189 papers
The paper introduces HLA-Resolve, a cost-effective workflow combining multiplexed long-read hybrid capture with a specialized typing algorithm to overcome the limitations of short-read sequencing and PCR-based methods, thereby enabling high-resolution, accurate HLA haplotyping across all classical loci and the Class III region for clinical and research applications.
This study demonstrates through genomic structural equation modeling that genetic factors influencing branched-chain amino acid (BCAA) metabolism independently contribute to the risk of type 2 diabetes and coronary artery disease, distinct from traditional risk factors like obesity and dyslipidemia.
In a Japanese cohort, a population-specific polygenic risk score derived from Japanese genome-wide association studies demonstrated a stronger and more consistent association with Mini-Mental State Examination-based cognitive decline than European-derived scores, highlighting the importance of ancestry-specific genetic risk assessment for identifying cognitive impairment in community settings.
This study demonstrates that low-activity genotypes of the serotonin transporter gene *SLC6A4* increase vulnerability to the co-occurrence of cognitive decline and depressive symptoms in later life through age-dependent epigenetic mechanisms, whereas high-activity genotypes confer resilience partly by preserving hippocampal volume.
The paper introduces **faers**, an open-source R/Bioconductor package that provides a high-fidelity, end-to-end framework for transforming raw FAERS data into analysis-ready formats through automated deduplication and MedDRA mapping, while enabling scalable, reproducible precision pharmacovigilance via integrated signal detection methods validated on large-scale drug safety datasets.
This study introduces Bone2Gene, a deep learning tool that leverages hand radiographs to achieve high accuracy in detecting and differentiating rare bone diseases, offering a promising next-generation phenotyping approach to improve early diagnosis and treatment.
This systematic review and meta-analysis of 15 studies reveals that while large language models augmented with external knowledge achieve higher diagnostic accuracy for rare diseases than standalone models, their performance is highly variable and dependent on benchmark disease composition, with all current evidence limited by high risk of bias and a lack of prospective clinical validation.
The paper introduces StratGWAS, a scalable framework that improves the power and specificity of genome-wide association studies for heterogeneous diseases by leveraging secondary clinical features to stratify cases and upweight those with higher inferred genetic liability, thereby identifying more significant genetic loci than standard methods.
This paper identifies a novel therapeutic strategy that leverages common heterozygous genetic variants to enable allele-specific, mutation-agnostic silencing of over 500 dominant and dispensable disease alleles across diverse physiological systems, thereby dramatically expanding the pool of treatable patients compared to mutation-specific approaches.
This study employs a multi-omics genome-wide analysis to systematically map the shared and differential genetic architectures between COVID-19 and various respiratory disorders, revealing distinct molecular mechanisms involving interferon signaling and surfactant metabolism that offer new targets for precision treatment and drug repurposing.
This paper presents the UPNAT framework, a scalable, disease-agnostic, and equitable system developed by UK experts to systematically prioritize patients and genetic targets for antisense oligonucleotide therapies within the NHS, aiming to standardize decision-making and bridge gaps in rare disease treatment.
This study demonstrates that lung adenocarcinoma histological subtypes defined by WHO classification exhibit distinct immune cell and transcriptomic profiles that correlate with survival and immunotherapy responsiveness, offering a more prognostically valuable framework than mutation analysis alone.
By analyzing whole-genome sequencing data from 490,549 UK Biobank participants using the scalable STAARpipelinePheWAS framework, researchers generated a comprehensive map of 49,121 rare coding and noncoding variant associations across 1,342 diseases and biomarkers, revealing numerous novel insights into drug targets and biological pathways that were previously undetected in exome or array-based studies.
This study demonstrates that a genotype-based severity scoring system, which classifies WFS1 variants by type and location, effectively correlates with the onset age of cardinal Wolfram syndrome symptoms, particularly diabetes mellitus and optic atrophy, thereby offering valuable insights for predicting disease progression and guiding clinical care.
This study demonstrates that polygenic scores significantly contribute to the variable expressivity of rare pathogenic copy-number variants and reveals that pervasive assortative mating, rather than collider bias, drives a positive correlation between these rare variants and polygenic background, leading to compounded genetic effects across mutational classes.
This study presents a comprehensive resource of whole-genome DNA methylation patterns in 404 high-grade serous ovarian cancer samples, revealing that the regulatory methylome is established early and remains stable during chemotherapy, with widespread promoter hypermethylation in treatment-resistant ascites cells driving therapy failure and offering detectable epigenetic signatures for liquid biopsy monitoring.
This study demonstrates that implementing a fully autonomous, in-country whole-exome sequencing workflow in Algeria successfully established molecular diagnoses for neurodevelopmental disorders in a consanguineous cohort, while simultaneously highlighting critical challenges such as variant interpretation limitations due to African population underrepresentation and underscoring the need for population-specific datasets and multidisciplinary frameworks to sustain genomic medicine in under-resourced settings.
This study employs an integrative cross-omic framework to demonstrate that the genetic relationship between Alzheimer's disease and type 2 diabetes is characterized not by a simple shared-risk model, but by locus-specific heterogeneity and antagonistic pleiotropy, where shared genetic signals often exert opposing effects on the two diseases.
CanVar-UK is a freely accessible online platform that supports the collaborative interpretation of germline variants in cancer susceptibility genes by aggregating diverse variant-level data, linking to clinical databases, and facilitating diagnostic discussions among a growing international community of users.
This study of over 1,000 older adults from the Berlin Aging Study II reveals that the third-generation epigenetic clock DunedinPACE, along with the fifth-generation SystemsAge framework, demonstrates the strongest and most consistent associations with cognitive decline, outperforming other first-, second-, and fourth-generation DNA methylation clocks.