Browsing by Author "Ghanty, Ismael"
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Publication Development and Validation of a Prediction Model for Early Diagnosis of SCN1A-Related Epilepsies(2022) Brunklaus, Andreas; Pérez, Eduardo; Ghanty, Ismael; Xinge, Ji; Brilstra, Eva; Ceulemans, Berten; Chemaly, Nicole; De Lange, Iris; Depienne, Christel; Guerrini, Renzo; Mei, Davide; Møller, Rikke; Nabbout, Rima; Regan, Brigid; Schneider, Amy; MGenCouns; Scheffer, Ingrid; Schoonjans, An; Symonds, Joseph; Weckhuysen, Sarah; Kattan, Michael; Zuberi, Sameer; Lal, DennisBackground and objectives: Pathogenic variants in the neuronal sodium channel α1 subunit gene (SCN1A) are the most frequent monogenic cause of epilepsy. Phenotypes comprise a wide clinical spectrum, including severe childhood epilepsy; Dravet syndrome, characterized by drug-resistant seizures, intellectual disability, and high mortality; and the milder genetic epilepsy with febrile seizures plus (GEFS+), characterized by normal cognition. Early recognition of a child's risk for developing Dravet syndrome vs GEFS+ is key for implementing disease-modifying therapies when available before cognitive impairment emerges. Our objective was to develop and validate a prediction model using clinical and genetic biomarkers for early diagnosis of SCN1A-related epilepsies. Methods: We performed a retrospective multicenter cohort study comprising data from patients with SCN1A-positive Dravet syndrome and patients with GEFS+ consecutively referred for genetic testing (March 2001-June 2020) including age at seizure onset and a newly developed SCN1A genetic score. A training cohort was used to develop multiple prediction models that were validated using 2 independent blinded cohorts. Primary outcome was the discriminative accuracy of the model predicting Dravet syndrome vs other GEFS+ phenotypes. Results: A total of 1,018 participants were included. The frequency of Dravet syndrome was 616/743 (83%) in the training cohort, 147/203 (72%) in validation cohort 1, and 60/72 (83%) in validation cohort 2. A high SCN1A genetic score (133.4 [SD 78.5] vs 52.0 [SD 57.5]; p < 0.001) and young age at onset (6.0 [SD 3.0] vs 14.8 [SD 11.8] months; p < 0.001) were each associated with Dravet syndrome vs GEFS+. A combined SCN1A genetic score and seizure onset model separated Dravet syndrome from GEFS+ more effectively (area under the curve [AUC] 0.89 [95% CI 0.86-0.92]) and outperformed all other models (AUC 0.79-0.85; p < 0.001). Model performance was replicated in both validation cohorts 1 (AUC 0.94 [95% CI 0.91-0.97]) and 2 (AUC 0.92 [95% CI 0.82-1.00]). Discussion: The prediction model allows objective estimation at disease onset whether a child will develop Dravet syndrome vs GEFS+, assisting clinicians with prognostic counseling and decisions on early institution of precision therapies (http://scn1a-prediction-model.broadinstitute.org/). Classification of evidence: This study provides Class II evidence that a combined SCN1A genetic score and seizure onset model distinguishes Dravet syndrome from other GEFS+ phenotypes.Publication Genotype-phenotype associations in 1018 individuals with SCN1A-related epilepsies(2024) Gallagher, Declan; Pérez; Pérez Palma, Eduardo; Bruenger, Tobias; Ghanty, Ismael; Brilstra, Eva; Ceulemans, Berten; Chemaly, Nicole; De Lange, Iris; Depienne, Christel; Guerrini, Renzo; Mei, Davide; Møller, Rikke; Nabbout, Rima; Regan, Brigid; Schneider, Amy; Scheffer, Ingrid; Schoonjans, An; Symonds, Joseph; Weckhuysen, Sarah; Zuber, Sameer; Lal, Dennis; Brunklaus, AndreasObjective: SCN1A variants are associated with epilepsy syndromes ranging from mild genetic epilepsy with febrile seizures plus (GEFS+) to severe Dravet syndrome (DS). Many variants are de novo, making early phenotype prediction difficult, and genotype-phenotype associations remain poorly understood. Methods: We assessed data from a retrospective cohort of 1018 individuals with SCN1A-related epilepsies. We explored relationships between variant characteristics (position, in silico prediction scores: Combined Annotation Dependent Depletion (CADD), Rare Exome Variant Ensemble Learner (REVEL), SCN1A genetic score), seizure characteristics, and epilepsy phenotype. Results: DS had earlier seizure onset than other GEFS+ phenotypes (5.3 vs. 12.0 months, p < .001). In silico variant scores were higher in DS versus GEFS+ (p < .001). Patients with missense variants in functionally important regions (conserved N-terminus, S4-S6) exhibited earlier seizure onset (6.0 vs. 7.0 months, p = .003) and were more likely to have DS (280/340); those with missense variants in nonconserved regions had later onset (10.0 vs. 7.0 months, p = .036) and were more likely to have GEFS+ (15/29, χ2 = 19.16, p < .001). A minority of protein-truncating variants were associated with GEFS+ (10/393) and more likely to be located in the proximal first and last exon coding regions than elsewhere in the gene (9.7% vs. 1.0%, p < .001). Carriers of the same missense variant exhibited less variability in age at seizure onset compared with carriers of different missense variants for both DS (1.9 vs. 2.9 months, p = .001) and GEFS+ (8.0 vs. 11.0 months, p = .043). Status epilepticus as presenting seizure type is a highly specific (95.2%) but nonsensitive (32.7%) feature of DS. Significance: Understanding genotype-phenotype associations in SCN1A-related epilepsies is critical for early diagnosis and management. We demonstrate an earlier disease onset in patients with missense variants in important functional regions, the occurrence of GEFS+ truncating variants, and the value of in silico prediction scores. Status epilepticus as initial seizure type is a highly specific, but not sensitive, early feature of DSPublication SCN9A should not be considered an epilepsy gene; Refuting a gene–disease association(2026) Ghanty, Ismael; Perez-Palma, Eduardo; Villaman, Camilo; Stobo, Daniel; Symonds, Joseph; Zuberi, Sameer; Lal, Dennis; Brunklaus, AndreasObjective The SCN9A gene is primarily expressed in nociceptive pathways within the peripheral nervous system, and pathogenic variants are associated with human pain disorders. In recent years, several studies have proposed SCN9A as a monogenic cause of epilepsy. Our objective was to critically appraise the SCN9A–epilepsy gene–disease relationship. Methods We assessed “epilepsy-associated” SCN9A variants from four sources: (1) the literature up to December 2023 (n = 27), (2) epilepsy patients referred for genetic testing at a regional service in Glasgow, UK over a 5-year period (n = 30), (3) the Human Genetics Mutation Database (n = 25), and (4) ClinVar (n = 1546). The latter two are genome-wide variant databases, accepting submissions from genetic laboratories and research groups. We checked whether each SCN9A variant is present in the Genome Aggregation Database (gnomAD) V4 (a reference population database for variant interpretation), and classified its pathogenicity based on the American College of Molecular Genetics and Genomics/Association of Molecular Pathologists guidelines. Results Only three SCN9A variants were classified as “likely pathogenic,” of which two were identified in healthy individuals in gnomAD. A total of 1540 of the 1546 SCN9A variants in ClinVar labeled as being associated with epilepsy were also reported in association with hereditary sensory and autonomic neuropathy. No further clinical data were provided in 1482 of these submissions. Significance There is no convincing genetic evidence to support SCN9A as a causative epilepsy gene. As such, the inclusion of SCN9A in epilepsy genetic testing panels should be reassessed. Research centers and genetic testing laboratories should be rigorous and consistent in their submissions to variant databases.