Bidirectional causal relationships between epilepsy subtypes and psychiatric disorders: A two-sample mendelian randomization study.

SELE is associated with reduced breast cancer susceptibility: Evidence from Mendelian randomization and single-cell transcriptome.

To investigate the causal effect of obesity on cataract risk and explores the potential mediating roles of metabolites using Mendelian randomization (MR). Summary-level data from large-scale genome-wide association studies to examine the relationship between obesity and cataract were utilized. Obesity-related traits, including body mass index (BMI), waist-to-hip ratio (WHR), and waist circumference (WC). A two-sample MR approach was employed to assess the causal effect of obesity on cataract risk, while potential mediators were identified from suitable genome-wide association studies (GWAS) datasets. Additionally, a metabolic pathway analysis was conducted. An increase of 1 standard deviation (SD) in BMI, WHR, and WC was associated with a significantly higher risk of cataract (BMI: odds ratio (OR) 1.0017, 95% confidence interval (CI): 1.0001-1.0032, P=0.0320; WHR: OR 1.0029, 95%CI: 1.0006-1.0051, P=0.0129; WC: OR 1.0020, 95%CI: 1.0001-1.0038, P=0.0390]. These associations remained robust after adjusting for confounding factors in multivariable MR analysis. Furthermore, a two-step MR analysis identified eight potential metabolic mediators, with one mediator showing a significant causal role in the relationship between obesity and cataract. This work highlights the importance of addressing obesity as a modifiable risk factor for cataracts, particularly through metabolic pathways.

Both genetic and environmental factors affect human stature, including overall height and familial short stature (FSS), and it is associated with various health outcomes. However, the study of genetic connections between stature and health conditions remains lacking in East Asian populations. Hence, we conducted parallel genome-wide association studies (GWAS) of body height and FSS in the Han Taiwanese population, aiming to elucidate the genetic influences of stature on health and facilitate the formulation of precision-health strategies. We analyzed large-scale GWAS data on adult height (120,301 Han Taiwanese) and FSS (FSS; 2,050 cases, 27,966 controls) to examine cross-trait genetic correlations across five East Asian biobanks, and applied phenome-wide association studies (PheWAS) and polygenic risk score (PRS) analyses to assess clinical outcomes using Cox proportional hazard models and Kaplan-Meier analyses. We identified 293 loci for height and five for FSS, with cross-biobank genetic correlations linking stature to body size, lung function, and cardiovascular/reproductive traits (atrial flutter/fibrillation [AF], menarche, and endometriosis). PheWAS showed that height PRS increased risks of AF and endometriosis, while FSS PRS had a protective effect against endometriosis. MR analyses showed that taller stature increased AF risk independently and endometriosis risk through menarche/weight, while shorter stature had a weak protective effect against endometriosis. Survival analyses showed the association of higher height PRS with greater AF risk and an earlier divergence of cumulative incidence curves. These time-to-event patterns were consistently replicated using meta-analysis-derived PRSs. The findings highlight stature-related genetic determinants, associated health outcomes, and polygenic risk scores as effective tools for early risk prediction and precision health strategies in East Asian populations.

Causal Associations between Pre-pregnancy Obesity Traits and Hypertensive Disorders of Pregnancy: A Two-Sample Mendelian Randomization Analyses.

Exploring causal relationships between child maltreatment and child internalising and externalising behavioural problems: A bidirectional mendelian randomization study.

Excessive exposure to heavy metals due to environmental pollution presents a significant challenge to public health. Prior studies have reported potential links between heavy metal exposure and multiple diseases, but the results have not been consistent and have lacked genetic evidence to prove causality. In this study, we employed a two-sample Mendelian randomization (MR) approach based on updated large-scale genome-wide association study (GWAS) data to comprehensively explore the causal relationships between common heavy metal exposures and multiple diseases. We identified 22 causal associations between six heavy metals (lead, arsenic, chromium, copper, cadmium, and nickel) and multiple diseases. Furthermore, sensitivity analyses confirmed the robustness of our findings. Our study provides insights into the susceptibility to multi-system diseases associated with excessive heavy metal exposure due to environmental pollution, highlighting the need for vigilance in populations exposed to high levels of these metals in polluted areas. This work offers potential evidence for the mechanisms underlying the relationship between heavy metals and disease onset.

Azithromycin remains an important agent in gonorrhea treatment, yet resistance is a growing global threat. To comprehensively define its genetic basis, we performed a large-scale genome-wide association study of 14,727 Neisseria gonorrhoeae genomes with linked azithromycin MICs from 66 countries. We identified 113 genetic variants significantly associated with elevated MICs. Beyond well-known mutations in 23S rRNA (A2059G, C2611T) and mtrCDE operon, we uncovered a broad repertoire of potential resistance determinants, including multiple amino acid substitutions in 16 ribosomal proteins (e.g., L2, L4, L13, L23) forming the nascent peptide exit tunnel (NPET), and porin PorB alterations (G120K, A121D/N). Systematic pairwise analysis revealed extensive synergistic interactions, particularly between variants affecting drug influx/efflux (PorB, MtrCDE) and ribosomal target affinity. Phylogenetic analysis identified successful, globally circulating lineages employing distinct resistance strategies: NPET-dominated, 23S rRNA-associated, and porin/efflux-mediated. Our findings demonstrate that azithromycin resistance is a polygenic trait shaped by functional complementarity and epistasis between target modification, membrane permeability, and efflux. This integrated model is essential for accurate resistance prediction from genomic data and highlights key lineages for focused surveillance.

Air pollution is a major global public health threat associated with increased morbidity and premature mortality. Growing evidence suggests that air pollution may also adversely affect brain health, contributing to cognitive impairment and mental disorders. However, most existing studies are observational and therefore vulnerable to residual confounding and reverse causation. Mendelian randomization (MR), which uses genetic variants as instrumental variables, offers a framework to strengthen causal inference regarding the neurological effects of air pollution. We conducted a 2-sample MR study using large-scale genome-wide association study data to investigate the causal effects of air pollution on neurodevelopmental and mental health outcomes. Exposures included ambient air pollutants (PM2.5, PM10, PM2.5-10, NO2, and NOx) and workplace-related air pollution, including self-reported "very dusty" workplace exposure, chemical or other fumes, and diesel exhaust. Neurodevelopmental and mental health outcomes comprised 17 genome-wide association studies datasets across 6 domains: cognitive function and intelligence, educational attainment, psychiatric disorders, emotional and behavioral disorders in children and adolescents, attention-deficit/hyperactivity disorder (ADHD), and neuroticism. Genetically proxied higher PM2.5 exposure was associated with lower intelligence and cognitive performance, reduced educational attainment, and increased risks of schizophrenia, depression, panic attacks, and vulnerability during youth. Elevated NOx exposure was associated with poorer cognition, lower educational attainment, and increased risks of anxiety, panic disorder, and attention-deficit/hyperactivity disorder. Higher NO2 levels were associated with an increased risk of schizophrenia and higher neuroticism scores. Workplace-related air pollution exposures were also associated with adverse outcomes. Self-reported "very dusty" workplace exposure was associated with poorer cognitive performance and educational attainment, while chemical fumes and diesel exhaust were linked to reduced academic achievement and increased risks of selected psychiatric outcomes. This MR study provides evidence supporting potential causal relationships between air pollution exposure and a wide range of neurodevelopmental and mental health outcomes, underscoring the importance of reducing air pollution exposure, particularly among vulnerable populations.

According to the diathesis-stress model, genetic liability and environmental exposures interact in the pathogenesis of depression. Polygenic risk scores for depression (PRSD) based on large-scale genome-wide association studies have opened new avenues for investigating gene-environment interaction (GxE) beyond candidate gene studies. To the best of our knowledge, this is the first systematic review of studies that have taken a polygenic score approach to study GxE interaction effects on depression phenotypes. Based on a preregistered, systematic literature search according to PRISMA guidelines, 56 studies were considered for qualitative analysis. Respective studies investigated a broad range of adverse and protective environmental exposures across the lifespan, e.g., trauma, stressful life events, social environments and (un)healthy lifestyle factors, using cross-sectional and longitudinal designs. While most studies reported significant main effects of an individual's PRSD and different environmental exposures on depression phenotypes, the overall evidence for GxE interactions was considerably heterogeneous. Findings of significant PRSDxE interactions mostly stem from large cohort studies comprising > 40000 participants, in particular, when recent environmental exposures were considered. Two general conclusions can be drawn from this review. First, PRSDxE interactions, if at all, add a small amount of explained variance in depression phenotypes to the corresponding additive model and may thus require large samples to be reliably detected. Second, in a considerable number of studies, different environmental exposures were found to depend on an individual's PRSD, indicating significant gene-environment correlation. We further discuss limitations, future directions and potential clinical relevance of PRSxE research in depression.

BackgroundGout is a prevalent inflammatory arthropathy driven by monosodium urate crystal deposition, yet the causal relationships between circulating biomarkers and disease susceptibility remain incompletely characterized. Establishing robust causal associations and mapping them to specific effector genes and tissues is essential for identifying mechanistically informed therapeutic targets.MethodsWe conducted a comprehensive multi-omics Mendelian randomization study integrating a meta-analysis of three large-scale gout genome-wide association studies (N = 1,538,494) with genome-wide data for 233 metabolites, 179 lipid species, and 926 plasma proteins. Findings were replicated in an independent cohort (N = 327,457). Summary-data-based Mendelian randomization and Bayesian colocalization (HyPrColoc) were applied to map causal biomarkers to tissue-specific effector genes using expression quantitative trait loci data from kidney, liver, and whole blood. Candidate genes were experimentally validated in monosodium urate-stimulated THP-1 macrophages.ResultsWe identified 32 metabolites, one lipid species (TAG 54:3), and two protective plasma proteins (ISLR2, ITIH3) with replicated causal associations with gout. Triglyceride-rich very-low-density lipoprotein particles and circulating isoleucine emerged as prominent risk factors. Multi-tissue transcriptomic mapping prioritized PRELID1 (kidney), NIPAL1 (liver), LMAN2 (whole blood), and CAD as high-confidence effector genes with strong colocalization evidence (posterior probability >0.70). Functional validation confirmed concordant transcriptional and translational dysregulation of these genes following inflammatory stimulation.ConclusionThis integrative analysis establishes a causal framework linking specific lipoprotein subfractions, amino acid metabolism, and novel effector genes to gout pathogenesis, elucidating the systemic metabolic architecture of the disease and identifying potential therapeutic candidates warranting further preclinical investigation before clinical translation.

Traditional genome-wide association studies (GWAS) aim to uncover the genetic variants associated with a single phenotype of interest (typically a disease), and to elucidate its genotypic architecture. However, many of today's GWAS simultaneously measure multiple related phenotypes, leading to the possibility of pursuing the reverse aim of elucidating the "phenotypic architecture" of a single genetic variant. In other words, we may ask what combination of measured phenotypes is associated with a given genotypic variant. ReverseGWAS is an algorithmic platform for answering such questions in the context of large-scale multi-phenotype GWAS. We demonstrate the effectiveness of ReverseGWAS on simulated data, showing its ability to identify logical combinations of phenotypes with a reasonable amount of noise. We then apply it to a selection of combined phenotypes from the UK Biobank, obtaining 719 candidate associations using autoimmune diseases and 205 using common ICD10 codes. We find that the majority of these associations (546/719 and 111/205, respectively) successfully replicate in an independent cohort, FinnGen. The source code of ReverseGWAS is freely available to non-commercial users as an installable R package at https://github.com/Leonardini/rgwas. Supplementary data are available at Bioinformatics online.

Proteome-wide multi-trait association analyses prioritize candidate proteins and therapeutic targets for psychiatric disorders.

Thyroid hormones profoundly modulate hepatic fatty acid and cholesterol synthesis and turnover. Although nonalcoholic fatty liver disease (NAFLD) shows epidemiological links to hypothyroidism, the genetic substrates of this relationship remain unresolved. Integrating large-scale genome-wide association studies with single-cell transcriptomics, spatial transcriptomics, and single-cell chromatin accessibility via state-of-the-art computational approaches, we interrogated the association between NAFLD and hypothyroidism across organ systems, cellular expression landscapes, and molecular-genetic strata. We uncovered pronounced spatial specificity in genetic risk within the liver, prioritized hepatocytes as the principal shared cell type affected, and, leveraging spatial transcriptomics, advanced a dynamic spatiotemporal two-hit model. We further nominated MAGI3, RRNAD1, and PRCC as high-confidence candidate genes and pinpointed a key risk locus, rs926103. These findings deliver a dynamic, testable framework for the full pathophysiological continuum linking NAFLD and hypothyroidism and yield new targets and leads for precision intervention.

This study aimed to explore the potential causal relationship between diabetes and shoulder periarthritis using the Mendelian randomization (MR) approach. Pooled data from large-scale genome-wide association studies were used to identify single nucleotide polymorphisms associated with type 2 diabetes (T2D), rather than a combined diabetes phenotype. T2D was selected as the exposure because existing clinical and epidemiological evidence links shoulder periarthritis primarily to metabolic dysfunction and insulin resistance characteristic of T2D, ensuring genetic independence between the 2. These single nucleotide polymorphisms were used as instrumental variables in a 2-sample MR analysis. The study primarily focused on European populations from publicly available databases. Multiple MR methods (inverse variance weighting, weighted median estimator, and MR-Egger regression) were employed to enhance result robustness. Heterogeneity tests, pleiotropy assessments, and “leave-one-out” sensitivity analyses were performed to validate the findings. The inverse variance weighting analysis showed that the causal effect of diabetes on shoulder periarthritis was odds ratio = 1.00 (95% confidence interval: 0.96–1.04, P = .822), indicating no significant association between diabetes and an increased risk of shoulder periarthritis. Further multi-effect tests revealed no bias, and sensitivity analyses supported the robustness of these results. This 2-sample MR analysis suggests that, based on current genetic data from European populations, diabetes is not an independent causal factor for shoulder periarthritis. These findings offer a genetic perspective on the epidemiological relationship between the 2 conditions, providing clinicians with insights for more accurate identification of risk factors when developing intervention strategies.

Alzheimer's disease (AD) represents a critical global health challenge, with its prevalence and associated costs expected to double significantly by 2030 and 2050. While lifestyle interventions are crucial, sporadic late-onset AD has a substantial genetic component (40-80% heritability), though known variants limit the scope of traditional precision medicine. Crucially, sex and gender are significant risk determinants, with women accounting for two-thirds of cases due to a complex interplay of biological and sociocultural factors. This review focuses on the influence of genetic and gender-related factors, examining large-scale genome-wide association studies (GWASs) and their role in developing advanced genetic risk scores (GRS) for precision genomics. We also explore the potential of Artificial Intelligence (AI) for multimodal big data analysis and digital health tools to promote personalized prevention and emerging concerns about ethics, privacy and data treatment. The convergence of these findings underscores the urgent need for a genetic-, sex- and gender-informed precision-medicine approach to AD.

BackgroundWhile gut microbiota dysbiosis is a hallmark of inflammatory bowel disease (IBD), the causal microbial drivers and their host-mediated mechanisms remain elusive. This study leverages an integrated multi-omics approach, combining Mendelian randomization (MR) and transcriptome analysis, to bridge the gap from microbial causality to host molecular pathways.MethodsWe performed a two-sample MR analysis using large-scale genome-wide association study (GWAS) data to identify specific gut microbiota taxa with a causal effect on IBD risk. Subsequently, we conducted a multi-level bioinformatic analysis of IBD patient transcriptomes to elucidate the downstream host genes, regulatory networks, and immune cell interactions modulated by these causal microbes.ResultsOur MR analysis established a robust causal protective effect of the family Bifidobacteriaceae against IBD. Integrating this finding with transcriptomic data, we identified three key host genes as potential mediators acting through distinct mechanisms: LCT, whose regulation may foster a protective prebiotic niche; MCM6, which appears to function as a hub driving the proliferation of pathogenic immune infiltrates; and UBXN4, a critical regulator of cellular proteostasis, the failure of which can precipitate inflammatory stress.ConclusionsThis study moves beyond association to delineate a causal protective role for Bifidobacteriaceae in IBD and pinpoints specific host genes (LCT, MCM6, UBXN4) through which this effect is likely orchestrated. These findings provide a novel mechanistic framework for host-microbiota interactions and highlight new pathways for therapeutic intervention in IBD.

Myopia is a prevalent ocular condition with marked heterogeneity in onset and progression. Although diagnosis is straightforward, predicting disease trajectories and identifying risks of high or pathologic myopia remain main clinical challenges. Advances in human genetics have substantially reshaped current understanding of myopia, revealing a complex architecture involving common polygenic susceptibility, rare high-impact variants, and cumulative genetic risk burden. Large-scale genome-wide association studies demonstrate that myopia-related variants are enriched in regulatory and signaling pathways that modulate retinal neuronal and glial responses to visual and metabolic stimuli, while exome sequencing studies highlight overlap between early-onset high myopia and inherited retinal or syndromic disorders. Polygenic risk scores further translate common-variant burden into quantitative measures of genetic susceptibility, enabling population-level risk stratification and early risk assessment, albeit with performance differences across ancestries and clinical outcomes. Together, these findings delineate a multilayered genetic framework for myopia and support the role of genetic information as a complementary component of prognostic assessment. Integration of genetic data with longitudinal clinical and environmental information may further improve the prediction of myopia trajectories and facilitate more individualized management strategies.

PurposeIron deficiency anemia may influence the development of periodontitis bidirectionally. This study aimed to examine the causal association of iron deficiency anemia and iron status on the occurrence of periodontitis predicted through Mendelian randomization (MR) analysis.Materials and MethodsThis two-sample MR study used summary data from large-scale genome-wide association studies of iron deficiency anemia (obtained through the meta-analysis of two large datasets), iron status, and periodontitis. Analysis was conducted using inverse variance weighted (IVW) as the main analysis and with weighted median, weighted mode, and MR-Egger regression methods as complementary analyses. Sensitivity analyses were evaluated using Cochran’s Q-test, MR-Egger regression, MR-PRESSO analysis, and leave-one-out analysis to assess the robustness and consistency of the results.ResultsGenetic predictions indicated a statistically significant association between transferrin saturation as the exposure and periodontitis as the outcome (OR=1.23, 95%CI: 1.08-1.41, p=0.002). No causal associations were observed between the other exposures (iron deficiency anemia, serum iron, serum ferritin, and TIBC) (all p>0.05). Cochran’s Q-test showed no statistically significant heterogeneity, and the MR-Egger regression results suggested that this analysis was not influenced by horizontal pleiotropy. The MR-PRESSO results indicated that there were no outliers.ConclusionThe results suggest the presence of a positive causal association between transferrin saturation and periodontitis, but not iron deficiency anemia, serum iron, serum ferritin, and TIBC as exposures. Hence, the findings provide genetic evidence that anemia may be a potential cause for periodontitis, suggesting that attention to and management of patients’ systemic hematological status may be important in the prevention and comprehensive treatment of periodontal disease.

Oxidative stress (OS) has been widely implicated in pathophysiology of major psychiatric disorder. However, establishing robust causal links and delineating the specific molecular mechanisms involved continue to pose significant research challenges. We performed a multi-omics analysis focusing on 817 oxidative stress-related genes (OSGs) in major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ). We applied summary data-based Mendelian randomization (SMR), integrating large-scale genome-wide association studies for MDD, BD, and SCZ with quantitative trait loci datasets from both blood and brain tissues, including measures of DNA methylation, gene expression, and protein abundance. Multi-omics integration yielded supportive evidence across blood and brain tissues implicating ACE and ACADVL in SCZ, where genetically predicted increases in their methylation, expression, and protein abundance were associated with reduced disease risk. IGF1R was associated with bipolar disorder (BD) risk in blood-specific analyses. Brain-specific analyses further nominated ENDOG as a candidate gene for SCZ. Single-cell SMR indicated that increased ENDOG expression was associated with higher SCZ risk in astrocytes, CD4+ naïve T cells, CD8+ effector T cells, and natural killer cells, suggesting a potential immune-brain interaction. This study provides multi-level genetic evidence supportive of a potential causal role for specific OSGs in major psychiatric disorders. We identify ACE, ACADVL, IGF1R, and ENDOG as candidate genes for further investigation, offering insights into epigenetic and transcriptional mechanisms that could inform future research on therapeutic targets.