L ennox‐ G astaut syndrome of unknown cause: Phenotypic characteristics of patients in the E pilepsy P henome/ G enome P roject

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/101778/1/epi12395.pd

Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy

Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data

Genome-wide identification and phenotypic characterization of seizure-associated copy number variations in 741,075 individuals

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

Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies

sem informaçãoThe epilepsies affect around 65 million people worldwide and have a substantial missing heritability component. We report a genome-wide mega-analysis involving 15,212 individuals with epilepsy and 29,677 controls, which reveals 16 genome-wide significant91sem informaçãosem informaçãosem informaçã

Genome-wide identification and phenotypic characterization of seizure-associated copy number variations in 741,075 individuals

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

Genomics of drug resistance in epilepsy

Difficulties identifying drug-resistant epilepsy (DRE) at disease onset and complex temporal patterns of epilepsy represent challenges in research and clinical practice. A better understanding of the underlying mechanisms of DRE is needed to enable biomarker development, early diagnosis, and personalised treatments. This work explores the influence of genomic variation on DRE through genome-wide association (GWAS) and heritability analyses. It is part of a collaborative, European Commission funded project: EpiPGX (Epilepsy Pharmacogenomics: delivering biomarkers for clinical use). Individuals with epilepsy were recruited from specialised clinical centres across Europe. Healthy controls were obtained from several publically available sources. To establish whether common genomic variants are associated with DRE, two GWAS were performed by the Author. The first analysis, comparing individuals with DRE and controls with drug-responsive epilepsy, did not reveal any variants with genome-wide significance. The second analysis, comparing individuals with DRE and healthy controls, revealed several loci with genome-wide significance. The top genome-wide association signal (rs75700350), located at 4q31.1, likely represents an artefact. Other findings include the signals at loci 5p13.2, and 11p13, pointing to potentially significant candidate genes, SLC1A2 and SLC1A3, implicated in glutamate reuptake and excitotoxicity. Furthermore, one of these loci has been linked to an important epilepsy comorbidity, autism. The functional variants driving these signals may represent risk factors for drug resistance, epilepsy susceptibility, or variants affecting pathophysiological pathways common to DRE and its comorbidities. The main limitations of these GWAS analyses were small sample sizes and the lack of replication. To explore if drug resistance in epilepsy has a polygenic inheritance component, a single nucleotide polymorphism (SNP) heritability analysis was performed. This analysis yielded an estimate of DRE SNP heritability of 0.22, showing that drug resistance in epilepsy is heritable

Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies

The epilepsies affect around 65 million people worldwide and have a substantial missing heritability component. We report a genome-wide mega-analysis involving 15,212 individuals with epilepsy and 29,677 controls, which reveals 16 genome-wide significant loci, of which 11 are novel. Using various prioritization criteria, we pinpoint the 21 most likely epilepsy genes at these loci, with the majority in genetic generalized epilepsies. These genes have diverse biological functions, including coding for ion-channel subunits, transcription factors and a vitamin-B6 metabolism enzyme. Converging evidence shows that the common variants associated with epilepsy play a role in epigenetic regulation of gene expression in the brain. The results show an enrichment for monogenic epilepsy genes as well as known targets of antiepileptic drugs. Using SNP-based heritability analyses we disentangle both the unique and overlapping genetic basis to seven different epilepsy subtypes. Together, these findings provide leads for epilepsy therapies based on underlying pathophysiology

Biobank participation of persons with epilepsy in South Wales

Introduction: The Swansea Neurology Biobank (SNB) has collected thousands of DNA bio-samples from people with epilepsy in South Wales. Analysis of biobank participation is important to optimise future recruitment for epilepsy research, meta-data analysis and gene / biomarker discovery. This will lead to a high-quality platform for the collection of biological specimens and data. Method: Participation data was extracted from over 2,500 patient records during SNB screening between 2016 and 2018. Biobank participation rates were calculated and linked to epilepsy prevalence using linked, anonymised primary care within the Secure Anonymised Information Linkage databank. Demographics, epilepsy characteristics and social deprivation status (measured using the Welsh Index of Multiple Deprivation – WIMD) were combined at a small geographical (Lower Super Output Area) scale. Factors hypothesised to influence biobank participation were analysed using bivariate and multivariate statistics. A proportion of biobank participants completed a questionnaire assessing attitudes to biobank consent. Results: 12.5% of people with epilepsy seen at epilepsy clinics within the Swansea area were represented in the SNB in 2018. Epilepsy prevalence in the study area (0.92%) was higher than the all Wales epilepsy prevalence (0.85%) and was highest in the most deprived areas. Older patients were more likely to donate compared to the youngest age grouping. Generalised onset epilepsy was underrepresented in the SNB with only 19% having generalised epilepsy. Nearly 20% of patients did not attend their appointment with the majority (59%) coming from the most deprived areas. A large proportion of non-attenders who had generalised epilepsy were diagnosed with Juvenile Myoclonic Epilepsy. Participation rates were lower in more deprived areas when compared to less deprived areas (36% WIMD quintile 1 compared to 41% quintile 4 and 5). Biobank participants were generally positive about biobank donation but there were uncertainties related to the broad reach of the consent process. Conclusion: Our results highlight the difficulty in encouraging research participation at levels representative of the local epilepsy population. Despite higher epilepsy prevalence in more deprived areas, participation rates are lower and non-attendance rates are higher. Mapping of epilepsy participation enables the identification of these low participation areas enabling focused recruitment strategies. Working with primary care and bringing services to the community may improve recruitment when compared to hospital clinic based recruitment