GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

Dissecting the genetic and causal relationship between sleep-related traits and common brain disorders

BACKGROUND

There is a profound connection between abnormal sleep patterns and brain disorders, suggesting a shared influential association. However, the shared genetic basis and potential causal relationships between sleep-related traits and brain disorders are yet to be fully elucidated.

METHODS

Utilizing linkage disequilibrium score regression (LDSC) and bidirectional two-sample univariable Mendelian Randomization (UVMR) analyses with large-scale GWAS datasets, we investigated the genetic correlations and causal associations across six sleep traits and 24 prevalent brain disorders. Additionally, a multivariable Mendelian Randomization (MVMR) analysis evaluated the cumulative effects of various sleep traits on each brain disorder, complemented by genetic loci characterization to pinpoint pertinent genes and pathways.

RESULTS

LDSC analysis identified significant genetic correlations in 66 out of 144 (45.8 %) pairs between sleep-related traits and brain disorders, with the most pronounced correlations observed in psychiatric disorders (66 %, 48/72). UVMR analysis identified 29 causal relationships (FDR<0.05) between sleep traits and brain disorders, with 19 associations newly discovered according to our knowledge. Notably, major depression, attention-deficit/hyperactivity disorder, bipolar disorder, cannabis use disorder, and anorexia nervosa showed bidirectional causal relations with sleep traits, especially insomnia's marked influence on major depression (IVW beta 0.468, FDR = 5.24E-09). MVMR analysis revealed a nuanced interplay among various sleep traits and their impact on brain disorders. Genetic loci characterization underscored potential genes, such as HOXB2, while further enrichment analyses illuminated the importance of synaptic processes in these relationships.

CONCLUSIONS

This study provides compelling evidence for the causal relationships and shared genetic backgrounds between common sleep-related traits and brain disorders.

Haploinsufficiency underlies the neurodevelopmental consequences of SLC6A1 variants

Heterozygous variants in SLC6A1, encoding the GAT-1 GABA transporter, are associated with seizures, developmental delay, and autism. The majority of affected individuals carry missense variants, many of which are recurrent germline de novo mutations, raising the possibility of gain-of-function or dominant-negative effects. To understand the functional consequences, we performed an in vitro GABA uptake assay for 213 unique variants, including 24 control variants. De novo variants consistently resulted in a decrease in GABA uptake, in keeping with haploinsufficiency underlying all neurodevelopmental phenotypes. Where present, ClinVar pathogenicity reports correlated well with GABA uptake data; the functional data can inform future reports for the remaining 72% of unscored variants. Surface localization was assessed for 86 variants; two-thirds of loss-of-function missense variants prevented GAT-1 from being present on the membrane while GAT-1 was on the surface but with reduced activity for the remaining third. Surprisingly, recurrent de novo missense variants showed moderate loss-of-function effects that reduced GABA uptake with no evidence for dominant-negative or gain-of-function effects. Using linear regression across multiple missense severity scores to extrapolate the functional data to all potential SLC6A1 missense variants, we observe an abundance of GAT-1 residues that are sensitive to substitution. The extent of this missense vulnerability accounts for the clinically observed missense enrichment; overlap with hypermutable CpG sites accounts for the recurrent missense variants. Strategies to increase the expression of the wild-type SLC6A1 allele are likely to be beneficial across neurodevelopmental disorders, though the developmental stage and extent of required rescue remain unknown.

Dissecting the Shared Genetic Architecture of Common Epilepsies With Cortical Brain Morphology

Background and Objectives

Epilepsies are associated with differences in cortical thickness (TH) and surface area (SA). However, the mechanisms underlying these relationships remain elusive. We investigated the extent to which these phenotypes share genetic influences.

Methods

We analyzed genome-wide association study data on common epilepsies (n = 69,995) and TH and SA (n = 32,877) using Gaussian mixture modeling MiXeR and conjunctional false discovery rate (conjFDR) analysis to quantify their shared genetic architecture and identify overlapping loci. We biologically interrogated the loci using a variety of resources and validated in independent samples.

Results

The epilepsies (2.4 k-2.9 k variants) were more polygenic than both SA (1.8 k variants) and TH (1.3 k variants). Despite absent genome-wide genetic correlations, there was a substantial genetic overlap between SA and genetic generalized epilepsy (GGE) (1.1 k), all epilepsies (1.1 k), and juvenile myoclonic epilepsy (JME) (0.7 k), as well as between TH and GGE (0.8 k), all epilepsies (0.7 k), and JME (0.8 k), estimated with MiXeR. Furthermore, conjFDR analysis identified 15 GGE loci jointly associated with SA and 15 with TH, 3 loci shared between SA and childhood absence epilepsy, and 6 loci overlapping between SA and JME. 23 loci were novel for epilepsies and 11 for cortical morphology. We observed a high degree of sign concordance in the independent samples.

Discussion

Our findings show extensive genetic overlap between generalized epilepsies and cortical morphology, indicating a complex genetic relationship with mixed-effect directions. The results suggest that shared genetic influences may contribute to cortical abnormalities in epilepsies.

Visual detection of seizures in mice using supervised machine learning

Seizures are caused by abnormally synchronous brain activity that can result in changes in muscle tone, such as twitching, stiffness, limpness, or rhythmic jerking. These behavioral manifestations are clear on visual inspection and the most widely used seizure scoring systems in preclinical models, such as the Racine scale in rodents, use these behavioral patterns in semiquantitative seizure intensity scores. However, visual inspection is time-consuming, low-throughput, and partially subjective, and there is a need for rigorously quantitative approaches that are scalable. In this study, we used supervised machine learning approaches to develop automated classifiers to predict seizure severity directly from noninvasive video data. Using the PTZ-induced seizure model in mice, we trained video-only classifiers to predict ictal events, combined these events to predict an univariate seizure intensity for a recording session, as well as time-varying seizure intensity scores. Our results show, for the first time, that seizure events and overall intensity can be rigorously quantified directly from overhead video of mice in a standard open field using supervised approaches. These results enable high-throughput, noninvasive, and standardized seizure scoring for downstream applications such as neurogenetics and therapeutic discovery.

Breaking down causes, consequences, and mediating effects of telomere length variation on human health

BACKGROUND

Telomeres form repeated DNA sequences at the ends of chromosomes, which shorten with each cell division. Yet, factors modulating telomere attrition and the health consequences thereof are not fully understood. To address this, we leveraged data from 326,363 unrelated UK Biobank participants of European ancestry.

RESULTS

Using linear regression and bidirectional univariable and multivariable Mendelian randomization (MR), we elucidate the relationships between leukocyte telomere length (LTL) and 142 complex traits, including diseases, biomarkers, and lifestyle factors. We confirm that telomeres shorten with age and show a stronger decline in males than in females, with these factors contributing to the majority of the 5.4% of LTL variance explained by the phenome. MR reveals 23 traits modulating LTL. Smoking cessation and high educational attainment associate with longer LTL, while weekly alcohol intake, body mass index, urate levels, and female reproductive events, such as childbirth, associate with shorter LTL. We also identify 24 traits affected by LTL, with risk for cardiovascular, pulmonary, and some autoimmune diseases being increased by short LTL, while longer LTL increased risk for other autoimmune conditions and cancers. Through multivariable MR, we show that LTL may partially mediate the impact of educational attainment, body mass index, and female age at childbirth on proxied lifespan.

CONCLUSIONS

Our study sheds light on the modulators, consequences, and the mediatory role of telomeres, portraying an intricate relationship between LTL, diseases, lifestyle, and socio-economic factors.

Genetic Testing in Pediatric Epilepsy: Tools, Tips, and Navigating the Traps

With the advent of high-throughput sequencing and computational methods, genetic testing has become an integral part of contemporary clinical practice, particularly in epilepsy. The toolbox for genetic testing has evolved from conventional chromosomal microarray and epilepsy gene panels to state-of-the-art sequencing techniques in the modern genomic era. Beyond its potential for therapeutic benefits through precision medicine, optimizing the choice of antiseizure medications, or exploring nonpharmacological therapeutic modalities, genetic testing carries substantial diagnostic, prognostic, and personal implications. Developmental and epileptic encephalopathies, the coexistence of neurodevelopmental comorbidities, early age of epilepsy onset, unexplained drug-refractory epilepsy, and positive family history have demonstrated the highest likelihood of yielding positive genetic test results. Given the diagnostic efficacy across different testing modalities, reducing costs of next-generation sequencing tests, and genetic diversity of epilepsies, exome sequencing or genome sequencing, where feasible and available, have been recommended as the first-tier test. Comprehensive clinical phenotyping at the outset, corroborative evidence from radiology and electrophysiology-based investigations, reverse phenotyping, and periodic reanalysis are some of the valuable strategies when faced with inconclusive test results. In this narrative review, the authors aim to simplify the approach to genetic testing in epilepsy by guiding on the selection of appropriate testing tools in the indicated clinical scenarios, addressing crucial aspects during pre- and post-test counseling sessions, adeptly navigating the traps posed by uncertain or negative genetic variants, and paving the way forward to the emerging testing modalities beyond DNA sequencing.

Oligoepilepsy and lifelong seizure susceptibility in epilepsy with generalized tonic-clonic seizures alone: Experience at an adult tertiary center

OBJECTIVE

Epilepsy with generalized tonic-clonic seizures alone (GTCA) is the least studied syndrome within the idiopathic generalized epilepsy (IGE) spectrum. We characterize a large cohort of adult patients with GTCA to understand natural history and drug responsiveness.

METHODS

In this retrospective single-center study using our epilepsy electronic record, we evaluated clinical characteristics, seizure outcomes, anti-seizure medication (ASM) response including seizure recurrence after ASM withdrawal, and sex differences in a cohort of GTCA patients aged ≥17 years.

RESULTS

Within a cohort of 434 IGE patients, 87 patients (20 %) with GTCA were included. The mean age was 34.9 years (range 17-73 years). Forty-six patients (52.8 %) were females. Seventy-two patients (82.8 %) were seizure-free and 15 (17.2 %) had active epilepsy over the previous 12 months. Thirty-four patients (39.1 %) had ≤5 lifetime seizures, aligning with a prior definition of 'oligoepilepsy'. Sixty-five patients (74.7 %) were treated with monotherapy, 19 (21.8 %) were treated with polytherapy, and three were not taking any ASM. Levetiracetam (37.9 %) was the most commonly prescribed ASM, followed by lamotrigine (32.1 %) and valproate (31 %). Seventeen patients (19.5 %) attempted to withdraw their ASM. The rate of seizure recurrence after ASM withdrawal was 88.2 % (15/17), including two patients who relapsed more than 20 years after ASM discontinuation. Females had more seizures in their lifetime and had trialed more ASM compared to males.

SIGNIFICANCE

GTCA has a relatively good prognosis, with most patients becoming seizure-free on monotherapy. The high rate of seizure recurrence after ASM withdrawal supports lifetime seizure susceptibility. We found potential sex differences in seizure outcomes and ASM response, although further research is needed to validate this finding.

Perampanel for the treatment of epilepsy with genetic aetiology: Real-world evidence from the PERMIT Extension study

Genetic factors contribute to the aetiology of epilepsy in >50% of cases, and information on the use of antiseizure medications in people with specific aetiologies will help guide treatment decisions. The PERMIT Extension study pooled data from two real-world studies (PERMIT and PROVE) to investigate the effectiveness and safety/tolerability of perampanel (PER) when used to treat people with focal and generalised epilepsy in everyday clinical practice. This post-hoc analysis of PERMIT Extension explored the use of PER when used to treat individuals presumed to have epilepsy with a genetic aetiology. Assessments included retention rate (evaluated at 3, 6 and 12 months), effectiveness (responder and seizure freedom rates; evaluated at 3, 6, 12 months and the last visit [last observation carried forward) and tolerability (adverse events [AEs]). Of the 6822 people with epilepsy included in PERMIT Extension, 1012 were presumed to have a genetic aetiology. The most common genetic aetiologies were idiopathic generalised epilepsy (IGE; 58.2%), tuberous sclerosis (1.1%), Dravet syndrome (0.8%) and genetic epilepsy with febrile seizures plus (GEFS+; 0.5%). Retention rates at 3, 6 and 12 months in the total genetic aetiology population were 89.3%, 79.7% and 65.9%, respectively. In the total genetic aetiology population, responder rates at 12 months and the last visit were 74.8% and 68.3%, respectively, and corresponding seizure freedom rates were 48.9% and 46.5%, respectively. For the specific aetiology subgroups, responder rates at 12 months and the last visit were, respectively: 90.4% and 84.4% (IGE), 100% and 57.1% (tuberous sclerosis), 100% and 71.4% (Dravet syndrome), and 33.3% and 20.0% (GEFS+). Corresponding seizure freedom rates were, respectively: 73.1% and 64.6% (IGE), 33.3% and 22.2% (tuberous sclerosis), 20.0% and 28.6% (Dravet syndrome), and 0% and 0% (GEFS+). The incidence of AEs was 46.5% for the total genetic aetiology population, 48.8% for IGE, 27.3% for tuberous sclerosis, 62.5% for Dravet syndrome, and 20% for GEFS+. Tolerability findings were consistent with PER's known safety profile. PER was effective and generally well tolerated when used in individuals with a presumed genetic epilepsy aetiology in clinical practice. PER was effective across a wide range of genetic aetiologies.

An integrated single-nucleus and spatial transcriptomics atlas reveals the molecular landscape of the human hippocampus

The hippocampus contains many unique cell types, which serve the structure's specialized functions, including learning, memory and cognition. These cells have distinct spatial topography, morphology, physiology, and connectivity, highlighting the need for transcriptome-wide profiling strategies that retain cytoarchitectural organization. Here, we generated spatially-resolved transcriptomics (SRT) and single-nucleus RNA-sequencing (snRNA-seq) data from adjacent tissue sections of the anterior human hippocampus across ten adult neurotypical donors. We defined molecular profiles for hippocampal cell types and spatial domains. Using non-negative matrix factorization and transfer learning, we integrated these data to define gene expression patterns within the snRNA-seq data and infer the expression of these patterns in the SRT data. With this approach, we leveraged existing rodent datasets that feature information on circuit connectivity and neural activity induction to make predictions about axonal projection targets and likelihood of ensemble recruitment in spatially-defined cellular populations of the human hippocampus. Finally, we integrated genome-wide association studies with transcriptomic data to identify enrichment of genetic components for neurodevelopmental, neuropsychiatric, and neurodegenerative disorders across cell types, spatial domains, and gene expression patterns of the human hippocampus. To make this comprehensive molecular atlas accessible to the scientific community, both raw and processed data are freely available, including through interactive web applications.

Systematic Analysis of the Relationship Between Elevated Zinc and Epilepsy

Previous studies have indicated a potential relationship between zinc and epilepsy. The aim of this study is to investigate the causal relationship between zinc, zinc-dependent carbonic anhydrase, and gray matter volume in brain regions enriched with zinc and epilepsy, as well as explore the possible mechanisms by which zinc contributes to epilepsy. First, this study assessed the risk causality between zinc, carbonic anhydrase, and gray matter volume alterations in zinc-enriched brain regions and various subtypes of epilepsy based on Two-sample Mendelian randomization analysis. And then, this study conducted GO/KEGG analysis based on colocalization analysis, MAGMA analysis, lasso regression, random forest model, and XGBoost model. The results of Mendelian randomization analyses showed a causal relationship between zinc, carbonic anhydrase-4, and generalized epilepsy (p = 0.044 , p = 0.010). Additionally, carbonic anhydrase-1 and gray matter volume of the caudate nucleus were found to be associated with epilepsy and focal epilepsy (p = 0.014, p = 0.003 and p = 0.022, p = 0.009). A colocalization relationship was found between epilepsy and focal epilepsy (PP.H4.abf = 97.7e - 2). Meanwhile, the MAGMA analysis indicated that SNPs associated with epilepsy and focal epilepsy were functionally localized to zinc-finger-protein-related genes (p < 1.0e - 5). The genes associated with focal epilepsy were found to have a molecular function of zinc ion binding (FDR = 2.3e - 6). After the onset of epilepsy, the function of the gene whose expression changed in the rats with focal epilepsy was enriched in the biological process of vascular response (FDR = 4.0e - 5). These results revealed mechanism of the increased risk of epilepsy caused by elevated zinc may be related to the increase of zinc ion-dependent carbonic anhydrase or the increase of the volume of zinc-rich caudate gray matter.

Unraveling the shared genetics of common epilepsies and general cognitive ability

Objective

Cognitive impairment is prevalent among individuals with epilepsy, and it is possible that genetic factors can underlie this relationship. Here, we investigated the potential shared genetic basis of common epilepsies and general cognitive ability (COG).

Methods

We applied linkage disequilibrium score (LDSC) regression, MiXeR and conjunctional false discovery rate (conjFDR) to analyze different aspects of genetic overlap between COG and epilepsies. We used the largest available genome-wide association study data on COG (n = 269,867) and common epilepsies (n = 27,559 cases, 42,436 controls), including the broad phenotypes 'all epilepsy', focal epilepsies and genetic generalized epilepsies (GGE), and as well as specific subtypes. We functionally annotated the identified loci using a variety of biological resources and validated the results in independent samples.

Results

Using MiXeR, COG (11.2k variants) was estimated to be almost four times more polygenic than 'all epilepsy', GGE, juvenile myoclonic epilepsy (JME), and childhood absence epilepsy (CAE) (2.5k - 2.9k variants). The other epilepsy phenotypes were insufficiently powered for analysis. We show extensive genetic overlap between COG and epilepsies with significant negative genetic correlations (-0.23 to -0.04). COG was estimated to share 2.9k variants with both GGE and 'all epilepsy', and 2.3k variants with both JME and CAE. Using conjFDR, we identified 66 distinct loci shared between COG and epilepsies, including novel associations for GGE (27), 'all epilepsy' (5), JME (5) and CAE (5). The implicated genes were significantly expressed in multiple brain regions. The results were validated in independent samples (COG: p = 1.0 × 10-14; 'all epilepsy': p = 5.6 × 10-3).

Significance

Our study demonstrates a substantial genetic basis shared between epilepsies and COG and identifies novel overlapping genomic loci. Enhancing our understanding of the relationship between epilepsies and COG may lead to the development of novel comorbidity-targeted epilepsy treatments.

A concerted neuron-astrocyte program declines in ageing and schizophrenia

Human brains vary across people and over time; such variation is not yet understood in cellular terms. Here we describe a relationship between people's cortical neurons and cortical astrocytes. We used single-nucleus RNA sequencing to analyse the prefrontal cortex of 191 human donors aged 22-97 years, including healthy individuals and people with schizophrenia. Latent-factor analysis of these data revealed that, in people whose cortical neurons more strongly expressed genes encoding synaptic components, cortical astrocytes more strongly expressed distinct genes with synaptic functions and genes for synthesizing cholesterol, an astrocyte-supplied component of synaptic membranes. We call this relationship the synaptic neuron and astrocyte program (SNAP). In schizophrenia and ageing-two conditions that involve declines in cognitive flexibility and plasticity1,2-cells divested from SNAP: astrocytes, glutamatergic (excitatory) neurons and GABAergic (inhibitory) neurons all showed reduced SNAP expression to corresponding degrees. The distinct astrocytic and neuronal components of SNAP both involved genes in which genetic risk factors for schizophrenia were strongly concentrated. SNAP, which varies quantitatively even among healthy people of similar age, may underlie many aspects of normal human interindividual differences and may be an important point of convergence for multiple kinds of pathophysiology.

Integrative analysis of epilepsy-associated genes reveals expression-phenotype correlations

Epilepsy is a highly prevalent neurological disorder characterized by recurrent seizures. Patients exhibit broad genetic, molecular, and clinical diversity involving mild to severe comorbidities. The factors that contribute to this phenotypic diversity remain unclear. Here we used publicly available datasets to systematically interrogate the expression pattern of 230 epilepsy-associated genes across human tissues, developmental stages, and central nervous system (CNS) cellular subtypes. We grouped genes based on their curated phenotypes into 3 broad classes: core epilepsy genes (CEG), where seizures are the dominant phenotype, developmental and epileptic encephalopathy genes (DEEG) that are associated with developmental and epileptic encephalopathy, and seizure-related genes (SRG), which are characterized by the presence of seizures and gross brain malformations. We find that compared to the other two groups of genes, DEEGs are highly expressed within the adult CNS, exhibit the highest and most dynamic expression in various brain regions across development, and are significantly enriched in GABAergic neurons. Our analysis provides an overview of the expression pattern of epilepsy-associated genes with spatiotemporal resolution and establishes a broad expression-phenotype correlation in epilepsy.

Identification of potential disease-associated variants in idiopathic generalized epilepsy using targeted sequencing

Many questions remain regarding the genetics of idiopathic generalized epilepsy (IGE), a subset of genetic generalized epilepsy (GGE). We aimed to identify the candidate coding variants of epilepsy panel genes in a cohort of affected individuals, using variant frequency information from a control cohort of the same region. We performed whole-exome sequencing analysis of 121 individuals and 10 affected relatives, focusing on variants of 950 candidate genes associated with epilepsy according to the Genes4Epilepsy curated panel. We identified 168 candidate variants (CVs) in 137 of 950 candidate genes in 88 of 121 affected individuals with IGE, of which 61 were novel variants. Notably, we identified five CVs in known GGE-associated genes (CHD2, GABRA1, RORB, SCN1A, and SCN1B) in five individuals and CVs shared by affected individuals in each of four family cases for other epilepsy candidate genes. The results of this study demonstrate that IGE is a disease with high heterogeneity and provide IGE-associated CVs whose pathogenicity should be proven by future studies, including advanced functional analysis. The low detection rate of CVs in the GGE-associated genes (4.1%) in this study suggests the current incompleteness of the Genes4Epilepsy panel for the diagnosis of IGE in clinical practice.

Coexistence of temporal lobe epilepsy and idiopathic generalized epilepsy

OBJECTIVE

We investigated the frequency of coexistence of temporal lobe epilepsy (TLE) and idiopathic generalized epilepsy (IGE) in a retrospective database study. We also explored the underlying pathomechanisms of the coexistence of TLE and IGE based on the available information, using bioinformatics tools.

METHODS

The first phase of the investigation was a retrospective study. All patients with an electro-clinical diagnosis of epilepsy were studied at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from 2008 until 2023. In the second phase, we searched the following databases for genetic variations (epilepsy-associated genetic polymorphisms) that are associated with TLE or syndromes of IGE: DisGeNET, genome-wide association study (GWAS) Catalog, epilepsy genetic association database (epiGAD), and UniProt. We also did a separate literature search using PubMed.

RESULTS

In total, 3760 patients with epilepsy were registered at our clinic; four patients with definitely mixed TLE and IGE were identified; 0.1% of all epilepsies. We could identify that rs1883415 of ALDH5A1, rs137852779 of EFHC1, rs211037 of GABRG2, rs1130183 of KCNJ10, and rs1045642 of ABCB1 genes are shared between TLE and syndromes of IGE.

CONCLUSION

While coexistence of TLE and IGE is a rare phenomenon, this could be explained by shared genetic variations.

Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders

DNA methylation (DNAm) is essential for brain development and function and potentially mediates the effects of genetic risk variants underlying brain disorders. We present INTERACT, a transformer-based deep learning model to predict regulatory variants impacting DNAm levels in specific brain cell types, leveraging existing single-nucleus DNAm data from the human brain. We show that INTERACT accurately predicts cell type-specific DNAm profiles, achieving an average area under the Receiver Operating Characteristic curve of 0.98 across cell types. Furthermore, INTERACT predicts cell type-specific DNAm regulatory variants, which reflect cellular context and enrich the heritability of brain-related traits in relevant cell types. Importantly, we demonstrate that incorporating predicted variant effects and DNAm levels of CpG sites enhances the fine mapping for three brain disorders-schizophrenia, depression, and Alzheimer's disease-and facilitates mapping causal genes to particular cell types. Our study highlights the power of deep learning in identifying cell type-specific regulatory variants, which will enhance our understanding of the genetics of complex traits.

Identification of potential crucial genes and therapeutic targets for epilepsy

BACKGROUND

Epilepsy, a central neurological disorder, has a complex genetic architecture. There is some evidence suggesting that genetic factors play a role in both the occurrence of epilepsy and its treatment. However, the genetic determinants of epilepsy are largely unknown. This study aimed to identify potential therapeutic targets for epilepsy.

METHODS

Differentially expressed genes (DEGs) were extracted from the expression profiles of GSE44031 and GSE1834. Gene co-expression analysis was used to confirm the regulatory relationship between newly discovered epilepsy candidate genes and known epilepsy genes. Expression quantitative trait loci analysis was conducted to determine if epilepsy risk single-nucleotide polymorphisms regulate DEGs' expression in human brain tissue. Finally, protein-protein interaction analysis and drug-gene interaction analysis were performed to assess the role of DEGs in epilepsy treatment.

RESULTS

The study found that the protein tyrosine phosphatase receptor-type O gene (PTPRO) and the growth arrest and DNA damage inducible alpha gene (GADD45A) were significantly upregulated in epileptic rats compared to controls in both datasets. Gene co-expression analysis revealed that PTPRO was co-expressed with RBP4, NDN, PAK3, FOXG1, IDS, and IDS, and GADD45A was co-expressed with LRRK2 in human brain tissue. Expression quantitative trait loci analysis suggested that epilepsy risk single-nucleotide polymorphisms could be responsible for the altered PTPRO and GADD45A expression in human brain tissue. Moreover, the protein encoded by GADD45A had a direct interaction with approved antiepileptic drug targets, and GADD45A interacts with genistein and cisplatin.

CONCLUSIONS

The results of this study highlight PTPRO and GADD45A as potential genes for the diagnosis and treatment of epilepsy.

Concerted neuron-astrocyte gene expression declines in aging and schizophrenia

Human brains vary across people and over time; such variation is not yet understood in cellular terms. Here we describe a striking relationship between people's cortical neurons and cortical astrocytes. We used single-nucleus RNA-seq to analyze the prefrontal cortex of 191 human donors ages 22-97 years, including healthy individuals and persons with schizophrenia. Latent-factor analysis of these data revealed that in persons whose cortical neurons more strongly expressed genes for synaptic components, cortical astrocytes more strongly expressed distinct genes with synaptic functions and genes for synthesizing cholesterol, an astrocyte-supplied component of synaptic membranes. We call this relationship the Synaptic Neuron-and-Astrocyte Program (SNAP). In schizophrenia and aging - two conditions that involve declines in cognitive flexibility and plasticity 1,2 - cells had divested from SNAP: astrocytes, glutamatergic (excitatory) neurons, and GABAergic (inhibitory) neurons all reduced SNAP expression to corresponding degrees. The distinct astrocytic and neuronal components of SNAP both involved genes in which genetic risk factors for schizophrenia were strongly concentrated. SNAP, which varies quantitatively even among healthy persons of similar age, may underlie many aspects of normal human interindividual differences and be an important point of convergence for multiple kinds of pathophysiology.

Rare copy-number variants as modulators of common disease susceptibility

BACKGROUND

Copy-number variations (CNVs) have been associated with rare and debilitating genomic disorders (GDs) but their impact on health later in life in the general population remains poorly described.

METHODS

Assessing four modes of CNV action, we performed genome-wide association scans (GWASs) between the copy-number of CNV-proxy probes and 60 curated ICD-10 based clinical diagnoses in 331,522 unrelated white British UK Biobank (UKBB) participants with replication in the Estonian Biobank.

RESULTS

We identified 73 signals involving 40 diseases, all of which indicating that CNVs increased disease risk and caused earlier onset. We estimated that 16% of these associations are indirect, acting by increasing body mass index (BMI). Signals mapped to 45 unique, non-overlapping regions, nine of which being linked to known GDs. Number and identity of genes affected by CNVs modulated their pathogenicity, with many associations being supported by colocalization with both common and rare single-nucleotide variant association signals. Dissection of association signals provided insights into the epidemiology of known gene-disease pairs (e.g., deletions in BRCA1 and LDLR increased risk for ovarian cancer and ischemic heart disease, respectively), clarified dosage mechanisms of action (e.g., both increased and decreased dosage of 17q12 impacted renal health), and identified putative causal genes (e.g., ABCC6 for kidney stones). Characterization of the pleiotropic pathological consequences of recurrent CNVs at 15q13, 16p13.11, 16p12.2, and 22q11.2 in adulthood indicated variable expressivity of these regions and the involvement of multiple genes. Finally, we show that while the total burden of rare CNVs-and especially deletions-strongly associated with disease risk, it only accounted for ~ 0.02% of the UKBB disease burden. These associations are mainly driven by CNVs at known GD CNV regions, whose pleiotropic effect on common diseases was broader than anticipated by our CNV-GWAS.

CONCLUSIONS

Our results shed light on the prominent role of rare CNVs in determining common disease susceptibility within the general population and provide actionable insights for anticipating later-onset comorbidities in carriers of recurrent CNVs.

O’Brien T, Perucca P, Ge Z, Anderson A. Applications for Deep Learning in Epilepsy Genetic Research

Epilepsy is a group of brain disorders characterised by an enduring predisposition to generate unprovoked seizures. Fuelled by advances in sequencing technologies and computational approaches, more than 900 genes have now been implicated in epilepsy. The development and optimisation of tools and methods for analysing the vast quantity of genomic data is a rapidly evolving area of research. Deep learning (DL) is a subset of machine learning (ML) that brings opportunity for novel investigative strategies that can be harnessed to gain new insights into the genomic risk of people with epilepsy. DL is being harnessed to address limitations in accuracy of long-read sequencing technologies, which improve on short-read methods. Tools that predict the functional consequence of genetic variation can represent breaking ground in addressing critical knowledge gaps, while methods that integrate independent but complimentary data enhance the predictive power of genetic data. We provide an overview of these DL tools and discuss how they may be applied to the analysis of genetic data for epilepsy research.