Gene family information facilitates variant interpretation and identification of disease-associated genes in neurodevelopmental disorders

Leveraging cancer mutation data to inform the pathogenicity classification of germline missense variants

Innovative and easy-to-implement strategies are needed to improve the pathogenicity assessment of rare germline missense variants. Somatic cancer driver mutations identified through large-scale tumor sequencing studies often impact genes that are also associated with rare Mendelian disorders. The use of cancer mutation data to aid in the interpretation of germline missense variants, regardless of whether the gene is associated with a hereditary cancer predisposition syndrome or a non-cancer-related developmental disorder, has not been systematically assessed. We extracted putative cancer driver missense mutations from the Cancer Hotspots database and annotated them as germline variants, including presence/absence and classification in ClinVar. We trained two supervised learning models (logistic regression and random forest) to predict variant classifications of germline missense variants in ClinVar using Cancer Hotspot data (training dataset). The performance of each model was evaluated with an independent test dataset generated in part from searching public and private genome-wide sequencing datasets from ~1.5 million individuals. Of the 2,447 cancer mutations, 691 corresponding germline variants had been previously classified in ClinVar: 426 (61.6%) as likely pathogenic/pathogenic, 261 (37.8%) as uncertain significance, and 4 (0.6%) as likely benign/benign. The odds ratio for a likely pathogenic/pathogenic classification in ClinVar was 28.3 (95% confidence interval: 24.2-33.1, p < 0.001), compared with all other germline missense variants in the same 216 genes. Both supervised learning models showed high correlation with pathogenicity assessments in the training dataset. There was high area under precision-recall curve values (0.847 and 0.829) and area under the receiver-operating characteristic curve values (0.821 and 0.774) for logistic regression and random forest models, respectively, when applied to the test dataset. With the use of cancer and germline datasets and supervised learning techniques, our study shows that cancer mutation data can be leveraged to improve the interpretation of germline missense variation potentially causing rare Mendelian disorders.

Exome sequencing of 20,979 individuals with epilepsy reveals shared and distinct ultra-rare genetic risk across disorder subtypes

Identifying genetic risk factors for highly heterogeneous disorders such as epilepsy remains challenging. Here we present, to our knowledge, the largest whole-exome sequencing study of epilepsy to date, with more than 54,000 human exomes, comprising 20,979 deeply phenotyped patients from multiple genetic ancestry groups with diverse epilepsy subtypes and 33,444 controls, to investigate rare variants that confer disease risk. These analyses implicate seven individual genes, three gene sets and four copy number variants at exome-wide significance. Genes encoding ion channels show strong association with multiple epilepsy subtypes, including epileptic encephalopathies and generalized and focal epilepsies, whereas most other gene discoveries are subtype specific, highlighting distinct genetic contributions to different epilepsies. Combining results from rare single-nucleotide/short insertion and deletion variants, copy number variants and common variants, we offer an expanded view of the genetic architecture of epilepsy, with growing evidence of convergence among different genetic risk loci on the same genes. Top candidate genes are enriched for roles in synaptic transmission and neuronal excitability, particularly postnatally and in the neocortex. We also identify shared rare variant risk between epilepsy and other neurodevelopmental disorders. Our data can be accessed via an interactive browser, hopefully facilitating diagnostic efforts and accelerating the development of follow-up studies.

Exome sequencing of 20,979 individuals with epilepsy reveals shared and distinct ultra-rare genetic risk across disorder subtypes

Identifying genetic risk factors for highly heterogeneous disorders like epilepsy remains challenging. Here, we present the largest whole-exome sequencing study of epilepsy to date, with >54,000 human exomes, comprising 20,979 deeply phenotyped patients from multiple genetic ancestry groups with diverse epilepsy subtypes and 33,444 controls, to investigate rare variants that confer disease risk. These analyses implicate seven individual genes, three gene sets, and four copy number variants at exome-wide significance. Genes encoding ion channels show strong association with multiple epilepsy subtypes, including epileptic encephalopathies, generalized and focal epilepsies, while most other gene discoveries are subtype-specific, highlighting distinct genetic contributions to different epilepsies. Combining results from rare single nucleotide/short indel-, copy number-, and common variants, we offer an expanded view of the genetic architecture of epilepsy, with growing evidence of convergence among different genetic risk loci on the same genes. Top candidate genes are enriched for roles in synaptic transmission and neuronal excitability, particularly postnatally and in the neocortex. We also identify shared rare variant risk between epilepsy and other neurodevelopmental disorders. Our data can be accessed via an interactive browser, hopefully facilitating diagnostic efforts and accelerating the development of follow-up studies.

Genotypic and phenotypic characteristics of sodium channel-associated epilepsy in Chinese population

Variants in voltage-gated sodium channel (VGSC) genes are implicated in seizures, epilepsy, and neurodevelopmental disorders, constituting a significant aspect of hereditary epilepsy in the Chinese population. Through retrospective analysis utilizing next-generation sequencing (NGS), we examined the genotypes and phenotypes of VGSC-related epilepsy cases from a cohort of 691 epilepsy subjects. Our findings revealed that 5.1% of subjects harbored VGSC variants, specifically 22 with SCN1A, 9 with SCN2A, 1 with SCN8A, and 3 with SCN1B variants; no SCN3A variants were detected. Among these, 14 variants were previously reported, while 21 were newly identified. SCN1A variant carriers predominantly presented with Dravet Syndrome (DS) and Genetic Epilepsy with Febrile Seizures Plus (GEFS + ), featuring a heightened sensitivity to fever-induced seizures. Statistically significant disparities emerged between the SCN1A-DS and SCN1A-GEFS+ groups concerning seizure onset and genetic diagnosis age, incidence of status epilepticus, mental retardation, anti-seizure medication (ASM) responsiveness, and familial history. Notably, subjects with SCN1A variants affecting the protein's pore region experienced more frequent cluster seizures. All SCN2A variants were of de novo origin, and 88.9% of individuals with SCN2A variations exhibited cluster seizures. This research reveals a significant association between variations in VGSC-related genes and the clinical phenotype diversity of epilepsy subjects in China, emphasizing the pivotal role of NGS screening in establishing accurate disease diagnoses and guiding the selection of ASM.

Boolean Modeling of Biological Network Applied to Protein-Protein Interaction Network of Autism Patients

Cellular molecules interact with one another in a structured manner, defining a regulatory network topology that describes cellular mechanisms. Genetic mutations alter these networks' pathways, generating complex disorders such as autism spectrum disorder (ASD). Boolean models have assisted in understanding biological system dynamics since Kauffman's 1969 discovery, and various analytical tools for regulatory networks have been developed. This study examined the protein-protein interaction network created in our previous publication of four ASD patients using the SPIDDOR R package, a Boolean model-based method. The aim is to examine how patients' genetic variations in INTS6L, USP9X, RSK4, FGF5, FLNA, SUMF1, and IDS affect mTOR and Wnt cell signaling convergence. The Boolean network analysis revealed abnormal activation levels of essential proteins such as β-catenin, MTORC1, RPS6, eIF4E, Cadherin, and SMAD. These proteins affect gene expression, translation, cell adhesion, shape, and migration. Patients 1 and 2 showed consistent patterns of increased β-catenin activity and decreased MTORC1, RPS6, and eIF4E activity. However, patient 2 had an independent decrease in Cadherin and SMAD activity due to the FLNA mutation. Patients 3 and 4 have an abnormal activation of the mTOR pathway, which includes the MTORC1, RPS6, and eIF4E genes. The shared mTOR pathway behavior in these patients is explained by a shared mutation in two closely related proteins (SUMF1 and IDS). Diverse activities in β-catenin, MTORC1, RPS6, eIF4E, Cadherin, and SMAD contributed to the reported phenotype in these individuals. Furthermore, it unveiled the potential therapeutic options that could be suggested to these individuals.

Genetic constraint at single amino acid resolution in protein domains improves missense variant prioritisation and gene discovery

BACKGROUND

One of the major hurdles in clinical genetics is interpreting the clinical consequences associated with germline missense variants in humans. Recent significant advances have leveraged natural variation observed in large-scale human populations to uncover genes or genomic regions that show a depletion of natural variation, indicative of selection pressure. We refer to this as "genetic constraint". Although existing genetic constraint metrics have been demonstrated to be successful in prioritising genes or genomic regions associated with diseases, their spatial resolution is limited in distinguishing pathogenic variants from benign variants within genes.

METHODS

We aim to identify missense variants that are significantly depleted in the general human population. Given the size of currently available human populations with exome or genome sequencing data, it is not possible to directly detect depletion of individual missense variants, since the average expected number of observations of a variant at most positions is less than one. We instead focus on protein domains, grouping homologous variants with similar functional impacts to examine the depletion of natural variations within these comparable sets. To accomplish this, we develop the Homologous Missense Constraint (HMC) score. We utilise the Genome Aggregation Database (gnomAD) 125 K exome sequencing data and evaluate genetic constraint at quasi amino-acid resolution by combining signals across protein homologues.

RESULTS

We identify one million possible missense variants under strong negative selection within protein domains. Though our approach annotates only protein domains, it nonetheless allows us to assess 22% of the exome confidently. It precisely distinguishes pathogenic variants from benign variants for both early-onset and adult-onset disorders. It outperforms existing constraint metrics and pathogenicity meta-predictors in prioritising de novo mutations from probands with developmental disorders (DD). It is also methodologically independent of these, adding power to predict variant pathogenicity when used in combination. We demonstrate utility for gene discovery by identifying seven genes newly significantly associated with DD that could act through an altered-function mechanism.

CONCLUSIONS

Grouping variants of comparable functional impacts is effective in evaluating their genetic constraint. HMC is a novel and accurate predictor of missense consequence for improved variant interpretation.

Lethal phenotypes in Mendelian disorders

PURPOSE

Existing resources that characterize the essentiality status of genes are based on either proliferation assessment in human cell lines, viability evaluation in mouse knockouts, or constraint metrics derived from human population sequencing studies. Several repositories document phenotypic annotations for rare disorders; however, there is a lack of comprehensive reporting on lethal phenotypes.

METHODS

We queried Online Mendelian Inheritance in Man for terms related to lethality and classified all Mendelian genes according to the earliest age of death recorded for the associated disorders, from prenatal death to no reports of premature death. We characterized the genes across these lethality categories, examined the evidence on viability from mouse models and explored how this information could be used for novel gene discovery.

RESULTS

We developed the Lethal Phenotypes Portal to showcase this curated catalog of human essential genes. Differences in the mode of inheritance, physiological systems affected, and disease class were found for genes in different lethality categories, as well as discrepancies between the lethal phenotypes observed in mouse and human.

CONCLUSION

We anticipate that this resource will aid clinicians in the diagnosis of early lethal conditions and assist researchers in investigating the properties that make these genes essential for human development.

The landscape of regional missense mutational intolerance quantified from 125,748 exomes

Missense variants can have a range of functional impacts depending on factors such as the specific amino acid substitution and location within the gene. To interpret their deleteriousness, studies have sought to identify regions within genes that are specifically intolerant of missense variation 1-12 . Here, we leverage the patterns of rare missense variation in 125,748 individuals in the Genome Aggregation Database (gnomAD) 13 against a null mutational model to identify transcripts that display regional differences in missense constraint. Missense-depleted regions are enriched for ClinVar 14 pathogenic variants, de novo missense variants from individuals with neurodevelopmental disorders (NDDs) 15,16 , and complex trait heritability. Following ClinGen calibration recommendations for the ACMG/AMP guidelines, we establish that regions with less than 20% of their expected missense variation achieve moderate support for pathogenicity. We create a missense deleteriousness metric (MPC) that incorporates regional constraint and outperforms other deleteriousness scores at stratifying case and control de novo missense variation, with a strong enrichment in NDDs. These results provide additional tools to aid in missense variant interpretation.

Lethal phenotypes in Mendelian disorders

Essential genes are those whose function is required for cell proliferation and/or organism survival. A gene's intolerance to loss-of-function can be allocated within a spectrum, as opposed to being considered a binary feature, since this function might be essential at different stages of development, genetic backgrounds or other contexts. Existing resources that collect and characterise the essentiality status of genes are based on either proliferation assessment in human cell lines, embryonic and postnatal viability evaluation in different model organisms, and gene metrics such as intolerance to variation scores derived from human population sequencing studies. There are also several repositories available that document phenotypic annotations for rare disorders in humans such as the Online Mendelian Inheritance in Man (OMIM) and the Human Phenotype Ontology (HPO) knowledgebases. This raises the prospect of being able to use clinical data, including lethality as the most severe phenotypic manifestation, to further our characterisation of gene essentiality. Here we queried OMIM for terms related to lethality and classified all Mendelian genes into categories, according to the earliest age of death recorded for the associated disorders, from prenatal death to no reports of premature death. To showcase this curated catalogue of human essential genes, we developed the Lethal Phenotypes Portal (https://lethalphenotypes.research.its.qmul.ac.uk), where we also explore the relationships between these lethality categories, constraint metrics and viability in cell lines and mouse. Further analysis of the genes in these categories reveals differences in the mode of inheritance of the associated disorders, physiological systems affected and disease class. We highlight how the phenotypic similarity between genes in the same lethality category combined with gene family/group information can be used for novel disease gene discovery. Finally, we explore the overlaps and discrepancies between the lethal phenotypes observed in mouse and human and discuss potential explanations that include differences in transcriptional regulation, functional compensation and molecular disease mechanisms. We anticipate that this resource will aid clinicians in the diagnosis of early lethal conditions and assist researchers in investigating the properties that make these genes essential for human development.

Evaluating the use of paralogous protein domains to increase data availability for missense variant classification

BACKGROUND

Classification of rare missense variants remains an ongoing challenge in genomic medicine. Evidence of pathogenicity is often sparse, and decisions about how to weigh different evidence classes may be subjective. We used a Bayesian variant classification framework to investigate the performance of variant co-localisation, missense constraint, and aggregating data across paralogous protein domains ("meta-domains").

METHODS

We constructed a database of all possible coding single nucleotide variants in the human genome and used PFam predictions to annotate structurally-equivalent positions across protein domains. We counted the number of pathogenic and benign missense variants at these equivalent positions in the ClinVar database, calculated a regional constraint score for each meta-domain, and assessed this approach versus existing missense constraint metrics for classifying variant pathogenicity and benignity.

RESULTS

Alternative pathogenic missense variants at the same amino acid position in the same protein provide strong evidence of pathogenicity (positive likelihood ratio, LR+  = 85). Additionally, clinically annotated pathogenic or benign missense variants at equivalent positions in different proteins can provide moderate evidence of pathogenicity (LR+  = 7) or benignity (LR+  = 5), respectively. Applying these approaches sequentially (through PM5) increases sensitivity for classifying pathogenic missense variants from 27 to 41%. Missense constraint can also provide strong evidence of pathogenicity for some variants, but its absence provides no evidence of benignity.

CONCLUSIONS

We propose using structurally equivalent positions across related protein domains from different genes to augment evidence for variant co-localisation when classifying novel missense variants. Additionally, we advocate adopting a numerical evidence-based approach to integrating diverse data in variant interpretation.

Use of big data and machine learning algorithms to extract possible treatment targets in neurodevelopmental disorders

Neurodevelopmental disorders (NDDs) impact multiple aspects of an individual's functioning, including social interactions, communication, and behaviors. The underlying biological mechanisms of NDDs are not yet fully understood, and pharmacological treatments have been limited in their effectiveness, in part due to the complex nature of these disorders and the heterogeneity of symptoms across individuals. Identifying genetic loci associated with NDDs can help in understanding biological mechanisms and potentially lead to the development of new treatments. However, the polygenic nature of these complex disorders has made identifying new treatment targets from genome-wide association studies (GWAS) challenging. Recent advances in the fields of big data and high-throughput tools have provided radically new insights into the underlying biological mechanism of NDDs. This paper reviews various big data approaches, including classical and more recent techniques like deep learning, which can identify potential treatment targets from GWAS and other omics data, with a particular emphasis on NDDs. We also emphasize the increasing importance of explainable and causal machine learning (ML) methods that can aid in identifying genes, molecular pathways, and more complex biological processes that may be future targets of intervention in these disorders. We conclude that these new developments in genetics and ML hold promise for advancing our understanding of NDDs and identifying novel treatment targets.

Stretch-activated ion channel TMEM63B associates with developmental and epileptic encephalopathies and progressive neurodegeneration

By converting physical forces into electrical signals or triggering intracellular cascades, stretch-activated ion channels allow the cell to respond to osmotic and mechanical stress. Knowledge of the pathophysiological mechanisms underlying associations of stretch-activated ion channels with human disease is limited. Here, we describe 17 unrelated individuals with severe early-onset developmental and epileptic encephalopathy (DEE), intellectual disability, and severe motor and cortical visual impairment associated with progressive neurodegenerative brain changes carrying ten distinct heterozygous variants of TMEM63B, encoding for a highly conserved stretch-activated ion channel. The variants occurred de novo in 16/17 individuals for whom parental DNA was available and either missense, including the recurrent p.Val44Met in 7/17 individuals, or in-frame, all affecting conserved residues located in transmembrane regions of the protein. In 12 individuals, hematological abnormalities co-occurred, such as macrocytosis and hemolysis, requiring blood transfusions in some. We modeled six variants (p.Val44Met, p.Arg433His, p.Thr481Asn, p.Gly580Ser, p.Arg660Thr, and p.Phe697Leu), each affecting a distinct transmembrane domain of the channel, in transfected Neuro2a cells and demonstrated inward leak cation currents across the mutated channel even in isotonic conditions, while the response to hypo-osmotic challenge was impaired, as were the Ca2+ transients generated under hypo-osmotic stimulation. Ectopic expression of the p.Val44Met and p.Gly580Cys variants in Drosophila resulted in early death. TMEM63B-associated DEE represents a recognizable clinicopathological entity in which altered cation conductivity results in a severe neurological phenotype with progressive brain damage and early-onset epilepsy associated with hematological abnormalities in most individuals.

Spinocerebellar ataxia 38: structure-function analysis shows ELOVL5 G230V is proteotoxic, conformationally altered and a mutational hotspot

Fatty acid elongase ELOVL5 is part of a protein family of multipass transmembrane proteins that reside in the endoplasmic reticulum where they regulate long-chain fatty acid elongation. A missense variant (c.689G>T p.Gly230Val) in ELOVL5 causes Spinocerebellar Ataxia subtype 38 (SCA38), a neurodegenerative disorder characterized by autosomal dominant inheritance, cerebellar Purkinje cell demise and adult-onset ataxia. Having previously showed aberrant accumulation of p.G230V in the Golgi complex, here we further investigated the pathogenic mechanisms triggered by p.G230V, integrating functional studies with bioinformatic analyses of protein sequence and structure. Biochemical analysis showed that p.G230V enzymatic activity was normal. In contrast, SCA38-derived fibroblasts showed reduced expression of ELOVL5, Golgi complex enlargement and increased proteasomal degradation with respect to controls. By heterologous overexpression, p.G230V was significantly more active than wild-type ELOVL5 in triggering the unfolded protein response and in decreasing viability in mouse cortical neurons. By homology modelling, we generated native and p.G230V protein structures whose superposition revealed a shift in Loop 6 in p.G230V that altered a highly conserved intramolecular disulphide bond. The conformation of this bond, connecting Loop 2 and Loop 6, appears to be elongase-specific. Alteration of this intramolecular interaction was also observed when comparing wild-type ELOVL4 and the p.W246G variant which causes SCA34. We demonstrate by sequence and structure analyses that ELOVL5 p.G230V and ELOVL4 p.W246G are position-equivalent missense variants. We conclude that SCA38 is a conformational disease and propose combined loss of function by mislocalization and gain of toxic function by ER/Golgi stress as early events in SCA38 pathogenesis.

Scorpion Peptides and Ion Channels: An Insightful Review of Mechanisms and Drug Development

The Buthidae family of scorpions consists of arthropods with significant medical relevance, as their venom contains a diverse range of biomolecules, including neurotoxins that selectively target ion channels in cell membranes. These ion channels play a crucial role in regulating physiological processes, and any disturbance in their activity can result in channelopathies, which can lead to various diseases such as autoimmune, cardiovascular, immunological, neurological, and neoplastic conditions. Given the importance of ion channels, scorpion peptides represent a valuable resource for developing drugs with targeted specificity for these channels. This review provides a comprehensive overview of the structure and classification of ion channels, the action of scorpion toxins on these channels, and potential avenues for future research. Overall, this review highlights the significance of scorpion venom as a promising source for discovering novel drugs with therapeutic potential for treating channelopathies.

Conserved patterns across ion channels correlate with variant pathogenicity and clinical phenotypes

Clinically identified genetic variants in ion channels can be benign or cause disease by increasing or decreasing the protein function. As a consequence, therapeutic decision-making is challenging without molecular testing of each variant. Our biophysical knowledge of ion-channel structures and function is just emerging, and it is currently not well understood which amino acid residues cause disease when mutated. We sought to systematically identify biological properties associated with variant pathogenicity across all major voltage and ligand-gated ion-channel families. We collected and curated 3049 pathogenic variants from hundreds of neurodevelopmental and other disorders and 12 546 population variants for 30 ion channel or channel subunits for which a high-quality protein structure was available. Using a wide range of bioinformatics approaches, we computed 163 structural features and tested them for pathogenic variant enrichment. We developed a novel 3D spatial distance scoring approach that enables comparisons of pathogenic and population variant distribution across protein structures. We discovered and independently replicated that several pore residue properties and proximity to the pore axis were most significantly enriched for pathogenic variants compared to population variants. Using our 3D scoring approach, we showed that the strongest pathogenic variant enrichment was observed for pore-lining residues and alpha-helix residues within 5Å distance from the pore axis centre and not involved in gating. Within the subset of residues located at the pore, the hydrophobicity of the pore was the feature most strongly associated with variant pathogenicity. We also found an association between the identified properties and both clinical phenotypes and functional in vitro assays for voltage-gated sodium channels (SCN1A, SCN2A, SCN8A) and N-methyl-D-aspartate receptor (GRIN1, GRIN2A, GRIN2B) encoding genes. In an independent expert-curated dataset of 1422 neurodevelopmental disorder pathogenic patient variants and 679 electrophysiological experiments, we show that pore axis distance is associated with seizure age of onset and cognitive performance as well as differential gain versus loss-of-channel function. In summary, we identified biological properties associated with ion-channel malfunction and show that these are correlated with in vitro functional readouts and clinical phenotypes in patients with neurodevelopmental disorders. Our results suggest that clinical decision support algorithms that predict variant pathogenicity and function are feasible in the future.

Delineation of functionally essential protein regions for 242 neurodevelopmental genes

Neurodevelopmental disorders (NDDs), including severe paediatric epilepsy, autism and intellectual disabilities are heterogeneous conditions in which clinical genetic testing can often identify a pathogenic variant. For many of them, genetic therapies will be tested in this or the coming years in clinical trials. In contrast to first-generation symptomatic treatments, the new disease-modifying precision medicines require a genetic test-informed diagnosis before a patient can be enrolled in a clinical trial. However, even in 2022, most identified genetic variants in NDD genes are 'variants of uncertain significance'. To safely enrol patients in precision medicine clinical trials, it is important to increase our knowledge about which regions in NDD-associated proteins can 'tolerate' missense variants and which ones are 'essential' and will cause a NDD when mutated. In addition, knowledge about functionally indispensable regions in the 3D structure context of proteins can also provide insights into the molecular mechanisms of disease variants. We developed a novel consensus approach that overlays evolutionary, and population based genomic scores to identify 3D essential sites (Essential3D) on protein structures. After extensive benchmarking of AlphaFold predicted and experimentally solved protein structures, we generated the currently largest expert curated protein structure set for 242 NDDs and identified 14 377 Essential3D sites across 189 gene disorders associated proteins. We demonstrate that the consensus annotation of Essential3D sites improves prioritization of disease mutations over single annotations. The identified Essential3D sites were enriched for functional features such as intermembrane regions or active sites and discovered key inter-molecule interactions in protein complexes that were otherwise not annotated. Using the currently largest autism, developmental disorders, and epilepsies exome sequencing studies including >360 000 NDD patients and population controls, we found that missense variants at Essential3D sites are 8-fold enriched in patients. In summary, we developed a comprehensive protein structure set for 242 NDDs and identified 14 377 Essential3D sites in these. All data are available at https://es-ndd.broadinstitute.org for interactive visual inspection to enhance variant interpretation and development of mechanistic hypotheses for 242 NDDs genes. The provided resources will enhance clinical variant interpretation and in silico drug target development for NDD-associated genes and encoded proteins.

Data-driven historical characterization of epilepsy-associated genes

Many epilepsy-associated genes have been identified over the last three decades, revealing a remarkable molecular heterogeneity with the shared outcome of recurrent seizures. Information about the genetic landscape of epilepsies is scattered throughout the literature and answering the simple question of how many genes are associated with epilepsy is not straightforward. Here, we present a computationally driven analytical review of epilepsy-associated genes using the complete scientific literature in PubMed. Based on our search criteria, we identified a total of 738 epilepsy-associated genes. We further classified these genes into two Tiers. A broad gene list of 738 epilepsy-associated genes (Tier 2) and a narrow gene list composed of 143 epilepsy-associated genes (Tier 1). Our search criteria do not reflect the degree of association. The average yearly number of identified epilepsy-associated genes between 1992 and 2021 was 4.8. However, most of these genes were only identified in the last decade (2010-2019). Ion channels represent the largest class of epilepsy-associated genes. For many of these, both gain- and loss-of-function effects have been associated with epilepsy in recent years. We identify 28 genes frequently reported with heterogenous variant effects which should be considered for variant interpretation. Overall, our study provides an updated and manually curated list of epilepsy-related genes together with additional annotations and classifications reflecting the current genetic landscape of epilepsy.

GABRG1 variant as a potential novel cause of epileptic encephalopathy, hypotonia, and global developmental delay

Epileptic encephalopathies (EEs) are severe brain disorders with excessive ictal (seizure) and interictal (electrographic epileptiform discharges) activity in developing brain which may result in progressive cognitive and neuropsychological deterioration. In contrast to regular epilepsy where the treatment goal is to prevent the seizure (ictal) recurrence, in patients with EE the goal is to treat both ictal as well as interictal activity to prevent further progression. With the introduction of genetic sequencing technologies over the past 20 years, there is growing recognition of the genetic basis of EE, with the majority due to monogenic causes. Monogenic etiologies of EE include pathogenic variants in the γ-aminobutyric acid type A receptor (GABA-A) encoding gene family. We present a 2-year-old patient with EE, hypotonia, and global developmental delays. Clinical trio exome sequencing showed a novel, de novo variant in GABRG1. GABRG1 encodes the γ1 subunit of the GABA-A receptor. To date, there has not been an association of EE with pathogenic variants in GABRG1. This variant is predicted to be damaging to protein structure and function, and the patient's phenotype is similar to those with pathogenic variants in other members of the GABA-A receptor encoding gene family.

Predicting functional effect of missense variants using graph attention neural networks

Accurate prediction of damaging missense variants is critically important for interpreting a genome sequence. Although many methods have been developed, their performance has been limited. Recent advances in machine learning and the availability of large-scale population genomic sequencing data provide new opportunities to considerably improve computational predictions. Here we describe the graphical missense variant pathogenicity predictor (gMVP), a new method based on graph attention neural networks. Its main component is a graph with nodes that capture predictive features of amino acids and edges weighted by co-evolution strength, enabling effective pooling of information from the local protein context and functionally correlated distal positions. Evaluation of deep mutational scan data shows that gMVP outperforms other published methods in identifying damaging variants in TP53, PTEN, BRCA1 and MSH2. Furthermore, it achieves the best separation of de novo missense variants in neuro developmental disorder cases from those in controls. Finally, the model supports transfer learning to optimize gain- and loss-of-function predictions in sodium and calcium channels. In summary, we demonstrate that gMVP can improve interpretation of missense variants in clinical testing and genetic studies.

Mendelian gene identification through mouse embryo viability screening

BACKGROUND

The diagnostic rate of Mendelian disorders in sequencing studies continues to increase, along with the pace of novel disease gene discovery. However, variant interpretation in novel genes not currently associated with disease is particularly challenging and strategies combining gene functional evidence with approaches that evaluate the phenotypic similarities between patients and model organisms have proven successful. A full spectrum of intolerance to loss-of-function variation has been previously described, providing evidence that gene essentiality should not be considered as a simple and fixed binary property.

METHODS

Here we further dissected this spectrum by assessing the embryonic stage at which homozygous loss-of-function results in lethality in mice from the International Mouse Phenotyping Consortium, classifying the set of lethal genes into one of three windows of lethality: early, mid, or late gestation lethal. We studied the correlation between these windows of lethality and various gene features including expression across development, paralogy and constraint metrics together with human disease phenotypes. We explored a gene similarity approach for novel gene discovery and investigated unsolved cases from the 100,000 Genomes Project.

RESULTS

We found that genes in the early gestation lethal category have distinct characteristics and are enriched for genes linked with recessive forms of inherited metabolic disease. We identified several genes sharing multiple features with known biallelic forms of inborn errors of the metabolism and found signs of enrichment of biallelic predicted pathogenic variants among early gestation lethal genes in patients recruited under this disease category. We highlight two novel gene candidates with phenotypic overlap between the patients and the mouse knockouts.

CONCLUSIONS

Information on the developmental period at which embryonic lethality occurs in the knockout mouse may be used for novel disease gene discovery that helps to prioritise variants in unsolved rare disease cases.

Concise Review: Stem Cell Models of SCN1A-Related Encephalopathies—Current Perspective and Future Therapies

Mutations in the SCN1A gene can cause a variety of phenotypes, ranging from mild forms, such as febrile seizures and generalized epilepsy with febrile seizures plus, to severe, such as Dravet and non-Dravet developmental epileptic encephalopathies. Until now, more than two thousand pathogenic variants of the SCN1A gene have been identified and different pathogenic mechanisms (loss vs. gain of function) described, but the precise molecular mechanisms responsible for the deficits exhibited by patients are not fully elucidated. Additionally, the phenotypic variability proves the involvement of other genetic factors in its final expression. This is the reason why animal models and cell line models used to explore the molecular pathology of SCN1A-related disorders are only of limited use. The results of studies based on such models cannot be directly translated to affected individuals because they do not address each patient’s unique genetic background. The generation of functional neurons and glia for patient-derived iPSCs, together with the generation of isogenic controls using CRISPR/Cas technology, and finally, the 3D brain organoid models, seem to be a good way to solve this problem. Here, we review SCN1A-related encephalopathies, as well as the stem cell models used to explore their molecular basis.

Emerging evidence of genotype–phenotype associations of developmental and epileptic encephalopathy due to KCNC2 mutation: Identification of novel R405G

Developmental and epileptic encephalopathies (DEEs) have high genetic heterogeneity, and DEE due to the potassium voltage-gated channel subfamily C member 2 (KCNC2) variant remains poorly understood, given the scarcity of related case studies. We report on two unrelated Chinese patients, an 11-year-old boy and a 5-year-old girl, diagnosed with global developmental delay (GDD), intellectual disability (ID), and focal impaired awareness seizure characterized by generalized spike and wave complexes on electroencephalogram (EEG) in the absence of significant brain lesions. Whole-exome sequencing (WES) and electrophysiological analysis were performed to detect genetic variants and evaluate functional changes of the mutant KCNC2, respectively. Importantly, we identified a novel gain-of-function KCNC2 variant, R405G, in both patients. Previously reported variants, V471L, R351K, T437A, and T437N, and novel R405G were found in multiple unrelated patients with DEE, showing consistent genotype–phenotype associations. These findings emphasize that the KCNC2 gene is causative for DEE and facilitates treatment and prognosis in patients with DEE due to KCNC2 mutations.

De novo variants in genes regulating stress granule assembly associate with neurodevelopmental disorders

Stress granules (SGs) are cytoplasmic assemblies in response to a variety of stressors. We report a new neurodevelopmental disorder (NDD) with common features of language problems, intellectual disability, and behavioral issues caused by de novo likely gene-disruptive variants in UBAP2L, which encodes an essential regulator of SG assembly. Ubap2l haploinsufficiency in mouse led to social and cognitive impairments accompanied by disrupted neurogenesis and reduced SG formation during early brain development. On the basis of data from 40,853 individuals with NDDs, we report a nominally significant excess of de novo variants within 29 genes that are not implicated in NDDs, including 3 essential genes (G3BP1, G3BP2, and UBAP2L) in the core SG interaction network. We validated that NDD-related de novo variants in newly implicated and known NDD genes, such as CAPRIN1, disrupt the interaction of the core SG network and interfere with SG formation. Together, our findings suggest the common SG pathology in NDDs.

Spectrum of Phenotypic, Genetic, and Functional Characteristics in Patients With Epilepsy With KCNC2 Pathogenic Variants

BACKGROUND AND OBJECTIVES

KCNC2 encodes Kv3.2, a member of the Shaw-related (Kv3) voltage-gated potassium channel subfamily, which is important for sustained high-frequency firing and optimized energy efficiency of action potentials in the brain. The objective of this study was to analyze the clinical phenotype, genetic background, and biophysical function of disease-associated Kv3.2 variants.

METHODS

Individuals with KCNC2 variants detected by exome sequencing were selected for clinical, further genetic, and functional analysis. Cases were referred through clinical and research collaborations. Selected de novo variants were examined electrophysiologically in Xenopus laevis oocytes.

RESULTS

We identified novel KCNC2 variants in 18 patients with various forms of epilepsy, including genetic generalized epilepsy (GGE), developmental and epileptic encephalopathy (DEE) including early-onset absence epilepsy, focal epilepsy, and myoclonic-atonic epilepsy. Of the 18 variants, 10 were de novo and 8 were classified as modifying variants. Eight drug-responsive patients became seizure-free using valproic acid as monotherapy or in combination, including severe DEE cases. Functional analysis of 4 variants demonstrated gain of function in 3 severely affected DEE cases and loss of function in 1 case with a milder phenotype (GGE) as the underlying pathomechanisms.

DISCUSSION

These findings implicate KCNC2 as a novel causative gene for epilepsy and emphasize the critical role of KV3.2 in the regulation of brain excitability.

Large-scale discovery of novel neurodevelopmental disorder-related genes through a unified analysis of single-nucleotide and copy number variants

Background

Previous large-scale studies of de novo variants identified a number of genes associated with neurodevelopmental disorders (NDDs); however, it was also predicted that many NDD-associated genes await discovery. Such genes can be discovered by integrating copy number variants (CNVs), which have not been fully considered in previous studies, and increasing the sample size.

Methods

We first constructed a model estimating the rates of de novo CNVs per gene from several factors such as gene length and number of exons. Second, we compiled a comprehensive list of de novo single-nucleotide variants (SNVs) in 41,165 individuals and de novo CNVs in 3675 individuals with NDDs by aggregating our own and publicly available datasets, including denovo-db and the Deciphering Developmental Disorders study data. Third, summing up the de novo CNV rates that we estimated and SNV rates previously established, gene-based enrichment of de novo deleterious SNVs and CNVs were assessed in the 41,165 cases. Significantly enriched genes were further prioritized according to their similarity to known NDD genes using a deep learning model that considers functional characteristics (e.g., gene ontology and expression patterns).

Results

We identified a total of 380 genes achieving statistical significance (5% false discovery rate), including 31 genes affected by de novo CNVs. Of the 380 genes, 52 have not previously been reported as NDD genes, and the data of de novo CNVs contributed to the significance of three genes (GLTSCR1, MARK2, and UBR3). Among the 52 genes, we reasonably excluded 18 genes [a number almost identical to the theoretically expected false positives (i.e., 380 × 0.05 = 19)] given their constraints against deleterious variants and extracted 34 “plausible” candidate genes. Their validity as NDD genes was consistently supported by their similarity in function and gene expression patterns to known NDD genes. Quantifying the overall similarity using deep learning, we identified 11 high-confidence (> 90% true-positive probabilities) candidate genes: HDAC2, SUPT16H, HECTD4, CHD5, XPO1, GSK3B, NLGN2, ADGRB1, CTR9, BRD3, and MARK2.

Conclusions

We identified dozens of new candidates for NDD genes. Both the methods and the resources developed here will contribute to the further identification of novel NDD-associated genes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01042-w.

Development and Validation of a Prediction Model for Early Diagnosis of SCN1A-Related Epilepsies

BACKGROUND AND OBJECTIVES

Pathogenic variants in the neuronal sodium channel α1 subunit gene (SCN1A) are the most frequent monogenic cause of epilepsy. Phenotypes comprise a wide clinical spectrum, including severe childhood epilepsy; Dravet syndrome, characterized by drug-resistant seizures, intellectual disability, and high mortality; and the milder genetic epilepsy with febrile seizures plus (GEFS+), characterized by normal cognition. Early recognition of a child's risk for developing Dravet syndrome vs GEFS+ is key for implementing disease-modifying therapies when available before cognitive impairment emerges. Our objective was to develop and validate a prediction model using clinical and genetic biomarkers for early diagnosis of SCN1A-related epilepsies.

METHODS

We performed a retrospective multicenter cohort study comprising data from patients with SCN1A-positive Dravet syndrome and patients with GEFS+ consecutively referred for genetic testing (March 2001-June 2020) including age at seizure onset and a newly developed SCN1A genetic score. A training cohort was used to develop multiple prediction models that were validated using 2 independent blinded cohorts. Primary outcome was the discriminative accuracy of the model predicting Dravet syndrome vs other GEFS+ phenotypes.

RESULTS

A total of 1,018 participants were included. The frequency of Dravet syndrome was 616/743 (83%) in the training cohort, 147/203 (72%) in validation cohort 1, and 60/72 (83%) in validation cohort 2. A high SCN1A genetic score (133.4 [SD 78.5] vs 52.0 [SD 57.5]; p < 0.001) and young age at onset (6.0 [SD 3.0] vs 14.8 [SD 11.8] months; p < 0.001) were each associated with Dravet syndrome vs GEFS+. A combined SCN1A genetic score and seizure onset model separated Dravet syndrome from GEFS+ more effectively (area under the curve [AUC] 0.89 [95% CI 0.86-0.92]) and outperformed all other models (AUC 0.79-0.85; p < 0.001). Model performance was replicated in both validation cohorts 1 (AUC 0.94 [95% CI 0.91-0.97]) and 2 (AUC 0.92 [95% CI 0.82-1.00]).

DISCUSSION

The prediction model allows objective estimation at disease onset whether a child will develop Dravet syndrome vs GEFS+, assisting clinicians with prognostic counseling and decisions on early institution of precision therapies (http://scn1a-prediction-model.broadinstitute.org/).

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that a combined SCN1A genetic score and seizure onset model distinguishes Dravet syndrome from other GEFS+ phenotypes.

Epilepsy Genetics and Precision Medicine in Adults: A New Landscape for Developmental and Epileptic Encephalopathies

This review aims to provide an updated perspective of epilepsy genetics and precision medicine in adult patients, with special focus on developmental and epileptic encephalopathies (DEEs), covering relevant and controversial issues, such as defining candidates for genetic testing, which genetic tests to request and how to interpret them. A literature review was conducted, including findings in the discussion and recommendations. DEEs are wide and phenotypically heterogeneous electroclinical syndromes. They generally have a pediatric presentation, but patients frequently reach adulthood still undiagnosed. Identifying the etiology is essential, because there lies the key for precision medicine. Phenotypes modify according to age, and although deep phenotyping has allowed to outline certain entities, genotype-phenotype correlations are still poor, commonly leading to long-lasting diagnostic odysseys and ineffective therapies. Recent adult series show that the target patients to be identified for genetic testing are those with epilepsy and different risk factors. The clinician should take active part in the assessment of the pathogenicity of the variants detected, especially concerning variants of uncertain significance. An accurate diagnosis implies precision medicine, meaning genetic counseling, prognosis, possible future therapies, and a reduction of iatrogeny. Up to date, there are a few tens of gene mutations with additional concrete treatments, including those with restrictive/substitutive therapies, those with therapies modifying signaling pathways, and channelopathies, that are worth to be assessed in adults. Further research is needed regarding phenotyping of adult syndromes, early diagnosis, and the development of targeted therapies.

Sequencing of selected chromatin remodelling genes reveals increased burden of rare missense variants in ASD patients from the Japanese population

Chromatin remodelling is an important process in neural development and is related to autism spectrum disorder (ASD) and schizophrenia (SCZ) aetiology. To further elucidate the involvement of chromatin remodelling genes in the genetic aetiology of ASD and SCZ in the Japanese population, we performed a case-control study. Targeted sequencing was conducted on coding regions of four BAF chromatin remodelling complex genes: SMARCA2, SMARCA4, SMARCC2, and ARID1B in 185 ASD, 432 SCZ patients, and 517 controls. 27 rare non-synonymous variants were identified in ASD and SCZ patients, including 25 missense, one in-frame deletion in SMRACA4, and one frame-shift variant in SMARCC2. Association analysis was conducted to investigate the burden of rare variants in BAF genes in ASD and SCZ patients. Significant enrichment of rare missense variants in BAF genes, but not synonymous variants, was found in ASD compared to controls. Rare pathogenic variants indicated by in silico tools were significantly enriched in ASD, but not statistically significant in SCZ. Pathogenic-predicted variants were located in disordered binding regions and may confer risk for ASD and SCZ by disrupting protein-protein interactions. Our study supports the involvement of rare missense variants of BAF genes in ASD and SCZ susceptibility.

Amelioration of a neurodevelopmental disorder by carbamazepine in a case having a gain-of-function GRIA3 variant

GRIA3 at Xq25 encodes glutamate ionotropic receptor AMPA type 3 (GluA3), a subunit of postsynaptic glutamate-gated ion channels mediating neurotransmission. Hemizygous loss-of-function (LOF) variants in GRIA3 cause a neurodevelopmental disorder (NDD) in male individuals. Here, we report a gain-of-function (GOF) variant at GRIA3 in a male patient. We identified a hemizygous de novo missense variant in GRIA3 in a boy with an NDD: c.1844C > T (p.Ala615Val) using whole-exome sequencing. His neurological signs, such as hypertonia and hyperreflexia, were opposite to those in previous cases having LOF GRIA3 variants. His seizures and hypertonia were ameliorated by carbamazepine, inhibiting glutamate release from presynapses. Patch-clamp recordings showed that the human GluA3 mutant (p.Ala615Val) had slower desensitization and deactivation kinetics. A fly line expressing a human GluA3 mutant possessing our variant and the Lurcher variant, which makes ion channels leaky, showed developmental defects, while one expressing a mutant possessing either of them did not. Collectively, these results suggest that p.Ala615Val has GOF effects. GRIA3 GOF variants may cause an NDD phenotype distinctive from that of LOF variants, and drugs suppressing glutamatergic neurotransmission may ameliorate this phenotype. This study should help in refining the clinical management of GRIA3-related NDDs.

The Role of Microtubule Associated Serine/Threonine Kinase 3 Variants in Neurodevelopmental Diseases: Genotype-Phenotype Association

Objective: To prove microtubule associated serine/threonine kinase 3 (MAST3) gene is associated with neurodevelopmental diseases (NDD) and the genotype-phenotype correlation.

Methods: Trio exome sequencing (trio ES) was performed on four NDD trios. Bioinformatic analysis was conducted based on large-scale genome sequencing data and human brain transcriptomic data. Further in vivo zebrafish studies were performed.

Results: In our study, we identified four de novo MAST3 variants (NM_015016.1: c.302C &gt; T:p.Ser101Phe; c.311C &gt; T:p.Ser104Leu; c.1543G &gt; A:p.Gly515Ser; and c.1547T &gt; C:p.Leu516Pro) in four patients with developmental and epileptic encephalopathy (DEE) separately. Clinical heterogeneities were observed in patients carrying variants in domain of unknown function (DUF) and serine-threonine kinase (STK) domain separately. Using the published large-scale exome sequencing data, higher CADD scores of missense variants in DUF domain were found in NDD cohort compared with gnomAD database. In addition, we obtained an excess of missense variants in DUF domain when compared autistic spectrum disorder (ASD) cohort with gnomAD database, similarly an excess of missense variants in STK domain when compared DEE cohort with gnomAD database. Based on Brainspan datasets, we showed that MAST3 expression was significantly upregulated in ASD and DEE-related brain regions and was functionally linked with DEE genes. In zebrafish model, abnormal morphology of central nervous system was observed in mast3a/b crispants.

Conclusion: Our results support the possibility that MAST3 is a novel gene associated with NDD which could expand the genetic spectrum for NDD. The genotype-phenotype correlation may contribute to future genetic counseling.

Distinct gene-set burden patterns underlie common generalized and focal epilepsies

Background

Analyses of few gene-sets in epilepsy showed a potential to unravel key disease associations. We set out to investigate the burden of ultra-rare variants (URVs) in a comprehensive range of biologically informed gene-sets presumed to be implicated in epileptogenesis.

Methods

The burden of 12 URV types in 92 gene-sets was compared between cases and controls using whole exome sequencing data from individuals of European descent with developmental and epileptic encephalopathies (DEE, n = 1,003), genetic generalized epilepsy (GGE, n = 3,064), or non-acquired focal epilepsy (NAFE, n = 3,522), collected by the Epi25 Collaborative, compared to 3,962 ancestry-matched controls.

Findings

Missense URVs in highly constrained regions were enriched in neuron-specific and developmental genes, whereas genes not expressed in brain were not affected. GGE featured a higher burden in gene-sets derived from inhibitory vs. excitatory neurons or associated receptors, whereas the opposite was found for NAFE, and DEE featured a burden in both. Top-ranked susceptibility genes from recent genome-wide association studies (GWAS) and gene-sets derived from generalized vs. focal epilepsies revealed specific enrichment patterns of URVs in GGE vs. NAFE.

Interpretation

Missense URVs affecting highly constrained sites differentially impact genes expressed in inhibitory vs. excitatory pathways in generalized vs. focal epilepsies. The excess of URVs in top-ranked GWAS risk-genes suggests a convergence of rare deleterious and common risk-variants in the pathogenesis of generalized and focal epilepsies.

Funding

DFG Research Unit FOR-2715 (Germany), FNR (Luxembourg), NHGRI (US), NHLBI (US), DAAD (Germany).

Three-dimensional missense tolerance ratio analysis

A wealth of genetic information is available describing single-nucleotide variants in the human population that appear to be well-tolerated and in and of themselves do not confer disease. These variant data sets contain signatures about the protein structure-function relationships and provide an unbiased view of various protein functions in the context of human health. This information can be used to determine regional intolerance to variation, defined as the missense tolerance ratio (MTR), which is an indicator of stretches of the polypeptide chain that can tolerate changes without compromising protein function in a manner that impacts human health. This approach circumvents the lack of comprehensive data by averaging the data from adjacent residues on the polypeptide chain. We reasoned that many motifs in proteins consist of nonadjacent residues, but together function as a unit. We therefore developed an approach to analyze nearest neighbors in three-dimensional space as determined by crystallography rather than on the polypeptide chain. We used members of the GRIN gene family that encode subunits of NMDA-type ionotropic glutamate receptors (iGluRs) to exemplify the differences between these methods. Our method, 3DMTR, provides new information about regions of intolerance within iGluRs, allows consideration of protein-protein interfaces in multimeric proteins, and moves this important research tool from one-dimensional analysis to a structurally relevant tool. We validate the improved 3DMTR score by showing that it more accurately classifies the functional consequences of a set of newly measured and published point mutations of Grin family genes than existing methods.

Computational Studies of the Structural Basis of Human RPS19 Mutations Associated With Diamond-Blackfan Anemia

Diamond-Blackfan Anemia (DBA) is an inherited rare disease characterized with severe pure red cell aplasia, and it is caused by the defective ribosome biogenesis stemming from the impairment of ribosomal proteins. Among all DBA-associated ribosomal proteins, RPS19 affects most patients and carries most DBA mutations. Revealing how these mutations lead to the impairment of RPS19 is highly demanded for understanding the pathogenesis of DBA, but a systematic study is currently lacking. In this work, based on the complex structure of human ribosome, we comprehensively studied the structural basis of DBA mutations of RPS19 by using computational methods. Main structure elements and five conserved surface patches involved in RPS19-18S rRNA interaction were identified. We further revealed that DBA mutations would destabilize RPS19 through disrupting the hydrophobic core or breaking the helix, or perturb the RPS19-18S rRNA interaction through destroying hydrogen bonds, introducing steric hindrance effect, or altering surface electrostatic property at the interface. Moreover, we trained a machine-learning model to predict the pathogenicity of all possible RPS19 mutations. Our work has laid a foundation for revealing the pathogenesis of DBA from the structural perspective.

Opportunities and challenges for the computational interpretation of rare variation in clinically important genes

Genome sequencing is enabling precision medicine-tailoring treatment to the unique constellation of variants in an individual's genome. The impact of recurrent pathogenic variants is often understood, however there is a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequential variants and associated mechanisms. Variants of uncertain significance (VUSs) in these genes are discovered at a rate that outpaces current ability to classify them with databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings: inborn errors of metabolism in newborns and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation.

The role of sodium channels in sudden unexpected death in pediatrics

BACKGROUND

Sudden Unexpected Death in Pediatrics (SUDP) is a tragic event, likely caused by the complex interaction of multiple factors. The presence of hippocampal abnormalities in many children with SUDP suggests that epilepsy-related mechanisms may contribute to death, similar to Sudden Unexplained Death in Epilepsy. Because of known associations between the genes SCN1A and SCN5A and sudden death, and shared mechanisms and patterns of expression in genes encoding many voltage-gated sodium channels (VGSCs), we hypothesized that individuals dying from SUDP have pathogenic variants across the entire family of cardiac arrhythmia- and epilepsy-associated VGSC genes.

METHODS

To address this hypothesis, we evaluated whole-exome sequencing data from infants and children with SUDP for variants in VGSC genes, reviewed the literature for all SUDP-associated variants in VGSCs, applied a novel paralog analysis to all variants, and evaluated all variants according to American College of Medical Genetics and Genomics (ACMG) guidelines.

RESULTS

In our cohort of 73 cases of SUDP, we assessed 11 variants as pathogenic in SCN1A, SCN1B, and SCN10A, genes with long-standing disease associations, and in SCN3A, SCN4A, and SCN9A, VGSC gene paralogs with more recent disease associations. From the literature, we identified 82 VGSC variants in SUDP cases. Pathogenic variants clustered at conserved amino acid sites intolerant to variation across the VGSC genes, which is unlikely to occur in the general population (p < .0001). For 54% of variants previously reported in literature, we identified conflicting evidence regarding pathogenicity when applying ACMG criteria and modern population data.

CONCLUSION

We report variants in several VGSC genes in cases with SUDP, involving both arrhythmia- and epilepsy-associated genes. Accurate variant assessment as well as future studies are essential for an improved understanding of the contribution of sodium channel-related variants to SUDP.