Mark R.
AU - Laing, Nigel G.
AU - Topf, Ana
AU - Kang, Peter B.
AU - Beggs, Alan H.
AU - North, Kathryn N.
AU - Straub, Volker
AU - Dowling, James J.
AU - Muntoni, Francesco
AU - Clarke, Nigel F.
AU - Cooper, Sandra T.
AU - Bönnemann, Carsten G.
AU - MacArthur, Daniel G.
AU - Ardlie, Kristin G.
AU - Getz, Gad
AU - Gelfand, Ellen T.
AU - Segrè, Ayellet V.
AU - Aguet, François
AU - Sullivan, Timothy J.
AU - Li, Xiao
AU - Nedzel, Jared L.
AU - Trowbridge, Casandra A.
AU - Hadley, Kane
AU - Engelhardt Martin, Barbara
AU - Akey, Joshua
N1 - Publisher Copyright:
© The Authors.
PY - 2017/4/19
Y1 - 2017/4/19
N2 - 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.
AB - 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.
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U2 - 10.1126/scitranslmed.aal5209
DO - 10.1126/scitranslmed.aal5209
M3 - Article
C2 - 28424332
AN - SCOPUS:85018570855
SN - 1946-6234
VL - 9
JO - Science Translational Medicine
JF - Science Translational Medicine
IS - 386
M1 - eaal5209
ER -