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Post by Admin on Sept 19, 2023 20:58:00 GMT
Sex-specific analyses There are known sex-related patterns in the epidemiology of epilepsy. Although females have a marginally lower incidence of epilepsy than males, GGE is known to occur more frequently in females34. To test whether this sex divergence has a genetic basis, we performed sex-specific GWAS for ‘all’, GGE and FE (Supplementary Figs. 17–19). These analyses revealed one female-specific genome-wide significant signal at 10q24.32 (lead SNP: rs72845653), containing KCNIP2. This locus was also implicated in our main GGE meta-analysis (lead SNP: rs11191156); however, the lead SNPs of these two signals show low allelic correlation (r2 = 0.05; D′ = 0.87). Interestingly, the direction of effect of this signal is opposite in females and males. This sex difference is further corroborated by significant sex heterogeneity (P = 1.54 × 10−8) and sex-differentiated GWAS (P = 5.6 × 10−9) (ref. 35). Sex-related differences in transcription levels in human heart have previously been reported for KCNIP2 (ref. 36). We did not find any sex-divergent signals for ‘all’ or FE. These analyses were limited by a reduction in sample size and prone to random fluctuation.
We used LDSC to assess the genetic correlation between male-only and female-only GWAS. The male and female GWAS of ‘all epilepsy,’ FE and GGE were strongly genetically correlated (all rG > 0.9), and none of these correlations were significantly different from 1 (all P > 0.05). These results suggest that, with the exception of the female-specific 10q24.32 signal, the overall genetic basis of common epilepsy appears largely similar between males and females.
Genetic overlap between epilepsy and other phenotypes To explore the genetic overlap of epilepsy with other diseases, we first used the GWAS Catalog37 to cross-reference the 26 genome-wide epilepsy loci with other traits with significant associations (P < 5 × 10−8) for the same SNP, or SNPs in strong LD with our lead SNPs (as detailed in Table 1). This analysis revealed 18 likely pleiotropic loci, with previous associations reported across a variety of traits, the most common being cognitive, sleep, psychiatric, coronary and blood cell-related (Supplementary Fig. 20). The remaining eight loci appear to be specific to epilepsy (3p22.3, 4p12, 5q31.2, 7p14.1, 8q23.1, 9q21.13, 21q21.1 and 21q22.1).
We then performed genetic correlation analyses between 18 selected traits (Supplementary Table 12) and ‘all’, GGE and FE using LDSC13. The selected traits had either, or a combination of, epilepsy as a common comorbidity or pleiotropic loci shared with epilepsy. Significant correlations (P < 0.05/54 = 0.0009) were found with febrile seizures, stroke, headache, ADHD, type 2 diabetes and intelligence (Fig. 2).
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Post by Admin on Sept 21, 2023 20:52:13 GMT
Gene prioritization We combined ten methods to prioritize the most likely biological candidate gene within each genome-wide significant locus. For each gene in each locus, we assessed the following criteria:
Missense: we assessed whether the SNPs tagged in the genome-wide significant locus contained an exonic missense variant in the gene, as annotated by ANNOVAR v2017-07-17.
TWAS: we assessed whether imputed gene expression was significantly associated with the epilepsy phenotype, based on the FUSION TWAS as described above, Bonferroni corrected for each mapped gene with expression information.
SMR: we assessed whether the gene had a significant SMR association with the epilepsy phenotype, based on the SMR analyses as described above, Bonferroni corrected for each mapped gene with expression information.
MAGMA: we assessed whether the gene was significantly associated with the epilepsy phenotype through a GWGAS analysis, Bonferroni corrected for each mapped gene.
PoPS: we calculated the polygenic priority score (PoPS)85, a method that combines GWAS summary statistics with biological pathways, gene expression and protein–protein interaction data, to pinpoint the most likely causal genes. We scored the gene with the highest PoPS score within each locus.
Brain expression: for each mapped gene, we calculated the mean expression in all brain and nonbrain tissues based on data from the GTEx project v8 (ref. 86). Next, we assessed whether the gene was more strongly expressed in brain tissues than nonbrain tissues, by comparing the average expression in all brain tissues with all nonbrain tissues.
Brain-coX: we assessed whether genes were prioritized as co-expressed with established epilepsy genes in more than a third of brain tissue resources used, using the tool brain-coX (Supplementary Fig. 26)87.
Target of AED: we assessed whether the gene is a known target of an anti-epileptic drug, as detailed in the drug–gene interaction database (www.DGidb.com; accessed on 26-11-2021) and a list of drug targets from a recent publication (Supplementary Data 10)88.
Knockout mouse: we assessed whether a knockout of the gene in a mouse model results in a nervous system (phenotype ID: MP:0003631) or a neurological/behavior phenotype (MP:0005386) in the Mouse Genome Informatics database (http://www.informatics.jax.org; accessed on 26-11-2021).
Monogenic epilepsy gene: we evaluated whether the gene is listed as a monogenic epilepsy gene, in a curated list maintained by the Epilepsy Research Center at the University of Melbourne89 (Supplementary Data 10).
Similar to previous studies4,90, we scored all genes based on the number of criteria being met (range: 0–10; all criteria had an equal weight). The gene with the highest score was chosen as the most likely implicated gene (see Supplementary Data 6 for a complete list of scores for all genes in each locus). We implicated both genes if they had an identical, highest score. We calculated Pearson correlation coefficients between the ten criteria (Supplementary Table 16) and note that most correlations were low (range: −0.13 to 0.39), suggesting that they convey complementary information.
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Post by Admin on Sept 24, 2023 20:19:13 GMT
GWAS in epilepsies ascertained from population biobanks Finally, we leveraged the data from several large-scale population biobanks and from deCODE genetics to explore the consistency of the epilepsy loci in cohorts that were less deeply phenotyped (total cases n = 21,734, total controls n = 1,023,989, phenotyped using International Classification of Diseases (ICD) codes; Methods; Supplementary Table 14). Forest plots showed a consistent direction of effect between the biobanks and our primary GWAS for all biobank-genotyped genome-wide significant top SNPs of the ‘all epilepsy’ GWAS and for all but one GGE top SNP (Supplementary Figs. 22 and 23). Although the biobank and deCODE genetics-specific GWAS did not identify any genome-wide significant loci for GGE or ‘all epilepsy,’ one significant locus at 2q22.1 (nearest gene, NXPH2) emerged for FE (Supplementary Fig. 24).
Meta-analysis of the biobank and deCODE genetics summary statistics with those from the primary epilepsy GWAS identified seven significant loci for the ‘all epilepsy’ phenotype. Six of these signals were previously identified in the primary ‘all epilepsy’ (n = 4) or the ‘GGE’ GWAS (n = 2). One locus (2q12.1) was new. The combined biobank and deCODE genetics meta-analysis for GGE identified five new loci, but four loci from our primary GWAS fell below the threshold of significance (Supplementary Fig. 25). The combined FE meta-analysis showed no significant associations. LDSC between the biobank/deCODE genetics and the primary GWAS results showed genetic correlations ranging between 0.31 and 0.74 (Supplementary Table 15).
Discussion In this study, we leveraged a substantial increase in sample size to uncover 26 common epilepsy risk loci, of which 16 have not been reported previously. Using a combination of ten post-GWAS analysis methods, we pinpointed 29 genes that most likely underlie these signals of association. These signals showed enrichment throughout the brain and indicate an important role for synapse biology in excitatory as well as inhibitory neurons. Drug prioritization from the genetic data highlighted licensed ASMs, ranked the ASMs broadly in line with clinical experience and pointed to drugs for potential repurposing. These findings further our understanding of the pathophysiology of common epilepsies and provide new leads for therapeutics.
The 26 associated loci included some notable monogenic epilepsy genes. These include the calcium channel gene CACNA2D2, an established epileptic encephalopathy gene42 that is directly targeted by ten currently licensed drugs, including two ASMs (gabapentin and pregabalin) as well as the Parkinson’s disease drug safinamide and the nonsteroidal anti-inflammatory drug celecoxib. Both safinamide and celecoxib have evidence of antiseizure activity43,44. SCN8A, which encodes a voltage-gated sodium channel, is an established epileptic encephalopathy gene and is associated here with common epilepsies. Nav1.6 (encoded by SCN8A) is targeted by commonly used sodium channel-blocking drugs, the most efficacious ASMs for people with monogenic SCN8A-related epilepsies, that are often caused by gain-of-function pathogenic variants45. Additional drugs targeting Nav1.6 include safinamide and quinidine. RYR2 encodes a ryanodine receptor, is an established cardiac disorder gene, has recently been implicated in epilepsy46,47 and is targeted by caffeine as well simvastatin, atorvastatin and carvedilol. The acetylcholine receptor gene CHRM3 has been previously associated with epilepsy48 and is targeted by drugs including solifenacin, used to treat urinary incontinence.
We found that GGE, in particular, has a strong contribution from common genetic variation. When analyzing individual GGE syndromes, we found that up to 90% of liability is attributable to common variants in the JAE subtype, making it among the highest of over 700 traits reported in a large GWAS atlas49 (albeit with relatively large CIs; Supplementary Table 10). The heritability estimates decrease to 40% for the collective GGE phenotype, possibly due to increased heterogeneity from combining syndromes with pleiotropic as well as syndrome-specific risk loci. Although statistical power drastically decreased when assessing specific GGE syndromes, three loci appeared specific to JME. These findings highlight the unique genetic architecture of the subtypes of common epilepsies, which are characterized by a high degree of both shared and syndrome-specific genetic risk.
In contrast to GGE, for FEs, we found only a minor contribution of common variants, with no variant reaching genome-wide significance. It would seem that FEs, as a group, are far more heterogeneous than GGE, lack (common-variation) loci with high effect sizes, have a higher degree of polygenicity and/or have a lower contribution of common heritable risk variation. Our attempt to mitigate this heterogeneity by performing subtype analysis contrasted with the results from GGE, suggesting different genetic architectures, consistent with the experience from studies of common9 and rare5 genetic variation and polygenic risk score analyses6. There is also emerging evidence for a substantial role of noninherited, somatic mutations in FEs50.
This work highlights the challenges of working with epilepsy cohorts ascertained through large biobanking initiatives. Accurate classification of epilepsy requires a combination of clinical features, electrophysiology and neuroimaging. Such details were absent from the biobanks we worked with. Rather, phenotypes were generally limited to ICD codes, which are prone to misclassification51. Population biobanks are also probably ascertaining milder epilepsies that are responsive to treatment, contrasting with the enrichment for refractory epilepsies at tertiary referral centers.
Moreover, a proportion of adults with epilepsy have an acquired brain lesion, such as stroke, tumors or head trauma. Biobanks typically provide self-reported clinical information and codes from primary care and inpatient hospital care episodes, but not neurological specialist outpatient records that would indicate whether previous brain insults were considered relevant to epilepsy. As a result, the inclusion of the biobank data appeared to introduce more heterogeneity. This contrasts with genetic mapping of other polygenic diseases like type 2 diabetes and migraine, which are relatively easy and reliable to diagnose and classify, resulting in a great increase in GWAS loci when including data from the same biobanks as included in our study52,53.
We found enrichment of GGE variants in brain-expressed genes, involving excitatory and inhibitory neurons, but not any other brain cell type. This contrasts with other neurological diseases. For example, microglia are involved in Alzheimer’s disease54 and multiple sclerosis55, whereas migraine does not appear to have brain cell specificity53. We further refine this signal by showing the involvement of synapse biology, primarily intracellular signal transduction and synapse excitability. These findings suggest an important role of synaptic processes in excitatory and inhibitory neurons throughout the brain, which could be a potential therapeutic target. Indeed, synaptic vesicle transport is a known target of the ASMs levetiracetam and brivaracetam56.
We confirmed that our GWAS-identified genes had substantial overlap with monogenic epilepsy genes. A similar convergence of common and rare variant associations has been observed for other neurological neuropsychiatric conditions including schizophrenia57 and ALS58. The genes prioritized in our GWAS signals also overlapped with known targets of current ASMs4, and we have provided a list of other drugs that directly target these genes. Moreover, using a systems-based approach39, we highlight drugs that are predicted to be efficacious when repurposed for epilepsy, based on their ability to perturb function and abundance in gene expression. Insights from GWAS of epilepsy have the potential to accelerate the development of new treatments via the identification of promising drug repurposing candidates for clinical trials59. We anticipate that follow-up studies of the highlighted drugs in this study could show clinical efficacy in epilepsy treatment.
In summary, these new data reveal markedly different genetic architectures between the milder and more common focal and generalized epilepsies, provide new biological insights to disease etiology and highlight drugs with predicted efficacy when repurposed for epilepsy treatment.
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Post by Admin on Sept 25, 2023 20:23:55 GMT
Myoclonic Seizures
Myoclonic seizures are characterized by brief, jerking spasms of a muscle or muscle group. They often occur with atonic seizures, which cause sudden muscle limpness.
WHAT YOU NEED TO KNOW The word “myoclonic” combines the Greek prefix for muscle — “myo” — with “clonus,” which means twitching. Myoclonic seizures do not cause any loss of awareness — the person is awake and conscious during the seizure. Infantile spasms and Lennox-Gastaut syndrome are two of the epilepsy syndromes characterized by myoclonic seizures, among other symptoms. Symptoms of Myoclonic Seizures A person having a myoclonic seizure experiences a sudden increases in muscle tone as if they have been jolted with electricity.
The mechanism is similar to a myoclonic jerk, the sudden spasm occasionally experienced by people as they are falling asleep. However, unlike myoclonic jerks, the “jolts” of myoclonic seizures occur in bouts.
Infantile Spasms This type of myoclonic epilepsy typically begins between the ages of 3 and 12 months and may persist for several years. Infantile spasms typically consist of a sudden jerk followed by stiffening.
During the characteristic seizures (spasms), the child’s arms fling outward as the knees pull up and the body bends forward. Each seizure lasts only a second or two but multiple episodes can occur close together in a series — or cluster. Sometimes the spasms are mistaken for colic, but colic cramps do not typically occur in a series.
Infantile spasms are most common just after waking up and rarely occur during sleep. This particularly severe form of epilepsy can have lasting effects on the child and should be treated without delay.
Lennox-Gastaut Syndrome This rare epilepsy syndrome affects young children and includes myoclonic seizures of the neck, shoulders, upper arms and face, along with other types of seizures.
Progressive Myoclonic Epilepsy Another rare seizure disorder, progressive myoclonic epilepsy, is characterized by a combination of myoclonic and tonic-clonic (grand mal) seizures. Treatment may provide relief for a while, but the patient’s condition worsens over time.
Treatment for Myoclonic Seizures Like other forms of seizures and epilepsy, myoclonic seizures are best addressed through an individualized approach. The doctor may recommend treatment with anti-seizure medication, nerve stimulation, dietary therapy or surgery.
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Post by Admin on Feb 8, 2024 15:01:13 GMT
Scientists at the Francis Crick Institute have found a new treatment target for CDKL5 deficiency disorder (CDD), one of the most common types of genetic epilepsy. CDD causes seizures and impaired development in children, and medications are limited to managing symptoms rather than tackling the root cause of the disease. The disorder involves losing the function of a gene producing the CDKL5 enzyme, which phosphorylates proteins, meaning it adds an extra phosphate molecule to alter their function. Following recent research from the same lab showing that a calcium channel could be a target for therapy for CDD, the team has now identified a new way to potentially treat CDD by boosting another enzyme's activity to compensate for the loss of CDKL5. In research published in Molecular Psychiatry, the scientists studied mice that don't make the CDKL5 enzyme. These mice show similar symptoms to people with CDD, such as impaired learning or social interaction. The researchers first identified that CDKL5 is active in nerve cells in mice but not in another type of brain cell called an astrocyte. In the nerve cells, they measured the level of phosphorylation of EB2, a molecule known to be targeted by CDKL5, to understand what happens when CDKL5 isn't produced. Interestingly, even in mice that don't produce CDKL5, there was still some EB2 phosphorylation taking place, which suggested that another similar enzyme must also be able to phosphorylate it. By looking at enzymes similar to CDKL5, the researchers identified that one called CDKL2 also targets EB2 and is present in human neurons. In mice without both CDKL5 and CDKL2, the remaining EB2 phosphorylation almost fully dropped off. The researchers concluded that although most activity comes from CDKL5, about 15% is from CDKL2, and the remaining < 5% from another enzyme yet to be identified. Their research suggests that increasing the level of CDKL2 in people who are deficient in CDKL5 could potentially treat some of the effects on the brain in early development. Sila Ultanir, Group Leader of the Kinases and Brain Development Laboratory at the Crick, said, "CDD is a devastating condition that impacts young children from birth, and we don't know a huge amount about why losing this one enzyme is so disastrous for the developing brain. Through this research, we've identified a potential way to compensate for the loss of CDKL5. If we can increase levels of CDKL2, we might one day be able to stop symptoms from developing or getting worse." The researchers are now investigating whether mice without CDKL5 can be treated by stimulating their brain cells to produce more CDKL2. The lab is also working with biotechnology companies to identify molecules that increase CDKL2 for potential new medicines for CDD. Margaux Silvestre, former Ph.D. student at the Crick and now postdoctoral researcher at the Max Planck Institute for Brain Research in Frankfurt, said, "Our discoveries offer fresh insights into the expression and regulation of CDKL5 in the brain. Moreover, the identification of CDKL2 as a potential compensatory enzyme provides hope for uncovering better treatments that could truly make a difference in the lives of the children with this devastating condition. This research owes its success to all the authors involved in the publication but also the unwavering support we received from the technical teams at the Crick—a big shoutout to them." More information: Margaux Silvestre et al, Cell-type specific expression, regulation and compensation of CDKL5 activity in mouse brain. Molecular Psychiatry. (2024). DOI: 10.1038/s41380-024-02434-7 www.nature.com/articles/s41380-024-02434-7
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