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We performed collapsing analyses on 454,796 UK Biobank (UKB) exomes to detect gene-level associations with diabetes. Recessive carriers of nonsynonymous variants in MAP3K15 were 30% less likely to develop diabetes ( P = 5.7 × 10 −10 ) and had lower glycosylated hemoglobin (β = −0.14 SD units, P = 1.1 × 10 −24 ). These associations were independent of body mass index, suggesting protection against insulin resistance even in the setting of obesity. We replicated these findings in 96,811 Admixed Americans in the Mexico City Prospective Study ( P < 0.05)Moreover, the protective effect of MAP3K15 variants was stronger in individuals who did not carry the Latino-enriched SLC16A11 risk haplotype ( P = 6.0 × 10 −4 ). Separately, we identified a Finnish-enriched MAP3K15 protein-truncating variant associated with decreased odds of both type 1 and type 2 diabetes ( P < 0.05) in FinnGen. No adverse phenotypes were associated with protein-truncating MAP3K15 variants in the UKB, supporting this gene as a therapeutic target for diabetes.

Despite great progress in the identification of neurodevelopmental disorder (NDD) risk genes, there are thousands that remain to be discovered. Computational tools that provide accurate gene-level predictions of NDD risk can significantly reduce the costs and time needed to prioritize and discover novel NDD risk genes. Here, we first demonstrate that machine learning models trained solely on single-cell RNA-sequencing data from the developing human cortex can robustly predict genes implicated in autism spectrum disorder (ASD), developmental and epileptic encephalopathy (DEE), and developmental delay (DD). Strikingly, we find differences in gene expression patterns of genes with monoallelic and biallelic inheritance patterns. We then integrate these expression data with 300 orthogonal features in a semi-supervised machine learning framework (mantis-ml) to train inheritance-specific models for ASD, DEE, and DD. The models have high predictive power (AUCs: 0.84 to 0.95) and top-ranked genes were up to two-fold (monoallelic models) and six-fold (biallelic models) more enriched for high-confidence NDD risk genes than genic intolerance metrics. Across all models, genes in the top decile of predicted risk genes were 60 to 130 times more likely to have publications strongly linking them to the phenotype of interest in PubMed compared to the bottom decile. Collectively, this work provides highly robust novel NDD risk gene predictions that can complement large-scale gene discovery efforts and underscores the importance of incorporating inheritance into gene risk prediction tools (https://nddgenes.com).

We performed collapsing analyses on 454,796 UK Biobank (UKB) exomes to detect gene-level associations with diabetes. Recessive carriers of nonsynonymous variants in

Trigeminal neuralgia, historically dubbed the “suicide disease,” is an exceedingly painful neurologic condition characterized by sudden episodes of intense facial pain. Unfortunately, the only U.S. Food and Drug Administration (FDA)–approved medication for trigeminal neuralgia carries substantial side effects, with many patients requiring surgery. Here, we identify the NRF2 transcriptional network as a potential therapeutic target. We report that cerebrospinal fluid from patients with trigeminal neuralgia accumulates reactive oxygen species, several of which directly activate the pain-transducing channel TRPA1. Similar to our patient cohort, a mouse model of trigeminal neuropathic pain also exhibits notable oxidative stress. We discover that stimulating the NRF2 antioxidant transcriptional network is as analgesic as inhibiting TRPA1, in part by reversing the underlying oxidative stress. Using a transcriptome-guided drug discovery strategy, we identify two NRF2 network modulators as potential treatments. One of these candidates, exemestane, is already FDA-approved and may thus be a promising alternative treatment for trigeminal neuropathic pain.

SummarySynonymous mutations change the DNA sequence of a gene without affecting the amino acid sequence of the encoded protein. Although emerging evidence suggests that synonymous mutations can impact RNA splicing, translational efficiency, and mRNA stability1, studies in human genetics, mutagenesis screens, and other experiments and evolutionary analyses have repeatedly shown that most synonymous variants are neutral or only weakly deleterious, with some notable exceptions. In their recent article, Shen et al. claim to have disproved these well-established findings. They perform mutagenesis experiments in yeast and conclude that synonymous mutations frequently reduce fitness to the same extent as nonsynonymous mutations2. Based on their findings, the authors state that their results “imply that synonymous mutations are nearly as important as nonsynonymous mutations in causing disease.” An accompanying News and Views argues that “revising our expectations about synonymous mutations should expand our view of the genetic underpinnings of human health”3. Considering potential technical concerns with these experiments4 and a large, coherent body of knowledge establishing the predominant neutrality of synonymous variants, we caution against interpreting this study in the context of human disease.

Large reference datasets of protein-coding variation in human populations have allowed us to determine which genes and genic subregions are intolerant to germline genetic variation. There is also a growing number of genes implicated in severe Mendelian diseases that overlap with genes implicated in cancer. We hypothesized that cancer-driving mutations might be enriched in genic subregions that are depleted of germline variation relative to somatic variation. We introduce a new metric, OncMTR (oncology missense tolerance ratio), which uses 125,748 exomes in the Genome Aggregation Database (gnomAD) to identify these genic subregions. We demonstrate that OncMTR can significantly predict driver mutations implicated in hematologic malignancies. Divergent OncMTR regions were enriched for cancer-relevant protein domains, and overlaying OncMTR scores on protein structures identified functionally important protein residues. Last, we performed a rare variant, gene-based collapsing analysis on an independent set of 394,694 exomes from the UK Biobank and find that OncMTR markedly improves genetic signals for hematologic malignancies.

Elucidating functionality in non-coding regions is a key challenge in human genomics. It has been shown that intolerance to variation of coding and proximal non-coding sequence is a strong predictor of human disease relevance. Here, we integrate intolerance to variation, functional genomic annotations and primary genomic sequence to build JARVIS: a comprehensive deep learning model to prioritize non-coding regions, outperforming other human lineage-specific scores. Despite being agnostic to evolutionary conservation, JARVIS performs comparably or outperforms conservation-based scores in classifying pathogenic single-nucleotide and structural variants. In constructing JARVIS, we introduce the genome-wide residual variation intolerance score (gwRVIS), applying a sliding-window approach to whole genome sequencing data from 62,784 individuals. gwRVIS distinguishes Mendelian disease genes from more tolerant CCDS regions and highlights ultra-conserved non-coding elements as the most intolerant regions in the human genome. Both JARVIS and gwRVIS capture previously inaccessible human-lineage constraint information and will enhance our understanding of the non-coding genome., Intolerance to variation is a strong indicator of disease relevance for coding regions of the human genome. Here, the authors present JARVIS, a deep learning method integrating intolerance to variation in non-coding regions and sequence-specific annotations to infer non-coding variant pathogenicity.

Advances in genetic discoveries have created substantial opportunities for precision medicine in neurodevelopmental disorders. Many of the genes implicated in these diseases encode proteins that regulate gene expression, such as chromatin-associated proteins, transcription factors, and RNA-binding proteins. The identification of targeted therapeutics for individuals carrying mutations in these genes remains a challenge, as the encoded proteins can theoretically regulate thousands of downstream targets in a considerable number of cell types. Here, we propose the application of a drug discovery approach originally developed for cancer called "transcriptome reversal" for these neurodevelopmental disorders. This approach attempts to identify compounds that reverse gene-expression signatures associated with disease states. ANN NEUROL 2021;89:199-211.

Synapses connect discrete neurons into vast networks that send, receive, and encode diverse forms of information. Synaptic function and plasticity, the neuronal process of adapting to diverse and variable inputs, depend on the dynamic nature of synaptic molecular components, which is mediated in part by cell adhesion signaling pathways. Here, we found that the enzyme biliverdin reductase (BVR) physically links together key focal adhesion signaling molecules at the synapse. BVR -null ( BVR −/− ) mice exhibited substantial deficits in learning and memory on neurocognitive tests, and hippocampal slices in which BVR was postsynaptically depleted showed deficits in electrophysiological responses to stimuli. RNA sequencing, biochemistry, and pathway analyses suggested that these deficits were mediated through the loss of focal adhesion signaling at both the transcriptional and biochemical level in the hippocampus. Independently of its catalytic function, BVR acted as a bridge between the primary focal adhesion signaling kinases FAK and Pyk2 and the effector kinase Src. Without BVR, FAK and Pyk2 did not bind to and stimulate Src, which then did not phosphorylate the N -methyl- d -aspartate (NMDA) receptor, a critical posttranslational modification for synaptic plasticity. Src itself is a molecular hub on which many signaling pathways converge to stimulate NMDAR-mediated neurotransmission, thus positioning BVR at a prominent intersection of synaptic signaling.

Genome-wide association studies have established the contribution of common and low frequency variants to metabolic biomarkers in the UK Biobank (UKB); however, the role of rare variants remains to be assessed systematically. We evaluated rare coding variants for 198 metabolic biomarkers, including metabolites assayed by Nightingale Health, using exome sequencing in participants from four genetically diverse ancestries in the UKB (N=412,394). Gene-level collapsing analysis – that evaluated a range of genetic architectures – identified a total of 1,303 significant relationships between genes and metabolic biomarkers (p−8), encompassing 207 distinct genes. These include associations between rare non-synonymous variants inGIGYF1and glucose and lipid biomarkers,SYT7and creatinine, and others, which may provide insights into novel disease biology. Comparing to a previous microarray-based genotyping study in the same cohort, we observed that 40% of gene-biomarker relationships identified in the collapsing analysis were novel. Finally, we applied Gene-SCOUT, a novel tool that utilises the gene-biomarker association statistics from the collapsing analysis to identify genes having similar biomarker fingerprints and thus expand our understanding of gene networks.

Diabetes mellitus is a chronic health condition that can result in significant end-organ complications and is estimated to impact at least 8.5% of the global adult population. Here, we performed gene-level collapsing analysis on exome sequences from 454,796 multi-ancestry UK Biobank participants to detect genetic associations with diabetes. Rare non-synonymous variants in GCK, GIGYF1, HNF1A, and HNF4A were significantly associated (P-8) with increased risk of diabetes, whereas rare non-synonymous variants in MAP3K15 were significantly associated with reduced risk of diabetes. Recessive carriers of rare non-synonymous variants in the X chromosome gene MAP3K15 had a 30% reduced risk of diabetes (OR=0.70, 95% CI: [0.62,0.79], P=5.7×10-10), along with reduced blood glucose (beta=-0.13, 95% CI: [-0.15,-0.10], P=5.5×10-18) and reduced glycosylated haemoglobin levels (beta=-0.14, 95% CI: [-0.16,-0.11], P=1.1×10-24). Hemizygous males carrying protein-truncating variants (PTVs) in MAP3K15 demonstrated a 40% reduced risk of diabetes (OR=0.60, 95% CI: [0.45,0.81], P=0.0007). These findings were independently replicated in FinnGen, with a MAP3K15 PTV associating with decreased risk of both type 1 diabetes (T1DM) and type 2 diabetes (T2DM) (pMAP3K15 loss on diabetes was independent of body mass index, suggesting its protective effect is unlikely to be mediated via the insulin resistance pathway. Tissue expression profile of MAP3K15 indicates a possible involvement of pancreatic islet cell or stress response pathways. No safety concerns were identified among heterozygous or recessive MAP3K15 PTV carriers across over 15,719 studied endpoints in the UK Biobank. Human population genetic evidence supports MAP3K15 inhibition as a novel therapeutic target for diabetes.

Idiopathic pulmonary fibrosis (IPF) is a fatal disorder characterised by progressive, destructive lung scarring. Despite substantial progress, the genetic determinants of this disease remain incompletely defined. Using whole genome and whole exome sequencing data from 752 individuals with sporadic IPF and 119,055 UK Biobank controls, we performed a variant-level exome-wide association study (ExWAS) and gene-level collapsing analyses. Our variant-level analysis revealed a novel association between a rare missense variant in SPDL1 and IPF (NM_017785.5:g.169588475 G > A p.Arg20Gln; p = 2.4 × 10−7, odds ratio = 2.87, 95% confidence interval: 2.03–4.07). This signal was independently replicated in the FinnGen cohort, which contains 1028 cases and 196,986 controls (combined p = 2.2 × 10−20), firmly associating this variant as an IPF risk allele. SPDL1 encodes Spindly, a protein involved in mitotic checkpoint signalling during cell division that has not been previously described in fibrosis. To the best of our knowledge, these results highlight a novel mechanism underlying IPF, providing the potential for new therapeutic discoveries in a disease of great unmet need., Ryan Dhindsa et al. conducted an exome-wide association study to identify a rare variant in SPDL1 as a risk factor for idiopathic pulmonary fibrosis (IPF). Their findings implicate mitotic checkpoint signalling as a new mechanism underlying IPF.

EBioMedicine 72, 103588 (2021). doi:10.1016/j.ebiom.2021.103588, Published by Elsevier, Amsterdam [u.a.]

Summary Both mild and severe epilepsies are influenced by variants in the same genes, yet an explanation for the resulting phenotypic variation is unknown. As part of the ongoing Epi25 Collaboration, we performed a whole-exome sequencing analysis of 13,487 epilepsy-affected individuals and 15,678 control individuals. While prior Epi25 studies focused on gene-based collapsing analyses, we asked how the pattern of variation within genes differs by epilepsy type. Specifically, we compared the genetic architectures of severe developmental and epileptic encephalopathies (DEEs) and two generally less severe epilepsies, genetic generalized epilepsy and non-acquired focal epilepsy (NAFE). Our gene-based rare variant collapsing analysis used geographic ancestry-based clustering that included broader ancestries than previously possible and revealed novel associations. Using the missense intolerance ratio (MTR), we found that variants in DEE-affected individuals are in significantly more intolerant genic sub-regions than those in NAFE-affected individuals. Only previously reported pathogenic variants absent in available genomic datasets showed a significant burden in epilepsy-affected individuals compared with control individuals, and the ultra-rare pathogenic variants associated with DEE were located in more intolerant genic sub-regions than variants associated with non-DEE epilepsies. MTR filtering improved the yield of ultra-rare pathogenic variants in affected individuals compared with control individuals. Finally, analysis of variants in genes without a disease association revealed a significant burden of loss-of-function variants in the genes most intolerant to such variation, indicating additional epilepsy-risk genes yet to be discovered. Taken together, our study suggests that genic and sub-genic intolerance are critical characteristics for interpreting the effects of variation in genes that influence epilepsy.

SummaryThe UK Biobank (UKB) represents an unprecedented population-based study of 502,543 participants with detailed phenotypic data and linkage to medical records. While the release of genotyping array data for this cohort has bolstered genomic discovery for common variants, the contribution of rare variants to this broad phenotype collection remains relatively unknown. Here, we use exome sequencing data from 177,882 UKB participants to evaluate the association between rare protein-coding variants with 10,533 binary and 1,419 quantitative phenotypes. We performed both a variant-level phenome-wide association study (PheWAS) and a gene-level collapsing analysis-based PheWAS tailored to detecting the aggregate contribution of rare variants. The latter revealed 911 statistically significant gene-phenotype relationships, with a median odds ratio of 15.7 for binary traits. Among the binary trait associations identified using collapsing analysis, 83% were undetectable using single variant association tests, emphasizing the power of collapsing analysis to detect signal in the setting of high allelic heterogeneity. As a whole, these genotype-phenotype associations were significantly enriched for loss-of-function mediated traits and currently approved drug targets. Using these results, we summarise the contribution of rare variants to common diseases in the context of the UKB phenome and provide an example of how novel gene-phenotype associations can aid in therapeutic target prioritisation.

Synapses are complex bridges that connect discrete neurons into vast networks that send, receive, and encode diverse forms of information. However, they must remain dynamic in order to adapt to changing inputs. Here, we report that the enzyme biliverdin reductase (BVR) physically links together key molecules in focal adhesion signaling at the synapse. In challenging mice with a battery of neurocognitive tasks, we first discover that BVR null (BVR-/-) mice exhibit profound deficits in learning and memory. We uncover that these deficits may be explained by a loss of focal adhesion signaling that is both transcriptionally and biochemically disrupted in BVR-/- hippocampi. We learn that BVR mediates focal adhesion signaling by physically bridging the initiatory kinases FAK/Pyk2 to the effector kinase Src. Activated Src normally promotes synaptic plasticity by phosphorylating the N-methyl-D-aspartate (NMDA) receptor, but FAK/Pyk2 are unable to bind and stimulate Src without BVR. Src itself is a molecular hub upon which many signaling pathways converge in order to stimulate NMDA neurotransmission, positioning BVR at a prominent intersection of synaptic signaling.

Idiopathic pulmonary fibrosis (IPF) is a fatal disorder characterised by progressive, destructive lung scarring. Despite significant progress, the genetic determinants of this disease remain incompletely defined. Using next generation sequencing data from 752 individuals with sporadic IPF and 119,055 controls, we performed both variant- and gene-level analyses to identify novel IPF genetic risk factors. Our variant-level analysis revealed a novel rare missense variant in SPDL1 (NM_017785.5 p.Arg20Gln; p = 2.4 × 10−7, odds ratio = 2.87). This signal was independently replicated in the FinnGen cohort (combined p = 2.2 × 10−20), firmly associating this variant as a novel IPF risk allele. SPDL1 encodes Spindly, a protein involved in mitotic checkpoint signalling during cell division that has not been previously described in fibrosis. Our results highlight a novel mechanism underlying IPF, providing the potential for new therapeutic discoveries in a disease of great unmet need.

Advances in genetic discoveries have created substantial opportunities for precision medicine in neurodevelopmental disorders. Many of the genes implicated in these diseases encode proteins that regulate gene expression, such as chromatin associated proteins, transcription factors, and RNA-binding proteins. The identification of targeted therapeutics for individuals carrying mutations in these genes remains a challenge, as the encoded proteins can theoretically regulate thousands of downstream targets in a considerable number of cell types. Here, we propose the application of a drug discovery approach called “transcriptome reversal” for these disorders. This approach, originally developed for cancer, attempts to identify compounds that reverse gene-expression signatures associated with disease states.

Heterozygous de novo loss-of-function mutations in the gene expression regulator HNRNPU cause an early-onset developmental and epileptic encephalopathy. To gain insight into pathological mechanisms and lay the potential groundwork for developing targeted therapies, we characterized the neurophysiologic and cell-type-specific transcriptomic consequences of a mouse model of HNRNPU haploinsufficiency. Heterozygous mutants demonstrated neuroanatomical abnormalities, global developmental delay, impaired ultrasonic vocalizations and increased seizure susceptibility, thus modeling aspects of the human disease. Single-cell RNA-sequencing of hippocampal and neocortical cells revealed widespread, yet modest, dysregulation of gene expression across mutant neuronal subtypes. We observed an increased burden of differentially-expressed genes in mutant excitatory neurons of the subiculum—a region of the hippocampus implicated in temporal lobe epilepsy. Evaluation of transcriptomic signature reversal as a therapeutic strategy highlights the potential importance of generating cell-type-specific signatures. Overall, this work provides insight into HNRNPU-mediated disease mechanisms, and provides a framework for using single-cell RNA-sequencing to study transcriptional regulators implicated in disease.

There is an urgent need to identify effective therapies for COVID-19. The SARS-CoV-2 host factor protease TMPRSS2 is required for viral entry and thus an attractive target for therapeutic intervention. In mouse, knockout of tmprss2 led to protection against SARS-CoV-1 with no deleterious phenotypes, and in human populations genetic loss of TMPRSS2 does not appear to be selected against. Here, we mined publicly available gene expression data to identify several compounds that down-regulate TMPRSS2. Recognizing the need for immediately available treatment options, we focused on FDA-approved drugs. We found 20 independent studies that implicate estrogenic and androgenic compounds as transcriptional modulators of TMPRSS2, suggesting these classes of drugs may be promising therapeutic candidates for clinical testing and observational studies of COVID-19. We also note that expression of TMPRSS2 is highly variable and skewed in humans, with a minority of individuals having extremely high expression. Combined with literature showing that inhibition of TMPRSS2 protease activity reduces SARS-CoV-2 viral entry in human cells, our results raise the hypothesis that modulation of TMPRSS2 expression is a promising therapeutic avenue for COVID-19.

There is an urgent need to identify effective therapies for COVID-19 given that a broadly available and effective vaccine is likely at least one year away. Here, we identify compounds that transcriptionally inhibit host proteins required for SARS-CoV-2 entry and should be evaluated for efficacy in SARS-CoV-2 viral infection assays. Recognizing the need for immediately available treatment options, we focused particular attention on FDA-approved drugs that could be immediately repurposed to treat COVID-19 patients. By mining publicly available gene expression data, we identify several compounds that down-regulate TMPRSS2, a protein required for SARS-CoV-2 entry that has emerged as a promising therapeutic target. Among these, we find twenty independent studies that implicate estrogen-related and androgen-related compounds as transcriptional modulators of TMPRSS2 expression, suggesting that these drugs and others acting on the pathway may be promising therapeutic candidates for COVID-19 for further testing. It is also noteworthy that TMPRSS2 has highly variable and skewed expression in humans, spanning two orders of magnitude with a small minority of individuals having extremely high expression. Combined with literature showing that TMPRSS2 loss-of-function in mouse is protective against SARS while anti-estrogen treatment predicted to increase TMPRSS2 expression exacerbates SARS, this observation raises the hypothesis that TMPRSS2 expression may positively correlate with severity in COVID-19.

Synonymous codon usage has been identified as an important determinant of translational efficiency and mRNA stability in model organisms and human cell lines. However, to date, population genetics studies have failed to observe evolutionary constraint on human codon usage, and synonymous variation has been largely overlooked as a component of human genetic diversity. Using genetic sequencing data from nearly 200,000 individuals, we uncover clear evidence that natural selection optimizes codon content in the human genome. We derive intolerance metrics to quantify gene-level constraint on synonymous variation and demonstrate that dosage-sensitive, DNA damage response, and cell cycle regulated genes are more intolerant to synonymous variation than other genes in the genome. Notably, we illustrate that reductions in codon optimality can attenuate the function of BRCA1. Our results reveal that synonymous mutations likely play an important and underappreciated role in human variation.

SUMMARYDe novo mutations in GNB1, encoding the Gβ1 subunit of G proteins, cause a neurodevelopmental disorder with global developmental delay and epilepsy. Mice carrying a pathogenic mutation, K78R, recapitulate aspects of the disorder, including developmental delay and frequent spike-wave discharges (SWD). Cultured mutant cortical neurons display aberrant bursting activity on multi-electrode arrays. Strikingly, the antiepileptic drug ethosuximide (ETX) restores normal neuronal network behavior in vitro and suppresses SWD in vivo. In contrast, while valproic acid suppresses SWD, it does not restore normal network behavior, suggesting that ETX has mechanistic specificity for the effects of aberrant Gβ1 signaling. Consistent with this, we show that K78R is a gain-of-function of G protein-coupled potassium channel (GIRK) activation that is potently inhibited by ETX. This work suggests that altered Gβ1 signaling causes disease in part through effects on GIRK channels, illustrates the utility of cultured neuronal networks in pharmacological screening, and establishes effective pre-clinical models for GNB1 Encephalopathy.

Sequencing-based studies have identified novel risk genes for rare, severe epilepsies and revealed a role of rare deleterious variation in common epilepsies. To identify the shared and distinct ultra-rare genetic risk factors for rare and common epilepsies, we performed a whole-exome sequencing (WES) analysis of 9,170 epilepsy-affected individuals and 8,364 controls of European ancestry. We focused on three phenotypic groups; the rare but severe developmental and epileptic encephalopathies (DEE), and the commoner phenotypes of genetic generalized epilepsy (GGE) and non-acquired focal epilepsy (NAFE). We observed that compared to controls, individuals with any type of epilepsy carried an excess of ultra-rare, deleterious variants in constrained genes and in genes previously associated with epilepsy, with the strongest enrichment seen in DEE and the least in NAFE. Moreover, we found that inhibitory GABAA receptor genes were enriched for missense variants across all three classes of epilepsy, while no enrichment was seen in excitatory receptor genes. The larger gene groups for the GABAergic pathway or cation channels also showed a significant mutational burden in DEE and GGE. Although no single gene surpassed exome-wide significance among individuals with GGE or NAFE, highly constrained genes and genes encoding ion channels were among the top associations, including CACNA1G, EEF1A2, and GABRG2 for GGE and LGI1, TRIM3, and GABRG2 for NAFE. Our study confirms a convergence in the genetics of common and rare epilepsies associated with ultra-rare coding variation and highlights a ubiquitous role for GABAergic inhibition in epilepsy etiology in the largest epilepsy WES study to date.

Here we present an open-source R package ‘meaRtools’ that provides a platform for analyzing neuronal networks recorded on Microelectrode Arrays (MEAs). Cultured neuronal networks monitored with MEAs are now being widely used to characterize in vitro models of neurological disorders and to evaluate pharmaceutical compounds. meaRtools provides core algorithms for MEA spike train analysis, feature extraction, statistical analysis and plotting of multiple MEA recordings with multiple genotypes and treatments. meaRtools functionality covers novel solutions for spike train analysis, including algorithms to assess electrode cross-correlation using the spike train tiling coefficient (STTC), mutual information, synchronized bursts and entropy within cultured wells. Also integrated is a solution to account for bursts variability originating from mixed-cell neuronal cultures. The package provides a statistical platform built specifically for MEA data that can combine multiple MEA recordings and compare extracted features between different genetic models or treatments. We demonstrate the utilization of meaRtools to successfully identify epilepsy-like phenotypes in neuronal networks from Celf4 knockout mice. The package is freely available under the GPL license (GPL>=3) and is updated frequently on the CRAN web-server repository. The package, along with full documentation can be downloaded from: https://cran.r-project.org/web/packages/meaRtools/.Author summaryCultured neuronal networks are widely used to study and characterize neuronal network activity. Among the many uses of neuronal cultures are the capabilities to evaluate neurotoxicity and the effects of pharmacological compounds on cellular physiology. Multi-well microelectrode arrays (MEAs) can collect high-throughput data from multiple neuronal cultures simultaneously, and thereby make possible hypotheses-driven inquiries into neurobiology and neuropharmacology. The analysis of MEA-derived information presents many computational challenges. High frequency data recorded simultaneously from hundreds of electrodes can be difficult to handle. The need to compare network activity across various drug treatments or genotypes recorded on the same plate from experiments lasting several weeks presents another challenge. These challenges inspired us to develop meaRtools; an MEA data analysis package that contains new methods to characterize network activity patterns, which are illustrated here using examples from a genetic mouse model of epilepsy. Among the highlights of meaRtools are novel algorithms designed to characterize neuronal activity dynamics and network properties such as bursting and synchronization, options to combine multiple recordings and use a robust statistical framework to draw appropriate statistical inferences, and finally data visualizations and plots. In summary, meaRtools provides a platform for the analyses of singular and longitudinal MEA experiments.

There is broad agreement that genetic mutations occurring outside of the protein-coding regions play a key role in human disease. Despite this consensus, we are not yet capable of discerning which portions of non-coding sequence are important in the context of human disease. Here, we present Orion, an approach that detects regions of the non-coding genome that are depleted of variation, suggesting that the regions are intolerant of mutations and subject to purifying selection in the human lineage. We show that Orion is highly correlated with known intolerant regions as well as regions that harbor putatively pathogenic variation. This approach provides a mechanism to identify pathogenic variation in the human non-coding genome and will have immediate utility in the diagnostic interpretation of patient genomes and in large case control studies using whole-genome sequences.

There is broad agreement that genetic mutations occurring outside of the protein-coding regions play a key role in human disease. Despite this consensus, we are not yet capable of discerning which portions of non-coding sequence are important in the context of human disease. Here, we present Orion, an approach that detects regions of the non-coding genome that are depleted of variation, suggesting that the regions are intolerant of mutations and subject to purifying selection in the human lineage. We show that Orion is highly correlated with known intolerant regions as well as regions that harbor putatively pathogenic variation. This approach provides a mechanism to identify pathogenic variation in the human non-coding genome and will have immediate utility in the diagnostic interpretation of patient genomes and in large case control studies using whole-genome sequences.

Epilepsy is one of the most prevalent serious neurological disorders worldwide. Genetic research is making a major impact on epilepsy in terms of both clinical management and understanding the underlying neurobiology to guide drug development. Critically, current antiepileptic drugs are ineffective in one-third of patients, almost certainly owing in large part to an insufficient understanding of the basic molecular underpinnings of disease in treatment-resistant patients. Next-generation sequencing approaches, however, are making tremendous progress in identifying the genetic causes of disease, which provide new directions for elucidating the molecular pathophysiological processes underlying epileptogenesis. Although there has been limited progress in understanding the genetics of common epilepsies, genetic and neurobiological discoveries are being made in the rare, strongly genetic epilepsies at an unprecedented rate. These findings have already led to some personalized treatment options in the clinic.

Epilepsy is a serious neurological disease with substantial genetic contribution. We have recently made major advances in understanding the genetics and etiology of the epilepsies. However, current antiepileptic drugs are ineffective in nearly one third of patients. Most of these drugs were developed without knowledge of the underlying causes of the epilepsy to be treated; thus, it seems reasonable to assume that further improvements require a deeper understanding of epilepsy pathophysiology. Although once the rate-limiting step, gene discovery is now occurring at an unprecedented rapid rate, especially in the epileptic encephalopathies. However, to place these genetic findings in a biological context and discover treatment options for patients, we must focus on developing an efficient framework for functional evaluation of the mutations that cause epilepsy. In this review, we discuss guidelines for gene discovery, emerging functional assays and models, and novel therapeutics to highlight the developing framework of precision medicine in the epilepsies.

Objective: To elucidate the functional consequences of epileptic encephalopathy–causing de novo mutations in DNM1 (A177P, K206N, G359A), which encodes a large mechanochemical GTPase essential for neuronal synaptic vesicle endocytosis. Methods: HeLa and COS-7 cells transfected with wild-type and mutant DNM1 constructs were used for transferrin assays, high-content imaging, colocalization studies, Western blotting, and electron microscopy (EM). EM was also conducted on the brain sections of mice harboring a middle-domain Dnm1 mutation ( Dnm1 Ftfl ). Results: We demonstrate that the expression of each mutant protein decreased endocytosis activity in a dominant-negative manner. One of the G-domain mutations, K206N, decreased protein levels. The G359A mutation, which occurs in the middle domain, disrupted higher-order DNM1 oligomerization. EM of mutant DNM1-transfected HeLa cells and of the Dnm1 Ftfl mouse brain revealed vesicle defects, indicating that the mutations likely interfere with DNM19s vesicle scission activity. Conclusion: Together, these data suggest that the dysfunction of vesicle scission during synaptic vesicle endocytosis can lead to serious early-onset epilepsies.

The identification of a set of genetic variations that are strongly associated with the risk of developing schizophrenia provides insights into the neurobiology of this destructive disease. See Article p.177 The strongest genetic association found in schizophrenia is its association to genetic markers across the major histocompatibility complex (MHC) locus, first described in three Nature papers in 2009. The association signal at the MHC is extremely complex. Here Steven McCarroll and colleagues report a dissection of the MHC association to schizophrenia. They find a strong contribution from many structurally diverse alleles of the complement component 4 (C4) genes. The linkage was higher for C4 alleles that promoted greater expression of C4A, measured in the brain tissues of adult post-mortem donors with or without schizophrenia. The authors suggest that C4 may work with other components of the classical complement cascade to promote synaptic pruning, and demonstrate that C4 mediates synaptic refinement in a mouse model.

Purpose: Despite the recognized clinical value of exome-based diagnostics, methods for comprehensive genomic interpretation remain immature. Diagnoses are based on known or presumed pathogenic variants in genes already associated with a similar phenotype. Here, we extend this paradigm by evaluating novel bioinformatics approaches to aid identification of new gene–disease associations. Genet Med 17 10, 774–781. Methods: We analyzed 119 trios to identify both diagnostic genotypes in known genes and candidate genotypes in novel genes. We considered qualifying genotypes based on their population frequency and in silico predicted effects we also characterized the patterns of genotypes enriched among this collection of patients. Genet Med 17 10, 774–781. Results: We obtained a genetic diagnosis for 29 (24%) of our patients. We showed that patients carried an excess of damaging de novo mutations in intolerant genes, particularly those shown to be essential in mice (P = 3.4 × 10−8). This enrichment is only partially explained by mutations found in known disease-causing genes. Genet Med 17 10, 774–781. Conclusion: This work indicates that the application of appropriate bioinformatics analyses to clinical sequence data can also help implicate novel disease genes and suggest expanded phenotypes for known disease genes. These analyses further suggest that some cases resolved by whole-exome sequencing will have direct therapeutic implications. Genet Med 17 10, 774–781.

Genetically targeted therapies for rare Mendelian conditions are improving patient outcomes. Here, we present the case of a 20-mo-old female suffering from a rapidly progressing neurological disorder. Although diagnosed initially with a possible autoimmune condition, analysis of the child's exome resulted in a diagnosis of Brown–Vialetto–Van Laere syndrome 2 (BVVLS2). This new diagnosis led to a change in the therapy plan from steroids and precautionary chemotherapy to high-dose riboflavin. Improvements were reported quickly, including in motor strength after 1 mo. In this case, the correct diagnosis and appropriate treatment would have been unlikely in the absence of exome sequencing and careful interpretation. This experience adds to a growing list of examples that emphasize the importance of early genome-wide diagnostics.