SEARCH · PubMed Health

Results for “Genomic biobank”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources

PubMed2025

Using Large Genomic Biobanks to Generate Insights into Genetic Kidney Disease.

Chronic kidney disease (CKD) affects approximately 9% of the global population, leading to increased risks of end-stage kidney disease (ESKD), cardiovascular disease (CVD), and mortality. Patients with CKD are a huge burden on health care resources globally. CKD is a complex condition influenced by a combination of genetic, environmental, and traditional risk factors. Family studies have suggested heritability rates for CKD ranging from 30% to 75%, and large genomic biobank studies have proven essential in identifying genes with substantial effects on CKD risk and in capturing cumulative genetic risk through polygenic risk scores. These biobanks are crucial for discovering new genes associated with kidney health and disease, and their growing size enhances the power to detect novel genetic associations. Integrating multi-omics technologies such as transcriptomics, metabolomics, and proteomics further enriches our understanding of CKD, while advanced computational tools continue to expand our insights into genetic data. Polygenic risk scores, derived from hundreds of genetic variants with small effect sizes, can help identify individuals at high risk of CKD. Genomic biobanks offer valuable opportunities for early identification and personalized treatment of monogenic kidney disorders, such as autosomal dominant polycystic kidney disease and Alport syndrome. These biobanks help fill knowledge gaps, particularly in individuals with milder or asymptomatic presentations who are often underrepresented in traditional studies. Expanding genomic biobank efforts globally, especially in diverse populations, is vital to enhancing our understanding of the genetic underpinnings of kidney disease. This review highlights the significant contributions of genomic biobanks to advancing our comprehension of the genetics of CKD.

Humans↗

PubMed2025

Streamlining large-scale genomic data management: Insights from the UK Biobank whole-genome sequencing data.

Biobank-scale whole-genome sequencing (WGS) studies are increasingly pivotal in unraveling the genetic bases of diverse health outcomes. However, managing and analyzing these datasets' sheer volume and complexity presents significant challenges. We highlight the annotated genomic data structure (aGDS) format, substantially reducing the WGS data file size while enabling seamless integration of genomic and functional information for comprehensive WGS analyses. The aGDS format yielded 23 chromosome-specific files for the UK Biobank 500k WGS dataset, occupying only 1.10 tebibytes of storage. We develop the vcf2agds toolkit that streamlines the conversion of WGS data from VCF to aGDS format. Additionally, the STAARpipeline equipped with the aGDS files enabled scalable, comprehensive, and functionally informed WGS analysis, facilitating the detection of common and rare coding and noncoding phenotype-genotype associations. Overall, the vcf2agds toolkit and STAARpipeline provide a streamlined solution that facilitates efficient data management and analysis of biobank-scale WGS data across hundreds of thousands of samples.

Humans↗

PubMed2026

Beyond statutory compliance: toward comprehensive genomic governance.

I welcome the discussion raised by Hernández-Huerta and colleagues. While legal provisions regulating the transfer of biological materials are significant, genomic governance goes beyond mere legal compliance. It encompasses transparency, accountability, benefit-sharing, and long-term stewardship. Recent international recommendations emphasize the need for comprehensive governance frameworks for national genomic initiatives.

Mexico↗

PubMed2025

Whole-genome sequencing of 490,640 UK Biobank participants.

Whole-genome sequencing provides an unbiased and complete view of the human genome and enables the discovery of genetic variation without the technical limitations of other genotyping technologies. Here we report on whole-genome sequencing of 490,640 UK Biobank participants, building on previous genotyping effort1. This advance deepens our understanding of how genetics associates with disease biology and further enhances the value of this open resource for the study of human biology and health. Coupling this dataset with rich phenotypic data, we surveyed within- and cross-ancestry genomic associations and identified novel genetic and clinical insights. Although most associations with disease traits were primarily observed in individuals of European ancestries, strong or novel signals were also identified in individuals of African and Asian ancestries. With the improved ability to accurately genotype structural variants and exonic variation in both coding and UTR sequences, we strengthened and revealed novel insights relative to whole-exome sequencing2,3 analyses. This dataset, representing a large collection of whole-genome sequencing data that is available to the UK Biobank research community, will enable advances of our understanding of the human genome, facilitate the discovery of diagnostics and therapeutics with higher efficacy and improved safety profile, and enable precision medicine strategies with the potential to improve global health.

Humans↗

PubMed2025

Extracting and calibrating evidence of variant pathogenicity from population biobank data.

Genomic medicine requires a robust evidence base of variant phenotypic impacts, which remains incomplete even in extensively studied genes with monogenic disease associations. Here, we evaluated the broad potential of using population cohort data to identify evidence that can be used in variant assessment. Across 41 genes related to 18 clinically actionable monogenic phenotypes, we calculated variant-level odds ratios of disease enrichment using data from 469,803 UK Biobank participants. We found significant differences in odds ratio values between ClinVar-labeled pathogenic and benign variants in 11 phenotypes, spanning both common and rare disorders. To facilitate clinical translation, we calibrated the strength of evidence provided by variant-level odds ratios to align with American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) interpretation guidelines (PS4 criterion) and found that odds ratios may reach "moderate," "strong," or "very strong" evidence, varying by phenotype and gene. Overall, we found that 2.6% (N = 12,350) of participants harbor a rare variant of uncertain significance (VUS) with at least moderate evidence of pathogenicity-an indication of potentially unrecognized disease risk. Finally, by incorporating computational and functional data alongside population-based odds ratios, we identified variants that met the criteria for clinical reclassification. Notably, using this approach, we identified that 12.4% of rare VUSs in LDLR seen in participants meet diagnostic criteria to be classified as likely pathogenic, demonstrating its potential to scale the reclassification of VUSs.

Humans↗

PubMed2026

Freely available genomic datasets for atrial fibrillation research: current resources and analytical pipeline.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, characterized by clinical and genetic heterogeneity. Increasing use of genomics and other omics approaches has driven reliance on publicly available AF datasets to advance biological discovery. Thus, this systematic review aimed to identify freely available genomic AF datasets through Mendeley Data and its interconnected repositories, and to characterize the most common analyses performed on these data. The search was conducted in adherence to the PRISMA 2020 guideline. Nineteen freely available genomic AF datasets were identified: Summary statistics for 'Biobank-driven genomic discovery yields new insight into atrial fibrillation biology', hum0014.v8.58qt.v1, AF GWAS in UK Biobank, UK Biobank (Publication 9659), GWAS summary statistics from a 2025 multi-ancestry AF meta-analysis, GSE115574, GSE128188, GSE14975, GSE2240, GSE238242, GSE254133, GSE261170, GSE271748, GSE271839, GSE293813, GSE294456, GSE31821, GSE41177, and GSE79768. The GEO datasets were further examined using differential gene expression, functional enrichment, protein-protein interaction networks, hub gene analysis, microRNA target prediction, and gene clustering, as well as, for the more recently deposited datasets, eQTL colocalization, single-cell/single-nucleus clustering, cell-cell communication analysis, and gene-dosage-dependent transcriptional and electrophysiological profiling. These analyses show some consistency but also considerable heterogeneity in initial conditions, data normalization, and analytical methodological settings. In conclusion, only a limited number of datasets are freely available, so additional, well-characterized and standardized datasets are needed to provide a complete picture of the AF pathology.

Mendeley Data↗

PubMed2025

MutBERT: probabilistic genome representation improves genomics foundation models.

MOTIVATION: Understanding the genomic foundation of human diversity and disease requires models that effectively capture sequence variation, such as single nucleotide polymorphisms (SNPs). While recent genomic foundation models have scaled to larger datasets and multi-species inputs, they often fail to account for the sparsity and redundancy inherent in human population data, such as those in the 1000 Genomes Project. SNPs are rare in humans, and current masked language models (MLMs) trained directly on whole-genome sequences may struggle to efficiently learn these variations. Additionally, training on the entire dataset without prioritizing regions of genetic variation results in inefficiencies and negligible gains in performance. RESULTS: We present MutBERT, a probabilistic genome-based masked language model that efficiently utilizes SNP information from population-scale genomic data. By representing the entire genome as a probabilistic distribution over observed allele frequencies, MutBERT focuses on informative genomic variations while maintaining computational efficiency. We evaluated MutBERT against DNABERT-2, various versions of Nucleotide Transformer, and modified versions of MutBERT across multiple downstream prediction tasks. MutBERT consistently ranked as one of the top-performing models, demonstrating that this novel representation strategy enables better utilization of biobank-scale genomic data in building pretrained genomic foundation models. AVAILABILITY AND IMPLEMENTATION: https://github.com/ai4nucleome/mutBERT.

Humans↗

PubMed2026

Evaluating a Genome-Wide Polygenic Score for Handgrip Strength and Its Interplay with Leisure-Time Physical Activity Across the IGEMS Twin Cohorts.

PURPOSE: Polygenic scores (PGSs) may help assess genetic predisposition to multifactorial traits. We examined whether age, sex, and leisure-time physical activity (LTPA) modify the association between a PGS for handgrip strength (HGS) and measured HGS in older adults. METHODS: PGS for HGS (PGS hgs) , based on Pan-UK Biobank genome-wide association study data, was calculated for 5103 participants (aged 40-96; 44% women) from eight twin cohorts in Denmark, Sweden, Australia, the United States, and Finland within the IGEMS consortium. Sex-standardized HGS and self-reported LTPA were assessed cross-sectionally. Linear mixed models estimated associations between PGS hgs and HGS, including interactions with age, country, and LTPA, as well as an association between PGS hgs and LTPA. Fixed-effect within-pair models were conducted to assess environmental contributions. RESULTS: Higher PGS hgs was associated with greater HGS (β = 2.14, SE = 0.15, P < 0.001), explaining 4.6% of HGS variance overall, with modest variation across countries. In sex-stratified models, PGS hgs explained 5.2% of the variance in females and 4.3% in males. No statistically significant interaction with age was found. A significant PGS hgs × LTPA interaction (β = -0.034, P = 0.013) indicated that the association between LTPA and HGS was more pronounced among individuals with lower PGS hgs . The within-pair models offered limited support for the independent environmental impact of LTPA. CONCLUSIONS: The PGS hgs was associated with measured HGS in the meta-analysis, highlighting the potential of PGSs to capture individual differences in strength-related traits across populations. The association of PGS hgs with HGS was moderated by LTPA, such that the beneficial impact of LTPA on HGS was greater among individuals with a lower genetic propensity for HGS.

Humans↗

PubMed2026

Development and Validation of a Clinical Polygenic Risk Report in U.S.-Based Health Systems for 8 Cardiovascular Conditions.

BACKGROUND: Polygenic risk scores (PRS) stratify inherited cardiovascular risk, but their path to clinical implementation remains unclear. OBJECTIVES: We aimed to develop and validate integrated PRS for 8 cardiovascular conditions and outline a framework for their clinical reporting. METHODS: We analyzed genotype and clinical data from 245,394 All of Us Research Program participants. Publicly available PRS for 8 traits-coronary artery disease, atrial fibrillation, type 2 diabetes, venous thromboembolism (VTE), thoracic aortic aneurysm (TAA), extreme hypertension, severe hypercholesterolemia, and elevated lipoprotein(a)-were combined using PRSmix, an elastic-net approach. Integrated PRS were externally validated in 53,306 Mass General Brigham Biobank participants using logistic regression, adjusting for age, sex, and ancestry. RESULTS: Of 53,306 genotyped Mass General Brigham Biobank participants (55.6% women, mean age 53 ± 17 years), integrated PRS demonstrated robust discrimination and appropriate calibration across 8 cardiovascular traits. Comparing high genetic risk (top 10% of PRS distribution, or top 20% for rarer TAA and VTE) vs average risk (26th-75th percentiles, or 21st-80th percentiles for TAA and VTE) yielded ORs: coronary artery disease (3.7 [95% CI: 3.4-4.1]), type 2 diabetes (3.1 [95% CI: 2.8-3.3]), atrial fibrillation (3.0 [95% CI: 2.7-3.3]), VTE (1.9 [95% CI: 1.6-2.0]), TAA (1.7 [95% CI: 1.5-1.9]), hypertension (2.1 [95% CI: 1.8-2.3]), hypercholesterolemia (4.1 [95% CI: 3.7-4.5]), and lipoprotein(a) (41.0 [95% CI: 27.0-62.2]). Incorporating integrated PRS into clinical models improved risk classification, while prospective analyses confirmed significant associations with incident cardiovascular outcomes. CONCLUSIONS: Integrated PRS offer an implementable framework for genetic risk reporting, and are now available as a clinically orderable test. Broader prospective validation studies are needed to further establish clinical utility.

Humans↗

PubMed2026

Polygenic Risk Scores for Cardiovascular Disease: Closing the Gap Between Prediction and Practice.

Explore the source record for details and available documents.

biobank↗

PubMed2025

Dietary patterns and risk of ischemic stroke: A two-sample Mendelian randomization study.

Diet and nutrition critically influence the development and outcomes of ischemic stroke (IS). However, observational studies often yield inconsistent findings due to confounding and measurement error. Mendelian randomization (MR) provides an alternative approach to strengthen causal inference. We conducted a 2-sample MR analysis to evaluate the causal associations between 22 dietary factors and IS risk. Genetic instruments for dietary exposures were derived from the UK Biobank genome-wide association study, and outcome data were obtained from the MEGASTROKE consortium. Inverse-variance weighted analysis served as the primary method, complemented by sensitivity analyses. Consumption of oily fish (β = -0.402, P = .022), cheese (β = -0.364, P = .001), dried fruit (β = -0.710, P = .0002), weekly red wine (β = -0.507, P = .024), and calcium supplements (β = -3.994, P = .015) was associated with reduced risk of IS. Conversely, dietary patterns characterized by high-sugar or high-protein intake showed suggestive associations with increased IS risk, although these did not remain significant after multiple-comparison correction. This 2-sample MR study provides evidence that specific dietary factors, including oily fish, cheese, dried fruit, red wine, and calcium, may reduce IS risk, while high-sugar and high-protein diets may confer increased risk. These findings underscore the importance of dietary management in stroke prevention and highlight the need for further studies to validate the role of potentially harmful dietary patterns.

Humans↗

PubMed2026

Inherited Susceptibility to Urinary Tract Infections from Kidney Papilla to Bladder.

Urinary tract infections (UTIs) are traditionally viewed as environmentally driven, yet their inherited susceptibility remains largely unexplored. We conducted a cross-biobank genome-wide association study of recurrent UTIs in 1,860,836 individuals (213,869 cases and 1,646,967 controls). We identified 36 genetic susceptibility loci and performed tissue-based multi-omic mapping to prioritize candidate causal genes. UTI risk alleles preferentially modulated epithelial gene expression in kidney and bladder, converging on urinary epithelia structure and function. PSCA, encoding a secreted epithelial surface protein, emerged as the strongest candidate under genetic control; the gene product is constitutively secreted into the urine from kidney papilla and bladder epithelia, binds uropathogenic E. coli, and inhibits bacterial growth in vitro. Our findings define the polygenic architecture of UTIs and highlight the critical role of uroepithelial surface defenses, providing a new framework for host-directed, non-antibiotic interventions.

Journal Article↗

PubMed2026

RBM20 Truncating Variants and Human Cardiomyopathy.

IMPORTANCE: Genetic diagnosis has become increasingly important to guide clinical decision-making for patients with dilated cardiomyopathy (DCM). Pathogenic or likely pathogenic (P/LP) missense variants in the gene RBM20 cause a highly penetrant arrhythmogenic DCM, but the role of RBM20 truncating variants (RBM20tvs) is unclear. OBJECTIVE: To assess the contribution of RBM20 variants to arrhythmogenic DCM. DESIGN, SETTING, AND PARTICIPANTS: In this cohort study, participants in the genome-first UK Biobank (UKB) and All of Us populations were evaluated to assess the etiologic fraction, natural history and penetrance of RBM20 variants. Retrospective data were collected from an international cohort of patients with DCM and RBM20 variants identified at centers of excellence for genetic heart disease and compared based on time to event. Study dates are not disclosed because the institutional review board did not authorize the sharing of this information. EXPOSURES: RBM20 variants were compared to known P/LP variants and variants of uncertain significance in RBM20 as well as titin truncating variants (TTNtvs). MAIN OUTCOMES AND MEASURES: Major ventricular arrhythmias, end-stage heart failure, and heart failure hospitalization as measured by medical record review (retrospective cohort) and diagnostic codes (UKB). RESULTS: Two main cohorts were studied for this project. In UK Biobank, a cohort of participants with RBM20tvs, RBM20 synonymous variants, and TTNtvs was studied. Of these 4249 participants, 1869 (44%) were male. The mean (SD) age at enrollment was 56 (8.2) years. In the RBM20 registry, of 179 patients, 105 (58.6%) were male, and the mean (SD) age at enrollment was 43.8 (19.1) years. A validation cohort from the All of Us biobank was also used. This consisted of 7002 participants, 4342 of whom (62.0%) were male, and the mean (SD) age was 52.7 (16.7) years. The etiologic fraction of RBM20 variants in arrhythmogenic DCM was 0.53 (95% CI, 0.32-0.67; P < .001). In genome-first biobanks, lifetime incidence of cardiomyopathy, heart failure, or major ventricular arrhythmia diagnosis was lower in participants with RBM20 variants than in those with TTNtvs (hazard ratio, 0.55; 95% CI, 0.36-0.84; P < .001). Patients with RBM20tvs and DCM presented to referral centers later in life than those with P/LP RBM20 and DCM (mean [SD], 53 [10] vs 34 [18] years; P < .001) and were less likely to have a family history of sudden cardiac arrest (2 of 10 [20%] vs 11 of 17 [65%]; P = .046) or cardiomyopathy (2 of 10 [20%] vs 14 of 18 [78%]; P < .001). There was no significant difference in age- and sex-adjusted incident major heart failure or arrhythmia events between patients with RBM20tv and DCM or those with P/LP RBM20 and DCM, though sex-adjusted lifetime hazard was reduced in those with RBM20tv and DCM (hazard ratio, 0.13; 95% CI, 0.03-0.56; P = .01). CONCLUSIONS AND RELEVANCE: This study found that RBM20 variants contributed to arrhythmogenic DCM phenotypes but conferred reduced lifetime disease penetrance compared to TTNtvs and milder disease severity alone than P/LP RBM20 variants. Their potential for additive interactions with other damaging variants should be considered in patients with DCM and their families.

Humans↗

PubMed2025

Interplay among lipoprotein(a), hepatic and vascular damage in individuals with metabolic dysfunction.

BACKGROUND: The relationship between plasma lipoprotein(a) [Lp(a)] levels and metabolic dysfunction-associated steatotic liver disease (MASLD) remains unclear. The aim of this study was to examine the combined effects of Lp(a) levels on liver and vascular damage. METHODS: The study was conducted using the Liver-Bible cohort of individuals with metabolic dysfunction (n = 859, 808 with genomic information) and the Milan Biobank (n = 6963). Genome-wide association studies (GWAS) and polygenic risk scores (PRS) were used to evaluate the inherited factors influencing plasma Lp(a) levels. RESULTS: In the Liver-Bible cohort, genetic variation in the LPA gene was the strongest determinant of Lp(a), followed by liver stiffness measurement (LSM). Additionally, circulating Lp(a) levels, but not genetic predisposition, were inversely related to LSM, suggesting that MASLD severity may affect Lp(a) secretion. Among participants with more severe insulin resistance (n = 250), Lp(a) levels (odds ratio 6.7, 95% CI 1.0-53.0, p = 0.046) and LSM (odds ratio 13.7, 95% CI 1.4-172.2, p = 0.023) were associated with greater prevalence of carotid atherosclerotic plaques, regardless of traditional cardiovascular risk factors. In the Milan Biobank, genetically predicted higher Lp(a) levels tended to increase the risk of liver-related outcomes, whereas genetically predicted MASLD was associated with lower circulating Lp(a) levels. CONCLUSIONS: The results of this study suggest that liver damage is more likely the cause of reduced plasma Lp(a) levels rather than a consequence. Assessing plasma Lp(a) levels and the extent of liver damage could improve the prediction of vascular damage.

Humans↗

PubMed2026

Dynamic Fusion of Genomics and Functional Network Connectivity in UK Biobank Reveals Schizophrenia-Related SNP Manifolds.

Many mental disorders show strong genetic influence. In parallel, dynamic functional network connectivity (dFNC) has shown high sensitivity to brain changes related to mental disorders. However, previous studies linking dFNC to genetics largely follow a paradigm to identify associations between one set of genetic factors and multiple sets of connectivity features from different dFNC states, ignoring the potential variability in genetic correlates across states. We propose a novel joint ICA (jICA)-based "dynamic fusion" framework to identify dynamically tuned genetic manifolds. A sliding window approach was utilized to estimate four dFNC states and compute subject-level state-average dFNC (sa-dFNC) features. The sa-dFNC features of each state were combined with schizophrenia risk single nucleotide polymorphisms (SNPs) within a jICA fusion framework, resulting in four parallel fusions in 32,861 individuals of the UK Biobank cohort. The extracted four sets of joint SNP-dFNC components were further validated for clinical relevance in a combined schizophrenia cohort of 820 individuals (348 patients). The similarity of SNP-dFNC components across four parallel fusions was evaluated as a measure of state variability. We observed a mixture of "state-invariant" and "state-variant" components for SNP and dFNC modalities. Particularly, the schizophrenia-related state-variant SNP components, or manifolds, complemented each other by capturing different SNPs involved in the same biological functions, revealing a partition of genomic risk particularly elicited by the dynamics of brain function. By augmenting the SNP factors to state-variant manifolds, this dynamic fusion framework promises additional insights into the underlying genetic risk of disease-related alterations in dynamic brain function.

Humans↗

PubMed2026

PMBB Geno-Pheno Toolkit: A suite of scalable, reproducible pipelines for cross-biobank association analyses.

Electronic health record (EHR)-linked biobanks generate unprecedented genomic and phenotypic datasets, but their scientific utility is constrained by data fragmentation across institutional silos and incompatible computing infrastructures, forcing researchers to rewrite ad-hoc scripts for each new environment. We present the PMBB Geno-Pheno Toolkit, a suite of modular Nextflow pipelines for biobank-scale association analyses. This note focuses on the toolkit's SAIGE family of pipelines - supporting genome-wide (GWAS), exome-wide (ExWAS), and phenome-wide (PheWAS) association testing - together with the companion GWAMA and ExWAS meta-analysis pipelines that enable cross-biobank replication. All components are containerized (Docker/Apptainer) and orchestrated with Nextflow, allowing the same workflows to run unmodified on local HPC clusters, cloud platforms, and the All of Us Research Workbench. Complementary toolkit pipelines for PLINK-based GWAS, polygenic scoring, LD-based clumping, and phenotype harmonization are also available and briefly noted.

Journal Article↗

PubMed2025

Germline Variants Influence Chronic Liver Disease Progression through Distinct Pathways.

Cirrhosis and hepatocellular carcinoma (HCC) are long-term complications of chronic liver disease (CLD). In this large multi-ancestry genome-wide association study of all-cause cirrhosis (35,481 cases, 2.36M controls) and HCC (6,680 cases, 1.76M controls), we identified 27 loci associated with cirrhosis (10 novel) and 11 with HCC (three novel). Three novel cirrhosis loci were replicated in independent cohorts (e.g. FGF21, RPTOR, and IFNL3/4). Fifteen cirrhosis loci exhibited differential effects on cirrhosis risk via underlying etiologies, and six HCC loci influenced HCC risk indirectly via cirrhosis. In a gene-burden analysis of rare variants from whole-genome sequencing data in the VA Million Veteran Program (n=102,677), we identified GSTA5 as a novel cirrhosis-associated gene, while APOB and ATP9B were associated with and replicated for HCC. A high genetic risk score for cirrhosis was associated with a nearly doubled risk of CLD progressing to cirrhosis (HR=1.94, P=2×10-68) and of cirrhosis progressing to HCC (HR=1.65, P=7×10-08). Finally, among individuals with chronic hepatitis C who underwent antiviral therapy, cirrhosis risk was modified by variants in PNPLA3, IFNL3/4, and CD81 following pegylated interferon-α therapy, and by APOE lead variant following direct-acting antiviral therapy. These findings provide new insights into the complex genetic architecture of CLD progression with potential clinical and therapeutic implications.

Journal Article↗

PubMed2024

Whole -genome survival analysis of 144 286 people from the UK Biobank identifies novel loci associated with blood pressure.

This study utilized UK Biobank data from 144 286 participants and employed whole-genome sequencing (WGS) data and time-to-event data over a 12-year follow-up period to identify susceptibility in genetic variants associated with hypertension. Following genotype quality control, 6 319 822 single nucleotide polymorphisms underwent analysis, revealing 31 significant variant-level associations. Among these, 29 were novel - 15 in Fibrillin-2 ( FBN2 ) and 4 in Junctophilin-2 ( JPH2 ). Mendelian randomization utilizing two identified variants (rs17677724 and rs1014754) suggested that a genetically induced decrease in heart FBN2 expression and an increase in adrenal gland JPH2 expression were causally linked to hypertension. Phenome-wide association (PheWAS) analysis using the FinnGen dataset confirmed positive associations of rs17677724 and rs1014754 with hypertension, assessed across 2727 traits in 377 277 individuals. Lastly, rs1014754 positively associated with kallistatin, whereas rs17677724 negatively associated with renin in the Fenland study, suggesting a counterregulatory response to high blood pressure. This study, employing WGS data, identified novel genetic loci and potential therapeutic targets for hypertension.

Humans↗