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Lookup NU author(s): Dr Leonela LuceORCiD, Dr Goknur Kocak, Jose Verdú-DíazORCiD, Professor Volker StraubORCiD, Dr Ana TopfORCiD, Professor Jordi Diaz ManeraORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2026 The Author(s). Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.Objective: Sarcoglycanopathies are among the most severe limb-girdle muscular dystrophies (LGMD), though milder presentations have been described. These diseases are primarily caused by missense variants, but the limited predictability of their effect on protein maturation, complex formation, and transport has hindered reliable genotype–phenotype correlations. This study aimed to establish accurate genotype–phenotype correlations for LGMDR3, LGMDR4, and LGMDR5. Methods: We analyzed the largest sarcoglycanopathy cohort to date (n = 541). Clinical data, including age at symptom onset and loss of ambulation, were collected and used to define phenotype severity. Predictive models were developed considering the impact of non-truncating variants on secondary structure, functional domains, evolutionary conservation, and intra-complex protein–protein interactions. Patients carrying two variants predicted to disrupt membrane trafficking were expected to present with severe phenotypes. Results: For LGMDR3, the best-performing model classified variants affecting β-sheets, cadherin-like, and ATP-binding domains as disruptive to membrane translocation, achieving 89% predictive power, 0.867 balanced accuracy, and 2.4% false-negative rate (clinically severe patients who were wrongly classified by the model as mild). For LGMDR4 and LGMDR5, the best-performing model involved conserved residues, β-sheets, EGF-like domain, and protein–protein interactions—reaching 80% predictive power, 0.689 balanced accuracy, and 3.1% false-negative rate (LGMDR4), and 90% predictive power, 0.536 balanced accuracy, with no false negatives (LGMDR5). Additionally, we developed an open-access predictive tool for clinical and research application. Interpretation: This study provides a robust genotype–phenotype correlation for sarcoglycanopathies, improving prognosis, patient management, and clinical trial recruitment.
Author(s): Luce L, Kocak GS, Verdu-Diaz J, Alonso-Perez J, Claeys KG, Stojkovic T, Fernandez-Eulate G, Laforet P, Miladi N, Di Pace F, Moreno CAM, Zanoteli E, Weihl CC, Straub V, Topf A, Diaz-Manera J, D'Amico A, Lopez de Munain A, Alonso-Jimenez A, Camacho-Salas A, Gangfuss A, Nascimento A, Sarkozy A, van der Kooi AJ, Fraga-Bau A, Melegh B, Schoser B, Udd B, Koritnik B, Ortez C, Marini Bettolo C, Panicucci C, Weiss C, Bruno C, Semplicini C, Dominguez-Gonzalez C, Garrido C, Gomez-Andres D, Malfatti E, Pegoraro E, De Vos E, Munell F, Dekomien G, Comi GP, Tasca G, Richard I, De Bleecker JL, Haberlova J, Storgaard JH, Palmio J, Vissing J, de Leon-Hernandez JC, Hadzsiev K, Costa-Comellas L, Leonardis L, ten Dam L, Gonzalez-Quereda L, Bello L, Politano L, Santos M, de Visser M, Rohlenova M, Garibaldi M, Guglieri M, Deconinck N, Lokken N, Abdel-Mannan O, Gallano P, Fernandez-Torron R, Schara-Schmidt U, Nigro V, Zangaro V
Publication type: Article
Publication status: Published
Journal: Annals of Clinical and Translational Neurology
Year: 2026
Pages: epub ahead of print
Online publication date: 19/03/2026
Acceptance date: 17/02/2026
Date deposited: 15/04/2026
ISSN (print): 2328-9503
ISSN (electronic): 2328-9503
Publisher: John Wiley and Sons Inc
URL: https://doi.org/10.1002/acn3.70361
DOI: 10.1002/acn3.70361
Data Access Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request
PubMed id: 41853897
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