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Objective: Epilepsy with eyelid myoclonia (EEM) spectrum is a generalized form of epilepsy characterized by eyelid myoclonia with or without absences, eye closure‐induced seizures with electroencephalographic paroxysms, and photosensitivity. Based on the specific clinical features, age at onset, and familial occurrence, a genetic cause has been postulated. Pathogenic variants in CHD2, SYNGAP1, NEXMIF, RORB, and GABRA1 have been reported in individuals with photosensitivity and eyelid myoclonia, but whether other genes are also involved, or a single gene is uniquely linked with EEM, or its subtypes, is not yet known. We aimed to dissect the genetic etiology of EEM. Methods: We studied a cohort of 105 individuals by using whole exome sequencing. Individuals were divided into two groups: EEM− (isolated EEM) and EEM+ (EEM accompanied by intellectual disability [ID] or any other neurodevelopmental/psychiatric disorder). Results: We identified nine variants classified as pathogenic/likely pathogenic in the entire cohort (8.57%); among these, eight (five in CHD2, one in NEXMIF, one in SYNGAP1, and one in TRIM8) were found in the EEM+ subcohort (28.57%). Only one variant (IFIH1) was found in the EEM− subcohort (1.29%); however, because the phenotype of the proband did not fit with published data, additional evidence is needed before considering IFIH1 variants and EEM− an established association. Burden analysis did not identify any single burdened gene or gene set. Significance: Our results suggest that for EEM, as for many other epilepsies, the identification of a genetic cause is more likely with comorbid ID and/or other neurodevelopmental disorders. Pathogenic variants were mostly found in CHD2, and the association of CHD2 with EEM+ can now be considered a reasonable gene–disease association. We provide further evidence to strengthen the association of EEM+ with NEXMIF and SYNGAP1. Possible new associations between EEM+ and TRIM8, and EEM− and IFIH1, are also reported. Although we provide robust evidence for gene variants associated with EEM+, the core genetic etiology of EEM− remains to be elucidated. [ABSTRACT FROM AUTHOR]

Keywords: epilepsy; machine learning; language model EN epilepsy machine learning language model 1195 1199 5 05/11/23 20230501 NES 230501 Epilepsy is a neurological disorder characterized by recurrent seizures, which can significantly impact the quality of life of affected individuals. However, these systems will need to be thoroughly tested and rigorously validated before they can be used in clinical care, in line with existing regulations on Software as a Medical Device or AI/ML-Enabled Medical Devices.[28] Ultimately, AI language models in epilepsy care will depend on developing robust and reliable systems as per the Ethics Guidelines for Trustworthy Artificial Intelligence,[29] driven by community-based data sharing and epilepsy-specific AI research. Fortunately, advances in artificial intelligence (AI) are providing new opportunities to improve the diagnosis and treatment of epilepsy. [Extracted from the article]

Objective: Psychoses affecting people with epilepsy increase disease burden and diminish quality of life. We characterized postictal psychosis, which comprises about one quarter of epilepsy‐related psychoses, and has unknown causation. Methods: We conducted a case–control cohort study including patients diagnosed with postictal psychosis, confirmed by psychiatric assessment, with available data regarding epilepsy, treatment, psychiatric history, psychosis profile, and outcomes. After screening 3,288 epilepsy patients, we identified 83 with psychosis; 49 had postictal psychosis. Controls were 98 adults, matched by age and epilepsy type, with no history of psychosis. Logistic regression was used to investigate clinical factors associated with postictal psychosis; univariate associations with a p value < 0.20 were used to build a multivariate model. Polygenic risk scores for schizophrenia were calculated. Results: Cases were more likely to have seizure clustering (odds ratio [OR] = 7.59, p < 0.001), seizures with a recollected aura (OR = 2.49, p = 0.013), and a family history of psychiatric disease (OR = 5.17, p = 0.022). Cases showed predominance of right temporal epileptiform discharges (OR = 4.87, p = 0.007). There was no difference in epilepsy duration, neuroimaging findings, or antiseizure treatment between cases and controls. Polygenic risk scores for schizophrenia in an extended cohort of postictal psychosis cases (n = 58) were significantly higher than in 1,366 epilepsy controls (R2 = 3%, p = 6 × 10−3), but not significantly different from 945 independent patients with schizophrenia (R2 = 0.1%, p = 0.775). Interpretation: Postictal psychosis occurs under particular circumstances in people with epilepsy with a heightened genetic predisposition to schizophrenia, illustrating how disease biology (seizures) and trait susceptibility (schizophrenia) may interact to produce particular outcomes (postictal psychosis) in a common disease. ANN NEUROL 2021;90:464–476 [ABSTRACT FROM AUTHOR]

Objective: Paroxysmal epileptiform abnormalities on electroencephalography (EEG) are the hallmark of epilepsies, but it is uncertain to what extent epilepsy and background EEG oscillations share neurobiological underpinnings. Here, we aimed to assess the genetic correlation between epilepsy and background EEG oscillations. Methods: Confounding factors, including the heterogeneous etiology of epilepsies and medication effects, hamper studies on background brain activity in people with epilepsy. To overcome this limitation, we compared genetic data from a genome‐wide association study (GWAS) on epilepsy (n = 12 803 people with epilepsy and 24 218 controls) with that from a GWAS on background EEG (n = 8425 subjects without epilepsy), in which background EEG oscillation power was quantified in four different frequency bands: alpha, beta, delta, and theta. We replicated our findings in an independent epilepsy replication dataset (n = 4851 people with epilepsy and 20 428 controls). To assess the genetic overlap between these phenotypes, we performed genetic correlation analyses using linkage disequilibrium score regression, polygenic risk scores, and Mendelian randomization analyses. Results: Our analyses show strong genetic correlations of genetic generalized epilepsy (GGE) with background EEG oscillations, primarily in the beta frequency band. Furthermore, we show that subjects with higher beta and theta polygenic risk scores have a significantly higher risk of having generalized epilepsy. Mendelian randomization analyses suggest a causal effect of GGE genetic liability on beta oscillations. Significance: Our results point to shared biological mechanisms underlying background EEG oscillations and the susceptibility for GGE, opening avenues to investigate the clinical utility of background EEG oscillations in the diagnostic workup of epilepsy. [ABSTRACT FROM AUTHOR]

Objective: Clinical genetic sequencing is frequently utilized to diagnose individuals with neurodevelopmental disorders (NDDs). Here we perform a meta‐analysis and systematic review of the success rate (diagnostic yield) of clinical sequencing through next‐generation sequencing (NGS) across NDDs. We compare the genetic testing yield across NDD subtypes and sequencing technology. Methods: We performed a systematic review of the PubMed literature until May 2020. We included clinical sequencing studies that utilized NGS in individuals with epilepsy, autism spectrum disorder (ASD), or intellectual disability (ID). Data were extracted, reviewed, and categorized according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. Two investigators performed clinical evaluation and grouping following the International League Against Epilepsy (ILAE) guidelines. Pooled rates of the diagnostic yield and 95% confidence intervals were estimated with a random‐effects model. Results: We identified 103 studies (epilepsy, N = 72; ASD, N = 14; ID, N = 21) across 32,331 individuals. Targeted gene panel sequencing was used in 73, and exome sequencing in 36 cohorts. Given highly selected patient cohorts, the diagnostic yield was 17.1% for ASD, 24% for epilepsy, and 28.2% for ID (23.7% overall). The highest diagnostic yield for epilepsy subtypes was observed in individuals with ID (27.9%) and early onset seizures (36.8%). The diagnostic yield for exome sequencing was higher than for panel sequencing, even though not statistically significant (27.2% vs 22.6%, P =.071). We observed that clinical sequencing studies are performed predominantly in countries with a high Inequality‐adjusted Human Development Index (IHDI) (countries with sequencing studies: IHDI median = 0.84, interquartile range [IQR] = 0.09 vs countries without sequencing studies: IHDI median = 0.56, IQR = 0.3). No studies from Africa, India, or Latin America were identified, indicating potential barriers to genetic testing. Significance: This meta‐analysis and systematic review provides a comprehensive overview of clinical sequencing studies of NDDs and will help guide policymaking and steer decision‐making in patient management. [ABSTRACT FROM AUTHOR]

Focal epilepsy (FE) is clinically highly heterogeneous. It has been shown recently that not only rare but also a subset of common genetic variants confer risk for FE. The relatively modest power of genetic studies in FE suggests a high genetic heterogeneity of FE when grouped as one disorder. We hypothesize that the clinical heterogeneity of FE is correlated with genetic heterogeneity on a common risk variant level. To test the hypothesis, we used an FE polygenic risk score "FE‐PRS" that combines small effect sizes of thousands of common variants from the largest FE‐GWAS (genome‐wide association study) into a single measure. We grouped 414 individuals with FE according to common clinical features into subgroups, either by one feature at a time or by all features combined in a cluster analysis. We examined their association with FE‐PRS compared to 20 435 matched population controls and observed heterogeneous FE‐PRS burden among the subgroups. The highest phenotypic variance explained by FE‐PRS was identified in a cluster analysis–defined FE subgroup where all individuals had unknown etiologies and psychiatric comorbidities, and the majority had early onset seizures. Our results indicate that genetic factors associated with FE have differential burden among FE subtypes. Future studies using better‐powered FE‐PRS might have clinical utility. [ABSTRACT FROM AUTHOR]

Objective: Autosomal dominant optic atrophy (ADOA) starts in early childhood with loss of visual acuity and color vision deficits. OPA1 mutations are responsible for the majority of cases, but in a portion of patients with a clinical diagnosis of ADOA, the cause remains unknown. This study aimed to identify novel ADOA-associated genes and explore their causality.Methods: Linkage analysis and sequencing were performed in multigeneration families and unrelated patients to identify disease-causing variants. Functional consequences were investigated in silico and confirmed experimentally using the zebrafish model.Results: We defined a new ADOA locus on 7q33-q35 and identified 3 different missense variants in SSBP1 (NM_001256510.1; c.113G>A [p.(Arg38Gln)], c.320G>A [p.(Arg107Gln)] and c.422G>A [p.(Ser141Asn)]) in affected individuals from 2 families and 2 singletons with ADOA and variable retinal degeneration. The mutated arginine residues are part of a basic patch that is essential for single-strand DNA binding. The loss of a positive charge at these positions is very likely to lower the affinity of SSBP1 for single-strand DNA. Antisense-mediated knockdown of endogenous ssbp1 messenger RNA (mRNA) in zebrafish resulted in compromised differentiation of retinal ganglion cells. A similar effect was achieved when mutated mRNAs were administered. These findings point toward an essential role of ssbp1 in retinal development and the dominant-negative nature of the identified human variants, which is consistent with the segregation pattern observed in 2 multigeneration families studied.Interpretation: SSBP1 is an essential protein for mitochondrial DNA replication and maintenance. Our data have established pathogenic variants in SSBP1 as a cause of ADOA and variable retinal degeneration. ANN NEUROL 2019;86:368-383. [ABSTRACT FROM AUTHOR]

Summary: Objective: With the exception of specific metabolic disorders, predictors of response to ketogenic dietary therapies (KDTs) are unknown. We aimed to determine whether common variation across the genome influences the response to KDT for epilepsy. Methods: We genotyped individuals who were negative for glucose transporter type 1 deficiency syndrome or other metabolic disorders, who received KDT for epilepsy. Genotyping was performed with the Infinium HumanOmniExpressExome Beadchip. Hospital records were used to obtain demographic and clinical data. KDT response (≥50% seizure reduction) at 3‐month follow‐up was used to dissect out nonresponders and responders. We then performed a genome‐wide association study (GWAS) in nonresponders vs responders, using a linear mixed model and correcting for population stratification. Variants with minor allele frequency <0.05 and those that did not pass quality control filtering were excluded. Results: After quality control filtering, the GWAS of 112 nonresponders vs 123 responders revealed an association locus at 6p25.1, 61 kb upstream of CDYL (rs12204701, P = 3.83 × 10−8, odds ratio [A] = 13.5, 95% confidence interval [CI] 4.07‐44.8). Although analysis of regional linkage disequilibrium around rs12204701 did not strengthen the likelihood of CDYL being the candidate gene, additional bioinformatic analyses suggest it is the most likely candidate. Significance: CDYL deficiency has been shown to disrupt neuronal migration and to influence susceptibility to epilepsy in mice. Further exploration with a larger replication cohort is warranted to clarify whether CDYL is the causal gene underlying the association signal. [ABSTRACT FROM AUTHOR]

Purpose Structural variations disrupting the gene encoding the neuron-specific splicing regulator RBFOX1 have been reported in three patients exhibiting epilepsy in comorbidity with other neuropsychiatric disorders. Consistently, the Rbfox1 knockout mouse model showed an increased susceptibility of seizures. The present candidate gene study tested whether exon-disrupting deletions of RBFOX1 increase the risk of idiopathic generalized epilepsies ( IGEs), representing the largest group of genetically determined epilepsies. Methods Screening of microdeletions (size: >40 kb, coverage >20 markers) affecting the genomic sequence of the RBFOX1 gene was carried out by high-resolution single-nucleotide polymorphism ( SNP) arrays in 1,408 European patients with idiopathic generalized epilepsy (IGE) and 2,256 population controls. Validation of RBFOX1 deletions and familial segregation analysis were performed by quantitative polymerase chain reaction (q PCR). Key Findings We detected five exon-disrupting RBFOX1 deletions in the IGE patients, whereas none was observed in the controls (p = 0.008, Fisher's exact test). The size of the exonic deletions ranged from 68 to 896 kb and affected the untranslated 5′-terminal RBFOX1 exons. Segregation analysis in four families indicated that the deletions were inherited, display incomplete penetrance, and heterogeneous cosegregation patterns with IGE. Significance Rare deletions affecting the untranslated 5′-terminal RBFOX1 exons increase risk of common IGE syndromes. Variable expressivity, incomplete penetrance, and heterogeneous cosegregation patterns suggest that RBFOX1 deletions act as susceptibility factor in a genetically complex etiology, where heterogeneous combinations of genetic factors determine the disease phenotype. [ABSTRACT FROM AUTHOR]

Summary Purpose: Genetic generalized epilepsies (GGEs) have a lifetime prevalence of 0.3% with heritability estimates of 80%. A considerable proportion of families with siblings affected by GGEs presumably display an oligogenic inheritance. The present genome-wide linkage meta-analysis aimed to map: (1) susceptibility loci shared by a broad spectrum of GGEs, and (2) seizure type-related genetic factors preferentially predisposing to either typical absence or myoclonic seizures, respectively. Methods: Meta-analysis of three genome-wide linkage datasets was carried out in 379 GGE-multiplex families of European ancestry including 982 relatives with GGEs. To dissect out seizure type-related susceptibility genes, two family subgroups were stratified comprising 235 families with predominantly genetic absence epilepsies (GAEs) and 118 families with an aggregation of juvenile myoclonic epilepsy (JME). To map shared and seizure type-related susceptibility loci, both nonparametric loci (NPL) and parametric linkage analyses were performed for a broad trait model (GGEs) in the entire set of GGE-multiplex families and a narrow trait model (typical absence or myoclonic seizures) in the subgroups of JME and GAE families. Key Findings: For the entire set of 379 GGE-multiplex families, linkage analysis revealed six loci achieving suggestive evidence for linkage at 1p36.22, 3p14.2, 5q34, 13q12.12, 13q31.3, and 19q13.42. The linkage finding at 5q34 was consistently supported by both NPL and parametric linkage results across all three family groups. A genome-wide significant nonparametric logarithm of odds score of 3.43 was obtained at 2q34 in 118 JME families. Significant parametric linkage to 13q31.3 was found in 235 GAE families assuming recessive inheritance (heterogeneity logarithm of odds = 5.02). Significance: Our linkage results support an oligogenic predisposition of familial GGE syndromes. The genetic risk factor at 5q34 confers risk to a broad spectrum of familial GGE syndromes, whereas susceptibility loci at 2q34 and 13q31.3 preferentially predispose to myoclonic seizures or absence seizures, respectively. Phenotype- genotype strategies applying narrow trait definitions in phenotypic homogeneous subgroups of families improve the prospects of disentangling the genetic basis of common familial GGE syndromes. [ABSTRACT FROM AUTHOR]