Deep Phenotyping and Phenotype-Guided Next Generation Sequencing Improve The Diagnosis of Neuromuscular Diseases

Introduction: The widespread accessibility of next-generation sequencing (NGS) is revolutionizing clinical neurology practice. Neuromuscular diseases (NMDs) present diagnostic challenges due to the extensive genetic heterogeneity, where multiple gene mutations exhibit similar clinical NMD phenotypes, and the significant phenotypic heterogeneity resulting from single gene mutations. A phenotypic-guided NGS-based approach enhances NMD diagnosis.

Methods: This study includes 15 patients with hereditary neuromuscular diseases who initially had no confirmed genetic diagnosis from previous tests (e.g., Sanger sequencing, gene panel, and whole exome sequencing). Medical information was systematically collected, and NGS, including whole exome and whole genome sequencing, was performed. Mutation detection involved both annotation-filtration and phenotype-based prioritization strategies, which were compared in this study.

Results: Pathogenic/likely pathogenic mutations were identified in all 15 patients with various NMDs, including congenital muscular dystrophies, congenital myopathies, limb-girdle muscular dystrophies, other rare NMDs, and syndromal diagnoses with neuromuscular manifestations. The annotation-filtration approach only requires genotypic information input, while the phenotype-based prioritization strategy necessitates both genotypic and phenotypic information. The phenotype-based prioritization strategy proved more efficient in prioritizing and identifying causal/potential causal variants when provided with accurate and sufficient phenotypes, as demonstrated through our case illustrations.

Conclusion: A major challenge of NGS as a diagnostic tool is interpreting variants of unknown significance. To address this issue, it is crucial to integrate NGS data with clinical phenotyping, bioinformatics tools, and data sharing. Interdisciplinary collaboration among clinicians, geneticists, muscle pathologists, and bioinformaticians is vital for translating NGS findings into clinical practice.