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Multicenter Study
. 2025 Aug;32(8):e70324.
doi: 10.1111/ene.70324.

The Clinical and Genetic Landscape of a French Multicenter Cohort of 2563 Epilepsy Patients Referred for Genetic Diagnosis

Jean-Madeleine de Sainte Agathe  1   2 , Pauline Monin  3 , Florence Riccardi  4   5 , Caroline Nava  1   2 , Lionel Arnaud  1 , Cyril Mignot  1 , Dorothée Ville  6 , Stéphane Auvin  7   8   9 , Sandrine Tardieu  1 , Kathy Larcher  1 , Isabelle Gourfinkel-An  10 , Mathilde Canon  11 , Vincent Navarro  10 , Bénédicte Héron  12 , Sophie Julia  13 , Diane Doummar  14 , Marie-Line Jacquemont  15   16 , Hélène Maurey  17 , Blandine Dozières-Puyravel  18 , Laurence Perrin  18 , Laurent Pasquier  19   20 , Christèle Dubourg  21   22 , Sylvie Odent  19   20 , Abdelhakim Bouazzaoui  21   23 , Wilfrid Carre  21 , Mélanie Fradin  19 , Florence Demurger  24 , Nicolas Chatron  3   25 , Damien Sanlaville  3   25 , Miriam Essid  3   25 , Vincent des Portes  6 , Eleni Panagiotakaki  26 , Anne-Lise Poulat  6 , Clotilde Rivier  27 , Catherine Sarret  28 , Ganaëlle Remerand  28 , Cecilia Altuzarra  29 , Radka Stoeva  30 , Sylvie Nguyen  31 , Juliette Piard  32   33 , Élise Boucher  32 , Vincent Flurin  34 , Anne-Marie Guerrot  35 , Sylvie Joriot  36 , Béatrice Desnous  37 , Nathalie Villeneuve  37 , Anne Lépine  37 , Caroline Hachon-Le Camus  38 , Laurent Villard  5   39 , Marie Faoucher  21   22 , Mathieu Milh  40 , Gaëtan Lesca  3   25 , Éric Leguern  1   2
Affiliations
Multicenter Study

The Clinical and Genetic Landscape of a French Multicenter Cohort of 2563 Epilepsy Patients Referred for Genetic Diagnosis

Jean-Madeleine de Sainte Agathe et al. Eur J Neurol. 2025 Aug.

Abstract

Background: Epileptic disorders are a heterogeneous group of neurological conditions, with many cases linked to monogenic causes, particularly in developmental and epileptic encephalopathies (DEE). Identifying pathogenic variants aids treatment, prognosis, and family planning. In France, genetic testing is coordinated through the EpiGene network.

Methods: We analyzed clinical and genetic data from 2563 epilepsy patients referred to four diagnostic labs (2016-2023). Epilepsy syndromes were classified via pre-test questionnaires, and genotyping used various gene panels, including a 68-gene core panel. Multivariate logistic regression assessed diagnostic rates and genotype-phenotype correlations.

Results: Overall, 27.0% of patients had pathogenic/likely pathogenic variants, mainly within the core panel (24%). SCN1A and KCNQ2 were the most frequently mutated genes. Diagnostic yield varied by syndrome, with Dravet Syndrome Spectrum (DSS) and early-infantile DEE (EIDEE) showing the highest rates (41% and 34%, respectively). Genetic heterogeneity differed across syndromes, from DSS (predominantly SCN1A) to Infantile Epileptic Spasms Syndrome (IESS, 12%), involving ≥ 26 genes. Outside DEE, self-limited neonatal epilepsy (SeLNE) had the highest yield (50%). Earlier seizure onset was associated with a higher likelihood of a positive molecular diagnosis, whereas intellectual disability severity and drug resistance were not independently predictive of diagnostic outcome. Genotype-phenotype correlations highlighted that objective clinical data (e.g., age of onset) can outperform syndrome labels (e.g., EIDEE) in predicting diagnosis.

Conclusion: This large cohort study refines the genetic landscape of epilepsy, informs classification challenges, and enhances genetic testing strategies, ultimately improving patient care and future research directions.

Keywords: Dravet syndrome; Lennox Gastaut syndrome; Spasms, infantile; genetic testing; genetics, medical.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Diagnostic rates in the cohort. Left: Multivariate analysis (logistic regression) to analyze the features independently associated with an increase of molecular diagnosis. Right, top: Diagnostic yield of the core panel (“core”), and genes diagnosed in Developmental and Epileptic Encephalopathy (DEE), N.B, the dual diagnoses were not counted twice; neg, negative result; pos, positive result. Bottom: diagnostic rate of the core panel in each epileptic category. ID, intellectual disability; SWAS, spike and wave activity during sleep. The diagnostic rate of the considered syndrome is compared to that of the entire cohort, with “*” when the difference is significant.
FIGURE 2
FIGURE 2
Seizure onset in self‐limited epilepsy, mostly neonatal or infantile, and Dravet Syndrome Spectrum categories.
FIGURE 3
FIGURE 3
Comparison of patients having DSS with those whose molecular diagnosis was a loss‐of‐function variant in SCN1A. GTC, generalized tonic clonic; ID, intellectual disability; normal_neuro_ex, normal neurologic examination; pLoF, putative loss‐of‐function variant.
FIGURE 4
FIGURE 4
Gene‐based analysis of epileptic phenotypes. Top, left: Summary of the gene‐to‐phenotype associations for the 14 top genes. Red: Odds Ratio > 1, Blue: Odds Ratio < 1, pale gray: Not significant (adjusted p > 0.05). EEG, electroencephalography; EE‐SWAS, epileptic encephalopathy with spike–wave activation in sleep; EIMFS, epilepsy of infancy with migrating focal seizures; GTC, generalized tonic–clonic; ID, intellectual disability; IESS, Infantile Epileptic Spasms Syndrome; here, “unspecified self‐limited epilepsy” referred to the category “SeLNE, SeLIE and other self‐limited epilepsies”. Top, right: multidimensional reduction plotting by t‐distributed stochastic neighbor embedding, using only the clinical characteristics as dimensions (sex, suspected syndrome, drug‐resistance, familial history, intellectual disability degree, seizure type, onset), without using the diagnosed gene. Bottom, left: seizure onset distribution of the main genes. Bottom, right: diagnostic proportion for all syndromes for each onset bin. The top‐3 genes of each bin are displayed in white.
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