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Abstract Copy number variants (CNV) are established risk factors for neurodevelopmental disorders with seizures or epilepsy. With the hypothesis that seizure disorders share genetic risk factors, we pooled CNV data from 10,590 individuals with seizure disorders, 16,109 individuals with clinically validated epilepsy, and 492,324 population controls and identified 25 genome-wide significant loci, 22 of which are novel for seizure disorders, such as deletions at 1p36.33, 1q44, 2p21-p16.3, 3q29, 8p23.3-p23.2, 9p24.3, 10q26.3, 15q11.2, 15q12-q13.1, 16p12.2, 17q21.31, duplications at 2q13, 9q34.3, 16p13.3, 17q12, 19p13.3, 20q13.33, and reciprocal CNVs at 16p11.2, and 22q11.21. Using genetic data from additional 248,751 individuals with 23 neuropsychiatric phenotypes, we explored the pleiotropy of these 25 loci. Finally, in a subset of individuals with epilepsy and detailed clinical data available, we performed phenome-wide association analyses between individual CNVs and clinical annotations categorized through the Human Phenotype Ontology (HPO). For six CNVs, we identified 19 significant associations with specific HPO terms and generated, for all CNVs, phenotype signatures across 17 clinical categories relevant for epileptologists. This is the most comprehensive investigation of CNVs in epilepsy and related seizure disorders, with potential implications for clinical practice.
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Summary Objective Copy number variations (
CNV s) represent a significant genetic risk for several neurodevelopmental disorders including epilepsy. As knowledge increases, reanalysis of existing data is essential. Reliable estimates of the contribution ofCNV s to epilepsies from sizeable populations are not available.Methods We assembled a cohort of 1255 patients with preexisting array comparative genomic hybridization or single nucleotide polymorphism array based
CNV data. All patients had “epilepsy plus,” defined as epilepsy with comorbid features, including intellectual disability, psychiatric symptoms, and other neurological and nonneurological features.CNV classification was conducted using a systematic filtering workflow adapted to epilepsy.Results Of 1097 patients remaining after genetic data quality control, 120 individuals (10.9%) carried at least one autosomal
CNV classified as pathogenic; 19 individuals (1.7%) carried at least one autosomalCNV classified as possibly pathogenic. Eleven patients (1%) carried more than one (possibly) pathogenicCNV . We identifiedCNV s covering recently reported (HNRNPU )RORB CNV carriers to those of noncarriers of pathogenicCNV s, we show that patients with nonneurological comorbidities, especially dysmorphism, were more likely to carry pathogenicCNV s (odds ratio = 4.09, confidence interval = 2.51‐6.68;P = 2.34 × 10−9). Meta‐analysis including data from published control groups showed that the presence or absence of epilepsy did not affect the detected frequency ofCNV s.Significance The use of a specifically adapted workflow enabled identification of pathogenic autosomal
CNV s in 10.9% of patients with epilepsy plus, which rose to 12.7% when we also considered possibly pathogenicCNV s. Our data indicate that epilepsy with comorbid features should be considered an indication for patients to be selected for a diagnostic algorithm includingCNV detection. Collaborative large‐scaleCNV reanalysis leads to novel declaration of pathogenicity in unexplained cases and can promote discovery of promising candidate epilepsy genes.
