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Atrial fibrillation (AF) is the most common heart rhythm abnormality and is a leading cause of heart failure and stroke. This large-scale meta-analysis of genome-wide association studies increased the power to detect single-nucleotide variant associations and found more than 350 AF-associated genetic loci. We identified candidate genes related to muscle contractility, cardiac muscle development and cell-cell communication at 139 loci. Furthermore, we assayed chromatin accessibility using assay for transposase-accessible chromatin with sequencing and histone H3 lysine 4 trimethylation in stem cell-derived atrial cardiomyocytes. We observed a marked increase in chromatin accessibility for our sentinel variants and prioritized genes in atrial cardiomyocytes. Finally, a polygenic risk score (PRS) based on our updated effect estimates improved AF risk prediction compared to the CHARGE-AF clinical risk score and a previously reported PRS for AF. The doubling of known risk loci will facilitate a greater understanding of the pathways underlying AF.
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http://dx.doi.org/10.1038/s41588-024-02072-3 | DOI Listing |
Cerebrovasc Dis
September 2025
Background: Intracranial aneurysm (IA), known as pathological dilation of cerebral arteries,commonly occurring at bifurcating arteries,carries a high risk of severe morbidity and mortality if left untreated.Although the treatment and early diagnosis have significantly improved,the complex pathophysiological process of IA formation presents significant challenges in the development of targeted therapies.Efficient disease-modifying therapies for IA are not yet available.
View Article and Find Full Text PDFHGG Adv
September 2025
Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA; Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA. Electronic address:
Pleiotropy, the phenomenon where a genetic region confers risk to multiple traits, is widely observed, even among seemingly unrelated traits. Knowledge of pleiotropy can improve understanding of biological mechanisms of diseases/traits, and can potentially guide identification of molecular targets or help predict side-effects in drug development. However, statistical approaches for identifying pleiotropy genome-wide are limited, particularly for two correlated traits or case-control traits with unknown sample overlap or for disease traits from family studies.
View Article and Find Full Text PDFExp Dermatol
September 2025
Department of Plastic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
This study utilised NHANES data from 2003 to 2006 and 2009 to 2014 to explore the association between the non-high-density lipoprotein to high-density lipoprotein cholesterol ratio (NHHR) and psoriasis. A total of 15 437 U.S.
View Article and Find Full Text PDFCurr Med Chem
September 2025
State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section of Ren Min Nan Rd., Chengdu, 610041, China.
Introduction: Current osteoporosis medications often prove ineffective for various reasons. Alongside optimizing available agents, new genetic targets should be proposed for drug development. Mendelian randomization (MR) may resolve throughput and confounding issues in traditional observational studies for druggable targets.
View Article and Find Full Text PDFSci Rep
September 2025
Jining Medical University School of Basic Medicine, Jining, 272067, China.
The causal relationship between vitamin B12 deficiency anaemia and the risk of developing lung diseases remains unclear. This study aimed to clarify that relationship using Mendelian randomization (MR) with Bayesian weighting, focusing on pulmonary fibrosis (PF), idiopathic PF (IPF), eosinophilic asthma (EA), squamous cell lung cancer (LUSC), chronic obstructive pulmonary disease (COPD), and pulmonary embolism (PE). We analysed genome-wide association study (GWAS) data, applied five MR models with Bayesian methods to assess causality, and validated the results via sensitivity analysis.
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