Genetic analysis and electrocardiographic patterns in Brugada syndrome

Abstract Background Brugada Syndrome (BrS) is a complex cardiac channelopathy marked by significant genotype and phenotype heterogeneity. While its clinical significance is well established, the underlying genetic architecture and the association with the clinical phenotype remains only partially understood (1,2). Purpose Our aim was to characterize the genetic landscape of consecutive patients referred to our centre with suspected Brugada Syndrome, analysing variant distribution across different electrocardiographic (ECG) patterns and exploring the molecular basis of the disease. Methods A cohort of 339 patients (230 males 68%, mean age 43.7±15.7 years) underwent multi-tiered genetic testing, including Sanger sequencing, targeted cardio panel, and whole exome sequencing. Variants were classified using ACMG criteria with a specialized framework for VUS interpretation (3) as likely pathogenic/pathogenic (LP/P), variant of unknown significance highly suspicious of pathogenicity (VUS/LP) and VUS. Statistical analyses were performed using R version 4.1.0. Results The genetic yield was 27.1% in the whole population (92/339) but varied according to the ECG pattern, being 17.5%, 31.6%, 32% and 32.4% in patients without an ECG pattern of Brugada, and those with a drug-induced, spontaneous intermittent, and spontaneous persistent ECG pattern, respectively (p=0.006) (Fig1). The difference in genetic yield was larger when including only likely causative variants (P/LP, VUS/LP) (5.2%, 15.3%, 18.5%, 24.3%, p=0.001). SCN5A was the main gene affected (55/92, 59.8%) with the highest pathogenic rate (52.7%). Likely causative SCN5A variants were most prevalent in spontaneous persistent pattern (60%), followed by drug-induced (55.6%) and spontaneous intermittent patterns (52.9%) (Fisher's exact test; p=0.042). A progressive increase in variant complexity was observed from no-Brugada to spontaneous persistent patterns, with additional genes potentially implicated including MYBPC3, KCNH2, LMNA, NEXN, MIB1, and NKX2-5. Conclusion This analysis confirms SCN5A as the predominant genetic substrate in BrS, with increasing genetic yield and genetic complexity correlating with ECG pattern severity (4). These findings may improve risk assessment and targeted genetic counseling for BrS patients and their families.