Sequencing-based studies have identified novel risk genes associated with severe epilepsies and revealed an excess of rare deleterious variation in less-severe forms of epilepsy. To identify the shared and distinct ultra-rare genetic risk factors for different types of epilepsies, we performed a whole-exome sequencing (WES) analysis of 9,170 epilepsy-affected individuals and 8,436 controls of European ancestry. We focused on three phenotypic groups: severe developmental and epileptic encephalopathies (DEEs), genetic generalized epilepsy (GGE), and non-acquired focal epilepsy (NAFE). We observed that compared to controls, individuals with any type of epilepsy carried an excess of ultra-rare, deleterious variants in constrained genes and in genes previously associated with epilepsy; we saw the strongest enrichment in individuals with DEEs and the least strong in individuals with NAFE. Moreover, we found that inhibitory GABA A receptor genes were enriched for missense variants across all three classes of epilepsy, whereas no enrichment was seen in excitatory receptor genes. The larger gene groups for the GABAergic pathway or cation channels also showed a significant mutational burden in DEEs and GGE. Although no single gene surpassed exome-wide significance among individuals with GGE or NAFE, highly constrained genes and genes encoding ion channels were among the lead associations; such genes included CACNA1G, EEF1A2, and GABRG2 for GGE and LGI1, TRIM3, and GABRG2 for NAFE. Our study, the largest epilepsy WES study to date, confirms a convergence in the genetics of severe and less-severe epilepsies associated with ultra-rare coding variation, and it highlights a ubiquitous role for GABAergic inhibition in epilepsy etiology.

DBA/2J (D2) and C57BL/6J (B6) mice exhibit differential sensitivity to seizures induced by various chemical and physical methods, with D2 mice being relatively sensitive and B6 mice relatively resistant. We conducted studies in mature D2, B6, F1, and F2 intercross mice to investigate behavioral seizure responses to pentylenetetrazol (PTZ) and to map the location of genes that influence this trait. Mice were injected with PTZ and observed for 45 min. Seizure parameters included latencies to focal clonus, generalized clonus, and maximal seizure. Latencies were used to calculate a seizure score that was used for quantitative mapping. F2 mice (n = 511) exhibited a wide range of latencies with two-thirds of the group expressing maximal seizure. Complementary statistical analyses identified loci on proximal (near D1Mit11) and distal chromosome 1 (near D1Mit17) as having the strongest and most significant effects in this model. Another locus of significant effect was detected on chromosome 5 (near D5Mit398). Suggestive evidence for additional PTZ seizure-related loci was detected on chromosomes 3, 4, and 6. Of the seizure-related loci identified in this study, those on chromosomes 1 (distal), 4, and 5 map close to loci previously identified in a similar F2 population tested with kainic acid. Results document that the complex genetic influences controlling seizure response in B6 and D2 mice are partially independent of the nature of the chemoconvulsant stimulus with a locus on distal chromosome 1 being of fundamental importance.

Cytogenic testing is routinely applied in most neurological centres for severe paediatric epilepsies. However, which characteristics of copy number variants (CNVs) confer most epilepsy risk and which epilepsy subtypes carry the most CNV burden, have not been explored on a genome-wide scale. Here, we present the largest CNV investigation in epilepsy to date with 10 712 European epilepsy cases and 6746 ancestry-matched controls. Patients with genetic generalized epilepsy, lesional focal epilepsy, non-acquired focal epilepsy, and developmental and epileptic encephalopathy were included. All samples were processed with the same technology and analysis pipeline. All investigated epilepsy types, including lesional focal epilepsy patients, showed an increase in CNV burden in at least one tested category compared to controls. However, we observed striking differences in CNV burden across epilepsy types and investigated CNV categories. Genetic generalized epilepsy patients have the highest CNV burden in all categories tested, followed by developmental and epileptic encephalopathy patients. Both epilepsy types also show association for deletions covering genes intolerant for truncating variants. Genome-wide CNV breakpoint association showed not only significant loci for genetic generalized and developmental and epileptic encephalopathy patients but also for lesional focal epilepsy patients. With a 34-fold risk for developing genetic generalized epilepsy, we show for the first time that the established epilepsy-associated 15q13.3 deletion represents the strongest risk CNV for genetic generalized epilepsy across the whole genome. Using the human interactome, we examined the largest connected component of the genes overlapped by CNVs in the four epilepsy types. We observed that genetic generalized epilepsy and non-acquired focal epilepsy formed disease modules. In summary, we show that in all common epilepsy types, 1.5–3% of patients carry epilepsy-associated CNVs. The characteristics of risk CNVs vary tremendously across and within epilepsy types. Thus, we advocate genome-wide genomic testing to identify all disease-associated types of CNVs.

Elucidation of cell subpopulations at high resolution is a key and challenging goal of single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) data analysis. Although unsupervised clustering methods have been proposed for de novo identification of cell populations, their performance and robustness suffer from the high variability, low capture efficiency and high dropout rates which are characteristic of scRNA-seq experiments. Here, we present a novel unsupervised method for Single-cell Clustering by Enhancing Network Affinity (SCENA), which mainly employed three strategies: selecting multiple gene sets, enhancing local affinity among cells and clustering of consensus matrices. Large-scale validations on 13 real scRNA-seq datasets show that SCENA has high accuracy in detecting cell populations and is robust against dropout noise. When we applied SCENA to large-scale scRNA-seq data of mouse brain cells, known cell types were successfully detected, and novel cell types of interneurons were identified with differential expression of gamma-aminobutyric acid receptor subunits and transporters. SCENA is equipped with CPU + GPU (Central Processing Units + Graphics Processing Units) heterogeneous parallel computing to achieve high running speed. The high performance and running speed of SCENA combine into a new and efficient platform for biological discoveries in clustering analysis of large and diverse scRNA-seq datasets.

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Transcriptomic studies of bulk neural tissue homogenates from persons with schizophrenia and controls have identified differential expressed genes in multiple brain regions. However, the heterogeneous nature prevents identification of relevant cell types. This study utilized single-nuclei transcriptomics of ∼311,000 nuclei from frozen human postmortem dorsolateral prefrontal cortex samples from individuals with schizophrenia (n = 14) and controls (n = 16). 2,846 differential expression events were identified in 2,195 unique genes in 19 of 24 transcriptomically-distinct cell populations. ∼97% of differential expressed genes occurred in five neuronal cell types, with ∼63% occurring in a subtype of PVALB+ inhibitory neurons and HTR2C+ layer V excitatory neurons. Differential expressed genes were enriched for genes associated with schizophrenia GWAS loci and cluster-specific changes in canonical pathways, upstream regulators and causal networks were identified. These results demonstrate cell type specific changes in the context of schizophrenia.

Better drugs are needed for common epilepsies. Drug repurposing offers the potential of significant savings in the time and cost of developing new treatments. In order to select the best candidate drug(s) to repurpose for a disease, it is desirable to predict the relative clinical efficacy that drugs will have against the disease. Common epilepsy can be divided into different types and syndromes. Different antiseizure medications are most effective for different types and syndromes of common epilepsy. For predictions of antiepileptic efficacy to be clinically translatable, it is essential that the predictions are specific to each form of common epilepsy, and reflect the patterns of drug efficacy observed in clinical studies and practice. These requirements are not fulfilled by previously published drug predictions for epilepsy. We developed a novel method for predicting the relative efficacy of drugs against any common epilepsy, by using its Genome Wide Association Study summary statistics and drugs’ activity data. The methodological advancement in our technique is that the drug predictions for a disease are based upon drugs’ effects on the function and abundance of proteins, and the magnitude and direction of those effects, relative to the importance, degree and direction of the proteins’ dysregulation in the disease. We used this method to predict the relative efficacy of all drugs, licensed for any condition, against each of the major types and syndromes of common epilepsy. Our predictions are concordant with findings from real-world experience and randomised clinical trials. Our method predicts the efficacy of existing antiseizure medications against common epilepsies; in this prediction, our method outperforms the best alternative existing method: area under receiver operating characteristic curve (mean ± standard deviation) 0.83 ± 0.03 and 0.63 ± 0.04, respectively. Importantly, our method predicts which antiseizure medications are amongst the more efficacious in clinical practice, and which antiseizure medications are amongst the less efficacious in clinical practice, for each of the main syndromes of common epilepsy, and it predicts the distinct order of efficacy of individual antiseizure medications in clinical trials of different common epilepsies. We identify promising candidate drugs for each of the major syndromes of common epilepsy. We screen five promising predicted drugs in an animal model: each exerts a significant dose-dependent effect upon seizures. Our predictions are a novel resource for selecting suitable candidate drugs that could potentially be repurposed for each of the major syndromes of common epilepsy. Our method is potentially generalizable to other complex diseases.

Background Prescription opioids (POs) are commonly used to treat moderate to severe chronic pain in the health system setting. Although they improve quality of life for many patients, more work is needed to identify both the clinical and genetic factors that put certain individuals at high risk for developing opioid use disorder (OUD) following use of POs for pain relief. With a greater understanding of important risk factors, physicians will be better able to identify patients at highest risk for developing OUD for whom non-opioid alternative therapies and treatments should be considered. Methods We are conducting a prospective observational study that aims to identify the clinical and genetic factors most stongly associated with OUD. The study design leverages an existing biobank that includes whole exome sequencing and array genotyping. The biobank is maintained within an integrated health system, allowing for the large-scale capture and integration of genetic and non-genetic data. Participants are enrolled into the health system biobank via informed consent and then into a second study that focuses on opioid medication use. Data capture includes validated self-report surveys measuring addiction severity, depression, anxiety, and nicotine use, as well as additional clinical, prescription, and brain imaging data extracted from electronic health records. Discussion We will harness this multimodal data capture to establish meaningful patient phenotypes in order to understand the genetic and non-genetic contributions to OUD.