Citations

Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood ( n = 6454) and lung ( n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank–derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.

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 Background Diagnostic interpretation of kidney allograft biopsies using the Banff classification remains variable, but the determinants of this variability are not fully defined. We performed a global, fully digital multi-reader study to identify the principal drivers of disagreement in Banff-based assessment. Methods Thirty-six kidney transplant biopsies were independently scored by 67 renal pathologists on a standardized digital platform. Readers assessed Banff lesions on hematoxylin and eosin, periodic acid–Schiff, and Jones’ silver stains; final diagnostic categories were assigned using prespecified Banff-based decision rules. Interobserver agreement was quantified with Gwet’s agreement coefficient (AC) statistics. Determinants of diagnostic agreement were evaluated using pairwise mixed-effects logistic regression, and reader similarity was examined by principal component analysis (PCA) with post hoc molecular annotation. Results Agreement for final diagnostic categories was moderate (Gwet’s AC1, 0.55; 95% CI, 0.47–0.63). Lesion-level agreement varied substantially, with lowest agreement for selected threshold-dependent inflammatory or semi-quantitative lesions, including interstitial inflammation in areas of IFTA, peritubular capillaritis and arteriolar hyalinosis. Diagnostic concordance differed markedly across biopsies, indicating strong case-level heterogeneity. In pairwise models, differences in active inflammatory and vascular lesion scoring were the strongest correlates of diagnostic disagreement; reader experience and geography contributed minimally. Principal component analysis showed reader variation was organized along two dominant axes: a rejection-calling threshold axis linked mainly to tubulointerstitial inflammatory injury, and a T cell-mediated (TCMR/TI) and antibody-mediated/microvascular (AMR/MVI) inflammation-oriented phenotypic classification axis. Conclusion Interobserver variation in Banff-based kidney transplant biopsy assessment is structured rather than random and driven mainly by how readers threshold and integrate key inflammatory lesion compartments rather than experience or geographic location.

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.

Exercise and physical activity are recognized as essential tools to optimize fitness and longevity, improve overall health, and reduce the risk of cardiovascular disease and other major comorbidities. However, there are inherent risks associated with participation in sports and exercise, regardless of the level of competition or skill required. As such, there is a need for cardiologists to understand the unique aspects of competitive and recreational athletics, as well as sport and exercise science, as it relates to overall heart health and cardiovascular risk. In this article, we provide an overview of the essential aspects of sports and exercise cardiology. We describe the roles and responsibilities of a sports and exercise cardiologist as a member of a comprehensive team of experts who provide care of athletes at all levels of sport, emphasize the importance of risk stratification and prevention of sudden cardiac arrest, the role of exercise for maintaining and optimizing overall health in the general population, describe the growing field of sports electrophysiology and also discuss unique aspects of sports cardiology applied to environmental health and care of the tactical athlete. Knowledge gaps and directions for future clinical and research investigation are reviewed.

Background/Objectives: To develop and evaluate a deep learning-based quality control system for Lumbar Spinal Digital Radiographs (LSDR), designed to automate and improve their evaluation and reduce reliance on manual reviews. Methods: This retrospective study utilized a deep learning workflow comprising image segmentation, feature extraction, and a classification model. The dataset, including anteroposterior (AP) and lateral (LAT) X-ray images, was expanded through data augmentation techniques. Four U-Net-based models were assessed: standard U-Net, Swin-UNet, Attention U-Net, and Attention U-Net with the weight map, with the latter selected for its superior performance. Extracted features, such as brightness, contrast, and anatomical positioning, were used in an XGBoost classifier, which was evaluated using mean intersection over union (mIoU), accuracy, sensitivity, specificity, and AUC. Results: The Attention U-Net with weighted attention outperformed the other models, achieving high mIoU scores in both AP and LAT views. The XGBoost classifier achieved the best performance in classifying images as “qualified” or “unqualified,” with an AUC of approximately 0.9, high accuracy, and balanced sensitivity and specificity. This approach effectively addressed class imbalances and improved model accuracy compared to traditional machine learning models such as MLP and SVM. Conclusions: The developed automated quality control system demonstrated potential for enhancing image quality, enhancing diagnostic reliability, and optimizing clinical workflow efficiency.

Abstract Background Data on respiratory syncytial virus (RSV) acute respiratory infection (ARI) among community-dwelling U.S. adults aged 18 to 64 years, particularly those with high-risk conditions (HRC), remain limited. This study estimated incidence and attack rates of RSV-ARI and RSV lower respiratory tract disease (LRTD) in adults with and without HRCs. Methods A multistate, community-based prospective cohort of adults aged 18 to 64 years with and without HRCs was followed from October 2022 to September 2024. Incidence rates and annual attack rates of RSV-ARI and RSV-LRTD were calculated by HRC presence and age group. Results Of 3375 adults with at least 1 HRC, the RSV-ARI incidence rate was 17.9 per 1000 person-years (attack rates: year 1, 1.87%; year 2, 1.56%), compared with 14.8 per 1000 person-years (year 1, 1.33%; year 2, 1.50%) among adults without HRCs. RSV-LRTD incidence was 10.0 per 1000 person-years in adults with HRCs versus 5.7 per 1000 person-years in those without. Asthma, congenital immunodeficiency, and immunosuppressive medication use were associated with increased incidence of RSV-ARI and RSV-LRTD; whereas adults with solid organ transplant and chronic kidney disease had a significantly higher incidence rate of RSV-LRTD compared to those without these conditions. Adults aged 18 to 49 years with HRCs had significantly higher RSV-LRTD incidence (9.6 vs 4.3 per 1000 person-years). After adjustment for age and sex, having ≥1 HRC was associated with a 62% increased RSV-LRTD risk, rising to 117% among adults aged 18 to 49 years. Conclusions Adults with HRCs—particularly asthma, congenital immunodeficiency, chronic kidney disease, immunosuppressive medication use, and solid organ transplant—experienced higher RSV-ARI and RSV-LRTD incidence. The elevated RSV-LRTD burden was most pronounced in adults aged 18 to 49 years with HRCs.