Yen-Chen Anne Feng

Background: Non-communicable diseases (NCDs) impose substantial population burden. Their polygenic architecture informs early risk stratification through polygenic risk scores (PRS). Previous studies reported elevated-PRS burden comparable to some modifiable risk factors (MRFs), yet evidence remains confined to European populations. We quantified the PRS-attributable NCD burden in Taiwan. Methods: Linking Taiwan Biobank and National Health Insurance Research Database (N ≈ 140K), we analyzed 42 high-burden NCDs. Elevated genetic risk ( PRS _e ; top 10%) was assessed using Cox models. We estimated population attributable fractions (PAFs) and disability-adjusted life years (DALYs) using Taiwan-specific estimates from Global Burden of Disease 2023, and examined associations between an integrated PRS (iPRS) and healthcare utilization. Findings: Strongest PRS _e associations occurred for atrial fibrillation (AFib; HR = 3.56 [95% CI: 3.23-3.92]) and gout (2.41 [2.33-2.49]), yielding PAFs of 20% and 12%, respectively, while population-level attributable DALYs were largest for type 2 diabetes (T2D), intracranial hemorrhage (ICH), and lung cancer. At the individual level, PRS _e corresponded to 1.50 [1.42-1.57] healthy life-years lost for T2D and 0.57 [0.40-0.76] for ICH, approaching 2.4 years for the top 1% PRS. Polygenic attributable burden was overall greater in men. For several diseases, PRS _e -associated PAF and DALYs exceeded those of individual MRFs (e.g., smoking, alcohol use). Top-decile iPRS was linked to 23.9% (15.0-33.6%) more hospital days, 19.1% (13.2-25.3%) more inpatient visits, and 9.6% (6.9-12.4%) more outpatient visits. Interpretation: Polygenic risk accounts for a measurable—and for several diseases, MRF-comparable—share of NCD burden in Taiwan, providing a quantitative foundation for integrating genetic liability into preventive care and health planning. Funding: National Taiwan University Academic Research-Career Development Project, Yushan Young Fellow Program from the Ministry of Education (MoE), and the Population Health Research Center from Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the MoE in Taiwan.

Background and ObjectivesEvidence remains inconclusive on whether paternal valproate exposure during spermatogenesis is associated with adverse offspring outcomes, with findings lacking beyond Nord...

Despite high heritability, much of the genetic architecture of schizophrenia (SZ) remains unexplained, and early-onset SZ may represent a more genetically homogeneous subtype. We examined genetically regulated expression risk scores (GeRS) to identify gene networks associated with early-onset SZ and characterize their biological functions. Using genotype data from patients in multiplex families (223 early-onset and 372 late-onset) and simplex families (matched for sex and onset age), SZ-GeRS were estimated based on single-nucleotide polymorphism (SNP)-expression prediction models derived from two transcriptomic reference panels (dorsolateral prefrontal cortex tissue from individuals with psychiatric disorders and brain cortex tissue from the general population). Panel-specific GeRS were constructed using transcriptome-wide association studies (TWAS)-derived effect sizes and integrated using a fixed-effects meta-analytic framework. Module-based SZ-GeRS were constructed by aggregating genes within empirically derived co-expression clusters, followed by network analysis to identify hub genes and functional mapping to infer biological context. Among 13 co-expression modules, meta-analysis identified a significant association between early-onset SZ and Module 10 GeRS (M10_sub-GeRS: adjusted odds ratio [aOR] = 1.22, 95%CI = 1.07-1.40). Hub genes prioritized by complementary network approaches also showed significant associations. Functional enrichment analyses indicated predominant involvement of excitatory neuronal processes and immune-related pathways. Sensitivity analyses using a variance-calibrated TWAS approach to account for polygenicity-driven inflation attenuated gene-level signals but did not materially alter module-level associations, with M10_sub-GeRS remaining robustly associated with early-onset SZ. These findings highlight gene regulatory networks linking genetic susceptibility to molecular mechanisms in early-onset SZ and may inform risk stratification and targeted interventions.

Objective: This study was undertaken to evaluate the predictive, stratification, and prognostic utility of polygenic risk scores (PRSs) for epilepsy in three Taiwanese cohorts comprising more than 7000 cases among more than 600 000 individuals, thereby addressing the limited evidence on PRS performance in non‐European populations. Methods: Leveraging results from the latest multiancestry genome‐wide association study (GWAS), we calculated PRS for all epilepsy, genetic generalized epilepsy (GGE), and focal epilepsy (FE) in two population cohorts—Taiwan Biobank–National Health Insurance Research Database (TWB‐NHIRD; N ~ 105 K) and Taiwan Precision Medicine Initiative (TPMI; N ~ 500 K), with cases defined by International Classification of Diseases codes and medications—and a clinically ascertained sample (Taiwanese subset of the Epi25 consortium [Epi25‐TWN]; N = 1140). PRS performance was assessed through lifetime risk models, age‐at‐onset stratifications, and phenome‐wide association studies (PheWASs). Results: PRS performance varied substantially by cohort, epilepsy type, and age at onset. GGE PRS had the strongest effect on GGE in Epi25‐TWN (per‐unit odds ratio [OR] = 1.65 [95% confidence interval (CI) = 1.24–2.23], p = 8.3 × 10 ⁻⁴ , R ² = 3.2%) but a weaker effect in TWB‐NHIRD and TPMI (ORs = 1.09–1.13 [95% CI = .98–1.23], p = .002–.057, R ² = .14%–.22%). FE PRS effects on FE were more consistent but modest (ORs = 1.02–1.16 [95% CI = .97–1.33]). No prognostic value of PRS was found in predicting epilepsy from syncope or unspecified seizures. Individuals in the top 5% GGE PRS had >4‐fold increased risk in Epi25‐TWN but <1.5‐fold in the others. Restricting to earlier onset cases strengthened PRS signals in population cohorts, particularly for GGE, with PheWAS analysis revealing epilepsy‐specific associations. PRS effects were generally comparable but attenuated relative to European studies. Significance: Cohort design and phenotyping accuracy strongly influence the assessment of the predictive value of epilepsy PRS, highlighting caution in clinical interpretation. Incorporating richer phenotypic data and more diverse GWASs will be crucial to enhancing its applicability and clinical potential.

Importance: Individuals with psychiatric disorders face elevated cardiometabolic risk which is linked to increased mortality. The extent to which this reflects shared pathogenesis or the downstream effects of illness and treatment remains poorly understood. Objective: To characterize the direct pleiotropic effects of psychiatric genetic liability on circulating metabolites and aggregate cardiometabolic risk, independent of psychiatric diagnosis and psychotropic medication use. Design, Setting and Participants: Cross-sectional analysis of Mass General Brigham Biobank participants with metabolomic profiling, genomic data, and linked electronic health records. Exposures: Genetic liability to nine psychiatric disorders quantified using polygenic risk scores (PRS): attention deficit/hyperactivity disorder (ADHD), anorexia nervosa (ANO), anxiety disorder (ANX), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), PTSD, schizophrenia (SCZ), and substance use disorder (SUD). Main Outcomes and Measures: 249 circulating metabolites and four metabolomic risk scores (MRS) for type 2 diabetes, myocardial infarction, ischemic stroke, and vascular dementia. PRS-metabolite associations were estimated using nested models adjusting for lifetime psychiatric diagnosis and psychotropic medication use. Results: Across 25,290 participants, we identified 604 significant PRS-metabolite associations (Bonferroni p< 1.36 × 10 ^-4 ), of which 89% persisted after adjustment for lifetime diagnosis and medication use, suggesting that the direct genetic effects on metabolism are largely independent of illness or treatment. PRS for MDD, PTSD, and ADHD showed the most extensive dysregulation, with a transdiagnostic pattern of elevated lipids and systemic inflammation, specifically triglycerides (β = 0.04 to 0.05, all p< 4.4 ×10 ^-13 ) and glycoprotein acetyls (β = 0.05, all p< 2.2 ×10 ^-16 ). Notably, PRS for SCZ and BD showed minimal metabolite dysregulation despite having the strongest association with their target diagnoses. PRS for MDD, PTSD, ADHD, and SUD were associated with increased MRS across cardiometabolic conditions (β = 0.03 to 0.08, all p< 2.1 ×10 ^-4 ). Sensitivity analyses controlling for BMI or excluding participants without any psychiatric history (N: 21,305 and 11,150, respectively) showed a similar pattern. Conclusions and Relevance: Psychiatric genetic liability is associated with systemic metabolic dysregulation independent of illness onset or treatment, supporting a partially pleiotropic basis for psychiatric-cardiometabolic comorbidity. Key findings: Question: Does genetic liability to psychiatric disorders influence peripheral metabolism independent of psychiatric diagnosis and treatment? Findings: In 25,290 participants, polygenic scores for major depressive disorder, posttraumatic stress disorder, and attention deficit hyperactivity disorder showed widespread associations with metabolic dysregulation, such as elevated triglycerides and systemic inflammation, which persisted after adjusting for diagnosis and medication use. Polygenic scores for schizophrenia and bipolar disorder showed minimal metabolite associations despite their well-established cardiometabolic burden. Meaning: Shared genetic pathways partially underlie psychiatric and cardiometabolic comorbidity, implicating transdiagnostic biomarker profiles.

Biomarkers can potentially improve the diagnosis, monitoring, and treatment of posttraumatic stress disorder (PTSD). However, PTSD biomarkers that are scalable and easily integrated into real-world clinical settings have not been identified. The analysis was conducted between June to November 2024 using genomic samples and laboratory test results recorded in the Mass General Brigham (MGB) Health System. The analysis included 23,743 European ancestry participants from the nested MGB Biobank study. The first exposure was polygenic risk score (PRS) for PTSD, calculated using the largest available European ancestry genome-wide association study (GWAS), employing a Bayesian polygenic scoring method. The second exposure was a clinical diagnosis of PTSD, determined by the presence of two or more instances of PTSD-related diagnostic codes in the longitudinal electronic health records (EHR). The primary outcomes were the inverse normal quantile transformed, median lab values of 241 laboratory traits with non-zero h²_SNP estimates. Sixteen unique laboratory traits across the cardiometabolic, hematologic, hepatic, and immune systems were implicated in both genomic and phenotypic lab-wide association scans (LabWAS). Two-sample Mendelian randomization analyses provided evidence of potential unidirectional causal effects of PTSD liability on hepatic (decreased albumin and total bilirubin), cardiometabolic (decreased HDL cholesterol and increased VLDL cholesterol), and hematologic (decreased mean platelet volume) markers. These findings demonstrate the potential of a triangulation approach to uncover scalable and clinically relevant biomarkers for PTSD.

Methylation quantitative trait loci (mQTL) studies have predominantly focused on European populations (EUR), limiting understanding of the genetic regulation of DNA methylation in other populations. We conduct an East Asian (EAS) mQTL analysis, integrating data from three independent samples comprising 7619 Han Chinese individuals. We identified 331,048 mCpGs, including 28,978 novel mCpGs in EAS. While observing substantial sharing of mQTL between EUR and EAS, we also identify EAS-specific mQTLs, often driven by variants with low minor allele frequencies in EUR. We found that mQTLs enriched for disease and trait heritability, especially for matched-ancestry mQTLs, underscoring their utility for interpreting GWAS results and highlighting the role of DNA methylation in diseases. Our EAS mQTL resource provides valuable insights into the genetic architecture of DNA methylation and its contribution to complex traits.

Whether delaying fatherhood leads to more mutations, thereby resulting in adverse psychiatric outcomes in offspring, remains under debate. No study has directly examined the role of de novo mutations (DNMs) between paternal age and offspring psychiatric outcomes. This study aimed to explore the association between paternal age, the number of DNMs, and age at onset of schizophrenia by sequencing the whole genome of multiplex schizophrenia families. Whole-genome sequencing (30x) was performed in 5 Taiwanese families, each comprising 3 co-affected siblings and healthy parents. Causal mediation analyses were used to explore the mediating role of DNMs in the paternal age effect. Paternal age predicted increased DNMs (+1.50 DNMs/year, 95% CI: 0.81, 2.19, p < 0.0001) over maternal age (+0.09 DNMs/year, 95% CI: −1.01, 1.19, p = 0.87). The effect of paternal age on the number of DNMs varied across families. Each additional DNM resulted in a 0.16-year earlier onset age of schizophrenia (95% CI: 0.04, 0.27, p = 0.009). The estimated direct effect of paternal age on the onset of schizophrenia was −0.82 (95% CI: −0.90, −0.73), while the indirect effect through DNMs was −0.32 (95% CI: −0.47, −0.17). The proportion mediated via DNMs was 28.04% (95% CI: 18.19%, 37.89%). The mediation analyses showed that 30% of the observed association of paternal age with onset age of schizophrenia might be mediated through paternal age-related DNMs. Our study, the first to directly quantify the mediating effect of DNMs, provides support for a causal role of paternal age-related mutations in the increased psychiatric risk in offspring.