Chung, Wendy K.

ABSTRACT Pathogenic variants in MAPK8IP3 (JIP3) cause Neurodevelopmental Disorder with or without variable Brain Abnormalities (NEDBA), characterized by cognitive impairment, global developmental delay, motor dysfunction, abnormal muscle tone, behavioral dysregulation including autism and attention deficit hyperactivity disorder (ADHD), seizures, and structural brain abnormalities. While more than 30 largely de novo MAPK8IP3 variants have been reported, the functional impact of variants across JIP3 protein structural domains is poorly defined. To address this knowledge gap, we compare clinical features of individuals with a truncating (p.E27X) or one of two missense (p.R578C and p.R1146C) variants from distinct JIP3 functional protein domains to corresponding knock-in mouse models. Our findings showed that all individuals, regardless of variant type, exhibited delays in language and gross motor function, but individual variants were associated with distinct motor, cognitive, and psychiatric symptoms. Corresponding homozygous p.E27X and p.R1147C variant mice resulted in embryonic lethality, consistent with essential roles for JIP3 in early neurodevelopment. Behavioral characterization of viable heterozygous mice revealed variant-specific locomotor, motor coordination, and hindlimb clasping defects that closely recapitulate clinical observations. Heterozygous p.R579C mice exhibited reduced locomotion, impaired motor performance, and hypertonia-like clasping, mirroring human motor deficits. In contrast, p.R1147C mice displayed hyperactivity, hindlimb clasping, and decreased brain weight, paralleling human clinical features. Together, our findings demonstrate that while distinct MAPK8IP3 variants lead to some shared phenotypes, they are also associated with distinct phenotypes that could reveal domain-specific aspects of JIP3 function. This work establishes the first domain-resolved in vivo rodent models of NEDBA and provides a validated translational platform for mechanistic investigation and preclinical therapeutic testing.

Abstract Polygenic risk scores (PRSs), which quantify inherited susceptibility to complex traits and diseases, have emerged as valuable tools for risk stratification and precision medicine. Despite their promise, PRS developed on European cohorts often demonstrate substantially reduced predictive accuracy in non-European populations, due to differences in genetic architecture. The disproportionate representation of European ancestry cohorts in genome-wide association studies (GWAS) leads to inequitable deployment of PRS technologies across diverse populations. Here, we introduce PRANA (Polygenic Risk Adaptation via Neural-network Architecture), a deep learning framework that adapts an existing PRS developed on one population to other ancestries. Unlike methods that require large-scale GWAS in the target population, PRANA leverages pre-trained PRS models derived from European cohorts and adapts them using modestly sized cohorts from the target population. We evaluated PRANA on seven complex traits in South Asian, East Asian and Ashkenazi Jewish populations, as well as in selected smaller East Asian subpopulations where the scarcity of training data poses a particular challenge. PRANA mostly improved predictive performance of the baseline PRS models by 5%-20% in terms of effect size (β) and Nagelkerke’s R², and, in most cases, outperformed existing cross-ancestry multi-PRS approaches. These results highlight PRANA as a scalable and practical strategy to reduce disparities in genomic risk prediction and advance the equitable application of PRS in diverse populations.

Abstract Objective To describe the development and implementation of an automated platform for genomic risk prediction that integrates multiple data types. Materials and methods Using the REDCap infrastructure, we constructed the R4 (Recruitment, Results, and Risk Reduction) platform to intake data from clinical sites, partner laboratories, participant surveys, and electronic health record (EHR) data across 13 institutions. Results The R4 Portal successfully integrated data to generate genome-informed risk assessments (GIRAs) across 11 conditions for a 23 840 person cohort. Testing phases and quality control led to network-wide protocols ensuring consistency and accuracy. Discussion As the science of estimating disease risk evolves, standardized and high-throughput methods of collecting and manipulating complex data are required. Platforms should be open-source, modular, and reusable, ensuring flexibility, security, and integration across healthcare environments. Conclusion The electronic MEdical Records and GEnomics (eMERGE) network successfully generated and returned comprehensive risk profiles using logic and data specific to 11 conditions in a secure and semi-automated fashion employing a customized REDCap database.

Abstract Patients with extremely rare pathogenic variants pose unique challenges to current healthcare systems. Nano-rare mutations have been defined as mutations with a known prevalence of <30 patients worldwide, but because of the small fraction of humans who have undergone genetic testing, neither the precise prevalence of individual mutations nor the total prevalence of patients with nano-rare mutations is known. n-Lorem is a non-profit founded in 2020 with the mission of equitably discovering, developing, and providing bespoke experimental antisense oligonucleotides (ASOs) for free, for life, to patients with nano-rare mutations that are amenable to ASO treatment. In this perspective, we provide an overview of the n-Lorem processes and systems, the characteristics of the first 329 patients who have applied for treatment for whom initial assessment was completed and suitability for ASO treatment determined, and a summary of the results of ASO treatment for patients treated to date. Detailed data on individual patients and the overall clinical safety and tolerability profiles of the ASOs for which there are clinical data are the subjects of other manuscripts.

Abstract Rare Mendelian disorders affect 300-400 million people globally. Although genetic testing has become widely adopted, gene-specific evidence for tailored variant interpretation remains scattered across resources. We present Gene Portals, a framework for gene-centered multimodal knowledge bases that co-localize expert-harmonized clinical data, functional assays, population variation, structural annotations and gene-specific ACMG/AMP specifications within a single resource. A modular interface integrates this unified evidence with VCEP-refined ACMG specifications to enable automated gene-specific variant classification, infer molecular mechanisms, and support cross-gene analyses. We demonstrate the framework’s utility across five Gene portals spanning eleven neurodevelopmental disorder-associated genes, integrating data from 4,423 individuals with 2,838 unique variants, 36,149 ClinVar submissions, and 1,044 expert-curated molecular readouts. By organizing evidence that is otherwise dispersed across multiple sources into a unified, queryable framework, the SCN, GRIN, CACNA1A, SATB2 and SLC6A1 Gene Portals became widely used community resources and provide an extensible template for standardized rare-disease variant interpretation and mechanism-aware discovery.

Abstract Objectives To implementation an automated multi-institutional pipeline that delivers breast-cancer risk integrated with polygenic risk scores, monogenic variants, family history, and clinical factors, emphasizing operational challenges and their solutions. Materials and Methods A five-stage process was executed at ten sites. Data streams from REDCap surveys, PRS and monogenic reports, and MeTree pedigrees were normalized and forwarded through a REDCap plug-in to the CanRisk API. Results Integrated risk was returned to >10 000 women; 3.6% were ≥25 % lifetime risk and 0.9% carried pathogenic variants. Pipeline generated score aligns well with manual generated ones. Major barriers such as heterogeneous pedigree formats, missing data, edge-case handling, and evolving model versions were identified and resolved through mapping rules, imputations, and iterative testing. Discussion Cross-platform data harmonization and stakeholder alignment were decisive for success. Borderline-risk communication and model-version drift remain open issues. Conclusion Large-scale PRS-integrated breast-cancer risk reporting is feasible but requires robust interoperability standards and iterative governance.

Background Right ventricular (RV) maladaptation to elevated pulmonary afterload is the primary determinant of outcomes in pulmonary artery (PA) hypertension; however, the pathobiological mechanisms underlying RV decompensation remain poorly understood. Methods We performed global untargeted metabolomics on plasma from 55 patients who underwent gold‐standard RV‐PA coupling measurements using multibeat pressure volume loop assessment in a single‐center cohort and from 1027 patients with coupling surrogate measurements in a larger multicenter cohort, the PVDOMICS (Pulmonary Vascular Disease Phenomics) study. Age and sex‐adjusted linear regression was performed to identify associations between metabolites and coupling metrics. Additionally, we performed a metabolic flux analysis using gene expression data from RV tissue in an independent cohort of 32 patients. Partial least squares–discriminant analysis was used to identify metabolites and reactions characteristic of the decompensated RV. Results RV‐PA coupling was negatively associated with tricarboxylic acid (TCA) cycle intermediate levels. Specifically, plasma α‐ketoglutarate and fumarate were significantly associated with all coupling metrics in both cohorts. Metabolic flux analysis indicated that decompensated RVs exhibited aberrant TCA cycle activity, including reduced acetyl coenzyme A entry and increased lactate elimination, suggesting a shift from the TCA cycle toward glycolysis at the RV tissue level. Conclusions We identify an association between circulating TCA cycle intermediate levels and RV‐PA uncoupling in 2 independent cohorts, and dysregulated TCA cycle metabolism in decompensated PA hypertension RVs, suggesting that aberrant TCA cycle metabolism could represent a hallmark of RV maladaptation in PA hypertension. Further study of this pathway is warranted to develop novel biomarkers of RV function or RV‐targeted therapies.

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGS BMI ) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R 2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R 2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGS BMI –covariate interaction effects, modifying PGS BMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R 2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R 2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGS BMI individuals have highest R 2 and increase in PGS effect. Using quantile regression, we show the effect of PGS BMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R 2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGS BMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R 2 (mean 23%) across datasets. Finally, creating PGS BMI directly from GxAge genome-wide association studies effects increased relative R 2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGS BMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGS BMI ) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R 2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R 2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGS BMI –covariate interaction effects, modifying PGS BMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R 2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R 2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGS BMI individuals have highest R 2 and increase in PGS effect. Using quantile regression, we show the effect of PGS BMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R 2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGS BMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R 2 (mean 23%) across datasets. Finally, creating PGS BMI directly from GxAge genome-wide association studies effects increased relative R 2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGS BMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.