Year: 2026
Type: article
Study design: Observational Study
Abstract: OBJECTIVE: Genomic sequencing leaves >50% of dystonia-affected individuals without a diagnosis. Where DNA-oriented approaches remain insufficient, integrating multiomics is essential to advance genome interpretation. Herein, we incorporated RNA sequencing (RNA-seq) data from 167 patients with dystonia across a range of ages and presentations. METHODS: We leveraged an RNA-seq analysis pipeline, focused on the identification of expression and splicing aberrations, on RNA-seq from skin biopsies. The recruited patients had early-onset dystonia in 85.0%, non-focal dystonia in 92.2%, and coexisting features in 76.0%. Thirty-six patient samples with pre-identified variants (36/167, 21.6%) and 131 samples with no previously prioritized diagnostic candidates from genomic sequencing (131/167, 78.4%) were evaluated. RESULTS: We found that >80% of dystonia-associated genes were detected by fibroblast RNA-seq. Expression and splicing aberration analyses produced a manageable number of significant RNA defects affecting dystonia-associated genes. The approach was especially successful in validating pathogenic effects of loss-of-function variants, with disease-relevant RNA-underexpression detected for 66.7% (10/15). Studying aberrant expression and splicing in the context of other pre-identified variant types yielded relevant results in 28.6% (6/21 samples). We obtained a 6.9% (9/131) diagnostic uplift for patients without prior candidates, all of whom exhibited combined dystonia with autosomal recessive inheritance. The new diagnoses from RNA-seq and genomic reanalysis were based on previously neglected splice-region (3/9) and deep(er) intronic (6/9) variants. For the observed events, integration of new machine-learning scores predicted corresponding aberrant gene expression in the brain. INTERPRETATION: Fibroblast-based RNA-seq in our selected cohort improved variant interpretation and offered a modest yield in patients without prior candidate variants. ANN NEUROL 2026;99:1363-1378.
Source: Annals of Neurology
DOI: 10.1002/ana.78171
Authors: Alice Saparov, Ivana Dzinovic, Theresa Brunet, Vicente A. Yépez, Florian Hölzlwimmer, Elisabetta Indelicato, Birgit Assmann, Susann Badmann, Diana Ballhausen, Steffen Berweck, Felix Brechtmann, Melanie Brugger, Kevork Derderian, Felix Distelmaier, Philip Harrer, Denisa Harvanova, Petra Havrankova, Ann‐Kathrin Jaroszynski, Miriam Kolnikova, Robert Kopajtich, Anne Koy, Magdalena Krygier, Lukas Kunc, Katarina Kusikova, Oliver Maier, Maria Mazurkiewicz‐Bełdzińska, Christian Mertes, Ava Oberlack, Timo Roser, Alexandra Sitzberger, Ugo Sorrentino, Antonia M. Stehr, Katharina Vill, Matias Wagner, Holger Prokisch, Sylvia Boesch, Jan Necpal, Robert Jech, Juliane Winkelmann, Elisabeth Graf, Julien Gagneur, Matej Skorvanek, Michael Zech
Institutions: Helmholtz Munich, Institut für Humangenetik, Technical University of Munich, Innsbruck Medical University, Universität Innsbruck, Heidelberg University, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Ostschweizer Kinderspital, Düsseldorf University Hospital, Heinrich Heine University Düsseldorf, University of Pavol Jozef Šafárik, Univerzitná Nemocnica Louisa Pasteura, Charles University, General University Hospital in Prague, University Hospital Bratislava, Národný ústav detských chorôb, Comenius University Bratislava, University of Cologne, University Hospital Cologne, Gdańsk Medical University, LMU Klinikum, Ludwig-Maximilians-Universität München, Munich Cluster for Systems Neurology, Deutsches Zentrum für Psychische Gesundheit
Language: English
Subfield: Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,700
Awards: G1940663492, G2062271078, G2300380025, GENOMIT - Die Variation im natürlichen Verlauf mitochondrialer Erkrankungen besser verstehen - Koordination und KI-Ansatz zur Verbesserung der Vorhersage, G2844082647, PREDYT - Prädiktive Biomarker für die Dystonie: Vom Paradigma der monogenen Subtypen zur Diagnose und Prognose der genetisch nicht-diagnostizierten Formen, GENOMIT - Ein Multi-Omics-Ansatz für die Diagnose und das Monitoring mitochondrialer Erkrankungen, Ganzgenom und Transkriptomsequenzierung zur Identifizierung neuer Krankheitsgene für Dystonie, Zentrale Koordinationseinheit des Next Generation Sequencing Kompetenznetzwerks, IT Infrastruktur für Computational Molecular Medicine, G7988924909, mitoNet - Deutsches Netzwerk für mitochondriale Erkrankungen - Teilprojekt Koordination, Teilprojekt 4.1 mitoREGISTRY
OpenAlex ID: W7127194582