Eduardo Pérez-Palma

Epilepsy affects more than 50 million individuals globally and has a substantial genetic component that remains to be completely understood. Traditional studies have focused on severe, early onset cases enrolled through clinical or research settings. Recent biobank‐based approaches, leveraging large‐scale population datasets, offer opportunities to explore genetic associations in broader epilepsy phenotypes, including milder, later onset forms. We analyzed data from more than 750 000 individuals across the UK Biobank, All of Us, and Massachusetts General Brigham Biobank, including 20 026 individuals with epilepsy. Rare coding variant burden testing revealed a significant association with LGI1 , a known epilepsy gene. Among the other top 10 associated genes, seven had prior evidence linking them to epilepsy ( GABRG2 , ATP1A3 ), neurological disorders with comorbid seizures ( HTRA2 , KRIT1 , STAG1 ), possible involvement in seizure phenotypes ( ADAM23 ), or roles in neuronal function ( PDCD4 ). Thus, we provide the first statistical evidence for ADAM23 as a candidate gene for epilepsy, based on the suggestive association signal combined with prior biological evidence from both animal (canine and murine) and one recent human epilepsy case study, potentially contributing to human epilepsy through its direct interaction with LGI1 . Phenome‐wide analyses highlighted the pleiotropic effects of epilepsy genes, with LGI1 and ADAM23 predominantly associated with epilepsy, whereas other genes such as KRIT1 , TSC1 , and TSC2 exhibited broader systemic involvement. Our study shows the potential of population‐scale genomic data and suggests that integrating these datasets with deep phenotyping will uncover more novel insights into epilepsy genetics in the future.

Background: Recessive genetic variation and extended runs of homozygosity (ROHs) may contribute to the unexplained heritability of Parkinson's disease (PD), particularly in diverse and understudied populations. Objective: We conducted the first large‐scale, multi‐ancestral investigation of PD to examine the impact of genome‐wide homozygosity on disease risk and age at onset (AAO). Using genotyping, imputed, and whole‐genome sequencing data from 36,127 PD cases and 19,475 controls across nine ancestral populations from the Global Parkinson's Genetics Program, we aimed to identify novel regions of homozygosity contributing to PD heritability. Methods: We analyzed ROHs for total length (SROH), number (NROH), average length (AVROH), and genomic inbreeding coefficient (FROH). ROHs were intersected with known PD, pallido‐pyramidal syndrome, and atypical parkinsonism gene regions and risk loci to assess pleomorphic or pleiotropic contributions. Homozygosity mapping identified ROH overlaps in families, consanguineous individuals, and early‐onset PD (EOPD) cases. Results: Significant differences in SROH, AVROH, NROH, and FROH were observed between case status across ancestries, persisting after excluding known PD‐associated recessive genes. Our analysis revealed distinct patterns of ROH enrichment associated with AAO, suggesting recessive genetic modifiers of PD. Homozygosity mapping was used to prioritize 52 variants either segregating in families or present in individuals with consanguinity. In total, 1,559 ROHs in consanguineous individuals and EOPD overlapped known PD gene regions and risk loci. Conclusions: ROH regions contribute to PD heritability across ancestries, partly reflecting recessive genetic architecture. Larger and more diverse whole‐genome sequencing studies are needed to identify rare recessive variants influencing PD risk. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Rett syndrome is a monogenic disorder with an incidence of 95% in women, characterized by the complexity of studying the associated phenotype due to the heterogeneity in patient tissues from the stochastic silencing of the affected X chromosome. Furthermore, we are largely unaware of the cascade of alterations that occur in neurons due to transcriptional changes induced by the affected MECP2 gene. To address these challenges, an in-depth network analysis was implemented using organoid single-cell transcriptomic data derived from human patients. We performed a Weighted correlation network analysis and trajectory analysis to understand the differences in the developmental processes between samples, we followed by the generation of gene regulatory networks for each relevant cell developmental pathways to assess the master regulator that are involved in this process, with the differential expressed genes with potential therapeutic implications being identified by integration with SFARI and Genes4Epi. The results were adapted into dynamic Boolean models fitted with the transcriptomic data for validation in which we evaluated the attractor field from each reachable state. These approaches allowed us to explore differences in regulatory behavior in the developmental pathways. Our study provides an insight that pinpoints the cellular stages on which the regulation and compensatory mechanism activate and regulate Rett syndrome. We identified 19 Master regulators for the Dopaminergic developmental trajectory, as well as 34 Master regulator genes for the Gabaergic developmental trajectory. Dynamic Boolean modeling of these systems showcased a comprehensive understanding of the disrupted developmental pathways of Rett syndrome, highlighting the transitional states of potential within maturation trajectories as the key point of divergence in regulation for Rett syndrome. After complementing with enrichment and clinical relevant variant analysis, we identify the key actors in this system as NR2F1 and TCF4, with TCF4 suggesting a symmetrical compensatory relationship with MeCP2, and NR2F1 as possible link with wider developmental conditions, this concluded with highlighting the possibility of regulation in this condition being affected by the MAPK-ERK pathway of transcriptional regulation, offering a novel angle for targeted research.

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.

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.

Objective: Rare diseases, including many rare genetic epilepsies and neurodevelopmental disorders, present significant challenges in timely diagnosis, treatment, and patient education due to their rare incidence, complex clinical nature and lack of standardized care pathways. Despite advancements in genetic testing, knowledge dissemination remains inadequate, contributing to delayed diagnosis and inconsistent management. Addressing these gaps requires innovative educational approaches tailored to diverse audiences, including patients, caregivers, and non-specialist healthcare providers. Methods: We explored the potential of AI-driven podcast generation as a scalable solution for rare disease education. We designed a tutorial for the community to use Google’s NotebookLM, a tool powered by Large Language Models and text-to-speech technology to generate podcasts. Results: Eight examples were created from research papers on rare epilepsies ( SCN2A -, CACNA1A -, SYNGAP1 -, chromosome 8p and SLC6A1 -related disorders) and a complex genetic research topic (epilepsy polygenic risk scores) in English, Spanish or German. The AI-generated podcasts featured conversational overviews delivered by virtual hosts, with customizable style and tone enabling personalized content creation for different audiences. Feedback from 20 stakeholders, including patient advocacy leaders, researchers, and clinicians, highlighted strong enthusiasm for this approach, particularly for under-resourced patient communities. Respondents praised the accessibility, quality of language translation and educational value of the podcasts, noting their potential to bridge the gap between complex research findings and practical patient care. Key recommendations for improvement included ensuring scientific accuracy through expert review, enhancing clinical depth, reducing redundancy, and incorporating structured episode elements such as introductions and summaries. Significance: The study underscores how AI-driven podcasting can democratize access to high-quality medical information, offering a flexible, multilingual platform that adapts to the needs of global rare disease communities. By refining this approach to include greater oversight and targeted content development, AI-generated educational podcasts could play a pivotal role in rare disease knowledge dissemination, ultimately improving patient outcomes and empowering stakeholders across healthcare systems. Key points: AI-driven podcasts bridge knowledge gaps in rare disease education via scalable, multilingual, and accessible formats. Our method enables rapid podcast generation, offers tailored content for diverse audiences in various languages with fair clinical depth. AI podcasts reduce barriers for caregivers, patients, and clinicians, democratizing access to medical knowledge. Human oversight and expert supervision are required and ensures scientific accuracy, balancing accessibility with professional rigor for impactful education.

Importance: Haploinsufficiency, a condition where a single functional copy of a gene is insufficient to maintain normal physiological function due to a mutation, represents the most prevalent disease mechanism underlying genetic epilepsies, particularly developmental and epileptic encephalopathies (DEEs). Current therapies for these patients primarily address symptoms and fail to alter the disease trajectory. Given the prospect of targeted gene therapies, accurately quantifying the burden of these disorders is paramount for optimizing resource allocation in patient care and facilitating the development of novel disease-modifying therapies. Objective: To estimate the global and U.S. incidence and prevalence of epilepsy caused by haploinsufficiency. Design, Setting, and Participants: First, we identified epilepsy genes, focusing on autosomal dominant genes with a high likelihood of haploinsufficiency. Second, we utilized a statistical framework to estimate the birth incidence of patients with protein-truncating variants in these genes, incorporating gene-specific mutation rates and birth rate data. Finally, we calculated prevalence by multiplying annual incidence by an effective disease duration, considering factors like sudden unexpected death in epilepsy. Main Outcomes and Measures: Birth incidence and prevalence of epilepsy attributable to de novo protein-truncating variants in haploinsufficient genes. Results: An estimated 228.81 (90% CI: 206.21–241.93) cases of haploinsufficiency-related epilepsy occur per 100,000 live births globally. This translates to approximately 313,595 new cases annually worldwide and 8,736 in the United States. The estimated global prevalence is 14,343,840 individuals. Conclusions and Relevance: This study provides the first data-driven estimates of the of haploinsufficiency-related epilepsy burden, highlighting a substantial population potentially benefiting from gene-targeting therapies. These findings have significant implications for research prioritization, resource allocation, and the development of precision medicine approaches for treating these severe neurological disorders.

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. AUTHOR SUMMARY: Despite numerous advancements in computational methods for predicting variant pathogenicity in the SCN2A gene, understanding the precise biophysical molecular mechanisms associated with each variant at the atomic level remains a challenge. Presently, variants are predominantly categorized as either gain or loss of function, often overlooking critical structural details associated with these variants. This study focuses on elucidating the molecular mechanisms linked to the four most common SCN2A variants using all-atom molecular dynamics simulations, employing three replicas for each system. Our findings offer insights into the potential mechanisms underlying these four variants, thereby providing explanations for the observed electrophysiological outcomes. This investigation significantly contributes to enhancing our comprehension of how SCN2A variants manifest in various diseases. It underscores the importance of unraveling the biophysical properties underlying potential disease mechanisms, which could potentially enhance diagnostic and therapeutic strategies for patients afflicted with SCN2A -related disorders.

Genetic variants in the SLC6A1 gene can cause a broad phenotypic disease spectrum by altering the protein function. Thus, systematically curated clinically relevant genotype-phenotype associations are needed to understand the disease mechanism and improve therapeutic decision-making. We aggregated genetic and clinical data from 172 individuals with likely pathogenic/pathogenic (lp/p) SLC6A1 variants and functional data for 184 variants (14.1% lp/p). Clinical and functional data were available for a subset of 126 individuals. We explored the potential associations of variant positions on the GAT1 3D structure with variant pathogenicity, altered molecular function and phenotype severity using bioinformatic approaches. The GAT1 transmembrane domains 1, 6 and extracellular loop 4 (EL4) were enriched for patient over population variants. Across functionally tested missense variants (n = 156), the spatial proximity from the ligand was associated with loss-of-function in the GAT1 transporter activity. For variants with complete loss of in vitro GABA uptake, we found a 4.6-fold enrichment in patients having severe disease versus non-severe disease (P = 2.9 × 10⁻³, 95% confidence interval: 1.5–15.3). In summary, we delineated associations between the 3D structure and variant pathogenicity, variant function and phenotype in SLC6A1-related disorders. This knowledge supports biology-informed variant interpretation and research on GAT1 function. All our data can be interactively explored in the SLC6A1 portal (https://slc6a1-portal.broadinstitute.org/).

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. Availability and implementation: The web application is freely available at https://cnv-ClinViewer.broadinstitute.org and the open-source code can be found at https://github.com/LalResearchGroup/CNV-clinviewer.