Aug 2026· Frontiers in Genetics· Vol 17· 0 citations· 98 references
TL;DR
Evidence across epilepsy subtypes is reviewed, focusing on convergence and divergence across the allelic spectrum, and how polygenic background and other modifiers may influence penetrance and clinical expressivity among carriers of rare pathogenic variants and CNVs is discussed.
Abstract
Epilepsy genetics has often been interpreted through a useful but simplified dichotomous framework in which severe epilepsies, particularly developmental and epileptic encephalopathies, are attributed mainly to rare, high-effect variants, whereas more common epilepsies are viewed as arising largely from the cumulative effects of common, small-effect variation. Although this framework has been instrumental for gene discovery, molecular diagnosis, and mechanism-based treatment, it does not fully explain incomplete penetrance, intrafamilial phenotypic heterogeneity, or marked differences in severity among individuals sharing the same molecular diagnosis. Evidence from exome sequencing, copy number variant (CNV) studies, and genome-wide association studies increasingly suggests that rare SNVs/indels, CNVs, and common variant should not be interpreted as entirely independent risk sources, but may partially converge on shared genes, pathways, cell types, and neurobiological processes relevant to neuronal excitability, network stability, and seizure susceptibility. Here, we review evidence across epilepsy subtypes, focusing on convergence and divergence across the allelic spectrum, and discuss how polygenic background and other modifiers may influence penetrance and clinical expressivity among carriers of rare pathogenic variants and CNVs. We also consider implications for variant interpretation, genetic counseling, risk stratification, and precision medicine, while emphasizing that most rare–common integrated models remain insufficiently validated for routine clinical decision-making.
This study finds that rare variants across hundreds of genes contribute to autism with variable phenotypic outcomes, and clusters them based on association evidence from large-scale studies of developmental disorders, schizophrenia, bipolar disorder, and epilepsy.
F. Satterstrom, C. Auwerx, J. Fu et al.· medRxiv· 0 citations
This review synthesizes contemporary insights into the genetic and molecular pathophysiology of seizures and epilepsy, with emphasis on mechanisms that destabilize excitation–inhibition balance, promote epileptogenesis, and drive pharmacoresistance and supports more refined approaches to epilepsy classification and future precision medicine strategies.
Mohammad Reza Seyedtaghia, Jina Babanzadeh, Marcello Scala et al.· Epilepsia Open· 0 citations
The study provides an integrated framework linking genetic variation to molecular dysfunction and clinical outcomes, offering valuable insights for future research and therapeutic development in pediatric neurology.
Varada Vidya Rani, Suryanarayana Reddy Kovvuri, D. Arya· Genetics and Molecular Resea...· 0 citations
Autism spectrum disorder (ASD) is genetically heterogeneous, involving rare and common variants that disrupt neurodevelopmental pathways. To explore this complexity, we performed whole-exome sequencing in children with ASD. Clinical phenotypes were systematically recorded, and severity was classified according to DSM-5 criteria. Variants were interpreted using ACMG guidelines, with recurrence analysis to identify genes shared across individuals and cohort enrichment testing against gnomAD. To examine genotype-phenotype relationships, we applied SKAT/SKAT-O across 16 phenotypes after covariate adjustment. Among 25 included individuals, pathogenic or likely pathogenic variants were found in 9, yielding a diagnostic yield of 36%. These involved genes linked to neurodevelopmental, epileptic, metabolic, and syndromic disorders. Recurrence analysis identified 586 genes present in at least two individuals, with PABPC1, GTF2I, PCLO, PKD1, and EP400 being the most frequent. SKAT/SKAT-O revealed the strongest burden associations for motor delay, aggressive behavior, mutism, anxiety, unresponsiveness to spoken voice, digestive disorder, and sleep disturbances, with limited overlap across phenotypes. Several recurrent genes also showed phenotype-specific associations. Overall, this integrative WES study provides clinically actionable diagnoses, highlights recurrent genes, and uncovers phenotype-specific signals, supporting convergent pathways with gene-level heterogeneity.
Zainab Gaouzi, Giulia Spoto, F. Polito et al.· International Journal of Mol...· 0 citations
This study shows that individual oligogenic analysis is implementable in selected patients and represents a feasible addition to monogenic analysis, and could not identify any definite oligogenic combination.
Sarah Duerinckx, Barbara Gravel, J. Soblet et al.· Epilepsia· 0 citations
Attention-deficit/hyperactivity disorder (ADHD) is heterogeneous in the age at which symptoms first lead to clinical diagnosis. Recent genomic analyses of a large Danish population-based cohort showed that individuals diagnosed in childhood differ from those diagnosed in adulthood in polygenic risk scores, rare-variant burden, and patterns of genetic correlation with other psychiatric traits [1]. Childhood cases exhibited stronger overlap with autism and higher rates of rare protein-truncating variants in constrained genes, whereas late-diagnosed cases showed greater genetic overlap with depression. The functional transcriptomic mechanisms underlying these differences have remained unclear.We performed a transcriptome-wide association study (TWAS) using S-PrediXcan and GTEx v8 MASHR models from six adult brain tissues: amygdala, anterior cingulate cortex, caudate, frontal cortex, hippocampus, and nucleus accumbens. Analyses were based on GWAS summary statistics from the iPSYCH study for childhood ADHD (N = 14,878) and late-diagnosed ADHD (N = 6,961). Thirteen curated gene sets were examined, spanning synaptic plasticity, immune function, glial markers, and housekeeping controls.The transcriptomic profiles of the two subgroups showed substantial overall similarity (Spearman ρ = 0.77 across 13,580 genes), with near-perfect directional concordance among nominally significant associations; because the two GWAS contrasted different case groups against the same 38,303 population controls, these similarity estimates should be interpreted as upper bounds rather than unbiased estimates of aetiological overlap. Despite this shared architecture, gene-set analyses suggested biologically interpretable divergences: late-diagnosed ADHD displayed stronger enrichment in long-term potentiation (enrichment ratio = 1.34, Mann-Whitney p = 1.42 × 10⁻⁸) and complement cascade pathways (1.22, p = 5.14 × 10⁻⁵), whereas childhood ADHD showed only nominally greater signals in microglia marker and MHC genes. Eighty-two genes differed in association strength between groups after FDR correction, although this delta-Z comparison should be read cautiously because the shared-control design violates the independence assumption of the test.These findings provide a functional explanation for previously reported genetic and comorbidity differences by age at diagnosis and identify an 82-gene differential list for follow-up. The enrichment patterns generate cautious, hypothesis-level questions regarding potential variation in stimulant response and tolerability, but no direct treatment, adherence, or pharmacogenomic data were analysed. This represents the first TWAS to dissect ADHD subtypes by age at first diagnosis and offers a bridge between statistical genetic associations and biologically interpretable pathways relevant to clinical heterogeneity.