A recurrent de novo splice site variant involving DNM1 exon 10a causes developmental and epileptic encephalopathy through a dominant-negative mechanism

Expanding the phenotypic spectrum of DNM1-related disorders: novel GTPase domain variants and their diverse neurological outcomes

BACKGROUND

Pathogenic DNM1 variants cause early-onset developmental and epileptic encephalopathy (DEE). The GTPase domain of the DNM1 protein has the most commonly affected sites.

AIM

This study aimed to delineate additional patients with DNM1-related disorders harboring novel GTPase domain variants.

METHODS

Trio whole-exome sequencing was performed on three Chinese probands with suspected encephalopathy, and Sanger sequencing was used to confirm the variants. Detailed evaluations were used to assess clinical features. Variant plasmids were constructed in vitro and transfected into cells, and the expression of mutant proteins was evaluated using western blotting (WB).

RESULTS

Three de novo heterozygous DNM1 variants were detected in the GTPase domain, namely, NM_004408.4: c.112_120delinsAGCGGCCAC, (p.Gly38_Gln40delinsSerGlyHis), c.457G > A, (p.Glu153Lys), and c.193 A > C, (p.Thr65Pro) in Patients 1, 2, and 3, respectively. Patients 2 and 3 exhibited typical DEE phenotypes with early-onset refractory seizures, profound developmental impairment, intellectual disability, and abnormal electroencephalography findings. However, Patient 1 did not have seizures and her clinical symptoms were autism features, mild hearing loss, subtle changes in the brain, and developmental delays. WB indicated that the expression of plasmids carrying the p.Thr65Pro and p.Glu153Lys variants was not significantly different from that in the wild-type control group and that the expression of the p.Gly38_Gln40delinsSerGlyHis plasmid was elevated.

CONCLUSIONS

This study expands the genetic and phenotypic spectrum of DNM1-associated disorders and reveals that de novo pathogenic variants in the GTPase domain can lead to divergent neurological outcomes ranging from infantile epileptic encephalopathy syndromes to predominant developmental delays without seizures.

Precision Therapeutics in Lennox-Gastaut Syndrome: Targeting Molecular Pathophysiology in a Developmental and Epileptic Encephalopathy

Lennox-Gastaut syndrome (LGS) is a severe childhood-onset developmental and epileptic encephalopathy characterized by multiple drug-resistant seizure types, cognitive impairment, and distinctive electroencephalographic patterns. Current treatments primarily focus on symptom management through antiseizure medications (ASMs), dietary therapy, epilepsy surgery, and neuromodulation, but often fail to address the underlying pathophysiology or improve cognitive outcomes. As genetic causes are identified in 30-40% of LGS cases, precision therapeutics targeting specific molecular mechanisms are emerging as promising disease-modifying approaches. This narrative review explores precision therapeutic strategies for LGS based on molecular pathophysiology, including channelopathies (SCN2A, SCN8A, KCNQ2, KCNA2, KCNT1, CACNA1A), receptor and ligand dysfunction (GABA/glutamate systems), cell signaling abnormalities (mTOR pathway), synaptopathies (STXBP1, IQSEC2, DNM1), epigenetic dysregulation (CHD2), and CDKL5 deficiency disorder. Treatment modalities discussed include traditional ASMs, dietary therapy, targeted pharmacotherapy, antisense oligonucleotides, gene therapy, and the repurposing of existing medications with mechanism-specific effects. Early intervention with precision therapeutics may not only improve seizure control but could also potentially prevent progression to LGS in susceptible populations. Future directions include developing computable phenotypes for accurate diagnosis, refining molecular subgrouping, enhancing drug development, advancing gene-based therapies, personalizing neuromodulation, implementing adaptive clinical trial designs, and ensuring equitable access to precision therapeutic approaches. While significant challenges remain, integrating biological insights with innovative clinical strategies offers new hope for transforming LGS treatment from symptomatic management to targeted disease modification.

Phenotypic analysis of 11,125 trio exomes in neurodevelopmental disorders

Genomic sequencing is widely used to identify causative genetic changes in neurodevelopmental disorders, such as autism, intellectual disability, and epilepsy. Most neurodevelopmental disorders also present with diverse clinical features, and delineating the interaction between causative genetic changes and phenotypic features is a key prerequisite for developing personalized therapies. However, assessing clinical features at a scale that parallels genomic sequencing remains challenging. Here, we standardize phenotypic information across 11,125 patient-parent trios with exome sequencing data using biomedical ontologies, analyzing 674,767 phenotypic terms. We find that individuals with de novo variants in 69 out of 261 neurodevelopmental genes exhibit statistically significant clinical similarities with distinct phenotypic fingerprints. We also observe that phenotypic relatedness follows a gradient, spanning from highly similar to dissimilar phenotypes, with intra-gene similarities suggesting clinically distinct subgroups for seven neurodevelopmental genes. For most genetic etiologies, only a small subset of highly phenotypically similar individuals carried de novo variants in the same gene, highlighting the heterogeneous and complex clinical landscape of neurodevelopmental disorders. Our study provides a large-scale overview of the dynamic relationship between genotypes and phenotypes in neurodevelopmental disorders, underscoring how the inherent complexity of these conditions can be deciphered through approaches that integrate genomic and phenotypic data.

Exploring the effects of missense mutations on protein thermodynamics through structure-based approaches: findings from the CAGI6 challenges

Missense mutations are known contributors to diverse genetic disorders, due to their subtle, single amino acid changes imparted on the resultant protein. Because of this, understanding the impact of these mutations on protein stability and function is crucial for unravelling disease mechanisms and developing targeted therapies. The Critical Assessment of Genome Interpretation (CAGI) provides a valuable platform for benchmarking state-of-the-art computational methods in predicting the impact of disease-related mutations on protein thermodynamics. Here we report the performance of our comprehensive platform of structure-based computational approaches to evaluate mutations impacting protein structure and function on 3 challenges from CAGI6: Calmodulin, MAPK1 and MAPK3. Our stability predictors have achieved correlations of up to 0.74 and AUCs of 1 when predicting changes in ΔΔG for MAPK1 and MAPK3, respectively, and AUC of up to 0.75 in the Calmodulin challenge. Overall, our study highlights the importance of structure-based approaches in understanding the effects of missense mutations on protein thermodynamics. The results obtained from the CAGI6 challenges contribute to the ongoing efforts to enhance our understanding of disease mechanisms and facilitate the development of personalised medicine approaches.

Genome Sequencing of Rare Disease Patients Through the Korean Regional Rare Disease Diagnostic Support Program

Affecting fewer than 20,000 people as defined in South Korea, rare diseases pose significant diagnostic challenges due to their diverse manifestations and genetic heterogeneity. Genome sequencing (GS) offers a promising solution by enabling simultaneous screening for thousands of rare genetic disorders. This study explores the diagnostic utility and necessity of GS within the government-funded Korean Regional Rare Disease Diagnostic Support Program (KR-RDSP), a collaborative initiative involving 11 regional rare disease centers across Korea. The program was launched as a proof-of-concept study in 2023 to equip the genetic clinics with a diagnostic tool to expedite the diagnoses for rare disease patients who reside outside the urban Seoul region where diagnostic resources are limited. The study leveraged GS to diagnose a cohort of 400 patients exhibiting a wide spectrum of symptoms. The overall diagnostic yield was 36.3% (145/400), with 4.8% (7/145) of the diagnosed patients being reported with variants that could not have been identified by chromosomal microarray or exome sequencing (ES), highlighting the added value of comprehensive genomic analysis. The implementation of a centralized GS analysis system streamlined the diagnostic process, enabling timely reporting within a reasonable turnaround time of ≤ 35 days. Segregation analysis by Sanger sequencing played a crucial role in confirming or reclassifying variant pathogenicity by elucidating inheritance patterns. Here, we summarize diagnostic statistics from the 400 GS dataset gathered from June 2023 to December 2023 and show interesting and informative case examples that illustrate the diagnostic efficacy of GS, highlighting its ability to uncover elusive genetic etiologies and provide personalized treatment insights. The study also highlights the successful implementation of the program for the 11 regional rare disease centers across Korea with a practical workflow, comprehensive testing, comparable diagnostic yield to previous reports, and, most importantly, reasonable turnaround time.

Protein isoform-centric therapeutics: expanding targets and increasing specificity

Most protein-coding genes produce multiple protein isoforms; however, these isoforms are commonly neglected in drug discovery. The expression of protein isoforms can be specific to a disease, tissue and/or developmental stage, and this specific expression can be harnessed to achieve greater drug specificity than pan-targeting of all gene products and to enable improved treatments for diseases caused by aberrant protein isoform production. In recent years, several protein isoform-centric therapeutics have been developed. Here, we collate these studies and clinical trials to highlight three distinct but overlapping modes of action for protein isoform-centric drugs: isoform switching, isoform introduction or depletion, and modulation of isoform activity. In addition, we discuss how protein isoforms can be used clinically as targets for cell type-specific drug delivery and immunotherapy, diagnostic biomarkers and sources of cancer neoantigens. Collectively, we emphasize the value of a focus on isoforms as a route to discovering drugs with greater specificity and fewer adverse effects. This approach could enable the targeting of proteins for which pan-inhibition of all isoforms is toxic and poorly tolerated.

Advances in big data and omics: Paving the way for discovery in childhood epilepsies

The insights gained from big data and omics approaches have transformed the field of childhood genetic epilepsy. With an increasing number of individuals receiving genetic testing for seizures, we are provided with an opportunity to identify clinically relevant subgroups and extract meaningful observations from this large-scale clinical data. However, the volume of data from electronic medical records and omics (e.g., genomics, transcriptomics) is so vast that standardized methods, such as the Human Phenotype Ontology, are necessary for reliable and comprehensive characterization. Here, we explore the integration of clinical and omics data, highlighting how these approaches pave the way for discovery in childhood epilepsies.

Splicing variant of WDR37 in a case of Neurooculocardiogenitourinary syndrome

BACKGROUND

Neurooculocardiogenitourinary syndrome (NOCGUS), a multisystemic syndrome characterized by motor disorder, intellectual disability, seizures, abnormal brain structure, ocular diseases, and cardiac diseases, has been reported with missense variant of WD repeat-containing protein 37 (WDR37) in humans. This report aimed to identify the cause of NOCGUS in an affected patient.

CASE PRESENTATION

We identified a de novo intronic 4-bp deletion of WDR37, c.727-27_727-24del, which were predicted to cause abnormal splicing by SpliceAI, in the patient with NOCGUS. Reverse transcription polymerase chain reaction (RT-PCR) revealed intron retention of 63 base pairs before exon 10 in messenger RNA, which was predicted to insert 21 additional aberrant amino acids (p.S242_I243insLCQKKLKISRKCLFWPSLWQQ). The patient had novel phenotypes, anal atresia, and polycystic kidney, in addition to intellectual disability, seizures, cerebellar vermian anomaly, and coloboma, which are typical in NOCGUS. We did not observe motor impairments or cardiovascular anomalies.

CONCLUSION

This is the first reported case of NOCGUS with the splicing variant of WDR37, which manifests with distinctive but variable features. Our findings may expand a possible phenotypic expression of NOCGUS.

Abnormal axonal development and severe epileptic phenotype in Dynamin-1 (DNM1) encephalopathy

Dynamin-1 (DNM1) is involved in synaptic vesicle recycling, and DNM1 mutations can lead to developmental and epileptic encephalopathy. The neuroimaging of DNM1 encephalopathy has not been reported in detail. We describe a severe phenotype of DNM1 encephalopathy showing characteristic neuroradiological features. In addition, we reviewed previously reported cases who have DNM1 pathogenic variants with white matter abnormalities. Our case presented drug-resistant seizures from 1 month of age and epileptic spasms at 2 years of age. Brain MRI showed no progression of myelination, progression of diffuse cerebral atrophy, and a thin corpus callosum. Proton magnetic resonance spectroscopy showed a decreased N-acetylaspartate peak and diffusion tensor imaging presented with less pyramidal decussation. Whole-exome sequencing revealed a recurrent de novo heterozygous variant of DNM1. So far, more than 50 cases of DNM1 encephalopathy have been reported. Among these patients, delayed myelination occurred in two cases of GTPase-domain DNM1 encephalopathy and in six cases of middle-domain DNM1 encephalopathy. The neuroimaging findings in this case suggest inadequate axonal development. DNM1 is involved in the release of synaptic vesicles with the inhibitory transmitter GABA, suggesting that GABAergic neuron dysfunction is the mechanism of refractory epilepsy in DNM1 encephalopathy. GABA-mediated signaling mechanisms play important roles in axonal development and GABAergic neuron dysfunction may be cause of white matter abnormalities in DNM1 encephalopathy.

Characterizing and predicting ccRCC-causing missense mutations in Von Hippel-Lindau disease

BACKGROUND

Mutations within the Von Hippel-Lindau (VHL) tumor suppressor gene are known to cause VHL disease, which is characterized by the formation of cysts and tumors in multiple organs of the body, particularly clear cell renal cell carcinoma (ccRCC). A major challenge in clinical practice is determining tumor risk from a given mutation in the VHL gene. Previous efforts have been hindered by limited available clinical data and technological constraints.

METHODS

To overcome this, we initially manually curated the largest set of clinically validated VHL mutations to date, enabling a robust assessment of existing predictive tools on an independent test set. Additionally, we comprehensively characterized the effects of mutations within VHL using in silico biophysical tools describing changes in protein stability, dynamics and affinity to binding partners to provide insights into the structure-phenotype relationship. These descriptive properties were used as molecular features for the construction of a machine learning model, designed to predict the risk of ccRCC development as a result of a VHL missense mutation.

RESULTS

Analysis of our model showed an accuracy of 0.81 in the identification of ccRCC-causing missense mutations, and a Matthew's Correlation Coefficient of 0.44 on a non-redundant blind test, a significant improvement in comparison to the previous available approaches.

CONCLUSION

This work highlights the power of using protein 3D structure to fully explore the range of molecular and functional consequences of genomic variants. We believe this optimized model will better enable its clinical implementation and assist guiding patient risk stratification and management.

AI-Driven Enhancements in Drug Screening and Optimization

The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, quality, and efficacy of the end product, streamlining these efforts toward compounds with success potential is pivotal for a more efficient and cost-effective process. The use of artificial intelligence (AI) within the pharmaceutical industry aims at just this, and has applications in preclinical screening for biological activity, optimization of pharmacokinetic properties for improved drug formulation, early toxicity prediction which reduces attrition, and pre-emptively screening for genetic changes in the biological target to improve therapeutic longevity. Here, we present a series of in silico tools that address these applications in small molecule development and describe how they can be embedded within the current pharmaceutical development pipeline.

Benchmarking splice variant prediction algorithms using massively parallel splicing assays

BACKGROUND

Variants that disrupt mRNA splicing account for a sizable fraction of the pathogenic burden in many genetic disorders, but identifying splice-disruptive variants (SDVs) beyond the essential splice site dinucleotides remains difficult. Computational predictors are often discordant, compounding the challenge of variant interpretation. Because they are primarily validated using clinical variant sets heavily biased to known canonical splice site mutations, it remains unclear how well their performance generalizes.

RESULTS

We benchmark eight widely used splicing effect prediction algorithms, leveraging massively parallel splicing assays (MPSAs) as a source of experimentally determined ground-truth. MPSAs simultaneously assay many variants to nominate candidate SDVs. We compare experimentally measured splicing outcomes with bioinformatic predictions for 3,616 variants in five genes. Algorithms' concordance with MPSA measurements, and with each other, is lower for exonic than intronic variants, underscoring the difficulty of identifying missense or synonymous SDVs. Deep learning-based predictors trained on gene model annotations achieve the best overall performance at distinguishing disruptive and neutral variants, and controlling for overall call rate genome-wide, SpliceAI and Pangolin have superior sensitivity. Finally, our results highlight two practical considerations when scoring variants genome-wide: finding an optimal score cutoff, and the substantial variability introduced by differences in gene model annotation, and we suggest strategies for optimal splice effect prediction in the face of these issues.

CONCLUSION

SpliceAI and Pangolin show the best overall performance among predictors tested, however, improvements in splice effect prediction are still needed especially within exons.

Genotypes and phenotypes of DNM1 encephalopathy

BACKGROUND

Variants in the dynamin-1 (DNM1) gene typically cause synaptopathy, leading to developmental and epileptic encephalopathy (DEE). We aimed to determine the genotypic and phenotypic spectrum of DNM1 encephalopathy beyond DEE.

METHODS

Electroclinical phenotyping and genotyping of patients with a DNM1 variant were conducted for patients undergoing next-generation sequencing at our centre, followed by a systematic review.

RESULTS

Six patients with heterozygous DNM1 variants were identified in our cohort. Three had a typical DEE phenotype characterised by epileptic spasms, tonic seizures and severe-to-profound intellectual disability with pathogenic variants located in the GTPase or middle domain. The other three patients had atypical phenotypes of milder cognitive impairment and focal epilepsy. Genotypically, two patients with atypical phenotypes had variants located in the GTPase domain, while the third patient had a novel variant (p.M648R) in the linker region between pleckstrin homology and GTPase effector domains. The third patient with an atypical phenotype showed normal development until he developed febrile status epilepticus. Our systematic review on 55 reported cases revealed that those with GTPase or middle domain variants had more severe intellectual disability (p<0.001) and lower functional levels of ambulation (p=0.001) or speech and language (p<0.001) than the rest.

CONCLUSION

DNM1-related phenotypes encompass a wide spectrum of epilepsy and neurodevelopmental disorders, with specific variants underlying different phenotypes.

Truncated DNM1 variant underlines developmental delay and epileptic encephalopathy

Background

Developmental and epileptic encephalopathies (DEEs) signify a group of heterogeneous neurodevelopmental disorder associated with early-onset seizures accompanied by developmental delay, hypotonia, mild to severe intellectual disability, and developmental regression. Variants in the DNM1 gene have been associated with autosomal dominant DEE type 31A and autosomal recessive DEE type 31B.

Methods

In the current study, a consanguineous Pakistani family consisting of a proband (IV-2) was clinically evaluated and genetically analyzed manifesting in severe neurodevelopmental phenotypes. WES followed by Sanger sequencing was performed to identify the disease-causing variant. Furthermore, 3D protein modeling and dynamic simulation of wild-type and mutant proteins along with reverse transcriptase (RT)-based mRNA expression were checked using standard methods.

Results

Data analysis of WES revealed a novel homozygous non-sense variant (c.1402G>T; p. Glu468*) in exon 11 of the DNM1 gene that was predicted as pathogenic class I. Variants in the DNM1 gene have been associated with DEE types 31A and B. Different bioinformatics prediction tools and American College of Medical Genetics guidelines were used to verify the identified variant. Sanger sequencing was used to validate the disease-causing variant. Our approach validated the pathogenesis of the variant as a cause of heterogeneous neurodevelopmental disorders. In addition, 3D protein modeling showed that the mutant protein would lose most of the amino acids and might not perform the proper function if the surveillance non-sense-mediated decay mechanism was skipped. Molecular dynamics analysis showed varied trajectories of wild-type and mutant DNM1 proteins in terms of root mean square deviation, root mean square fluctuation and radius of gyration. Similarly, RT-qPCR revealed a substantial reduction of the DNM1 gene in the index patient.

Conclusion

Our finding further confirms the association of homozygous, loss-of-function variants in DNM1 associated with DEE type 31B. The study expands the genotypic and phenotypic spectrum of pathogenic DNM1 variants related to DNM1-associated pathogenesis.

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.

Splicing defects in rare diseases: transcriptomics and machine learning strategies towards genetic diagnosis

Genomic variants affecting pre-messenger RNA splicing and its regulation are known to underlie many rare genetic diseases. However, common workflows for genetic diagnosis and clinical variant interpretation frequently overlook splice-altering variants. To better serve patient populations and advance biomedical knowledge, it has become increasingly important to develop and refine approaches for detecting and interpreting pathogenic splicing variants. In this review, we will summarize a few recent developments and challenges in using RNA sequencing technologies for rare disease investigation. Moreover, we will discuss how recent computational splicing prediction tools have emerged as complementary approaches for revealing disease-causing variants underlying splicing defects. We speculate that continuous improvements to sequencing technologies and predictive modeling will not only expand our understanding of splicing regulation but also bring us closer to filling the diagnostic gap for rare disease patients.

Reversal of cell, circuit and seizure phenotypes in a mouse model of DNM1 epileptic encephalopathy

Dynamin-1 is a large GTPase with an obligatory role in synaptic vesicle endocytosis at mammalian nerve terminals. Heterozygous missense mutations in the dynamin-1 gene (DNM1) cause a novel form of epileptic encephalopathy, with pathogenic mutations clustering within regions required for its essential GTPase activity. We reveal the most prevalent pathogenic DNM1 mutation, R237W, disrupts dynamin-1 enzyme activity and endocytosis when overexpressed in central neurons. To determine how this mutation impacted cell, circuit and behavioural function, we generated a mouse carrying the R237W mutation. Neurons from heterozygous mice display dysfunctional endocytosis, in addition to altered excitatory neurotransmission and seizure-like phenotypes. Importantly, these phenotypes are corrected at the cell, circuit and in vivo level by the drug, BMS-204352, which accelerates endocytosis. Here, we demonstrate a credible link between dysfunctional endocytosis and epileptic encephalopathy, and importantly reveal that synaptic vesicle recycling may be a viable therapeutic target for monogenic intractable epilepsies.

A deep intronic variant in DNM1 in a patient with developmental and epileptic encephalopathy creates a splice acceptor site and affects only transcript variants including exon 10a

DNM1 developmental and epileptic encephalopathy (DEE) is characterized by severe to profound intellectual disability, hypotonia, movement disorder, and refractory epilepsy, typically presenting with infantile spasms. Most of the affected individuals had de novo missense variants in DNM1. DNM1 undergoes alternative splicing that results in expression of six different transcript variants. One alternatively spliced region affects the tandemly arranged exons 10a and 10b, producing isoforms DNM1A and DNM1B, respectively. Pathogenic variants in the DNM1 coding region affect all transcript variants. Recently, a de novo DNM1 NM_001288739.1:c.1197-8G > A variant located in intron 9 has been reported in several unrelated individuals with DEE that causes in-frame insertion of two amino acids and leads to disease through a dominant-negative mechanism. We report on a patient with DEE and a de novo DNM1 variant NM_001288739.2:c.1197-46C > G in intron 9, upstream of exon 10a. By RT-PCR and Sanger sequencing using fibroblast-derived cDNA of the patient, we identified aberrantly spliced DNM1 mRNAs with exon 9 spliced to the last 45 nucleotides of intron 9 followed by exon 10a (NM_001288739.2:r.1196_1197ins[1197-1_1197-45]). The encoded DNM1A mutant is predicted to contain 15 novel amino acids between Ile398 and Arg399 [NP_001275668.1:p.(Ile398_Arg399ins15)] and likely functions in a dominant-negative manner, similar to other DNM1 mutants. Our data confirm the importance of the DNM1 isoform A for normal human brain function that is underscored by previously reported predominant expression of DMN1A transcripts in pediatric brain, functional differences of the mouse Dnm1a and Dnm1b isoforms, and the Dnm1 fitful mouse, an epilepsy mouse model.

Benchmarking splice variant prediction algorithms using massively parallel splicing assays

Background

Variants that disrupt mRNA splicing account for a sizable fraction of the pathogenic burden in many genetic disorders, but identifying splice-disruptive variants (SDVs) beyond the essential splice site dinucleotides remains difficult. Computational predictors are often discordant, compounding the challenge of variant interpretation. Because they are primarily validated using clinical variant sets heavily biased to known canonical splice site mutations, it remains unclear how well their performance generalizes.

Results

We benchmarked eight widely used splicing effect prediction algorithms, leveraging massively parallel splicing assays (MPSAs) as a source of experimentally determined ground-truth. MPSAs simultaneously assay many variants to nominate candidate SDVs. We compared experimentally measured splicing outcomes with bioinformatic predictions for 3,616 variants in five genes. Algorithms' concordance with MPSA measurements, and with each other, was lower for exonic than intronic variants, underscoring the difficulty of identifying missense or synonymous SDVs. Deep learning-based predictors trained on gene model annotations achieved the best overall performance at distinguishing disruptive and neutral variants. Controlling for overall call rate genome-wide, SpliceAI and Pangolin also showed superior overall sensitivity for identifying SDVs. Finally, our results highlight two practical considerations when scoring variants genome-wide: finding an optimal score cutoff, and the substantial variability introduced by differences in gene model annotation, and we suggest strategies for optimal splice effect prediction in the face of these issues.

Conclusion

SpliceAI and Pangolin showed the best overall performance among predictors tested, however, improvements in splice effect prediction are still needed especially within exons.

The current landscape of epilepsy genetics: where are we, and where are we going?

PURPOSE OF REVIEW

In this review, we aim to analyse the progress in understanding the genetic basis of the epilepsies, as well as ongoing efforts to define the increasingly diverse and novel presentations, phenotypes and divergences from the expected that have continually characterized the field.

RECENT FINDINGS

A genetic workup is now considered to be standard of care for individuals with an unexplained epilepsy, due to mounting evidence that genetic diagnoses significantly influence treatment choices, prognostication, community support, and increasingly, access to clinical trials. As more individuals with epilepsy are tested, novel presentations of known epilepsy genes are being discovered, and more individuals with self-limited epilepsy are able to attain genetic diagnoses. In addition, new genes causative of epilepsy are being uncovered through both traditional and novel methods, including large international data-sharing collaborations and massive sequencing efforts as well as computational methods and analyses driven by the Human Phenotype Ontology (HPO).

SUMMARY

New approaches to gene discovery and characterization are advancing rapidly our understanding of the genetic and phenotypic architecture of the epilepsies. This review highlights relevant and groundbreaking studies published recently that have pushed forward the field of epilepsy genetics.