DSpace Repository :: Browsing by Author "Lal, Dennis"

Browsing by Author "Lal, Dennis"

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Analysing an allelic series of rare missense variants of CACNA1I in a Swedish schizophrenia cohort
(2021) Baez, David; Allen, Andrew; Akers, Seth; Yang, Lingling; Budnik, Nikita; Pupo, Amaury; Cheul, Young; Genovese, Giulio; Liao, Maofu; Pérez, Eduardo; Heyne, Henrike; Lal, Dennis; Lipscombe, Diane; Pan, Jen
CACNA1I is implicated in the susceptibility to schizophrenia by large-scale genetic association studies of single nucleotide polymorphisms. However, the channelopathy of CACNA1I in schizophrenia is unknown. CACNA1I encodes CaV3.3, a neuronal voltage-gated calcium channel that underlies a subtype of T-type current that is important for neuronal excitability in the thalamic reticular nucleus and other regions of the brain. Here, we present an extensive functional characterization of 57 naturally occurring rare and common missense variants of CACNA1I derived from a Swedish schizophrenia cohort of more than 10 000 individuals. Our analysis of this allelic series of coding CACNA1I variants revealed that reduced CaV3.3 channel current density was the dominant phenotype associated with rare CACNA1I coding alleles derived from control subjects, whereas rare CACNA1I alleles from schizophrenia patients encoded CaV3.3 channels with altered responses to voltages. CACNA1I variants associated with altered current density primarily impact the ionic channel pore and those associated with altered responses to voltage impact the voltage-sensing domain. CaV3.3 variants associated with altered voltage dependence of the CaV3.3 channel and those associated with peak current density deficits were significantly segregated across affected and unaffected groups (Fisher's exact test, P = 0.034). Our results, together with recent data from the SCHEMA (Schizophrenia Exome Sequencing Meta-Analysis) cohort, suggest that reduced CaV3.3 function may protect against schizophrenia risk in rare cases. We subsequently modelled the effect of the biophysical properties of CaV3.3 channel variants on thalamic reticular nucleus excitability and found that compared with common variants, ultrarare CaV3.3-coding variants derived from control subjects significantly decreased thalamic reticular nucleus excitability (P = 0.011). When all rare variants were analysed, there was a non-significant trend between variants that reduced thalamic reticular nucleus excitability and variants that either had no effect or increased thalamic reticular nucleus excitability across disease status. Taken together, the results of our functional analysis of an allelic series of >50 CACNA1I variants in a schizophrenia cohort reveal that loss of function of CaV3.3 is a molecular phenotype associated with reduced disease risk burden, and our approach may serve as a template strategy for channelopathies in polygenic disorders.
CNV-ClinViewer: enhancing the clinical interpretation oflarge copy-number variants online
(2023) Macnee, Marie; Pérez Palma, Eduardo; Brünger, Tobias; Klöckner, Chiara; Platzer, Konrad; Stefansk, Arthur; Montanucci, Ludovica; Bayat, Allan; Radtke, Maximilian; Collins, Ryan; Talkowski, Michael; Blankenberg, Daniel; Møller, Rikke; Lemke, Johannes; Nothnagel, Michael; May, Patrick; Lal, Dennis
Motivation: Pathogenic copy-number variants (CNVs) can cause a heterogeneous spectrum of rare and severe disorders. However, most CNVs are benign and are part of natural variation in human genomes. CNV pathogenicity classification, genotype-phenotype analyses, and therapeutic target identification are challenging and time-consuming tasks that require the integration and analysis of information from multiple scattered sources by experts. Results: Here, we introduce the CNV-ClinViewer, an open-source web application for clinical evaluation and visual exploration of CNVs. The application enables real-time interactive exploration of large CNV datasets in a user-friendly designed interface and facilitates semi-automated clinical CNV interpretation following the ACMG guidelines by integrating the ClassifCNV tool. In combination with clinical judgment, the application enables clinicians and researchers to formulate novel hypotheses and guide their decision-making process. Subsequently, the CNV-ClinViewer enhances for clinical investigators' patient care and for basic scientists' translational genomic research.
Conserved missense variant pathogenicity and correlated phenotypes across paralogous genes
(2025) Brünger, Tobias; Ivaniuk, Alina; Pérez Palma, Eduardo; Montanucci, Ludovica; Cohen, Stacey; Smith, Lacey; Parthasarathy, Shridhar; Helbig, Ingo; Nothnagel, Michael; May, Patrick; Lal, Dennis
Background: The majority of missense variants in clinical genetic tests are classified as variants of uncertain significance. Prior research shows that the deleterious effects and the subsequent molecular consequences of variants are often conserved among paralogous protein sequences within a gene family. Here, we systematically quantify on an exome-wide scale whether the existence of pathogenic variants in paralogous genes at a conserved position can serve as evidence for the pathogenicity of a new variant. For the gene family of voltage-gated sodium channels, where variants and expert-curated clinical phenotypes are available, we also assess whether phenotype patterns of multiple disorders for each gene are conserved across variant positions within the gene family. Results: Mapping 590,000 pathogenic and 1.9 million population variants onto 9928 genes grouped into 2054 paralogous families increases the number of residues with classifiable evidence 5.1-fold compared with gene-specific data alone. The presence of a pathogenic variant in a paralogous gene is associated with a positive likelihood ratio of 13.0 for variant pathogenicity. Across ten genes encoding voltage-gated sodium channels and 22 expert-curated disorders, we identify cross-paralog correlated phenotypes based on 3D structure spatial position. For example, multiple established loss-of-function related disorders across SCN1A, SCN2A, SCN5A, and SCN8A show overlapping spatial variant clusters. Finally, we show that phenotype integration in paralog variant selection improves variant classification. Conclusion: Conserved pathogenic missense variants in paralogous genes provide robust, quantifiable support for clinical variant interpretation, and phenotype-informed mapping further improves predictions.
Conserved patterns across ion channels correlate with variant pathogenicity and clinical phenotypes
(2022) Brünger, Tobias; Pérez, Eduardo; Montanucci, Ludovica; Nothnagel, Michael; Møller, Rikke; Schorge, Stephanie; Zuberi, Sameer; Symonds, Joseph; Lemke, Johannes; Brunklaus, Andreas; Traynelis, Stephen; May, Patrick; Lal, Dennis
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.
Copy Number Variation Analysis from SNP Genotyping Microarrays in Large Cohorts of Neurological Disorders
(2022) Pérez, Eduardo; Niestroj, Lisa; Martínez, Miguel; Villaman, Camilo; Irem, Elif; Lal, Dennis; Mata, Ignacio
Copy number variants (CNVs) are a major source of genetic variation in the human genome, and they are highly heterogeneous in type, size, and frequency. CNVs represent the largest portion of genomic variation between humans, and a subset of CNVs has been associated with multiple rare and common neurological disorders. Although recent sequencing-based methods deliver increased resolution and greater power in detecting CNVs, SNP genotyping microarrays still provide a scalable opportunity to analyze CNVs in large cohorts of neurological disorders. In the past 15 years, case-control genome-wide association studies and population-based biobanks have widely used SNP genotyping microarrays to understand the heritability of common variants. As a result, massive amounts of SNP microarray data are available and provide a costefficient opportunity to repurpose the data and study large and rare CNVs. Here we describe a workflow to detect and analyze CNVs from SNP genotyping microarrays. We describe established CNV quality control procedures, CNV downstream analyses, case-control burden analysis, and validation protocols with particular focus on nervous system disorders and non-European datasets.
Data-driven historical characterization of epilepsy-associated genes
(2022) Macnee, Marie; Pérez, Eduardo; López, Javier; Ivaniuk, Alina; May, Patrick; Møller, Rikke; Lal, Dennis
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.
Delineation of functionally essential protein regions for 242 neurodevelopmental genes
(2022) Iqbal, Sumaiya; Brünger, Tobias; Pérez, Eduardo; Macnee, Marie; Brunklaus, Andreas; Daly, Mark; Campbell, Arthur; Hoksza, David; May, Patrick; Lal, Dennis
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.
Development and Validation of a Prediction Model for Early Diagnosis of SCN1A-Related Epilepsies
(2022) Brunklaus, Andreas; Pérez, Eduardo; Ghanty, Ismael; Xinge, Ji; Brilstra, Eva; Ceulemans, Berten; Chemaly, Nicole; De Lange, Iris; Depienne, Christel; Guerrini, Renzo; Mei, Davide; Møller, Rikke; Nabbout, Rima; Regan, Brigid; Schneider, Amy; MGenCouns; Scheffer, Ingrid; Schoonjans, An; Symonds, Joseph; Weckhuysen, Sarah; Kattan, Michael; Zuberi, Sameer; Lal, Dennis
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.
Evaluating novel in silico tools for accurate pathogenicity classification in epilepsy-associated genetic missense variants
(2024) Montanucci, Ludovica; Brünger, Tobias; Boßelmann, Christian; Ivaniuk, Alina; Pérez Palma, Eduardo; Lhatoo, Samden; Leu, Costin; Lal, Dennis
Objective: Determining the pathogenicity of missense variants in clinical genetic tests for individuals with epilepsy is crucial for guiding personalized treatment. However, achieving a definitive pathogenic classification remains challenging, with most missense variants still classified as variants of uncertain significance (VUS) and with the availability of many computational tools which may provide conflicting predictions. Here, we aim to evaluate the performance of state-of-the-art computational tools in pathogenicity prediction of missense variants in epilepsy-associated genes. This will assist in selecting the most appropriate tool and critically assess their use in clinical setting. Methods: We assessed the performance of nine in silico pathogenicity prediction tools for missense variants in epilepsy-associated genes on three carefully curated data sets. The first two data sets comprise missense variants in epilepsy associated genes that have been uploaded to ClinVar in the last year and were, therefore, not part of the training set of any of the nine considered tools. These two data sets are based on two different lists of epilepsy-associated genes and comprise ~700 and ~ 250 missense variants, respectively. The third data set includes ~400 missense variants within epilepsy-associated genes for which the functional effects have been determined experimentally and are therefore used here to infer pathogenicity. These three data sets represent the best available approximation to blind and independent test sets. Results: Among the nine assessed tools, AlphaMissense (area under the curve [AUC]: .93, .88, and .95) and REVEL (AUC: .93, .88, and .93) showed the best classification performance, also outperforming other tools in the number of classified variants. Significance: We show which recently developed prediction tools achieve higher performance in epilepsy-associated genes and should be integrated, therefore, into the American College of Medical Genetics and Genomics/Association of Molecular Pathology (AGMC/AMP) variant classification process. Periodic reevaluation of genetic test results with newly developed or updated tools should be incorporated into standard clinical practice to improve diagnostic yield and better inform precision medicine.
Gene variant effects across sodium channelopathies predict function and guide precision therapy
(2022) Brunklaus, Andreas; Feng, Tony; Brünger, Tobias; Pérez, Eduardo; Heyne, Henrike; Matthews, Emma; Semsarian, Christopher; Symonds, Joseph; Zuberi, Sameer; Lal, Dennis; Schorge, Stephanie
Pathogenic variants in the voltage-gated sodium channel gene family lead to early onset epilepsies, neurodevelopmental disorders, skeletal muscle channelopathies, peripheral neuropathies and cardiac arrhythmias. Disease-associated variants have diverse functional effects ranging from complete loss-of-function to marked gain-of-function. Therapeutic strategy is likely to depend on functional effect. Experimental studies offer important insights into channel function but are resource intensive and only performed in a minority of cases. Given the evolutionarily conserved nature of the sodium channel genes, we investigated whether similarities in biophysical properties between different voltage-gated sodium channels can predict function and inform precision treatment across sodium channelopathies. We performed a systematic literature search identifying functionally assessed variants in any of the nine voltage-gated sodium channel genes until 28 April 2021. We included missense variants that had been electrophysiologically characterized in mammalian cells in whole-cell patch-clamp recordings. We performed an alignment of linear protein sequences of all sodium channel genes and correlated variants by their overall functional effect on biophysical properties. Of 951 identified records, 437 sodium channel-variants met our inclusion criteria and were reviewed for functional properties. Of these, 141 variants were epilepsy-associated (SCN1/2/3/8A), 79 had a neuromuscular phenotype (SCN4/9/10/11A), 149 were associated with a cardiac phenotype (SCN5/10A) and 68 (16%) were considered benign. We detected 38 missense variant pairs with an identical disease-associated variant in a different sodium channel gene. Thirty-five out of 38 of those pairs resulted in similar functional consequences, indicating up to 92% biophysical agreement between corresponding sodium channel variants (odds ratio = 11.3; 95% confidence interval = 2.8 to 66.9; P < 0.001). Pathogenic missense variants were clustered in specific functional domains, whereas population variants were significantly more frequent across non-conserved domains (odds ratio = 18.6; 95% confidence interval = 10.9-34.4; P < 0.001). Pore-loop regions were frequently associated with loss-of-function variants, whereas inactivation sites were associated with gain-of-function (odds ratio = 42.1, 95% confidence interval = 14.5-122.4; P < 0.001), whilst variants occurring in voltage-sensing regions comprised a range of gain- and loss-of-function effects. Our findings suggest that biophysical characterisation of variants in one SCN-gene can predict channel function across different SCN-genes where experimental data are not available. The collected data represent the first gain- versus loss-of-function topological map of SCN proteins indicating shared patterns of biophysical effects aiding variant analysis and guiding precision therapy. We integrated our findings into a free online webtool to facilitate functional sodium channel gene variant interpretation (http://SCN-viewer.broadinstitute.org).
Genomic analysis of AlphaFold2-predicted structures identifies maps of 3D essential sites in 243 neurodevelopmental disorder-associated proteins
(2022) Iqbal, Sumaiya; Brunger, Tobias; Pérez Palma, Eduardo; Hoksza, David; Campbell, Arthur; Daly, Mark; May, Patrick; Lal, Dennis
We propose a new methodology to identify maps of essential sites (i.e., amino acid residues) in 3D, called Essential 3D sites.
Genotype-phenotype associations in 1018 individuals with SCN1A-related epilepsies
(2024) Gallagher, Declan; Pérez; Pérez Palma, Eduardo; Bruenger, Tobias; Ghanty, Ismael; Brilstra, Eva; Ceulemans, Berten; Chemaly, Nicole; De Lange, Iris; Depienne, Christel; Guerrini, Renzo; Mei, Davide; Møller, Rikke; Nabbout, Rima; Regan, Brigid; Schneider, Amy; Scheffer, Ingrid; Schoonjans, An; Symonds, Joseph; Weckhuysen, Sarah; Zuber, Sameer; Lal, Dennis; Brunklaus, Andreas
Objective: SCN1A variants are associated with epilepsy syndromes ranging from mild genetic epilepsy with febrile seizures plus (GEFS+) to severe Dravet syndrome (DS). Many variants are de novo, making early phenotype prediction difficult, and genotype-phenotype associations remain poorly understood. Methods: We assessed data from a retrospective cohort of 1018 individuals with SCN1A-related epilepsies. We explored relationships between variant characteristics (position, in silico prediction scores: Combined Annotation Dependent Depletion (CADD), Rare Exome Variant Ensemble Learner (REVEL), SCN1A genetic score), seizure characteristics, and epilepsy phenotype. Results: DS had earlier seizure onset than other GEFS+ phenotypes (5.3 vs. 12.0 months, p < .001). In silico variant scores were higher in DS versus GEFS+ (p < .001). Patients with missense variants in functionally important regions (conserved N-terminus, S4-S6) exhibited earlier seizure onset (6.0 vs. 7.0 months, p = .003) and were more likely to have DS (280/340); those with missense variants in nonconserved regions had later onset (10.0 vs. 7.0 months, p = .036) and were more likely to have GEFS+ (15/29, χ2 = 19.16, p < .001). A minority of protein-truncating variants were associated with GEFS+ (10/393) and more likely to be located in the proximal first and last exon coding regions than elsewhere in the gene (9.7% vs. 1.0%, p < .001). Carriers of the same missense variant exhibited less variability in age at seizure onset compared with carriers of different missense variants for both DS (1.9 vs. 2.9 months, p = .001) and GEFS+ (8.0 vs. 11.0 months, p = .043). Status epilepticus as presenting seizure type is a highly specific (95.2%) but nonsensitive (32.7%) feature of DS. Significance: Understanding genotype-phenotype associations in SCN1A-related epilepsies is critical for early diagnosis and management. We demonstrate an earlier disease onset in patients with missense variants in important functional regions, the occurrence of GEFS+ truncating variants, and the value of in silico prediction scores. Status epilepticus as initial seizure type is a highly specific, but not sensitive, early feature of DS
Identification and quantification of oligogenic loss-of-function disorders
(2021) Stefanski, Arthur; Pérez, Eduardo; Mrdjen, Marko; McHugh, Megan; Leu, Costin; Lal, Dennis
Purpose: Monogenic disorders can present clinically heterogeneous symptoms. We hypothesized that in patients with a monogenic disorder caused by a large deletion, frequently additional loss-of-function (LOF)-intolerant genes are affected, potentially contributing to the phenotype. Methods: We investigated the LOF-intolerant gene distribution across the genome and its association with benign population and pathogenic classified deletions from individuals with presumably monogenic disorders. For people with presumably monogenic epilepsy, we compared Human Phenotype Ontology terms in people with large and small deletions. Results: We identified LOF-intolerant gene dense regions that were enriched for ClinVar and depleted for population copy number variants. Analysis of data from >143,000 individuals with a suspected monogenic disorder showed that 2.5% of haploinsufficiency disorder-associated deletions can affect at least 1 other LOF-intolerant gene. Focusing on epilepsy, we observed that 13.1% of pathogenic and likely pathogenic ClinVar deletions <3 megabase pair, covering the diagnostically most relevant genes, affected at least 1 additional LOF-intolerant gene. Those patients have potentially more complex phenotypes with increasing deletion size. Conclusion: We could systematically show that large deletions frequently affected admditional LOF-intolerant genes in addition to the established disease gene. Further research is needed to understand how additional potential disease-relevant genes influence monogenic disorders to improve clinical care and the efficacy of targeted therapies.
Incidence and prevalence of major epilepsy-associated brain lesions
(2022) López; Javier; Smuk, Victoria; Leu, Costin; Nasr, Gaelle; Vegh, Deborah; Stefanski, Arthur; Pérez, Eduardo; Busch, Robyn; Jehi, Lara; Najm, Imad; Blümcke, Ingmar; Lal, Dennis
Epilepsy surgery is an effective treatment option for drug-resistant focal epilepsy patients with associated structural brain lesions. However, little epidemiological data are available regarding the number of patients with these lesions. We reviewed data regarding (1) the prevalence and incidence of epilepsy; (2) the proportion of epilepsy patients with focal epilepsy, drug-resistant epilepsy, and drug-resistant focal epilepsies; and (3) the number of epilepsy presurgical evaluations and surgical resections. We also assessed the relative proportion of brain lesions using post-surgical histopathological findings from 541 surgical patients from the Cleveland Clinic and 9,523 patients from a European multi-center cohort. Data were combined to generate surgical candidate incidence and prevalence estimates and the first lesion-specific estimates for hippocampal sclerosis (HS), low-grade epilepsy-associated brain tumors (LEAT), malformations of cortical development (MCD), glial scars, vascular malformations, and encephalitis. The most frequently diagnosed brain lesions were HS (incidence = 2.32 ± 0.26 in 100,000, prevalence = 19.40 ± 2.16 in 100,000) for adults and MCD (incidence = 1.15 ± 0.34 in 100,000, prevalence = 6.52 ± 1.89 in 100,000) for children. Our estimates can guide patient advocacy groups, clinicians, researchers, policymakers in education, development of health care strategy, resource allocation, and reimbursement schedules.
Molecular dynamics simulations reveal molecular mechanisms for the gain and loss of function effects of four SCN2A variants
(2024) Bhattarai, Nisha; Montanucci, Ludovica; Brünger, Tobias; Pérez Palma, Eduardo; Martin, William; Smith, Iris; Eng, Charis; Cheng, Feixiong; Helbig, Ingo; Müller, Rikke; Brunklaus, Andreas; Schorge, Stephanie; Lal, Dennis
SCN2A gene disorders cover a wide range of medical conditions, from epileptic encephalopathies to neurodevelopmental disorders. The variants of these disorders, studied through electrophysiology, show complex behaviors that go beyond simple classification as either gain or loss of function. In our study, we simulated the biophysical effects of variants (R937C, V208E, S1336Y, and R853Q) to understand their impact. Our findings reveal that all these variants negatively affect the structural stability of the gene, with R937C being the most unstable. Specifically, R937C disrupts important charged interactions affecting sodium ion flow, while S1336Y introduces a new interaction that impacts the channel’s inactivation gate. Conversely, the variants V208E and R853Q, which are located in the voltage-sensing domains, have opposite effects: R853Q increases compactness and interaction, whereas V208E shows a decrease. Our computer-based method offers a scalable way to gain crucial insights into how genetic variants influence channel dysfunction and contribute to neurodevelopmental disorders.
Polygenic score analysis identifies distinct genetic risk profiles in Alzheimer’s disease comorbidities
(2025) Hernández, Carlos F.; Villaman, Camilo; Leu, Costin; Lal, Dennis; Mata, Ignacio; Klein, Andrés; Pérez-Palma, Eduardo
Alzheimer’s disease (AD) is usually accompanied by comorbidities such as type 2 diabetes (T2D), epilepsy, major depressive disorder (MDD), and migraine headaches (MH) that can significantly affect patient management and progression. As AD, these comorbidities have their own cumulative common genetic risk component that can be explored in a single individual through polygenic scores. Utilizing data from the UK Biobank, we investigated the correlation between polygenic scores (PGS) for these comorbidities and their actual presentation in AD patients. We show that individuals with higher PGS values showed an elevated risk of developing T2D (OR 2.1, p = 1.07 × 10−11) and epilepsy (OR 1.5, p = 0.0176). High T2D-PGS is also associated with an earlier AD onset in individuals at high genetic risk for AD (AD-PGS). In contrast, no significant genetic associations were found for MDD and MH. Our findings show distinct common genetic risk factors for T2D and epilepsy carried by AD patients that are associated with increased prevalence and earlier disease onset. These results highlight the contribution of common genetic variation to the broader clinical landscape of AD and will contribute to future tailored patient management strategies for individuals at high genetic risk.
Polygenic scores contribution to Parkinson's disease comorbidities
(2025) Hernández, Carlos; Villaman, Camilo; Tejos, Cristian; Repetto, Gabriela; Leu, Costin; Lal, Dennis; Mata, Ignacio; Klein, Andrés; Pérez Palma, Eduardo
Comorbidities are common in Parkinson's disease and significantly impact the disease progression and management. While polygenic scores have been widely used to assess genetic risk for complex diseases, their role in comorbidity presentation in Parkinson's disease remains unclear. This study investigates whether genetic predisposition to comorbidities, as measured by polygenic scores, differs between individuals with Parkinson's disease and the general population and explores how genetic risk influences disease onset and sex-related differences. We analysed data from 4144 individuals with Parkinson's disease and 370 480 individuals from the general population in the UK Biobank, focusing on four comorbidities with high-quality genome-wide association study data: Type 2 diabetes, major depressive disorder, migraine headaches and epilepsy. We first compared polygenic score distributions between individuals with Parkinson's disease and the general population. While our findings indicate that comorbidities and polygenic risk scores do not significantly differ between individuals with Parkinson's disease and the general population, we show an association with disease onset and sex-specific differences. Individuals with earlier disease onset (50-70 years old) had higher genetic risk for major depressive disorder (odds ratio: 2.19, P-value: 1.27 × 10⁻¹⁵) and epilepsy (odds ratio: 1.58, P-value: 0.00845). Additionally, a female participant with Parkinson's disease exhibited higher genetic risk scores for major depressive disorder (odds ratio: 1.5, P-value: 0.0119) and migraine headaches (odds ratio: 2.1, P-value: 0.0155), while a male participant displayed higher genetic risk scores for Type 2 diabetes (odds ratio: 2.7, P-value: 2.11 × 10⁻¹⁷). Comorbidity-polygenic score did not differ between people with versus without Parkinson's disease, yet within Parkinson's disease, a higher genetic burden for specific comorbidities was linked to earlier onset and sex-specific presentation, implicating common variants as modifiers of clinical heterogeneity rather than the primary disease risk. These results enhance our understanding of the genetic influences shaping the broader clinical presentation of Parkinson's disease and highlight the need for further research into the interplay between genetic risk factors, comorbidities and disease heterogeneity.
Quantitative Phenotype Morbidity Description of SATB2 - Associated Syndrome
(2023) Zarate, Yuri; Bosanko, Katherine; Kannan, Amrit; Thomason, Ashlen; Nutt, Beth; Kumar, Nihit; Simmons, Kirt; Hiegert, Aaron; Hartzell, Larry; Johnson, Adam; Prater, Tabitha; Pérez Palma, Eduardo; Brünger, Tobias; Stefanski, Arthur; Lal, Dennis; Caffrey, Aisling
Characterized by developmental delay with severe speech delay, dental anomalies, cleft palate, skeletal abnormalities, and behavioral difficulties, SATB2-associated syndrome (SAS) is caused by pathogenic variants in SATB2. The SAS phenotype range of severity has been documented previously in large series. Using data from the SAS registry, we present the SAS severity score, a comprehensive scoring rubric that encompasses 15 different individual neurodevelopmental and systemic features. Higher (more severe) systemic and total (sum of neurodevelopmental and systemic scores) scores were seen for null variants located after amino acid 350 (the start of the CUT1 domain), the recurrent missense Arg389Cys variant (n=10), intragenic deletions, and larger chromosomal deletions. The Arg389Cys variant had the highest cognitive, verbal, and sialorrhea severity scores, while large chromosomal deletions had the highest expressive, ambulation, palate, feeding and growth, neurodevelopmental, and total scores. Missense variants not located in the CUT1 or CUT2 domain scored lower in several subcategories. We conclude that the SAS severity score allows quantitative phenotype morbidity description that can be used in routine clinical counseling. Further refinement and validation of the SAS severity score are expected over time. All data from this project can be interactively explored in a new portal.
SCN9A should not be considered an epilepsy gene; Refuting a gene–disease association
(2026) Ghanty, Ismael; Perez-Palma, Eduardo; Villaman, Camilo; Stobo, Daniel; Symonds, Joseph; Zuberi, Sameer; Lal, Dennis; Brunklaus, Andreas
Objective The SCN9A gene is primarily expressed in nociceptive pathways within the peripheral nervous system, and pathogenic variants are associated with human pain disorders. In recent years, several studies have proposed SCN9A as a monogenic cause of epilepsy. Our objective was to critically appraise the SCN9A–epilepsy gene–disease relationship. Methods We assessed “epilepsy-associated” SCN9A variants from four sources: (1) the literature up to December 2023 (n = 27), (2) epilepsy patients referred for genetic testing at a regional service in Glasgow, UK over a 5-year period (n = 30), (3) the Human Genetics Mutation Database (n = 25), and (4) ClinVar (n = 1546). The latter two are genome-wide variant databases, accepting submissions from genetic laboratories and research groups. We checked whether each SCN9A variant is present in the Genome Aggregation Database (gnomAD) V4 (a reference population database for variant interpretation), and classified its pathogenicity based on the American College of Molecular Genetics and Genomics/Association of Molecular Pathologists guidelines. Results Only three SCN9A variants were classified as “likely pathogenic,” of which two were identified in healthy individuals in gnomAD. A total of 1540 of the 1546 SCN9A variants in ClinVar labeled as being associated with epilepsy were also reported in association with hereditary sensory and autonomic neuropathy. No further clinical data were provided in 1482 of these submissions. Significance There is no convincing genetic evidence to support SCN9A as a causative epilepsy gene. As such, the inclusion of SCN9A in epilepsy genetic testing panels should be reassessed. Research centers and genetic testing laboratories should be rigorous and consistent in their submissions to variant databases.
SimText: a text mining framework for interactive analysis and visualization of similarities among biomedical entities
(2021) Macnee, Marie; Pérez Palma, Eduardo; Schumacher-Bass, Sarah; Dalton, Jarrod; Leu, Costin; Blankenberg, Daniel; Lal, Dennis
Literature exploration in PubMed on a large number of biomedical entities (e.g. genes, diseases or experiments) can be time-consuming and challenging, especially when assessing associations between entities. Here, we describe SimText, a user-friendly toolset that provides customizable and systematic workflows for the analysis of similarities among a set of entities based on text. SimText can be used for (i) text collection from PubMed and extraction of words with different text mining approaches, and (ii) interactive analysis and visualization of data using unsupervised learning techniques in an interactive app.

Browsing by Author "Lal, Dennis"

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