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Krenn, M.* ; Schmidt, A.* ; Wagner, M. ; Ernst, M.* ; Graf, E.* ; Zulehner, G.* ; Cetin, H.* ; Zimprich, F.* ; Rath, J.*

AlphaMissense prediction for the evaluation of missense variants in the diagnostic setting of neuromuscular disorders.

J. Neuromuscul. Dis., DOI: 10.1177/22143602251370957:22143602251370957 (2025)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Next-generation sequencing has improved diagnostic outcomes for neuromuscular disorders, but interpreting rare missense variants remains challenging. We evaluated AlphaMissense, a recently developed machine learning tool, for predicting missense variant pathogenicity, using 45 (likely) pathogenic variants and 21 variants of uncertain significance from 58 deeply phenotyped patients. AlphaMissense predicted 69% of pathogenic variants correctly, but also classified 62% of variants of uncertain significance as pathogenic. Median AlphaMissense scores were not significantly different between pathogenic and uncertain variants. Overall, AlphaMissense accurately predicted the pathogenicity of most missense variants, but may be limited in certain functional contexts, highlighting the need for disease-specific interpretation approaches.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Alphamissense ; Missense Variants ; Neuromuscular Disorder ; Next-generation Sequencing; Mutations
ISSN (print) / ISBN 2214-3599
e-ISSN 2214-3602
Zeitschrift Journal of neuromuscular diseases
Quellenangaben Band: , Heft: , Seiten: , Artikelnummer: 22143602251370957 Supplement: ,
Verlag IOS Press
Verlagsort Amsterdam
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Neurogenomics (ING)