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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)
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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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Publication type Article: Journal article
Document type Scientific Article
Keywords Alphamissense ; Missense Variants ; Neuromuscular Disorder ; Next-generation Sequencing; Mutations
Language english
Publication Year 2025
HGF-reported in Year 2025
ISSN (print) / ISBN 2214-3599
e-ISSN 2214-3602
Journal Journal of neuromuscular diseases
Quellenangaben Volume: , Issue: , Pages: , Article Number: 22143602251370957 Supplement: ,
Publisher IOS Press
Publishing Place Amsterdam
Reviewing status Peer reviewed
Institute(s) Institute of Neurogenomics (ING)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Genetics and Epidemiology
PSP Element(s) G-503200-001
DOI 10.1177/22143602251370957
WOS ID 001564500000001
PubMed ID 40910438
Erfassungsdatum 2025-11-18
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