Unlocking the genetic blueprint of duchenne muscular dystrophy: A personalized approach with MLPA and WES

Background: Duchenne muscular dystrophy (DMD) is a progressive X-linked disorder causing muscle degeneration and multisystem involvement, requiring precise genetic diagnosis for timely intervention and treatment. Objective: To investigate the genetic landscape of DMD using a two-tiered diagnostic approach combining MLPA and WES, and to correlate genetic findings with clinical outcomes for improved management. Materials and methods: A cross-sectional study of 80 male DMD patients was conducted using a sequential genetic approach, combining MLPA and WES, with bioinformatics and statistical analyses to explore genotype-phenotype correlations. Results: Pathogenic variants were identified in 65 cases (81.2 %), with deletions (67.5 %) being the most common, followed by duplications (6.3 %), splice-site (3.8 %), and nonsense variants (3.8 %). WES identified additional pathogenic variants in MLPA-negative cases, including novel mutations, expanding the known genetic spectrum of DMD. The combined MLPA-WES approach significantly improved diagnostic yield (χ² = 12.90, p < 0.001). Functional analysis revealed disruptions in glycogen metabolism (46 %), calcium transport (24 %), and mitochondrial function (12 %), with dystrophin-associated proteins (DAG1, SGCD) critically involved in muscle stability. Out-of-frame deletions were significantly associated with early disease onset (χ² = 49.03, p < 0.001) and severe phenotypes (χ² = 47.04, p < 0.001), supporting exon-skipping therapy. In-frame deletions correlated with milder progression, while nonsense variants posed a 2.5-fold increased risk of early cardiomyopathy (p = 0.002), emphasizing the need for early intervention. Conclusion: Combining MLPA and WES enhances DMD diagnostic accuracy, enabling timely clinical interventions. Integrating functional analysis with genotype-phenotype correlations supports personalized therapeutic strategies, improving patient outcomes.