189 papers
The BEACONS-NBS study developed and validated a comprehensive list of 777 genetic conditions for multi-state genomic newborn screening, involving diverse stakeholders to ensure clinical relevance and feasibility for prospective population-wide implementation.
The paper introduces gnomAD v4, a massive database of 807,162 individuals, alongside refined loss-of-function annotation and a novel Bayesian framework integrating clinical literature to significantly enhance gene discovery and rare disease diagnosis.
This integrative systematic review and meta-analysis synthesizes data from 31 studies to characterize the general population's preferences for expanded carrier screening program attributes and estimates a median willingness-to-pay of $107, despite noting a high risk of bias in most included studies.
The GenomeIndia study addresses the underrepresentation of South Asian populations in global genomics by presenting a comprehensive whole-genome dataset of nearly 10,000 individuals from 83 diverse Indian groups, revealing unique genetic architectures, identifying millions of novel variants, and providing essential resources to advance precision medicine and equity in global health.
This study demonstrates that FTA Elute technology enables reliable, rapid, and cost-effective prenatal diagnosis of sickle cell disease via amniocentesis in resource-limited settings like Kinshasa, while also characterizing the socioeconomic and genetic profiles of the couples seeking these services.
This study identifies a distinct genetic architecture for schistosomiasis-induced hepatic fibrosis through a genome-wide association study and multi-omics integration, pinpointing DGKG as a critical mediator linking lipid metabolism and immune signaling that drives fibrosis severity and represents a potential therapeutic target.
This study presents an in silico framework demonstrating that integrating polygenic risk scores into clinical trial designs to enrich for high-risk participants significantly improves statistical power, reduces required sample sizes, and accelerates event accrual across various disease contexts, though optimal enrichment thresholds must be balanced against population availability.
The paper introduces S-MiXcan, a scalable summary-statistics-based framework that infers cell-type-specific transcriptome-wide associations from bulk transcriptomics and GWAS data by jointly modeling genetically regulated expression across multiple cell types without requiring individual-level data.
This study demonstrates that blood-based RNA sequencing of 5,412 individuals with rare diseases in the National Genomic Research Library successfully identifies clinically relevant diagnostic candidates by detecting expression and splicing outliers across a diverse range of disorders.
This study identifies a novel association between genetic variants in the autophagy gene ATG4B and asthma using UK Biobank GWAS data, suggesting ATG4B as a potential target for future drug development.
This pilot genome-wide association study in a Kazakh cohort identifies two genome-wide significant loci (UGT1A and ACTR3C) and prioritizes the CSMD1 gene as candidates for ischemic heart disease with co-occurring arterial hypertension, highlighting the need for replication in larger Central Asian populations.
This study reveals that young cancer survivors exhibit a significantly higher prevalence of loss of Y chromosome in their sperm compared to controls, suggesting this genetic alteration serves as a valuable biomarker for genomic instability, reduced fertility, and disease predisposition.
A randomized Phase II sub-study reveals that high- and low-dose dietary n-3 PUFA supplementation elicit distinct, directionally discordant effects on DNA methylation in breast adipose tissue, with high doses reducing epigenetic heterogeneity and enriching inflammation pathways, thereby suggesting a novel mechanism for breast cancer prevention and highlighting the critical importance of dose selection in future trials.
This study introduces a novel graphical modeling framework applied to large-scale electronic health records to disentangle genetic effects on disease, medication selection, and treatment response, successfully identifying specific variants influencing blood pressure therapy and age-specific changes while controlling for confounding factors.
This study identifies thousands of context-dependent methylation quantitative trait loci in immune cells that reveal how genetic variation moderates the epigenetic embedding of childhood adversity, thereby driving chronic immune alterations and increasing susceptibility to diverse psychiatric and physical disorders.
This study utilizes genomic structural equation modeling on a large European cohort to identify distinct genetic architectures for the personality meta-traits of stability and plasticity, revealing 81 and 13 significant loci respectively and demonstrating a bi-directional genetic relationship where higher stability protects against psychopathology.
By integrating large-scale GWAS data, haplotype analysis, and long-read sequencing, this study refines the genetic architecture of the TMEM106B locus into four distinct haplotypes characterized by unique structural variants and epigenetic patterns that differentially influence Alzheimer's disease risk and cognitive longevity.
This study validates a novel polygenic scoring framework based on Genomic Independent Component Analysis (genomICA) that successfully disentangles shared genetic effects across thousands of brain imaging traits into independent components, which effectively predict diverse behavioral, clinical, and lifestyle phenotypes while confirming mutual independence and distinct biological associations.
Established in 2017 to standardize the interpretation of cancer susceptibility gene variants in the UK, the Cancer Variant Interpretation Group UK (CanVIG-UK) has expanded its multidisciplinary network to include training, consensus frameworks, and digital platforms, with a 2025 survey confirming that its guidance is highly utilized and regarded as extremely useful by the clinical community.
This study employs a genetic epidemiological pipeline to identify candidate biomarkers and establish causal links between clozapine metabolism and metabolic dysfunction, offering a foundation for predictive, precision medicine approaches to mitigate clozapine-induced metabolic risks.