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PubMed2026

Joint Effects of Long-Term Obesity and Genetic Susceptibility on Sex-Specific Brain Aging.

OBJECTIVE: This study aimed to examine the associations of longitudinal obesity trajectories and polygenic risk with sex-specific brain aging. METHODS: We analyzed 35,092 UK Biobank participants (16,484 males and 18,608 females). Sex-specific XGBoost models estimated multimodal brain age. We derived 16-year longitudinal obesity trajectories from repeatedly collected anthropometric measurements. Polygenic risk scores were constructed based on 55 independent genetic loci. Multivariable logistic regression examined associations of obesity trajectories and genetic risk with brain age acceleration. RESULTS: A total of 8198 (49.73%) males and 9089 (48.84%) females had accelerated brain aging. High genetic risk significantly increased brain age acceleration odds (males: OR = 1.39; females: OR = 1.34). Crucially, the high-stable obesity trajectory exerted a stronger effect in males (OR = 1.90, 95% CI: 1.64-2.21) than in females (OR = 1.25, 95% CI: 1.12-1.40), with the joint presence of high genetic risk and high-stable obesity amplifying risk to an OR of 2.78 in males and 1.57 in females. Conversely, shifting from obesity to non-obesity reduced risk by 30% in males and 18% in females. CONCLUSIONS: These findings underscore long-term obesity as a critical, sex-dimorphic driver of accelerated brain aging, and midlife weight management offers robust neuroprotection even in genetically susceptible individuals.

brain aging↗

PubMed2026

Prostate Cancer, Genetic Susceptibility, and Risk of Chronic Non-Urological Complications.

BACKGROUND: Chronic nonurological complications are common among prostate cancer (PCa) survivors; however, their spectrum, magnitude, and genetic contribution remain poorly characterized. METHODS: We evaluated 15 commonly reported nonurological complications and tested their associations with exposure to PCa and disease-specific polygenic risk scores (PRS) in the UK Biobank (UKB; N = 219,133). Analyses were conducted using cause-specific Cox proportional-hazards models within a full-cohort framework with time-updated PCa status, delayed entry at study recruitment, and age as the underlying time scale. RESULTS: After recruitment, incident PCa was diagnosed in 13,780 men, of which 1,656 (12.02%) had metastatic PCa (mPCa). Risks for seven complications were higher among men with PCa, adjusting for genetic background (all p < 0.05), including osteoporosis, venous thromboembolism, depression, and four primary cancers (bladder, kidney, colorectal, and pancreatic). Risks were consistently stronger among men exposed to mPCa than non-mPCa; for example, the hazard ratio (HR) (95% confidence interval) for osteoporosis was 1.88 (1.63-2.18) for any PCa, 4.67 (3.11-7.01) for mPCa, and 1.75 (1.50-2.04) for non-mPCa. Elevated risks for three additional complications (coronary artery disease, type 2 diabetes and chronic obstructive pulmonary disease) were observed only among men with mPCa. The risks for these ten complications were further increased among men with higher disease-specific PRS; for example, the HR for osteoperosis in mPCa patients in the top PRS quartile was 13.57 (8.24-22.34) compared with men without PCa (p < 0.001). CONCLUSION: PCa diagnosis and inherited genetic susceptibility jointly contribute to increased risks of multiple chronic non-urological complications among survivors.

Humans↗

PubMed2026

The Childhood Cancer and Leukemia International Consortium (CLIC): Expanding global collaboration in pediatric cancer etiology research.

Childhood cancers are rare, but incidence has risen modestly in countries with robust registration, partly reflecting improved diagnosis. In high-income countries, cancer is the leading cause of disease-related death in children. Marked inequities in incidence, survival, and research capacity underscore the need for large-scale collaboration to identify environmental, genetic, and contextual determinants of risk. The Childhood Cancer and Leukemia International Consortium (CLIC) was established in 2007 to study the etiology of childhood leukemia and later expanded in 2019 to include other childhood cancers, principally solid tumors. CLIC pools harmonized, individual-level data from case-control and cohort studies, obtained through interviews, record linkage (insurance claims, registries), or geographic information systems, and integrates germline genomic data where available. Membership has grown from 13 studies in 9 countries to 57 studies in 21 countries; recruitment spans the early 1960s to the present and encompasses approximately 150,000 cases across all tumor types and 300,000 controls with clinical, demographic, and exposure data, centralized via harmonized data dictionaries at the Data Coordination Center, established in 2014 at the International Agency for Research on Cancer, and supported by a secure analysis platform. Pooled analyses across diverse populations have implicated parental age, prenatal vitamin or folic acid use, mode of delivery, fetal growth, selected congenital anomalies, occupational or household exposures (e.g., pesticides), paternal smoking, and markers of early-life immune modulation (e.g., breastfeeding, daycare attendance) in leukemia risk, informing carcinogen evaluation and prevention. The integration of genetic ancestry and germline susceptibility data is clarifying ancestry-related differences in leukemia biology and outcomes, while confirming risk loci with population-specific effects. CLIC is now adding polygenic risk scores and exposomic data to refine etiologic subtyping and identify modifiable pathways, while broadening representation from underserved regions through partnership-building and capacity-strengthening.

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PubMed2026

Multi-omic biomarkers in cardiovascular disease: Discovery to clinical translation.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, necessitating improved risk stratification and early detection strategies. Multiomics approaches that integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics offer unprecedented opportunities for biomarker discovery and precision medicine in cardiovascular care. This narrative review examines the current landscape of multiomics biomarkers for CVD, tracing their evolution from discovery to clinical translation. We synthesize evidence from recent studies evaluating the clinical utility of integrated omics approaches across diverse cardiovascular conditions, including atherosclerotic cardiovascular disease, heart failure, and atrial fibrillation. High-throughput proteomics has identified novel protein signatures that enhance cardiovascular risk prediction beyond traditional risk factors. Metabolomics has revealed pathway-specific biomarkers, including trimethylamine N-oxide and lipid species, associated with atherogenesis. Polygenic risk scores derived from genomic data demonstrate incremental value when combined with clinical risk scores. Multiomics biomarkers represent a transformative approach to cardiovascular risk assessment and disease management.

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PubMed2026

Predicting risk of ischemic stroke: A transformer model using genomic data.

BACKGROUND AND OBJECTIVE: Ischemic stroke is a leading cause of mortality and long-term disability worldwide. Genetic factors contribute to IS susceptibility, yet conventional polygenic risk score approaches are primarily based on additive effects and may not fully capture non-linear relationships or positional context and interactions among genetic variants. This study aimed to develop and evaluate a transformer-based genomic model incorporating position-wise genotype embedding for IS risk prediction. METHODS: We conducted a genome-wide association study using the UK Biobank dataset to identify IS-associated loci. Gene prioritisation was subsequently performed using tissue-specific expression quantitative trait locus-based Mendelian randomisation and colocalization analyses in whole blood and brain cortex. We then developed a transformer-based model that encoded genotype and SNP-position information using a position-wise embedding layer. Model performance was evaluated across three UK Biobank control definitions and externally assessed in the independent All of Us cohort. Performance metrics included the area under the receiver operating characteristic curve (AUROC), precision, recall, and F1 score. RESULTS: Across the three UK Biobank control definitions, the proposed method achieved the numerically highest discrimination among the evaluated models, with AUROCs of 0.8109, 0.7843, and 0.7468 using MRF-negative, combined, and MRF-positive controls, respectively. In the external All of Us cohort, the proposed method achieved an AUROC of 0.7251 and retained the highest AUROC among the evaluated models. In a separate incident-stroke survival analysis, medium- and high-score groups had hazard ratios of 1.13 and 1.21, respectively, relative to the low-score group. A total of 18 IS-associated loci were identified. Among the tissue-specific MR results, EDEM2 in the brain cortex remained significant after Bonferroni correction, while DCHS2 showed a nominal association. CONCLUSIONS: The proposed transformer-based framework provides a genomic modelling approach that achieved the highest discrimination among the evaluated models in this study and retained comparative performance in an independent external cohort. In further applications, integrating this genomic framework with conventional clinical, lifestyle, and environmental risk factors may support more comprehensive and personalised IS risk assessment. Prospective, population-representative, and multi-ancestry validation will be important to establish its potential role in future prevention-oriented risk management.

Genomics and bioinformatics↗

PubMed2024

Evaluating the impact of modeling choices on the performance of integrated genetic and clinical models.

PURPOSE: The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results. We performed multiple modeling experiments integrating clinical and demographic data from electronic health records with genetic data to understand which decisions may affect performance. METHODS: Clinical data in the form of structured diagnostic codes, medications, procedural codes, and demographics were extracted from 2 large independent health systems, and polygenic risk scores (PRS) were generated across all patients of European ancestry with genetic data in the corresponding biobanks. Crohn's disease was studied based on its substantial genetic component, established electronic health records-based definition, and sufficient prevalence for training and testing. We investigated the impact of choices regarding the PRS integration method, training sample, model complexity, and performance metrics. RESULTS: Overall, our results showed that including PRS resulted in higher performance, but this gain was only robust in situations with limited clinical information. We found consistent performance increases from more compute-intensive models, such as random forest, but the impact of other decisions varied by site. CONCLUSION: This work highlights the importance of considering methodological decision points in interpreting the impact of PRS on prediction performance in clinical models.

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PubMed2025

Covering medical care costs for participants in the eMERGE Network: Challenges for equity and implementation.

PURPOSE: To investigate the complexities of covering study-recommended medical care costs for individuals (in order to prevent lack of adherence due to financial reasons), which have received little attention. METHODS: We explored the deliberations, decisions, and challenges faced by the Electronic Medical Records and Genomics (eMERGE) Network during the implementation of a genomic research project recommending clinical care based on high-risk results defined largely by polygenic risk scores. Two surveys were disseminated to eMERGE sites: to identify preferences about payment for specific care recommendations (survey 1) and to understand the operational processes of covering medical care costs (survey 2). RESULTS: Paying for a subset of care recommendations for the funded study duration was identified as the most feasible approach for covering medical care costs for participants who received high-risk genomic results. Each eMERGE site, by necessity, used diverse approaches to pay for medical care costs. CONCLUSION: eMERGE researchers balanced competing concerns about bias, equity, study design, regulatory compliance, and cost in designing a unified approach to cover some of the recommended medical care costs in the study. Many implementation challenges were encountered. Findings can inform researchers and regulatory bodies about the implications and complications of covering medical care costs in translational research studies focused on prevention.

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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

Urban heat island and risk of rheumatoid arthritis: Insights from genetic predisposition and proteomics.

BACKGROUND: Urban heat island (UHI) exposure is an increasingly common consequence of urbanization and climate warming, but its association with rheumatoid arthritis (RA) risk remains unclear. OBJECTIVE: To investigate the association between UHI exposure and incident RA, and to further assess the roles of genetic susceptibility and plasma proteomic profiles in this association. METHODS: This study included 400,628 urban residents with UHI exposure data and free of RA at baseline. Cox proportional hazards models were used to evaluate the association between UHI exposure and incident RA. Polygenic risk scores were used to assess effect modification by genetic susceptibility. Proteomic analyses identified candidate proteins and enriched pathways underlying the association. Mendelian randomization, colocalization, and mediation analyses assessed causal relevance and mediation. RESULTS: Over a median follow-up of 14.05 years, 5397 incident RA cases were documented. Each standard-deviation increase in UHI exposure was associated with a 17% higher risk of RA. This association was more pronounced among older adults and individuals with lower socioeconomic status. An additive interaction was observed between UHI exposure and genetic risk for RA. Proteomic analyses suggested that this association may involve not only canonical immune-inflammatory pathways, but also hypoxia response and protein transport, with CD40, VCAM1, and SUGP1 emerging as potential molecular mediators. CONCLUSIONS: UHI exposure may be a modifiable environmental risk factor for RA and provide new insights into the biological mechanisms underlying this association.

Humans↗

PubMed2026

Osteoarthritis Year in Review 2026: Genetics, genomics and epigenetics.

OBJECTIVE: The purpose of this narrative review is to highlight advances made over the past 12 months in the field of osteoarthritis (OA) genetics, genomics and epigenomics, with a particular focus on the interpretation of OA risk loci through functional genomic and regulatory approaches. DESIGN: PubMed and Europe PMC were searched to identify studies relevant to OA genetics, genomics and epigenomics published between 1st March 2025 and 30th April 2026. Searches used combinations of terms relating to genetics, genomics, epigenomics, functional genomics, molecular quantitative trait loci, chromatin accessibility and enhancer biology. Studies were limited to human subjects and English-language publications, with additional articles identified through citation screening and expert knowledge of the field. RESULTS: Over the past year, the field has continued to transition from large-scale locus discovery towards biological interpretation of OA genetic risk. Major advances included the largest OA genome-wide association study to date, further development of polygenic risk score approaches, and increasing integration of molecular quantitative trait loci, chromatin accessibility, and enhancer biology datasets to prioritise effector genes and elucidate regulatory mechanisms. Several studies highlighted the highly context-dependent nature of OA genetic risk mechanisms, demonstrating that distinct tissues, cell types, and regulatory layers can identify different candidate effector genes at the same locus. Additional developments included increasing application of singlecell and multi-omic technologies to study OA-relevant tissues. CONCLUSION: Recent advances in OA genetics have shifted the field from locus discovery towards mechanistic interpretation. Emerging evidence demonstrates that the biological consequences of genetic variation are highly dependent upon tissue, cell state and disease context, with different functional genomic approaches often prioritising distinct candidate genes and regulatory mechanisms at the same susceptibility locus. Together, these findings suggest that OA risk loci should increasingly be viewed as dynamic regulatory systems rather than simple variant-to-gene relationships, providing a framework for future studies aimed at resolving causal mechanisms, defining disease endotypes, and identifying therapeutic targets.

Genetics↗

PubMed2025

Rare variants and survival of patients with idiopathic pulmonary fibrosis: analysis of a multicentre, observational cohort study with independent validation.

BACKGROUND: Rare pathogenic variants in telomere-related genes are associated with poorer clinical outcomes in idiopathic pulmonary fibrosis (IPF). We aimed to assess whether rare qualifying variants in monogenic adult-onset pulmonary fibrosis genes are associated with IPF survival. Using polygenic risk scores (PRS), we also evaluated the influence of common IPF risk variants in patients carrying the qualifying variants. METHODS: We identified qualifying variants in telomere and non-telomere genes using whole-genome sequences from individuals clinically diagnosed with IPF and enrolled in the Pulmonary Fibrosis Foundation Patient Registry (PFFPR), a large multicentre, observational cohort study (March 29, 2016 to June 15, 2018, n=888). We also derived a PRS for IPF (PRS-IPF) from known common sentinel IPF variants. The primary outcome was the association between qualifying variants and survival. The secondary outcome was the association between qualifying variants and PRS-IPF. We used logistic regression models adjusted for sex, age at diagnosis, and principal components of genetic heterogeneity to examine the mutual relationship of qualifying variants and PRS-IPF. The association between qualifying variants and PRS-IPF with survival was tested using Cox proportional hazard models adjusted for baseline confounders. Validation of the results was sought in data from an independent multicentre, prospective, observational cohort study of IPF in the UK (PROFILE, May 17, 2010 to Sept 5, 2017, n=472), and results were meta-analysed under a fixed-effects model. FINDINGS: We included 888 patients from PFFPR and 472 from PROFILE, totalling 1360 participants. In the PFFPR, carriers of qualifying variants in monogenic adult-onset pulmonary fibrosis genes were associated with lower PRS-IPF (odds ratio 1·79 [95% CI 1·15-2·81]; p=0·010) and shorter survival (hazard ratio 1·53 [1·12-2·10]; p=7·33 × 10-3). Individuals with the lowest PRS-IPF also had worse survival (1·61 [1·25-2·07]; p=1·87 × 10-4). These findings were validated in PROFILE and the meta-analysis of the results showed a consistent direction of effect across both cohorts. INTERPRETATION: We found non-additive effects between qualifying variants and common risk variants in IPF survival, suggesting distinct disease subtypes and raising the possibility of using PRS to guide sequencing prioritisation. Assessing the carrier status for qualifying variants and modelling PRS-IPF promises to further contribute to predicting disease progression among patients with IPF. FUNDING: Instituto de Salud Carlos III; Instituto Tecnológico y de Eenergías Renovables; Cabildo Insular de Tenerife; Fundación DISA; National Heart, Lung, and Blood Institute of the US National Institutes of Health; and UK Medical Research Council.

Humans↗

PubMed2026

Joint effects of childhood adversity and genetic risk for psychosis on psychopathology in the UK Biobank.

BACKGROUND: The individual effects of genetic factors and adverse childhood experiences (ACEs) on risk of psychosis, including schizophrenia (SCZ) and bipolar disorder (BIP), have been widely acknowledged, but their interaction effects on individual psychopathological symptoms remain unclear. METHODS: Based on data from 163,704 individuals in the UK Biobank, we investigated the joint effects of polygenic risk scores (PRSs) of SCZ and BIP and ACEs on psychopathology. ACEs status and 55 psychopathological symptoms from seven domains were measured retrospectively using an online mental health questionnaire in 2016. Recent genome-wide association studies for SCZ and BIP were combined with genotype data to generate PRSs. Logistic regression analyses were then conducted to explore univariate and joint main effects of PRSs and ACEs on psychopathological symptoms, as well as their additive and multiplicative interaction effects. RESULTS: The interaction mechanisms for PRSs and ACEs varied across symptom domains: additive interactions were observed on the depression (RERIBIP-ACEs = 0.20-0.25), anxiety (RERISCZ-ACEs = 0.20; RERIBIP-ACEs = 0.22-0.26), help-seeking (RERISCZ-ACEs = 0.24; RERIBIP-ACEs = 0.23), and cognition domains (RERISCZ-ACEs = -0.23 to -0.17), whereas multiplicative interactions were only detected on the psychotic (betaSCZ-ACEs = -0.543; betaBIP-ACEs = -0.181), mania (betaBIP-ACEs = -0.195), self-harm or suicide (betaSCZ-ACEs = -0.118), and cognitive domains (betaSCZ-ACEs = -0.204 to -0.157). CONCLUSIONS: The interplay mechanisms for genetic liability to SCZ and BIP and ACEs vary across symptom domains. This study reveals heterogeneity in gene-ACEs interaction mechanisms underlying psychosis and may provide personalized guidance for psychological care after ACEs.

Humans↗

PubMed2026

Genetic heterogeneity affects the risk of incident depression, comorbidity, and response to environment: A prospective trajectory study.

BACKGROUND: Depression exhibits significant heterogeneity in its genetic underpinnings. The role of genetic components in the development of depression and its comorbidities remains insufficiently explored. METHODS: First, depression risk loci from a large-scale genome-wide meta-analysis were annotated to Gene Ontology (GO) terms by functional enrichment. GO-based polygenic risk scores (GO-PRS) were then calculated for individuals in the UK Biobank. Principal component analysis (PCA) was applied for dimensionality reduction, followed by cluster analysis to identify genetic subtypes of depression. Multistate models were applied to assess the impact of genetic patterns on the trajectory from healthy status to incident depression, and depression to 26 subsequent diseases, as well as the associations between environmental factors and disease trajectories across genetic subtypes. RESULTS: Participants were categorized into three genetic subtypes: immune-dominant, neuro-dominant, and comprehensive-risk. Significant differences in risk of depression and subsequent diseases, and susceptibility to environmental factors were observed across subtypes. Comprehensive-risk subtype showed higher risks of depression compared to immune-dominant (HR: 1.10, 95% CI: 1.05-1.15) and neuro-dominant subtype (HR: 1.12, 95% CI: 1.08-1.16). Comprehensive-risk subtype exhibited higher risks of transition from depression to subsequent diseases, such as anemia compared to immune-dominant subtype, and diseases of the digestive system compared to neuro-dominant subtype. Environmental factors were more strongly associated with the transition from depression to subsequent diseases in immune-dominant and comprehensive-risk subtypes, including cardiovascular, respiratory, and metabolic diseases. CONCLUSIONS: Our findings highlight the genetic heterogeneity of depression and comorbidities, and shed light on how genetic components modulate responses to environmental factors.

Humans↗

PubMed2024

Longitudinal characterization of impulsivity phenotypes boosts signal for genomic correlates and heritability.

Genomic correlates of impulsivity have been identified in several genome-wide association studies (GWAS) using cross-sectional designs, but no studies have investigated the molecular genetic correlates of impulsivity phenotypes using longitudinally constructed traits. In 3860 unrelated European participants in the Avon Longitudinal Study of Parents and Children (ALSPAC), we constructed longitudinal phenotypes for delay discounting and impulsive personality traits (as measured by the UPPS-P impulsive behavior scales) via assessment at ages 24, 26, and 28. We conducted GWASs of impulsivity using both cross-sectional and longitudinal phenotypes, estimated heritability and their phenotypic and genetic correlations, and evaluated their association with recently-developed polygenic risk scores (PRSs) for the impulsivity indicators themselves and also related psychiatric conditions. Latent growth curve modeling revealed a stable intercept over time for all impulsivity phenotypes. High genetic correlation of cross-sectional measures over time suggested a stable genetic component for delay discounting (rg = 0.53-0.99) and sensation seeking (rg = 0.99). Heritability estimates of the stable longitudinal phenotypes substantively improved as compared to their cross-sectional counterparts, revealing a significant SNP-heritability for delay discounting (0.22; p = 0.03) and sensation seeking (0.35; p = 0.0007). Consistent with previous reports, GWAS and gene-based analyses revealed associations between specific longitudinal impulsivity indicators and CADM2 and NCAM1 genes. The PRSs for the impulsivity indicators and disorders related to self-regulation were also significantly associated with longitudinal impulsivity traits. Finally, we validated the associations between longitudinal impulsivity phenotypes and their PRSs in an independent 13-wave longitudinal study (n = 1019) and the benefit of longitudinal phenotypes in simulation studies. In this first longitudinal genetic study of impulsivity traits, the results revealed stable genomic correlates of delay discounting and sensation seeking over time and further validated the utility of recently-developed PRSs, both in relation to the observed traits and in connecting them to psychiatric disorders. More generally, these findings support using latent intercepts as novel longitudinal phenotypes to boost signal for heritability and genomic correlates of mechanisms contributing to psychiatric disease liability.

Humans↗

PubMed2025

Genetic and epigenetic analysis of plasma glial fibrillary acidic protein (GFAP) levels in PTSD.

Glial fibrillary acidic protein (GFAP) is an astrocytic marker that can be assessed in blood using single molecule array technology. Recent studies suggest that individuals with posttraumatic stress disorder (PTSD) have suppressed circulating levels of this CNS biomarker. This study examined the hypothesis that PTSD and plasma GFAP levels share common genetic and epigenetic pathways. Using data from 1096 veterans and civilians, we computed a PTSD polygenic risk score (PRS) derived from a prior PTSD genomewide association study (GWAS) and found that PTSD severity and the PRS were each associated with reduced levels of GFAP. To clarify the basis of the PRS association, we performed a GWAS of GFAP which identified 20 genomewide-significant loci including genes implicated in independent GWASs of PTSD and neurodegenerative disease (e.g., PRKN, NFIA). Comparison of the PTSD and GFAP GWAS results showed that PTSD-associated genes were significantly enriched in the GFAP results with notable overlap involving NPSR1 and the protocadherin alpha (PCDHA) gene cluster. Similarly, we performed an epigenomewide association study (EWAS) of GFAP, which identified 4 genomewide-significant associations (including loci in MCT4 and SREBF1) and then compared those results to the findings of a PTSD EWAS. Results again showed significantly greater overlap than would be expected by chance and included loci implicated in prior studies of depression, dementia, and inflammation. This study clarifies the genetic and epigenetic basis of the association between PTSD and plasma GFAP levels and should encourage future research into the role of GFAP in the pathophysiology of PTSD.

Humans↗

PubMed2026

Distinct contributions of schizophrenia and neurotransmitter pathway genetic liability to neurocognition and antipsychotic efficacy in drug-naïve first-episode schizophrenia.

The genetic mechanisms underlying heterogeneity in symptom presentation and antipsychotic response in schizophrenia remain unclear, limiting the development of personalized treatment. We integrated genome-wide schizophrenia polygenic risk scores (SZ-PRS) and pathway-specific PRSs (pPRSs) for four major neurotransmitter systems to examine their associations with clinical phenotypes across the course of illness. Primary analyses were conducted in 394 drug-naïve, first-episode patients from the Chinese First-Episode Schizophrenia Trial (CNFEST) to investigate associations with baseline symptom severity, neurocognitive impairment, and longitudinal treatment response. The CNFEST cohort included 52-week longitudinal assessments of symptoms and neurocognition using the Positive and Negative Syndrome Scale and a modified version of the MATRICS Consensus Cognitive Battery. An independent case-control cohort evaluated associations with schizophrenia diagnosis, while a cohort of 514 healthy adults assessed whether PRS-cognition associations are specific to schizophrenia. Higher SZ-PRS predicted schizophrenia diagnosis (OR = 2.28, Pfdr = 0.003) and poorer baseline executive function (β = -0.44, Pfdr = 0.006) and working memory (β = -0.49, Pfdr = 0.018), but these associations were absent in healthy adults. In contrast, pPRSs showed weaker associations with diagnosis and baseline cognition but were more informative for treatment outcomes: higher serotonin-pPRS predicted greater improvement in depressive symptoms (Pfdr = 0.023-0.032), and higher GABA-pPRS predicted greater improvement in overall symptoms (Pfdr = 0.038-0.043) during weeks 4-24. Exploratory drug-specific analyses further suggested that treatment response varied across antipsychotics and was differentially associated with pPRSs. These findings demonstrate that genome-wide and pathway-specific PRSs contribute distinctly to schizophrenia phenotypes, supporting their integration for personalized stratification and treatment.

Humans↗

PubMed2022

Polygenic heterogeneity in antidepressant treatment and placebo response.

The genetic architecture of antidepressant response is poorly understood. Polygenic risk scores (PRS), exploration of placebo response and the use of sub-scales might provide insights. Here, we investigate the association between PRSs for relevant complex traits and response to vortioxetine treatment and placebo using clinical scales, including sub-scales and self-reported assessments. We collected a clinical test sample of Major Depressive Disorder (MDD) patients treated with vortioxetine (N = 907) or placebo (N = 455) from seven randomized, double-blind, clinical trials. In parallel, we obtained data from an observational web-based study of vortioxetine-treated patients (N = 642) with self-reported response. PRSs for antidepressant response, psychiatric disorders, and symptom traits were derived using summary statistics from well-powered genome-wide association studies (GWAS). Association tests were performed between the PRSs and treatment response in each of the two test samples and empirical p-values were evaluated. In the clinical test sample, no PRSs were significantly associated with response to vortioxetine treatment or placebo following Bonferroni correction. However, clinically assessed treatment response PRS was nominally associated with vortioxetine treatment and placebo response given by several secondary outcome scales (improvement on HAM-A, HAM-A Psychic Anxiety sub-scale, CPFQ & PDQ), (P ≤ 0.026). Further, higher subjective well-being PRS (P ≤ 0.033) and lower depression PRS (P = 0.01) were nominally associated with higher placebo response. In the self-reported test sample, higher schizophrenia PRS was significantly associated with poorer self-reported response (P = 0.0001). The identified PRSs explain a low proportion of the variance (1.2-5.3%) in placebo and treatment response. Although the results were limited, we believe that PRS associations bear unredeemed potential as a predictor for treatment response, as more well-powered and phenotypically similar GWAS bases become available.

Humans↗

PubMed2026

Multi-population GWAS meta-analysis identifies bladder cancer susceptibility loci and highlights genetic regulation of smoking-related risk.

Bladder cancer is the ninth most common cancer worldwide, caused by genetic and environmental risk factors. Here, we report the findings of a multi-population meta-analysis of genome-wide association studies, including 32,470 individuals with and 1,753,462 without bladder cancer. We identify 70 independent risk loci, of which 43 are novel. Using a 70-marker polygenic risk score (HR = 1.63 per standard deviation), we increase the area under the curve from 0.71 (baseline risk model) to 0.75. Integrative analyses reveal the enrichment of the associated variants within accessible chromatin regions, and of the prioritized genes within pathways for xenobiotic metabolism and smoking behavior. Specifically, we show that the 15q25.1 variant rs71581744-ACCCC/A co-localizes with tissue-specific CHRNA3 expression, modulates mRNA stability, and associates with risk of muscle-invasive bladder cancer among current smokers. Together, these findings substantially expand the known genetic architecture of bladder cancer risk and highlight the germline regulation of smoking behavior as a mechanism driving bladder cancer susceptibility.

Humans↗