SEARCH · Search PubMed

Results for “Biobank”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

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

At least 487 records · Page 27Linked to original sources

PubMed2024

Polycystic Ovary Syndrome Physiologic Pathways Implicated Through Clustering of Genetic Loci.

CONTEXT: Polycystic ovary syndrome (PCOS) is a heterogeneous disorder, with disease loci identified from genome-wide association studies (GWAS) having largely unknown relationships to disease pathogenesis. OBJECTIVE: This work aimed to group PCOS GWAS loci into genetic clusters associated with disease pathophysiology. METHODS: Cluster analysis was performed for 60 PCOS-associated genetic variants and 49 traits using GWAS summary statistics. Cluster-specific PCOS partitioned polygenic scores (pPS) were generated and tested for association with clinical phenotypes in the Mass General Brigham Biobank (MGBB, N = 62 252). Associations with clinical outcomes (type 2 diabetes [T2D], coronary artery disease [CAD], and female reproductive traits) were assessed using both GWAS-based pPS (DIAMANTE, N = 898,130, CARDIOGRAM/UKBB, N = 547 261) and individual-level pPS in MGBB. RESULTS: Four PCOS genetic clusters were identified with top loci indicated as following: (i) cluster 1/obesity/insulin resistance (FTO); (ii) cluster 2/hormonal/menstrual cycle changes (FSHB); (iii) cluster 3/blood markers/inflammation (ATXN2/SH2B3); (iv) cluster 4/metabolic changes (MAF, SLC38A11). Cluster pPS were associated with distinct clinical traits: Cluster 1 with increased body mass index (P = 6.6 × 10-29); cluster 2 with increased age of menarche (P = 1.5 × 10-4); cluster 3 with multiple decreased blood markers, including mean platelet volume (P = 3.1 ×10-5); and cluster 4 with increased alkaline phosphatase (P = .007). PCOS genetic clusters GWAS-pPSs were also associated with disease outcomes: cluster 1 pPS with increased T2D (odds ratio [OR] 1.07; P = 7.3 × 10-50), with replication in MGBB all participants (OR 1.09, P = 2.7 × 10-7) and females only (OR 1.11, 4.8 × 10-5). CONCLUSION: Distinct genetic backgrounds in individuals with PCOS may underlie clinical heterogeneity and disease outcomes.

Humans↗

PubMed2025

Genetic Determinants of Leisure-Time Physical Activity in the Taiwanese Population: A Genome-Wide Association Study.

BACKGROUND: Physical inactivity contributes to systemic disease burden and premature mortality worldwide. Leisure-time physical activity (LTPA) improves health outcomes; however, its genetic determinants, particularly in Asian populations, remain unclear. This study aimed to identify genetic loci associated with LTPA in the Taiwanese population. METHODS: We conducted genome-wide association studies in 122,258 Taiwan Biobank participants. LTPA was assessed both as a binary trait (regular exerciser vs non-exerciser) and an ordinal trait (categorized by MET-hours per week into low, moderate, and high physical activity levels). Logistic and ordinal logistic regression models were used under an additive genetic model, adjusting for age, age 2 , sex, body mass index, smoking, and the first 10 genetic principal components. Candidate nonsynonymous mutations were further examined in 1494 whole-genome sequenced participants. RESULTS: Binary trait genome-wide association studies identified genome-wide significant (GWS) loci at ATXN2 (12q24.12), FTO (16q12.2), and NOTCH4 (6p21.32), with associations for FTO and NOTCH4 only observed in body mass index (BMI)-adjusted models. Ordinal trait analysis (<10, 10-<20, ≥20 MET·h·wk -1 ) identified a single GWS locus at BRAP (12q24.12). Fine-mapping of 12q24.12 revealed multiple GWS single-nucleotide polymorphisms (SNPs) in strong linkage disequilibrium with lead variants; these signals largely disappeared after conditional analysis, consistent with a single underlying association. Whole-genome sequencing and linkage disequilibrium analysis identified three GWS nonsynonymous mutations, with ALDH2 rs671 emerging as the most likely causal variant. CONCLUSIONS: ATXN2-ALDH2 region on chromosome 12q24.12 was identified as a key locus for LTPA in Taiwanese individuals. These findings enhance our understanding of the genetic basis of physical activity and may inform future precision medicine and public health strategies.

Adult↗

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↗

PubMed2025

Simple scaling laws control the genetic architectures of human complex traits.

Genome-wide association studies have revealed that the genetic architectures of complex traits vary widely, including in terms of the numbers, effect sizes, and allele frequencies of significant hits. However, at present we lack a principled way of understanding the similarities and differences among traits. Here, we describe a probabilistic model that combines the effects of mutation, drift, and stabilizing selection at individual sites with a genome-scale model of phenotypic variation. In this model, the architecture of a trait arises from the distribution of selection coefficients of mutations and from two scaling parameters. We fit this model for 95 highly polygenic quantitative traits of different kinds from the UK Biobank. Notably, we infer that all these traits have fairly similar, though not identical, distributions of selection coefficients. This similarity suggests that differences in architectures of highly polygenic traits arise mainly from the two scaling parameters: the mutational target size and heritability per site, which vary by orders of magnitude among traits. When these two scale factors are accounted for, we find that the architectures of all 95 traits are very similar.

Humans↗

PubMed2025

Tumor-immune partitioning and clustering algorithm for identifying tumor-immune cell spatial interaction signatures within the tumor microenvironment.

BACKGROUND: Growing evidence supports the importance of characterizing the organizational patterns of various cellular constituents in the tumor microenvironment in precision oncology. Most existing data on immune cell infiltrates in tumors, which are based on immune cell counts or nearest neighbor-type analyses, have failed to fully capture the cellular organization and heterogeneity. METHODS: We introduce a computational algorithm, termed Tumor-Immune Partitioning and Clustering (TIPC), that jointly measures immune cell partitioning between tumor epithelial and stromal areas and immune cell clustering versus dispersion. As proof-of-principle, we applied TIPC to a prospective cohort incident tumor biobank containing 931 colorectal carcinoma cases. TIPC identified tumor subtypes with unique spatial patterns between tumor cells and T lymphocytes linked to certain molecular pathologic and prognostic features. T lymphocyte identification and phenotyping were achieved using multiplexed (multispectral) immunofluorescence. In a separate hepatocellular carcinoma cohort, we replaced the stromal component with specific immune cell types-CXCR3+CD68+ or CD8+-to profile their spatial relationships with CXCL9+CD68+ cells. RESULTS: Six unsupervised TIPC subtypes based on T lymphocyte distribution patterns were identified, comprising two cold and four hot subtypes. Three of the four hot subtypes were associated with significantly longer colorectal cancer (CRC)-specific survival compared to a reference cold subtype. Our analysis showed that variations in T-cell densities among the TIPC subtypes did not strictly correlate with prognostic benefits, underscoring the prognostic significance of immune cell spatial patterns. Additionally, TIPC revealed two spatially distinct and cell density-specific subtypes among microsatellite instability-high colorectal cancers, indicating its potential to upgrade tumor subtyping. TIPC was also applied to additional immune cell types, eosinophils and neutrophils, identified using morphology and supervised machine learning; here two tumor subtypes with similarly low densities, namely 'cold, tumor-rich' and 'cold, stroma-rich', exhibited differential prognostic associations. Lastly, we validated our methods and results using The Cancer Genome Atlas colon and rectal adenocarcinoma data (n = 570). Moreover, applying TIPC to hepatocellular carcinoma cases (n = 27) highlighted critical cell interactions like CXCL9-CXCR3 and CXCL9-CD8. CONCLUSIONS: Unsupervised discoveries of microgeometric tissue organizational patterns and novel tumor subtypes using the TIPC algorithm can deepen our understanding of the tumor immune microenvironment and likely inform precision cancer immunotherapy.

Humans↗

PubMed2025

Robust pleiotropy-decomposed polygenic scores identify distinct contributions to elevated coronary artery disease polygenic risk.

BACKGROUND: Polygenic risk score (PRS) have proved to offer robust risk prediction for coronary artery disease (CAD). However, the global CAD PRS summarizes the joint effects of all the markers in the genome, masking potential genetic heterogeneity that may be important for disease interpretation and targeted interventions. METHODS: Using summary-level data, we identified 43 significant CAD-related traits based on genetic correlations, and further classified them into eight pleiotropy clusters based on their biological functions. We then partitioned the genome into 2,353 near-independent regions. Variants in each region were assigned to the trait most genetically similar to CAD, and then were labeled with the corresponding pleiotropy cluster. We grouped variants without labels into a ninth, non-specific cluster. The Pleiotropy Decomposed (PD) PRSs for each of the nine clusters were calculated using variants assigned to each cluster for 407,903 samples of European ancestry from the UK Biobank (UKBB). RESULTS: We decomposed the CAD PRS into nine PD-PRSs and further stratified individuals with high CAD-PRS into nine subgroups. Each PD-PRS accounted for a higher proportion of the global CAD-PRS within its corresponding subgroup than in the remaining subjects with high CAD-PRS (e.g., 25.2% (0.07) vs. 10.06% (0.07) for lipids-PD-PRS). Additionally, these subgroups showed distinct clinical features. For example, in the lipids-related subgroup, lipoprotein(a) and LDL-cholesterol levels were 67.5% and 18.3% higher, respectively, compared to the remaining high-risk individuals. Furthermore, significant interactions were observed between blood pressure and BP PD-PRS, and between current smoking and respiratory system PD-PRS. CONCLUSION: Our findings suggest that PD-PRSs may reveal substantial genetic and phenotypic heterogeneity among individuals with high CAD-PRS. The unique PD-PRS compositions of each individual can highlight the relative importance of different pleiotropic regions.

Humans↗

PubMed2026

Protein mediators of chronic kidney disease in Type 2 diabetes: A mendelian randomization study.

BACKGROUND: Chronic kidney disease (CKD) occurs in 20-50% of the people living with Type 2 diabetes (T2D) and is the leading cause of kidney failure worldwide. The cause of CKD is not fully understood, and few interventions prevent CKD in individuals living with diabetes. Here, we use large-scale proteomics data to identify circulating proteins that mediate the relationship between T2D and kidney disorders. METHODS AND FINDINGS: First, we used two-sample mendelian randomization (MR) and identified 71 circulating proteins whose levels were altered by genetic predisposition to T2D based on circulating proteomic GWAS from deCODE with 35,559 individuals and T2D GWAS with 80,154 cases. Then, we used cis-genetic variants to proxy the causal effect of some of these T2D-influenced circulating proteins and found that, collectively, five proteins (INHBC, GNPTG, LPO, AGRN, and CTSD) affected three kidney traits (blood urea nitrogen [BUN], estimated glomerular filtration rate [eGFR] and CKD risk) based on GWAS with up to 1,004,040 participants. Notably, we found that higher levels of circulating INHBC protein were estimated to lead to a lower eGFR and higher BUN based on MR analyses. We then replicated this MR analysis with proteomic GWAS from four additional cohorts, namely, UKB-PPP, Fenland, ARIC, and EPIC-Norfolk. We observed a consistent direction of effect across all four proteomic GWAS datasets, supporting the robustness of our results against platform and cohort variation. In observational analyses, increased circulating INHBC levels were associated with increased hazard for kidney disease diagnosis in 37,854 UK Biobank participants. We estimated that circulating INHBC levels mediate 1.3% (95% confidence interval [0.85%, 1.9%]) of the association between T2D and kidney disease diagnosis. There are important limitations in this study. Firstly, although we observed limited evidence for violations to the MR assumptions, some are untestable. Secondly, our study was not based on individuals with diabetic kidney diseases, but rather independent population-based studies assessing diabetes and kidney function separately. Therefore, additional functional analyses in disease specific cohort are needed. CONCLUSIONS: Collectively, these findings suggest that T2D influences the risk of CKD, in part, through increased circulating INHBC levels.

Humans↗

PubMed2024

Assessing the causal link between liver function and acute pancreatitis: A Mendelian randomisation study.

A correlation has been reported to exist between exposure factors (e.g. liver function) and acute pancreatitis. However, the specific causal relationship remains unclear. This study aimed to infer the causal relationship between liver function and acute pancreatitis using the Mendelian randomisation method. We employed summary data from a genome-wide association study involving individuals of European ancestry from the UK Biobank and FinnGen. Single-nucleotide polymorphisms (SCNPs), closely associated with liver function, served as instrumental variables. We used five regression models for causality assessment: MR-Egger regression, the random-effect inverse variance weighting method (IVW), the weighted median method (WME), the weighted model, and the simple model. We assessed the heterogeneity of the SNPs using Cochran's Q test. Multi-effect analysis was performed using the intercept term of the MR-Egger method and leave-one-out detection. Odds ratios (ORs) were used to evaluate the causal relationship between liver function and acute pancreatitis risk. A total of 641 SNPs were incorporated as instrumental variables. The MR-IVW method indicated a causal effect of gamma-glutamyltransferase (GGT) on acute pancreatitis (OR = 1.180, 95%CI [confidence interval]: 1.021-1.365, P = 0.025), suggesting that GGT may influence the incidence of acute pancreatitis. Conversely, the results for alkaline phosphatase (ALP) (OR = 0.997, 95%CI: 0.992-1.002, P = 0.197) and aspartate aminotransferase (AST) (OR = 0.939, 95%CI: 0.794-1.111, P = 0.464) did not show a causal effect on acute pancreatitis. Additionally, neither the intercept term nor the zero difference in the MR-Egger regression attained statistical significance (P = 0.257), and there were no observable gene effects. This study suggests that GGT levels are a potential risk factor for acute pancreatitis and may increase the associated risk. In contrast, ALP and AST levels did not affect the risk of acute pancreatitis.

Humans↗

PubMed2003

Ethical issues and GenomEUtwin.

The post-genomic era is witnessing a proliferation of large-scale and population based genetic and genomic research projects. Many countries have or are establishing research biobanks and, as with GenomEUtwin, there is great interest in building multinational projects that link genotypic and phenotypic information from different centers. Clearly, the conduct of these projects raises multiple ethical issues, and the knowledge generated will continually recast the ethical, legal and social implications (ELSI) of such research. Maximising the scientific profit from this work while minimizing the risks to the participants requires full integration of ethics components into the structure and functioning of these projects. GenomEUtwin is organized around five intellectual cores, including an Ethics Core which operates across the entire project. This paper describes the role of the Ethics Core and presents an overview of the guidelines on which the principles followed in GenomEUtwin are based. We outline the major ethical concerns of our project and highlight complexities arising from diverse national legislations. Finally, the role of empirically based ethics research is discussed for understanding the ethical, legal, social and economic implications of human genetics and genomics research.

Confidentiality↗

PubMed2025

Alterations in DNA Methylation, Proteomic, and Metabolomic Profiles in African Ancestry Populations with APOL1 Risk Alleles.

KEY POINTS: We aimed to elucidate potential methylation, proteomic, and metabolomic mechanisms by which APOL1 variants may be linked to kidney disease. We report distinct methylation profiling between APOL1 risk allele carriers and noncarriers, many near APOL gene family. We report higher APOL1 protein and lower C18:1 cholesteryl ester in two risk allele carriers. BACKGROUND: The APOL1 high-risk haplotype has been associated with CKD and the deterioration of kidney function, particularly in populations with West African ancestry. However, the mechanisms by which APOL1 risk variants increase the risk for kidney disease and its progression have not been fully elucidated. METHODS: We compared methylation (N=3191; 715 [22%] carriers), proteomic (N=1240; 169 [14%] carriers), and metabolomic (N=6309; 674 [11%] carriers) profiles in African and Hispanic/Latino carriers of two APOL1 high-risk alleles (G1/G1, G2/G2, G1/G2) and noncarriers (G0/G0), excluding heterozygotes (G0/G1, G0/G2), from the Population Architecture using Genomics and Epidemiology Consortium and UK Biobank. In each study, the associations between the APOL1 high-risk haplotype and up to 722,719 cytosine-phosphate-guanine (CpG) sites, 2923 proteins, or 836 metabolites were estimated using covariate-adjusted linear regression models, followed by fixed-effects sample size–weighted meta-analyses. RESULTS: Significant associations were observed between APOL1 high-risk haplotype and methylation at 52 CpG sites, with 48 located on chromosome 22 and 18 in the vicinity of APOL1–4 and MYH9. All significant CpG sites near APOL2 were hypomethylated, whereas those near APOL3 and APOL4 were hypermethylated. APOL1-associated CpG sites were also identified in genes involved in ion transport and mitochondrial stress pathways. Sensitivity analyses indicated consistent yet attenuated effects among heterozygotes, supporting an additive effect of APOL1 risk alleles. Further analyses of the 52 CpG sites identified two near APOL4 exhibiting G1-specific effects, eight associated with CKD but none with eGFR, and three showing heterogeneity by CKD status. In addition, carrying two APOL1 risk alleles was associated with higher plasma APOL1 protein (β=1.12, PFDR = 2.26e-70) and lower C18:1 cholesteryl ester metabolite (Z=−4.50, PFDR = 4.83e-3). CONCLUSIONS: Our results demonstrate differential methylation, proteomic, and metabolomic profiles associated with APOL1 high-risk haplotypes.

APOL1↗

PubMed2025

Network Interactions of Circulating FGF23, HRG-HMGB1, and Cardiac Disease in CKD.

KEY POINTS: Multitrait analysis of genome-wide association study boosts the statistical power to identify novel genetic traits for fibroblast growth factor 23. A functional genomics approach aided network discovery to identify histidine-rich glycoprotein (HRG) and high-mobility group protein box 1 (HMGB1) as key regulators of cardiac disease in CKD. Integration of clinical and genetic data enhances the discovery power and is crucial for understanding the genetic underpinnings of mineral bone disorder related to CKD. BACKGROUND: Genome-wide association studies (GWAS) have identified numerous genetic loci associated with mineral metabolism markers but have exclusively focused on single-trait analysis. In this study, we performed a multitrait analysis of GWAS (MTAG) of mineral metabolism, exploring overlapping genetic architecture between traits to identify novel genetic associations for fibroblast growth factor 23 (FGF23). METHODS: We applied MTAG to variants common to GWAS of five genetically correlated mineral metabolism markers in participants of European ancestry. We integrated UK Biobank GWAS for blood levels for phosphate, 25-hydroxyvitamin D, and calcium (n=366,484) and Cohorts for Heart and Aging Research in Genetic Epidemiology GWAS for parathyroid hormone (n=29,155) and FGF23 (n=13,716). We then used supervised and unsupervised deep machine learning to identify novel associations between genetic traits and FGF23. RESULTS: MTAG increased the effective sample size for mineral metabolism markers to n=50,325 for FGF23. After clumping, MTAG identified independent genome-wide significant single-nucleotide polymorphisms for all traits, including 62 loci for FGF23. Many of these loci have not been previously reported in single-trait analyses. Through a functional genomics approach, we identified histidine-rich glycoprotein (HRG) and high-mobility group box 1 (HMGB1) as master regulators of downstream canonical pathways associated with circulating FGF23, and both genes were highly enriched in hypertrophied cardiac tissue of deceased hemodialysis patients. In addition, we found that DNMT3A was associated with uremic toxin, 8-hydroxy-2-deoxyguanosine, a biomarker of DNA damage. In silico gene perturbation analysis revealed that DNMT3A is protective in patients with heart failure caused by hypertrophied or dilated cardiomyopathy. CONCLUSIONS: Our findings highlight the importance of MTAG analysis of mineral metabolism markers to boost the number of genome-wide significant loci for FGF23 to identify novel genetic traits. Functional genomics revealed novel networks that inform unique cellular functions and identified HRG and HMGB1 as key master regulators of FGF23 and cardiovascular disease in CKD.

bones, stones, and mineral metabolism↗

PubMed2023

Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection.

The genetic architecture of human diseases and complex traits has been extensively studied, but little is known about the relationship of causal disease effect sizes between proximal SNPs, which have largely been assumed to be independent. We introduce a new method, LD SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs, depending on their allele frequencies, LD, and functional annotations; LDSPEC produced robust estimates in simulations across various genetic architectures. We applied LDSPEC to 70 diseases and complex traits from the UK Biobank (average N=306K), meta-analyzing results across diseases/traits. We detected significantly nonzero effect correlations for proximal SNP pairs (e.g., -0.37±0.09 for low-frequency positive-LD 0-100bp SNP pairs) that decayed with distance (e.g., -0.07±0.01 for low-frequency positive-LD 1-10kb), varied with allele frequency (e.g., -0.15±0.04 for common positive-LD 0-100bp), and varied with LD between SNPs (e.g., +0.12±0.05 for common negative-LD 0-100bp) (because we consider derived alleles, positive-LD and negative-LD SNP pairs may yield very different results). We further determined that SNP pairs with shared functions had stronger effect correlations that spanned longer genomic distances, e.g., -0.37±0.08 for low-frequency positive-LD same-gene promoter SNP pairs (average genomic distance of 47kb (due to alternative splicing)) and -0.32±0.04 for low-frequency positive-LD H3K27ac 0-1kb SNP pairs. Consequently, SNP-heritability estimates were substantially smaller than estimates of the sum of causal effect size variances across all SNPs (ratio of 0.87±0.02 across diseases/traits), particularly for certain functional annotations (e.g., 0.78±0.01 for common Super enhancer SNPs)-even though these quantities are widely assumed to be equal. We recapitulated our findings via forward simulations with an evolutionary model involving stabilizing selection, implicating the action of linkage masking, whereby haplotypes containing linked SNPs with opposite effects on disease have reduced effects on fitness and escape negative selection.

Journal Article↗

PubMed2026

The Association of Allergic Rhinitis with Chronic Adenotonsillar Diseases and Chronic Rhinosinusitis: A Mendelian Randomization Study.

INTRODUCTION: Allergic rhinitis (AR) has long been considered to be associated with chronic adenotonsillar disease (CATD). However, their causal relationship remains unclear. This study aims to investigate the causal relationship between AR and CATD and to examine the mediating role of chronic rhinosinusitis (CRS) in this association. METHODS: This study employed a two-sample Mendelian randomization (MR) design using genetic instrumental variable analysis. Data for allergic rhinitis (AR) were obtained from the MRC IEU OpenGWAS data infrastructure, data for chronic adenotonsillar disease (CATD) from the FinnGen biobank, and data for chronic rhinosinusitis (CRS) from the GWAS Catalog. Several MR methods were applied. In addition, a two-step MR approach was used to investigate the mediating role of CRS in the relationship between AR and CATD. RESULTS: MR analysis identified a positive correlation between AR and CATD. IVW and weighted median analyses showed significant causal effects (beta = 0.55, 95% CI: 0.26 to 0.84); p <0.001). No causal association was found between CATD and AR. AR and CRS showed a positive correlation (beta = 1.38, 95% CI: 0.78 to 1.98; p = 6.5 × 10-6). CRS had a beta value of 0.15 (95% CI: 0.06 to 0.24; p = 0.001) for CATD. CRS mediates 37.6% of the AR to CATD pathway (mediation effect = 0.20, 95% CI: 0.04 to 0.37; p = 0.013). DISCUSSION: These findings indicate that AR may contribute to CATD risk through CRS, highlighting the need for further research to explore underlying biological mechanisms and validate these findings. CONCLUSIONS: This study suggests a positive causal relationship between AR and CATD, with CRS acting as a mediator.

Mendelian Randomization Analysis↗

PubMed2026

Genetic Association Between Sleep Traits and Vertigo Risk: A Two-sample Bidirectional Mendelian Randomization Study.

BACKGROUND: Observational studies suggest the potential association between sleep traits and vertigo; however, causal evidence remains limited. OBJECTIVE: This study aimed to explore the relationship between genetically predicted sleep traits and vertigo with the Mendelian randomization (MR) method. METHODS: Instrumental variables for sleep traits (snoring, sleep duration, insomnia, daytime sleepiness, daytime napping, and chronotype) were adopted from genomewide association studies (GWAS) data of European ancestry from UK Biobank. The summary-level datasets of vertigo were retrieved from the GWAS of FinnGen. Inversevariance weighted (IVW) method was adopted as the main analysis. RESULTS: IVW analysis revealed a significant association between genetically predicted daytime napping (OR = 1.51, 95% CI =1.08-2.12, P = 0.016) and chronotype (OR = 1.13, 95% CI =1.01-1.26, P = 0.033), both of which were associated with an increased risk of vertigo. However, we did not find evidence for a causal effect of snoring, overall sleep duration, long sleep duration, short sleep duration, insomnia, and excessive daytime sleepiness on vertigo. No reverse causality was detected. CONCLUSION: Our findings suggest that abnormal sleep patterns may serve as risk factors for vertigo disorders and offer opportunities for the prevention and management of vertigo disorders.

Humans↗

PubMed2006

Pharmacogenomic data sample collection and storage: ethical issues and policy approaches.

This perspective report will focus on the ethical, legal and social issues raised by pharmacogenomic research using large population-based databases. Access to databases established or developed at the level of whole populations or communities (e.g., the Estonian Genome Project, the UK Biobank, CARTaGENE, GenomEUtwin, and so on) will become increasingly important in pharmacogenomic research for the purpose of confirming associations between genetic variations and drug-related effects. The capacity of database creators and managers, along with that of researchers, to meet the ethical issues raised by such vast public projects will determine the integration of pharmacogenomics into mainstream clinical practice.

Computer Security↗

PubMed2026

Mendelian Randomization Using a Japanese GWAS Identifies an HLA-Linked Causal Effect of Chronic Hepatitis B on Cholangiocarcinoma Risk.

BACKGROUND: Cholangiocarcinoma (CCA) is a highly malignant cancer that develops in the bile ducts. Its incidence is particularly high in East Asian populations, but the underlying genetic factors remain unclear. To investigate potential risk factors for CCA, we conducted a Mendelian randomization study to infer causality. METHODS: Using large-scale genome-wide association study data from the BioBank Japan resource, we systematically investigated the causal effects of genetic predisposition to seven conditions, chronic hepatitis B (CHB), chronic hepatitis C, autoimmune hepatitis, type 1 diabetes, type 2 diabetes, chronic gastritis, and chronic pancreatitis, on CCA risk. RESULTS: Our analysis reveals a significant association between genetic susceptibility to CHB with a 24% higher likelihood of developing CCA than non-susceptible individuals (Inverse-Variance Weighted Odds Ratio = 1.24, 95% Confidence Interval: 1.08-1.42; p = 0.002). This genetic association is significantly driven by instrumental variables enriched in the immune-regulatory HLA class II region (6p21), suggesting a plausible biological mechanism. For the primary outcome (CCA), statistical significance was assessed across seven exposures at a Bonferroni-corrected threshold (two-sided p<0.0071). Notably, the CHB-CCA association remains significant after correction. This primary finding is strongly supported by comprehensive sensitivity analyses that showed no evidence of confounding by horizontal pleiotropy or heterogeneity. Conversely, no significant causal effects on CCA were identified for the other six conditions. CONCLUSIONS: Our MR analysis supports a causal role of HBV infection in CCA development, highlighting the importance of targeted HBV screening and surveillance.

Female↗

PubMed2026

Safeguarding biomedical AI: a critical scoping review of privacy-enhancing technologies, hybrid approaches, and deployment models.

BACKGROUND: Biomedical artificial intelligence (AI) requires the integration of privacy-enhancing technologies (PETs) to safeguard sensitive clinical, imaging, and genomic data while preserving analytical utility. OBJECTIVES: This review critically and systematically maps applications of PETs across the biomedical AI lifecycle in accordance with PRISMA-ScR guidelines and evaluates their technical trade-offs, deployment feasibility, and residual risks. METHODS: We systematically searched PubMed, IEEE Xplore, ACM Digital Library, and Scopus for studies published between 2015 and 2025. Eligible studies addressed differential privacy, federated learning, secure multiparty computation, homomorphic encryption, or hybrid approaches in biomedical AI. Data were charted on PET type, modality, lifecycle stage, utility metrics, privacy parameters, and deployment considerations. A critical appraisal rubric assessed threat-model adequacy, methodological clarity, reproducibility, privacy-utility transparency, and deployment realism. Additionally, we hand-searched major venues (USENIX Security, NeurIPS, AAAI) and screened Google Scholar for grey literature, applying de-duplication across sources. RESULTS: We identified 87 studies spanning clinical decision support, genomics, and medical imaging. From 25,761 initial records, 3,754 underwent title/abstract screening and 1,968 underwent full-text assessment. PETs demonstrated distinct strengths and limitations: differential privacy provided provable guarantees but reduced performance on imbalanced data; federated learning improved data access but remained vulnerable to gradient leakage; and cryptographic methods ensured confidentiality at high computational cost. Synthetic data generation supported privacy-conscious data sharing and benchmarking but remained sensitive to disclosure risk, fidelity loss, and subgroup representation. Hybrid and emerging approaches, including trusted execution environments, zero-knowledge proofs, and privacy-preserving transformer architectures, mitigated composability gaps yet lacked full end-to-end assurance. Case studies at hospital and biobank scale illustrated practical feasibility and infrastructure demands. CONCLUSIONS: Situating PETs within technical and operational contexts clarifies their capabilities, limitations, and deployment challenges. Residual risks persist, including fairness concerns, inference-time leakage, and overreliance on PETs as compliance proxies. Sustained technical innovation and institutional governance remain essential for the trustworthy integration of PETs in biomedical AI.

biomedical AI↗

PubMed2026

Potential mitochondria-associated pathogenic genes in sepsis: a multi-omics Mendelian randomization study.

BACKGROUND: Mitochondrial dysfunction has been implicated in the pathophysiology of sepsis. However, human genetic evidence linking mitochondria-related genes to sepsis susceptibility remains limited. This study aimed to identify mitochondria-related genes associated with sepsis risk using a multi-omics Mendelian randomization framework. METHODS: Summary-data-based Mendelian randomization (SMR) was applied using sepsis genome-wide association study (GWAS) summary statistics from the UK Biobank and FinnGen databases. Expression, methylation, single-cell, and protein quantitative trait loci (QTLs) were used as genetic instruments. Colocalization analyses were conducted to evaluate whether SMR associations were driven by shared genetic variants. Expression of prioritized candidate genes was further examined in clinical septic samples, and correlations with disease severity (SOFA scores) were assessed. RESULTS: SMR analysis prioritized 13 mitochondria-related genes associated with sepsis risk. Immune cell-specific eQTL analysis suggested that genetically predicted SURF1 expression in memory B cells and naïve T cells was associated with sepsis risk. Differential expression of 12 candidate genes was confirmed in septic patients by qPCR, and PPOX expression showed a negative correlation with SOFA scores. Integration of mQTL and eQTL data supported a regulatory relationship between methylation at cg06661924 and AK4 expression. Increased genetically predicted AK4 expression was associated with higher sepsis risk (OR = 1.21, 95% CI 1.02-1.42). Protein-level analysis identified DUT as a potential sepsis-associated candidate, with consistent evidence across streptococcal and pneumococcal septicemia subtypes. Subtype analyses also suggested heterogeneous genetic signals across different sepsis subtypes. CONCLUSION: This study prioritized several mitochondria-related genes associated with sepsis susceptibility based on human genetic evidence. These findings provide candidate targets for further mechanistic and translational investigation.

Humans↗