Shared genetic architecture of posttraumatic stress disorder with cardiovascular imaging, risk, and diagnoses

Shared genetic architecture of posttraumatic stress disorder with cardiovascular imaging, risk, and diagnoses Item Type info:eu-repo/semantics/article Authors Shen, Jie; Valentim, Wander; Friligkou, Eleni; Overstreet, Cassie; Choi, Karmel W.; Koller, Dora; O’Donnell, Christopher J.; Stein, Murray B.; Gelernter, Joel; Koenen, Karestan C.; Ressler, Kerry J.; Zwart, John Anker; Zoellner, Lori A.; Zhao, Hongyu; Zervas, Mark; Zai, Gwyneth C.; Zai, Clement C.; Young, Keith A.; Young, Ross Mc D.; Yehuda, Rachel; Xiong, Ying; Xia, Yan; Wolf, Christiane; Wolf, Erika J.; Winternitz, Sherry; Winsvold, Bendik S.; Williamson, Douglas E.; Williams, Michelle A.; Werge, Thomas; Wendt, Frank R.; Weber, Heike; Waszczuk, Monika; Wang, Yunpeng; Wang, Zhewu; Voisey, Joanne; Vinkers, Christiaan H.; Vermetten, Eric; van Rooij, Sanne J.H.; Van Hooff, Miranda; van den Heuvel, Leigh Luella; Valdimarsdóttir, Unnur; Ursano, Robert J.; Uddin, Monica; Trapido, Edward; Tiwari, Arun K.; Thompson, Wesley K.; Teicher, Martin H.; Sumner, Jennifer A.; Stevens, Jennifer S.; Stensland, Synne; Stein, Dan J.; Sponheim, Scott R.; Smoller, Jordan W.; Smith, Alicia K.; Silove, Derrick; Sheerin, Christina M.; Shabalin, Andrey; Seng, Julia S.; Seedat, Soraya; Seah, Carina; Santoro, Marcos; Sanchez, Sixto E.; Sampson, Laura; Salum, Giovanni Abrahão; de Viteri, Stacey Saenz; Rutten, Bart P.F.; Runz, Heiko; Rung, Ariane; Ruggiero, Kenneth J.; Roy-Byrne, Peter; Rothbaum, Alex O.; Rothbaum, Barbara O.; Roberts, Andrea L.; Risbrough, Victoria B.; Ratanatharathorn, Andrew; Qin, Xue Jun; Powers, Abigail; Porjesz, Bernice; Polusny, Melissa A.; Pietrzak, Robert H.; Peverill, Matthew; Peterson, Alan L.; Peters, Edward S.; Panizzon, Matthew S.; Pan, Pedro M.; Orcutt, Holly K.; O’Donnell, Meaghan; Nugent, Nicole R.; Norman, Sonya B.; Nordentoft, Merete; Nelson, Elliot C.; Mufford, Mary S.; Mortensen, Preben Bo; Mors, Ole; Morris, Charles Phillip; Morey, Rajendra A.; Miller, Mark W.; Milberg, William; Milani, Lili; Mikita, Elizabeth A. DOI 10.1038/s41467-025-60487-w Publisher Nature Research Journal Nature Communications Rights info:eu-repo/semantics/openAccess; Attribution 4.0 International Download date 04/11/2025 01:16:48 Item License http://creativecommons.org/licenses/by/4.0/ Link to Item http://hdl.handle.net/10757/686703 Article https://doi.org/10.1038/s41467-025-60487-w Shared genetic architecture of posttraumatic stress disorder with cardiovascular imaging, risk, and diagnoses Jie Shen 1,2 , Wander Valentim 2,3 ,EleniFriligkou 2,4 , Cassie Overstreet 2,4 , Karmel W. Choi 5,6 , Dora Koller 2,4,7 ,ChristopherJ.O’Donnell 8 , Murray B. Stein 9,10,11 , Joel Gelernter 2,4 , Posttraumatic Stress Disorder Working Group of the Psychiatric Genomics Consortium*, Haitao Lv 1 ,LingSun 1 , Guido J. Falcone 12 ,RenatoPolimanti 2,4,13 &GitaA.Pathak 2,4 Patients with post-traumatic stress disorder face increased cardiovascular risk. This study examines shared genetic regions between post-traumatic stress disorder and 246 cardiovascular conditions across electronic health records, 82 cardiac imaging, and health behaviors defined by Life’sEssential8.Posttraumatic stress disorder is genetically correlated with cardiovascular diagnoses in 33 regions, imaging traits in 4 regions, and health behaviors in 44 regions. Potentially shared causal variants between post-traumatic stress disorder and 17 cardiovascular conditions were observed in 11 regions. Subsequent observational analysis in AllofUS cohort showed post-traumatic stress disorder is associated with 13 diagnoses even after accounting for socioeconomic factors and depression. Genetically regulated proteome expression in brain and blood tissues identified 33 blood and 122 brain genes shared betweenthetwoconditions,revealingneuronal,immune,metabolic,and calcium-related mechanisms, with several genes as targets for existing drugs. These findings exhibit shared risk loci and genes are involved in tissue-specific mechanisms. Given that cardiovascular (CV)-related outcomes, including diseases and risk factors, are the leading cause of morbidity and mortality worldwide1, it is imperative to extend our understanding of associations beyond traditionally known CV risk factors. Recently, the American Heart Association (AHA) recognized the influence of psychological stress on adverse CV health2. Posttraumatic stress disorder (PTSD) is considered a stress-related mental disorder with a lifetime prevalence ranging from 2% to 25%3. PTSD and CV diseases are highly comorbid. Specifically, PTSD has been associated with CVD4, hypertension5, diabetes6, ischemic heart disease7,stroke 8, Received: 24 August 2024 Accepted: 21 May 2025 Check for updates 1 Department of Cardiology, Children’s Hospital of Soochow University, Suzhou, China. 2 Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA. 3 Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo Horizonte, State of Minas Gerais, Brazil. 4 VA Connecticut Healthcare Center, West Haven, CT, USA. 5 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 6 Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA. 7 Department of Genetics, Microbiology, and Statistics, Faculty of Biology, University of Barcelona, Barcelona, Catalonia, Spain. 8 Cardiology Section, Department of Medicine, Veterans Affairs Boston Healthcare System, West Roxbury, Massachusetts, USA. 9 Department of Psychiatry, University of California San Diego, La Jolla, CA, USA. 10 Veterans Affairs San Diego Healthcare System, Psychiatry Service, San Diego, CA, USA. 11 University of California San Diego, School of Public Health, La Jolla, CA, USA. 12 Department of Neurology; Center for Brain and Mind Health Yale University New Haven CT USA, Yale University New Haven, New Haven, CT, USA. 13 Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut, USA. *A list of authors and their affiliations appears at the end of the paper. e-mail: r[email protected]; [email protected] Nature Communications | (2025) 16:5631 1 1234567890():,; 1234567890():,; carotid intima-media thickness9, and coronary artery disease10. PTSD and CVD have been reported to share several pathophysiological features. For instance, patients with PTSD have higher adrenergic activity both at baseline and after being subjected to stress-inducing situations11, imposing a chronically increased burden on both the heart and the circulatory system. High adrenergic states are also associated with CVD symptoms, including increased heart rate and acutely increased blood pressure12. Additionally, PTSD can increase the risk of CVDs by increasing the risk of dyslipidemia and diabetes, although the biology of this association is not yet fully understood13,14. Both PTSD and CVD have substantial genetic components, with heritability estimates of 30–40% for PTSD13,15 and 15–57% for hypertension, 26% for heart failure (HF), and 40-60% for coronary artery disease16–20. Recently, our group showed PTSD polygenic risk being associated with several cardiovascular symptoms and disorders21 and showed potential genetic causality towards cardiac arrhythmias21, ischemic stroke22, coronary artery disease, and hypertension23. Previous studies reporting overlap between PTSD and cardiovascular disease (CVD) either relied solely on epidemiological13 or genetic data, examined only a limited range of CVD outcomes23–25 often from a single source, or lacked a comprehensive exploration of mechanistic insights26,27. Additionally, these studies often did not adequately address potential confounders, such as socioeconomic and behavioral factors. Therefore, several critical gaps remain in understanding the shared genetic architecture between PTSD and CVD which were highlighted by the experts from AHA and the National Heart, Lung, and Blood Institute28, including exploring genomic regions linked to both PTSD and cardiovascular disease, identifying specific disruptions in brain circuitry, and utilizing cell-specific methods to investigate how PTSD-related neural pathways may causally contribute to cardiovascular dysfunction. In this study, we address these knowledge gaps by leveraging the latest and largest GWAS (genome-wide association study) data to study genetic overlap between the PTSD29 and CV outcomes from three different domains including EHR diagnoses, heart imaging30 and Life’s Essential 8 (LE8) key measures for improving and maintaining –evidencebased cardiovascular health as factors and behaviors recently outlined by the American Heart Association2.Specifically, we aimed to (i) identify loci that are shared between PTSD and CV conditions, (ii) test the specificity of PTSD-CVD comorbidity accounting for socioeconomic factors (BMI, smoking, deprivation index) and diagnosis of depression, and (iii) infer biological mechanisms underlying PTSD and CV outcomes based on tissue-specific transcriptomic regulation and proteomic gene-associations that overlap between PTSD and CVD traits (Fig. 1). Fig. 1 | Study design. We investigated loci shared between posttraumatic stress disorder (PTSD) and cardiovascular (CV)-related traits, including the American Heart Association’s Life Essential 8 factors, CV diagnoses derived from electronic health records (EHR),and cardiac imaging phenotypes. Afteridentifying genetically correlated loci between PTSD and CV conditions, we investigated shared causal variants. For the EHR-based CV diagnoses, we performed replication of shared causal variants in the UK Biobank and a follow-up analysis in the All of US Research Program. The traits with evidence of PTSD-CV shared causal variants were tested with respect to tissue-specific transcriptomic and proteomic profiles. The overlapping genes were investigated for overrepresented pathways and drug targets. Article https://doi.org/10.1038/s41467-025-60487-w Nature Communications | (2025) 16:5631 2 Results Genetically correlated loci between PTSD and CVD Traits We investigated 76 genome-wide significant (GWS) risk loci associated with the PTSD GWAS (P<5×10⁻⁸, Supplementary Data S1–3) for local genetic correlation with GWAS of 246 CV-related phecodes from MVP, 82 cardiac imaging traits from UKBB, and LE8-related phenotypes from various studies. We observed statistically significant SNP-based heritability at 73 loci for all the phenotypes investigated (Supplementary 1p31.1 2p24.1 2q22.3 3p24.3 3p21.31 4p15.2 5q12.1 5q21.3 5q33.1 5q33.2 5q33.3 6p22.2 6q25.1 7p22.3 7p21.3 7q21.11 7q31.1 7q31.2 8q24.3 10q22.2 11q12.1 11q23.2 12q23.2 13q14.3 14q21.3 14q24.1 15q26.1 17q21.31 17q24.2 18q21.2 19p13.11 Aneurysm of iliac artery Aortic aneurysm Atrial fibrillation Atrial fibrillation and flutter Cardiac dysrhythmias Cardiomyopathy Cerebrovascular disease Chronic venous insufficiency [CVI] Congestive heart failure (CHF) NOS Congestive heart failure; nonhypertensive Coronary atherosclerosis Diabetes mellitus Disorders of fluid, electrolyte, and acid−based balance Disorders of lipoid metabolism Essential hypertension Heart failure with preserved EF [Diastolic heart failure] Heart failure with reduced EF [Systolic or combined heart failure] Hyperlipidemia Hypertension Hypertensive heart and/or renal disease Hypothyroidism Hypothyroidism NOS Ischemic Heart Disease Morbid obesity Myocardial infarction Nonrheumatic aortic valve disorders Nonspecific chest pain Obesity Occlusion and stenosis of precerebral arteries Other aneurysm Other chronic ischemic heart disease, unspecified Overweight, obesity and other hyperalimentation Palpitations Pericarditis Peripheral vascular disease Peripheral vascular disease, unspecified Premature beats Primary/intrinsic cardiomyopathies Supraventricular premature beats Type 1 diabetes with renal manifestations Type 2 diabetes Type 2 diabetes with neurological manifestations Type 2 diabetes with renal manifestations Unstable angina (intermediate coronary syndrome) Varicose veins of lower extremity, symptomtic EHR - Phecodes (MVP) descending aorta minimum area_DAo_min_area 3p21.31 7q22.3 15q26.1 17q21.31 global myocardial−wall thickness at end−diastole_WT_global left ventricular end−systolic volume_LVESV regional myocardial−wall thickness at end−diastole_WT_AHA_10 regional myocardial−wall thickness at end−diastole_WT_AHA_11 regional myocardial−wall thickness at end−diastole_WT_AHA_12 regional myocardial−wall thickness at end−diastole_WT_AHA_13 regional myocardial−wall thickness at end−diastole_WT_AHA_14 regional myocardial−wall thickness at end−diastole_WT_AHA_16 regional myocardial−wall thickness at end−diastole_WT_AHA_4 regional myocardial−wall thickness at end−diastole_WT_AHA_7 regional myocardial−wall thickness at end−diastole_WT_AHA_9 regional peak circumferential strain_Ecc_AHA_15 regional radial strain_Err_AHA_15 Heart Imaging - UKBB 1p34.3 1p31.3 1p31.1 1q32.3 2p24.1 2p23.3 2q22.3 2q24.2 3p24.3 3p22.1 3p21.31 4p15.2 5q12.1 5q15 5q21.2 5q21.3 6p22.2 7p22.3 7p21.3 7p15.3 7q22.3 7q31.1 7q31.2 7q31.31 9q22.31 10q22.2 10q25.1 11p14.1 11q12.1 11q13.1 11q23.2 12p12.1 12q24.22 13q14.3 13q21.33 14q32.32 15q24.3 15q26.1 17q11.2 17q24.2 18q12.1 18q21.2 19p13.11 20q13.12 BMI(WHR) Diet_Intake−Carbohydrate Diet_Intake−Protein Meta−Hypertension Difficulty in falling asleep−Insomnia Waking up in the night−Insomnia Physical Activity Smoking Total Cholesterol Type 2 Diabetes AHA’s Life Essential 8 Genetic correlation 1.0 0.5 0.3 0.0 −0.3 −0.5 −1.0 (a) (b) (c) Fig. 2 | Local genetic correlation between PTSD and CV conditions. Matrix plot of local genetic correlation between PTSD and conditions grouped by their CV category (a)Life’sEssential8fromdifferentsources(b) heart imaging from UKBB and cthe EHR-based CV definitions (i.e., phecodes) are from Million Veteran Program. The x-axis shows loci as cytoband positions. The positive correlation is denoted in orange, cyan indicates negative correlation, and the size of the squares corresponds to the magnitude of the genetic correlation. Article https://doi.org/10.1038/s41467-025-60487-w Nature Communications | (2025) 16:5631 3 Data S4). For CVphecodes, we identified 112 local genetic correlations with PTSD across 33 loci (FDR q< 0.05; Fig. 2,SupplementaryDataS5). Among these, 67 were related to circulatory system, and 45 to endocrine/metabolic phecodes. Additionally, four PTSD-associated loci presented local genetic correlations between PTSD and 14 heart imaging traits (FDR q<0.05; Fig. 2, Supplementary Data S5). For LE8factors, 92 local genetic correlations with PTSD were identified across 44 loci (Fig. 2, Supplementary Data S5). Notably, fat dietary intake was the only LE8trait that was not genetically correlated with PTSD at any of the investigated loci. Overall, most local genetic correlations were positive. Among the few negative local genetic correlations, four were between PTSD and total cholesterol across different loci and six at 15q26.1 between PTSD and multiple phenotypes (Fig. 2,Supplementary Data S5). Considering locus-specific results, the 17q21.31 region exhibited the highest number of genetically correlated traits (N=21; Fig. 2, Supplementary Data S5), including 10 CV-related phecodes (e.g., cardiac dysrhythmias, nonrheumatic aortic valve disorders, diabetes mellitus, overweight, and hypothyroidism) and 11 heart imaging traits (e.g., various sections of myocardial-wall thickness at end-diastole and descending aorta minimum area). Shared causal variants between PTSD and CV conditions within genetically correlated loci We investigated whether the local genetic correlation between PTSD and CV-related phenotypes is due to a shared causal variant or both traits are associated but with distinct causal variants (colocalization hypotheses H4—shared causal variant, or H3—regional colocalization, respectively)31. EHR: Using colocalization approach, we identified 20 CV-related phecodes that shared the same causal SNP with PTSD across 11 loci (H4-PP ≥80%, Fig. 3; Supplementary Data S6). Under the H3 hypothesis (H3-PP ≥80%), 19 CV diagnoses shared potentially causal but distinct PTSD variants (Fig. 3; Supplementary Data S6). To replicate the colocalization findings observed using MVP phecodes, we repeated the analysis using UKBB phecodes and other cohorts that incorporate a combination of EHR and self-report definitions. Specifically, we matched 24 CV phecodes in UK Biobank (N=420,531) 32 and 11 GWASs from major consortia comprising of self-reported and/or clinical data (N= ~1,320,016)33–39. In UKBB, 13 out of the 24 examined traits displayed replications (H4/H3-PP ≥70%), revealing consistent colocalization patterns with PTSD across 8 loci (Supplementary Data S6; Supplementary Fig. S1&2). In the other cohorts, validation was achieved for 5 phenotypes (i.e., myocardial infarction, coronary artery disease, atrial fibrillation, type 2 diabetes, and BMI) across 8 distinct loci (H4/H3-PP ≥70%; Supplementary Data S6; Supplementary Fig. S1&2). Across discovery (in MVP) and replication (in UKBB and other cohorts), obesity and being overweight demonstrated the highest posterior probability of sharing the same causal variant with PTSD in locus 4p15.2 (H4-PP = 1, a shared causal SNP - rs34811474 - ANAPC4). Imaging traits: Among the heart imaging traits, there was colocalization on two loci for 8 traits. Within locus 17q21.31, variants were associated with PTSD and seven different levels of measurements of regional myocardial-wall thickness at end-diastole, and the global myocardial-wall thickness at end-diastole (H3-PP ≥80.0%). In locus 15q21.31, PTSD and the global myocardial-wall thickness at enddiastole share the same causal trait (H4-PP = 92.5%, causal SNP - rs17514846FURIN). rs11130221(IP6K1) rs13237518(TMEM106B) rs13237518(TMEM106B) rs13237518(TMEM106B) rs13237518(TMEM106B) rs7792410(TMEM106B) rs13237518(TMEM106B) rs13237518(TMEM106B) rs7792410(TMEM106B) rs13237518(TMEM106B) rs13237518(TMEM106B) rs13237518(TMEM106B) rs13237518(TMEM106B) rs13237518(TMEM106B) rs11764590(MAD1L1) rs11764590(MAD1L1) rs11764590(MAD1L1) rs11767176(ELFN1) rs6461115(MAD1L1) rs11767176(ELFN1) rs11764590(MAD1L1) rs6461115(MAD1L1) rs73046323(ELFN1) rs34809719(MAD1L1) rs11763750(MAD1L1) rs11763750(MAD1L1) rs34809719(MAD1L1) rs34809719(MAD1L1) rs34811474(ANAPC4) rs34811474(ANAPC4) rs34811474(ANAPC4) rs34811474(ANAPC4) rs34811474(ANAPC4) rs34811474(ANAPC4) rs34811474(ANAPC4) rs34811474(ANAPC4) rs61676547(BPTF) rs1976054(BPTF) rs61676547(BPTF) rs61676547(BPTF) rs73338706(C17orf58) rs34872586(BPTF) rs7408312(LSM4) rs12128161(−) rs11581459(−) rs4269101(SATB1) rs9855153(SATB1) rs4269101(SATB1) rs9855153(SATB1) rs4269101(SATB1) rs4269101(SATB1) rs748832(PLCL2) rs11135202(PPP1R2B) rs11135202(PPP1R2B) rs11756123(ESR1) rs9479138(ESR1) 3p21.31 7p21.3 7p22.3 4p15.2 17q24.2 19p13.11 1p31.1 3p24.3 5q33.3 6q25.1 75 100 75 100 75 100 Meta−SlpWakePM−Insomnia|PMID.35835914+MVP Meta−SlpFall−Insomnia|PMID.35835914+MVP Phe_427_21|MVP|Atrial fibrillation Phe_427_2|MVP|Atrial fibrillation and flutter Phe_418|UKBB|Nonspecific chest pain Phe_418|MVP|Nonspecific chest pain Phe_278_1|OTHER|PMID.30239722|BMI Phe_278_1|UKBB|Obesity Phe_278_1|MVP|Obesity Phe_278|OTHER|PMID.30239722|BMI Phe_278|UKBB|Overweight Phe_278|MVP|Overweight Phe_250_22|MVP|Type 2 diabetes with renal manifestations Phe_250_2|OTHER|PMID.35551307|Type 2 Diabetes Diet_Intake−Protein|PMID.34426670 Phe_278_1|OTHER|PMID.30239722|BMI Phe_278|OTHER|PMID.30239722|BMI Meta−Hypertension|Phe401−MVP+UKBB Phe_411_8|OTHER|PMID.33532862|Myocardial infarction Phe_411_8|OTHER|PMID.35915156|Coronary artery disease Phe_411_8|MVP|Other chronic ischemic heart disease Phe_411_4|OTHER|PMID.35915156|Coronary artery disease Phe_411_4|MVP|Coronary atherosclerosis Phe_411|OTHER|PMID.33532862|Myocardial infarction Phe_411|OTHER|PMID.35915156|Coronary artery disease Phe_411|MVP|Ischemic Heart Disease Phe_401_1|MVP|Essential hypertension Phe_401|MVP|Hypertension Phe_250_2|MVP|Type 2 diabetes Phe_250|MVP|Diabetes mellitus Phecode | Source | Description Total Cholesterol|PMID.34887591 Phe_443|MVP|Peripheral vascular disease Phe_428_1|MVP|Congestive heart failure (CHF) NOS Phe_428|MVP|Congestive heart failure; nonhypertensive Phe_250_24|MVP|Type 2 diabetes with neurological manifestations Phe_250_2|OTHER|PMID.35551307|Type 2 Diabetes Smoking|PMID.33082346 Meta−SlpWakePM−Insomnia|PMID.35835914+MVP Meta−Hypertension|Phe401−MVP+UKBB Phe_401_1|UKBB|Essential hypertension Phe_401_1|MVP|Essential hypertension Phe_401|UKBB|Hypertension Phe_401|MVP|Hypertension Meta−Hypertension|Phe401−MVP+UKBB Physical Activity|PMID.36071172 Phe_428_1|MVP|Congestive heart failure (CHF) NOS Phe_428|MVP|Congestive heart failure; nonhypertensive Phe_278_11|MVP|Morbid obesity Phe_278_1|UKBB|Obesity Phe_278_1|MVP|Obesity Phe_278|UKBB|Overweight Phe_278|MVP|Overweight Smoking|PMID.33082346 Phe_276|MVP|Disorders of fluid Phe_418|UKBB|Nonspecific chest pain Phe_418|MVP|Nonspecific chest pain H4 Colocalization probability Cardiovascular conditions Category EHR LE8 Fig. 3 | Shared causal variants between PTSD and CV conditions. The x-axis shows on top shows H4 colocalization probability i.e., same causal variant between traits (gene closest to the variant), between PTSD and CV traits. The y-axis is CV conditions, grouped by EHR (green) and AHA’s Life’s essential 8 traits (purple). Article https://doi.org/10.1038/s41467-025-60487-w Nature Communications | (2025) 16:5631 4 LE8 traits: Considering LE8 checklist, we observed statistically significant PTSD colocalization (H4/H3 ≥80%) in 31 loci (Fig. 3;Supplementary Data S6; Supplementary Fig. S1&S2). While no colocalization was observed between PTSD and dietary carbohydrate intake, the other LE8-related phenotypes shared the same PTSD casual SNP in 20 loci collectively (H4-PP ≥80%; closest gene (shared causal variant), ANAPC4 (rs34811474), ARHGAP15 (rs10191758), BPTF (rs34872586), CDH2 (rs7243332), FOXP2 (rs1476535, rs8180817), IP6K1 (rs11130221), KMT2E (rs2470937), LSM4 (rs7408312), MAD1L1 (rs34809719), NCAM1 (rs7106434), NCOA5 (rs6032660), PDE4B (rs2310819), PLCL2 (rs748832), PROX1 (rs340874), SATB1 (rs4269101), TANK (rs197261), TMEM106B (rs13237518), non-coding regions (rs11581459, rs325500, rs4275621)) Supplementary Data S6; Supplementary Fig. S1&2). Interestingly, physical activity shared the same causal variant with PTSD in locus 4p15.2 (H4-PP = 99%) that we observed with respect to obesity and being overweight (shared causal SNP - rs34811474 - ANAPC4). Both total cholesterol and BMI–adjusted-waist-to-hip ratio had the H3 highest posterior probability with PTSD in 6p22.2 and 3p21.31, respectively (H3-PP = 100%, Supplementary Data S6; Supplementary Fig. S1-S3). Overall, 7p21.3, 7p22.3, 4p15.2, and 17q24.2 regions exhibited the highest number of H4 probability-based colocalized CV-related phenotypes (Fig. 3). While 17q21.31 and 3p21 showed colocalization between PTSD and CV phecodes, heart imaging phenotypes, and LE8 checklist, mostly due to H3 hypothesis. (Supplementary Fig. S4). To prioritize genes within the colocalized regions, we first identified 506 genes physically located in the 38 loci that showed evidence of colocalization between PTSD and CV conditions. We leveraged multitissue molecular profiles (gene, proteome, and splicing expression) and CV traits available from OpenTargets platform40,41.Weidentified 270 genes that had H4 or H3 hypothesis probability ≥0.6 of colocalizing with 201 different CV conditions across 124 tissues. Among the highest H4 associations (H4-PP = 100%), there were multiple phenotypes related to the body electrical impedance, BMI, blood lipids, blood pressure, hemoglobin A1c in multiple tissues (gene/splicing/proteome expression in various tissues), smoking, sleep duration, and hypothyroidism. (Supplementary Data S7). Observational association of PTSD with CV-related diagnoses in the All of US cohort To further investigate the comorbidity between PTSD, and 13 CV diagnoses (lifetime prevalence) observed in our genetically informed analysis, we conducted an observational analysis using EHR data for circulatory and metabolic diagnoses from AoU cohort (as per phecodes: 244-hypothyroidism, 244.4-hypothyroidism NOS, 250-diabetes mellitus, 250.2-Type 2 diabetes, 278-Overweight, obesity, and hyperalimentation, 278.1-obesity, 401-hypertension, 401.1-essential hypertension, 411.8-other chronic ischemic heart disease, 418-nonspecific chest pain, 427-Cardiac dysrhythmias, 427.2 atrial fibrillation and flutter, and 411.4-coronary atherosclerosis). Specifically, we tested the association of PTSD (13,877 cases) with 13 CV diagnoses (Supplementary Data S8) considering three adjustment models: i) base-model (covariates:- age, sex, and self-reported race); ii) SES-model (base-model covariates and deprivation index, smoking, and BMI), iii) depression-model (SES-model and depression diagnosis-[phecode 296.2]). PTSD was significantly associated with all 13 CV phecodes across all three models (p<5.15×10 −6; Supplementary Data S8). However, while there was no difference between the estimates obtained from base and SES-models, we observed a reduction in effect sizes observed in the depression model compared to the base model ranging from 84% for chronic ischemic heart disease (odds ratio, OR = 2.61 vs 1.41, p-difference = 5.44 × 10−19) to 48% for hypothyroidism (OR = 1.78 vs 1.2, p-difference=5.7×10 −25). Nevertheless, the effect sizes observed accounting for depression comorbidity confirm the relationship linking PTSD to CV-related traits. Partitioned heritability to identify tissues overlapping between PTSD, CV diagnoses, heart imaging, and LE8 traits To gain biological insights into PTSD and CV conditions, we further employed in-silico genetic approaches to identify tissues that might overlap due to similar gene expression profiles. We limited our analyses to CV traits that showed multiple levels of evidence for genetic overlap: 13 CV diagnoses (meta-analyzed between UKBB and MVP; Supplementary Data S9), 8 heart imaging traits and eight LE8 traits. We conducted a partitioned-heritability analysis, which systematically models tissue-specific gene expression data from 205 cell-type and gene expression annotations. This approach integrates GWAS data for the trait of interest to prioritize disease-specific causal tissues. We identified 6 tissues that are enriched based on gene expression in PTSD: the limbic system (p=4×10 −6), the cerebral cortex (p=6×10 −5), the brain (p=4×10 −6), the entorhinal cortex (p=4×10 −4), the hippocampus (p=10 −3), and the brain cortex (p=9×10 −4). These tissues were not FDR significant in other traits, but were nominally significant for nonspecificchestpain,overweight,‘overweight, obesity and other hyperalimentation’, physical activity, smoking, diet intake proportion of protein, and insomnia (SlpFalldifficulty in falling asleep, and SlpWakePM-difficulty in falling asleep after waking up in the middle of the night) (p< 0.05; Supplementary Data S10; Supplementary Fig. S5). No heart imaging trait remained significant in the multi-tissue enrichment analysis. Proteome-Wide Association Study (PWAS): integrating genetic variants from GWAS and proteome expression in brain and blood tissues PWAS studies combine effect-estimate of genetic variants on diseases, and abundance or expression of proteins, thereby prioritizing genes that may be associated with disease/traits via altered proteome expression. We tested GWAS of PTSD, and CV phenotypes with genetically regulated proteome expression in dlPFC and blood. The CV phenotypes included 13 phecodes, seven heart imaging traits, and LE8 factors. To maximize statistical power, we meta-analyzed GWAS of each of the 13 CV diagnoses from MVP and UKBB to improve statistical power for gaining insights into overlapping mechanistic pathways [N total= 865,527]. Details regarding each meta-analyzed CV diagnosis are available in Supplementary Data S9. Leveraging weights derived from dlPFC-specificpQTLs,weidentified 122 genes associated with both PTSD and CVD phenotypes (FDR q<0.05; Fig. 4, Supplementary Data S11). The majority of these PTSD associations were shared with LE8 factors (N= 109). Several genes demonstrated proteomic associations across CV phecodes, heart imaging phenotypes, and LE8 factors (e.g., ATG7,CCDC92,CNNM2,DNM1,FAM134 A,SIRPA,SNX32,and TR0IM47). Other pleiotropic genes included CCDC92,SIRPA and LRRC37A2 that were associated with PTSD and 20 or more CV-related phenotypes (Supplementary Data S11). The strongest proteome-wide association with PTSD was observed with ICA1L (Z = 6.89, p=5×10 −12) that was also associated with several CV phecodes such as atrial fibrillation (Z = 2.9, p=4 × 10 −59), coronary atherosclerosis (Z = 16.2, p=5 × 10 −50), and T2D [EHR-MVP + UKBB] (Z = 3.87, p=1.07 × 10 −4). While these associations were positively related to increased ICA1L proteomic expression, we also observed an inverse relationship with total cholesterol (Z = −18.86, p=2×10 −79), insomnia (Z = −6.03, p=2× 10−9), physical activity (Z = −3.41, p=6×10 −4), and smoking (Z = −4.85, p=10 −6).ThestrongestinverseassociationwithPTSDwasKHK (Z = −6.86, p=7 × 10 −12), which was also negatively associated with several other phenotypes, such as T2D (Z = −5.22, p=2×10 −7), hypertension (Z = −4.59, p=4 × 10 −6), unspecified chronic ischemic heart disease (Z = −4.04, p=5×10 −5), hypothyroidism (Z = −3.94, p=8×10 −5) proteinintake(Z=−3.84, p=10 −4), coronary atherosclerosis (Z = −3.82, p=10 −4), and nonspecific chest pain (Z = −3.32, p=9×10 −4). KHK was positively associated with BMI (Z = 3.49, p=5 × 10 −4) and LE8-T2D (Z = 3.56, p=4 × 10 −4). Total cholesterol demonstrated the highest Article https://doi.org/10.1038/s41467-025-60487-w Nature Communications | (2025) 16:5631 5 number of overlapping genes with PTSD, with 56 genes overlapping in the brain proteome including genes with the highest effect estimate such as PLCG1 (Z = −18.349, p=3.37 × 10 −75). Among the significant genes, we applied FOCUS to prioritize causalgenes that mediate risk to the trait via proteome abundance. We found 11 genes in credible set that were causal for PTSD and at least one CV trait (Supplementary Data S12, Fig. 4A). Leveraging blood-proteome expression from two different studies, ARIC (using FUSION), and UKBB-PPP(using SMR), we identified 33 genes associated with both PTSD and at least one or more of the 13 CV diagnoses, 8 heart imaging traits, and LE8 factors investigated (Supplementary Data S11). The strongest positive association with PTSD was FES (Z = 6.15, p=8 × 10 −10), which was also positively associated with smoking (Z = 5.95, p=3×10 −9) and physical activity (Z = 4.19, p=3 ×10 −5). FES proteomic expression exhibited negative associations with essential hypertension (Z = −9.92, p=3×10 −23), unspecified chronic ischemic myocardial disease (Z = −8.51, p=2×10 −17), and global myocardial wall-thickness at end-diastole (Z = −4.08, p=5 × 10 −5). The strongest negative association with PTSD was observed with CD40 (Z = −5.27, p=10 −7), which was also negatively associated with atrial fibrillation and flutter (Z = −3.73, p=2×10 −4) and positively associated with total cholesterol (Z = 7.17, p=8×10 −13). Considering both blood and dlPFC, SIRPA,MANF,andPOR exhibited cross-tissue proteomewide associations with PTSD and CVD traits (Supplementary Data S11). To prioritize genes statistically causal genes, we applied FOCUS and HEIDI tests on ARIC, and UKBB-PPP respectively. We observed 10 genes that were causal for PTSD and CV traits (Supplementary Data S12 Fig. 4B). By comparing genes prioritized from colocalized regions and genes identified from PWAS, we identified 403 distinct genes with 17 EHR - Meta−Phecode(MVP/UKBB) EHR - Meta−Phecode(MVP/UKBB) Life’s Essential 8 C3orf18 CACNA2D2 CAMK2G CDC42BPB CNNM2 CYSTM1 GMPPB GPX1 KHK NOS1 SUGP1 PTSD Atrial fibrillation and flutter Cardiac dysrhythmias Coronary atherosclerosis Diabetes mellitus Essential hypertension Hypertension Nonspecific chest pain Obesity Other chronic ischemic heart disease, unspecified Overweight, obesity and other hyperalimentation Type 2 diabetes BMI(WHR) Diet_Intake−Protein Waking up in the night−Insomnia Physical Activity Smoking Total Cholesterol Type 2 Diabetes Diagnosis/Descrption ARIC−Plasma UKB_PPP LE8 EPHA10 MANF MST1 CD40 CDH6 CGREF1 CNTN2 FKBPL KHK TWF2 PTSD Atrial fibrillation and flutter Cardiac dysrhythmias Coronary atherosclerosis Diabetes mellitus Essential hypertension Hypertension Hypothyroidism Hypothyroidism NOS Nonspecific chest pain Obesity Other chronic ischemic heart disease, unspecified Overweight, obesity and other hyperalimentation Type 2 diabetes BMI(WHR) Physical Activity Total Cholesterol (a) DLPFC - PWAS (b) Plasma - PWAS Difficulty in falling asleep−Insomnia Fig. 4 | Shared genes between PTSDand CV conditions based on proteome-wide associations. Distribution of z-scores across significant PWAS genes between PTSD and CV conditions using A) brain proteome in blue and B) blood proteome in red. Genes are grouped based on two blood-based proteome panels/brain-based panel (y-axis) and respective CV conditions (x-axis). Significant genes are shown as red (blood) or blue (brain) triangles, wherein triangles facing up and down represent positive and negative z-scores (two-sided), respectively. Article https://doi.org/10.1038/s41467-025-60487-w Nature Communications | (2025) 16:5631 6 overlapping between the two methods: BTN2A1,BTN3A2,C3orf18, CACNA2D2,CD40,DAG1,FES,FURIN,GMPPB,GPX1,HYAL1,LRRC37A2, MST1,NCAM1,SEMA3F,SERPING1,UBE2L6. Pathway enrichment Considering genes identified by the tissue-specificPWAS,weidentified 25 pathways overrepresented by the PTSD&CV proteome-wide significant genes in the dlPFC, and 36 pathways in the blood (Supplementary Data S13). Among the dlPFC PWAS genes, in addition to basic cellular functions (Supplementary Fig. S6; Supplementary Data S13), we observed metabolic and calcium modulating pathways: “oxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor”(FDR P-value = 3.92 × 10−2), “Calmodulin-induced events” (FDR P-value = 3.97 × 10−2), the “CaM pathway”(FDR P-value = 3.97 × 10−2), and “Ca-dependent events”(FDR P-value = 4.95 × 10−2). We also performed pathway enrichment on 11 statistically causal genes identified, and observed seven pathways, including “Pathways of neurodegeneration-multiple diseases”,“Adrenergic signaling in cardiomyocytes”,“Oxytocin signaling pathway”,“Metabolic pathways”,and “Calcium signaling pathway”(FDR P-value < 0.05) (Supplementary Data S14). Among the pathways overrepresented by the blood PWAS genes, in addition to biological and cellularprocesses (Supplementary Fig. S7; Supplementary Data S13), we observed several immune and neuronal processes Such as “response to stimulus”(FDR P-value = 5.84 × 10−4), “regulation of immune response”(FDR P-value = 4.98 × 10−3), “regulation of immune system process”(FDR P-value = 7.33 × 10−3), “regulation of response to stimulus”(FDR P-value = 0.011), neuron projection development (FDR P-value = 0.014). None of the significant pathways enriched in blood and brain tissues were overlapping. The pathway enrichment for genes with a single causal variant for PTSD and CV traits did not identify any significant pathways. Drug repurposing in research context To contextualize the role of reported shared genes between PTSD and CV conditions, we aimed (i) to identify which of these shared genes/ proteins are known therapeutic targets, (ii) determine if any of these shared genes are targeted by medications known to treat either condition, and/or (iii) if they exhibit opposing side effects. This analysis helps clarify why the observed genes may not be specifictoa single disease but instead contribute to both conditions and the potential risks and benefits of the drugs involved. We identified 74 approved drugs targeting 30 genes that were either designated for psychiatric (as a range of psychiatric drugs can be used for treating PTSD and associated symptoms) or cardiovascular conditions (Supplementary Data S15, Fig. 5). Looking at gene targets that overlap between psychiatric and CV conditions, we found DRD2, GFAP,POR and NOS1 to be targets of several CV medications that address conditions including hypo/hypertension, diabetes, cholesterol and heart failure. Only Prazosin, an alpha-1 adrenergic receptor antagonist was the only drug that is known to treat hypertension, and has off-label benefit for PTSD-associated nightmares42,43.Among genes, DRD2 had the highest number of interactions with both psychiatric and CV drugs. While interaction type between each drug and gene target is not available, we observed that 87.5% of drugs are inhibitors, and 21.5% are agonists to DRD2. We also investigated adverse effects of these drugs, and among the 45 psychiatric drugs, most cardiovascular-system-related adverse effects included increased weight or weight fluctuations, tachycardia, and QT prolongation, which indicates a disturbance in the heart ventricle chamber signal transmission44. Other CV adverse effects of psychiatric drugs include blood glucose increase, diabetes, orthostatic hypotension, and dyslipidemia. Conversely, among the 30 CVD drugs, adverse psychiatric effects were less observed, and included depression, suicide attempt, cognitive disorder, and hallucination. (Supplementary Data S16). Discussion To our knowledge, this is the first work that includes a comprehensive evaluation of CV measurements from diverse sources, such as imaging, diagnoses, and risk factors. By leveraging additional GWAS data of EHR-based phecodes for CV diagnoses from the MVP, the study generates statistically well-powered GWAS results, addressing phenotypes that previously lacked sufficient power or were analyzed using data from a single source, such as the UKBB. Additionally, the study validates genetic associations between PTSD and CV conditions using Drug Classes Drugs for psychiatric conditions Gene targets Drugs for cardiovascular conditions Drug Classes Anesthesia; Hypnotics and Sedatives,Adjuvants Anti−anxiety Agents Antidepressive Agents Antidyskinetics; Antipsychotic Agents Antiemetics; Antipsychotic Agents Antipsychotic Agents Hypnotics and Sedatives PTSD Second−Generation; Antipsychotic Agents,Antidepressive Agents antipsychotic agent,antiemetic atypical,antipsychotic agent ACETOPHENAZINE AMISULPRIDE AMOXAPINE ARIPIPRAZOLE LAUROXIL BREXPIPRAZOLE BUSPIRONE CARIPRAZINE CARPHENAZINE CHLORPROTHIXENE CLOZAPINE FLUPHENAZINE HYDROCHLORIDE FLUSPIRILENE HALOPERIDOL DECANOATE ILOPERIDONE LOXAPINE LUMATEPERONE LURASIDONE HYDROCHLORIDE MELATONIN MESORIDAZINE METHOTRIMEPRAZINE MIDAZOLAM HYDROCHLORIDE MINAPRINE MOLINDONE OLANZAPINE PALIPERIDONE PERPHENAZINE PIMOZIDE PRAZOSIN PROPIOMAZINE QUETIAPINE FUMARATE REMOXIPRIDE RISPERIDONE SERTINDOLE SULPIRIDE THIORIDAZINE TRIFLUOPERAZINE TRIFLUPROMAZINE ZIPRASIDONE ZUCLOPENTHIXOL DRD2 GFAP NOS1 POR APIXABAN BROMOCRIPTINE CARVEDILOL ENALAPRIL MALEATE MEPHENTERMINE METARAMINOL METHYLDOPA ANHYDROUS NIMODIPINE PHENTOLAMINE PIOGLITAZONE HYDROCHLORIDE PRAVASTATIN SODIUM PRAZOSIN RIVAROXABAN ROSIGLITAZONE TOLAZOLINE Antihypertensive Agents Antihypertensive Agents,for treatment of erectile dysfunction Antihypertensive Agents; Vasodilator Agents Antihypertensive Agents; antispasmodics Antihypotensive Agents; Vasoconstrictor Agents anticholesterolaemic agent,antihypecholesterolemic agent antidiabetic antithrombotic cardiovascular agent,for treatment of congestive heart failure ATORVASTATIN CALCIUM TRIHYDRATE Fig. 5 | Comparing common drugs and their gene-targets between PTSD and CV conditions. This Sankey plot shows the psychiatric drugs and their classes (first & second panel) that target genes –DRD2,GFAP,POR and NOS1 (third panel), which are also targeted by CV drugs (third panel) and their corresponding CV categories (fourth & fifth panel) (see Supplementary Data for more details). Article https://doi.org/10.1038/s41467-025-60487-w Nature Communications | (2025) 16:5631 7 105. Bick, A. G. et al. Genomic data in the All of Us Research Program. Nature 627,340–346 (2024). 106. Luo, Y. et al. Estimating heritability and its enrichment in tissuespecific gene sets in admixed populations. Hum. Mol. Genet 30, 1521–1534 (2021). 107. Kolberg, L. et al. g:Profiler-interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update). Nucleic Acids Res. 51,W207–W212 (2023). 108. Cannon, M. et al. DGIdb 5.0: rebuilding the drug-gene interaction database for precision medicine and drug discovery platforms. Nucleic Acids Res. 52, D1227-D1235 (2024). Acknowledgements This study was supported by grants from the National Institutes of Health (RF1 MH132337-RP, R33 DA047527-R.P., and K99 AG078503-G.A.P.), One Mind-RP, the Alzheimer’s Association (Research Fellowship AARF22-967171-G.A.P.), Horizon 2020 (Marie Sklodowska-Curie Individual Fellowship 101028810-D.K.), Yale Women’s Faculty Forum-GAP, and Suzhou Municipal Health Commission (LCZX202207)-J.S., L.S., H.L. We also acknowledge the contribution of the participants, and the investigators involved in the UK Biobank, the Million Veteran Program, the All of Us Research Program, and the Psychiatric Genomics Consortium. Major financial support for the PTSD-PGC was provided by the Cohen Veterans Bioscience, Stanley Center for Psychiatric Research at the Broad Institute, and the National Institute of Mental Health (NIMH; R01MH106595, R01MH124847, R01MH124851-PGC-PTSD). The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276. Author contributions G.A.P. and R.P. supervised the study. J.S., G.A.P., R.P., and G.J.F. contributed to the study design. J.S. led the analysis, W.V., E.F., C.O., and D.K. contributed to the analysis and data collection. K.W.C., C.J.O., M.B.S., H.L., L.S., and J.G. contributed to the result interpretations. The Posttraumatic Stress Disorder Working Group of the Psychiatric Genomics Consortium provided data. All authors contributed to the manuscript drafting and revisions. Competing interests R.P. reports a research grant from Alkermes outside the scope of this study. R.P. and J.G. are paid for their editorial work on the journal Complex Psychiatry. J.G. is named as an inventor on PCT patent application no. 15/ 878,640 entitled “Genotype-guided dosing of opioid agonists”,filed January 24, 2018. M.B.S. has in the past 3 years received consulting income from Actelion, Acadia Pharmaceuticals, Aptinyx, atai Life Sciences, Boehringer Ingelheim, Bionomics, BioXcel Therapeutics, Clexio, Delix Pharmaceuticals, EmpowerPharm, Engrail Therapeutics, GW Pharmaceuticals, Janssen, Jazz Pharmaceuticals, and Roche/Genentech; has stock options in Oxeia Biopharmaceuticals and EpiVario; and has been paid for editorial work on Depression and Anxiety (Editor-in-Chief), Biological Psychiatry (Deputy Editor), and UpToDate (Co-Editor-in-Chief for Psychiatry). C.J.O an employee of Novartis Pharmaceuticals). D.K. is the founder and CEO of EndoCare Therapeutics, but the company conducts research unrelated to the present study. The other authors declare no competing interests. Additional information Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41467-025-60487-w. Correspondence and requests for materials should be addressed to Renato Polimanti or Gita A. Pathak. Peer review information Nature Communications thanks William Reay and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available. Reprints and permissions information is available at http://www.nature.com/reprints Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creativecommons.org/licenses/by-nc-nd/4.0/. © The Author(s) 2025 Posttraumatic Stress Disorder Working Group of the Psychiatric Genomics Consortium Caroline M. Nievergelt 9,14,15 ,AdamX.Maihofer 9,14,15 , Elizabeth G. Atkinson 16 , Chia-Yen Chen 17 , Jonathan RI Coleman 18,19 , Nikolaos P. Daskalakis 20,21,22 , Laramie E. Duncan 23 , Cindy Aaronson 24 , Ananda B. Amstadter 25 , Soren B. Andersen 26 , Ole A. Andreassen 27,28 ,PaulA.Arbisi 29,30 , Allison E. Ashley-Koch 31 ,S.BrynAustin 32,33,34 , Esmina Avdibegoviç 35 , Dragan Babic 36 , Silviu-Alin Bacanu 37 , Dewleen G. Baker 9,10,14 , Anthony Batzler 38 ,JeanC.Beckham 39,40,41 , Sintia Belangero 42,43 , Corina Benjet 44 , Carisa Bergner 45 , Linda M. Bierer 46 , Joanna M. Biernacka 38,47 ,LauraJ.Bierut 48 , Jonathan I. Bisson 49 ,MarcoP.Boks 50 , Elizabeth A. Bolger 21,51 ,AmberBrandolino 52 , Gerome Breen 19,53 , Rodrigo Affonseca Bressan 54,55 ,RichardA.Bryant 56 ,AngelaC.Bustamante 57 , Jonas Bybjerg-Grauholm 58,59 , Marie Bækvad-Hansen 58,59 ,AndersD.Børglum 59,60,61 ,SigridBørte 62,63 ,LeahCahn 24 , Joseph R. Calabrese 64,65 , Article https://doi.org/10.1038/s41467-025-60487-w Nature Communications | (2025) 16:5631 14 Jose Miguel Caldas-de-Almeida 66 , Chris Chatzinakos 20,21,67 , Sheraz Cheema 68 ,SeanA.P.Clouston 69,70 ,LucíaColodroConde 71 , Brandon J. Coombes 38 ,CarlosS.Cruz-Fuentes 72 ,AndersM.Dale 73 , Shareefa Dalvie 74 , Lea K. Davis 75 , Jürgen Deckert 76 , Douglas L. Delahanty 77 , Michelle F. Dennis 39,40,41 , Terri deRoon-Cassini 52 ,FrankDesarnaud 24 , Christopher P. DiPietro 20,67 ,SethG.Disner 78,79 , Anna R. Docherty 80,81 , Katharina Domschke 82,83 ,GreteDyb 28,84 , Alma Dzubur Kulenovic 85 , Howard J. Edenberg 86,87 ,AlexandraEvans 49 , Chiara Fabbri 19,88 , Negar Fani 89 , Lindsay A. Farrer 90,91,92,93,94 ,AdrianaFeder 24 , Norah C. Feeny 95 ,JanineD.Flory 24 ,DavidForbes 96 , Carol E. Franz 9 , Sandro Galea 97 , Melanie E. Garrett 31 ,BizuGelaye 5 , Elbert Geuze 98,99 , Charles F. Gillespie 89 , Aferdita Goci 100 , Slavina B. Goleva 75,101 ,ScottD.Gordon 71 ,LanaRuvoloGrasser 102 , Camila Guindalini 103 , Magali Haas 104 , Saskia Hagenaars 18,19 , Michael A. Hauser 39 ,AndrewC.Heath 105 , Sian MJ Hemmings 106,107 ,VictorHesselbrock 108 , Ian B. Hickie 109 ,KelleighHogan 9,14,15 , David Michael Hougaard 58,59 , Hailiang Huang 20,110 ,LauraM.Huckins 111 , Kristian Hveem 62 , Miro Jakovljevic 112 , Arash Javanbakht 102 , Gregory D. Jenkins 38 , Jessica Johnson 113 , Ian Jones 114 , Tanja Jovanovic 89 , Karen-Inge Karstoft 26,115 , Milissa L. Kaufman 21,51 , James L. Kennedy 116,117,118,119 , Ronald C. Kessler 120 , Alaptagin Khan 21,51 ,NathanA.Kimbrel 39,41,121 , Anthony P. King 122 ,NastassjaKoen 123 ,RomanKotov 124 , Henry R. Kranzler 125,126 ,KristiKrebs 127 , William S. Kremen 9 , Pei-Fen Kuan 128 ,BruceR.Lawford 129 ,LaurenA.M.Lebois 21,22 , Kelli Lehto 127 ,DanielF.Levey 2,4 ,CatrinLewis 49 ,IsraelLiberzon 130 , Sarah D. Linnstaedt 131 ,MarkW.Logue 93,132,133 , Adriana Lori 89 ,YiLu 134 , Benjamin J. Luft 135 , Michelle K. Lupton 71 ,JurjenJ.Luykx 99,136 ,IouriMakotkine 24 , Jessica L. MaplesKeller 89 ,ShelbyMarchese 137 ,CharlesMarmar 138 , Nicholas G. Martin 139 ,GabrielaA.Martínez-Levy 72 , Kerrie McAloney 71 , Alexander McFarlane 140 , Katie A. McLaughlin 141 ,SamuelA.McLean 131,142 , Sarah E. Medland 71 ,DivyaMehta 129,143 , Jacquelyn Meyers 144 , Vasiliki Michopoulos 89 ,ElizabethA.Mikita 9,14,15 , Lili Milani 127 , William Milberg 145 , Mark W. Miller 132,133 , Rajendra A. Morey 146 , Charles Phillip Morris 129 ,OleMors 59,147 ,PrebenBoMortensen 59,60,148,149 , Mary S. Mufford 74 ,ElliotC.Nelson 48 , Merete Nordentoft 59,150 , Sonya B. Norman 9,14,151 ,NicoleR.Nugent 152,153,154 , Meaghan O’Donnell 155 , Holly K. Orcutt 156 ,PedroM.Pan 157 , Matthew S. Panizzon 9 ,EdwardS.Peters 158 , Alan L. Peterson 159,160 , Matthew Peverill 161 ,RobertH.Pietrzak 2,162 , Melissa A. Polusny 29,79,163 , Bernice Porjesz 144 , Abigail Powers 89 , Xue-Jun Qin 164 , Andrew Ratanatharathorn 5,165 , Victoria B. Risbrough 9,14,15 , Andrea L. Roberts 166 , Barbara O. Rothbaum 89 ,AlexO.Rothbaum 167,168 , Peter Roy-Byrne 169 , Kenneth J. Ruggiero 170 ,ArianeRung 171 , Heiko Runz 172 ,BartP.F.Rutten 173 , Stacey Saenz de Viteri 174 , Giovanni Abrahão Salum 175,176 ,LauraSampson 5,94 , Sixto E. Sanchez 177 ,MarcosSantoro 178 ,CarinaSeah 137 , Soraya Seedat 179,180 ,JuliaS.Seng 181,182,183,184 , Andrey Shabalin 81 , Christina M. Sheerin 25 , Derrick Silove 185 ,AliciaK.Smith 89,186 ,JordanW.Smoller 5,20,187 ,ScottR.Sponheim 29,188 , Dan J. Stein 123 , Synne Stensland 63,84 , Jennifer S. Stevens 89 , Jennifer A. Sumner 189 , Martin H. Teicher 21,190 , Wesley K. Thompson 191,192 ,ArunK.Tiwari 116,117,118 , Edward Trapido 171 , Monica Uddin 193 ,RobertJ.Ursano 194 , Unnur Valdimarsdóttir 195,196 , Leigh Luella van den Heuvel 106,107 ,MirandaVanHooff 197 , Sanne JH van Rooij 89 , Eric Vermetten 198,199,200 ,ChristiaanH.Vinkers 201,202,203 , Joanne Voisey 129,143 , Zhewu Wang 204,205 , Yunpeng Wang 206 , Monika Waszczuk 207 ,HeikeWeber 76 , Frank R. Wendt 208 , Thomas Werge 59,209,210 , Michelle A. Williams 5 , Douglas E. Williamson 39,40 ,BendikS.Winsvold 62,63,211 ,SherryWinternitz 21,51 ,ErikaJ.Wolf 133,212 , Christiane Wolf 76 , Yan Xia 20,110 , Ying Xiong 134 , Rachel Yehuda 24,213 ,RossMcDYoung 214,215 , Keith A. Young 216,217 , Clement C. Zai 20,116,117,118,119,218 , Gwyneth C. Zai 116,117,118,119,219 , Mark Zervas 104 , Hongyu Zhao 220 , Lori A. Zoellner 161 ,JohnAnker Zwart 9,10,11,28,62,63 ,KerryJ.Ressler 21,51,89 & Karestan C. Koenen 5,20,187 14 Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA. 15 Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA. 16 Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA. 17 Biogen Inc., Translational Sciences, Cambridge, MA, USA. 18 King’s College London, National Institute for Health and Care Research Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, Great Britain. 19 King’s College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, Great Britain. 20 Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA. 21 Department of Psychiatry, Harvard Medical School, Boston, MA, USA. 22 McLean Hospital, Center of Excellence in Depression and Anxiety Disorders, Belmont, MA, USA. 23 Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA. 24 Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 25 Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA. 26 The Danish Veteran Centre, Research and Knowledge Centre, Ringsted, Sjaelland, Denmark. 27 Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway. 28 University of Oslo, Institute of Clinical Medicine, Oslo, Norway. 29 Minneapolis VA Health Care System, Mental Health Service Line, Minneapolis, MN, USA. 30 Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA. 31 Duke Molecular Physiology Institute, Duke University, Durham, NC, USA. 32 Division of Adolescent and Young Adult Medicine, Boston Children’s Hospital, Boston, MA, USA. 33 Department of Pediatrics, Harvard Medical School, Boston, MA, USA. 34 Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 35 Department of Psychiatry, University Clinical Center of Tuzla, Tuzla, Bosnia-Herzegovina. 36 Department of Psychiatry, University Clinical Center of Mostar, Mostar, Bosnia-Herzegovina. 37 Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA. 38 Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA. 39 Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA. 40 Durham VA Health Care System, Research, Durham, NC, USA. 41 VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center (MIRECC), Genetics Research Laboratory, Durham, NC, USA. 42 Department of Morphology and Genetics, Universidade Federal de São Paulo, São Paulo, SP, Brazil. 43 Departament of Psychiatry, Universidade Federal de São Paulo, Laboratory of Integrative Neuroscience, São Paulo, SP, Brazil. 44 Instituto Nacional Article https://doi.org/10.1038/s41467-025-60487-w Nature Communications | (2025) 16:5631 15 de Psiquiatraía Ramón de la Fuente Muñiz, Center for Global Mental Health, Mexico City, CDMX, Mexico. 45 Medical College of Wisconsin, Comprehensive Injury Center, Milwaukee, WI, USA. 46 Department of Psychiatry, James J. Peters VA Medical Center, Bronx, NY, USA. 47 Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA. 48 Department of Psychiatry, Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA. 49 Cardiff University, National Centre for Mental Health, MRC Centre for Psychiatric Genetics and Genomics, Cardiff, South Glamorgan, Great Britain. 50 Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht, UT, Netherlands. 51 McLean Hospital, Belmont, MA, USA. 52 Division of Trauma & Acute Care Surgery, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA. 53 King’s College London, NIHR Maudsley BRC, London, Great Britain. 54 Department of Psychiatry, Universidade Federal de São Paulo, São Paulo, SP, Brazil. 55 Department of Psychiatry, Universidade Federal de São Paulo, Laboratory of Integrative Neuroscience, São Paulo, SP, Brazil. 56 University of New South Wales, School of Psychology, Sydney, NSW, Australia. 57 Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA. 58 Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark. 59 The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark. 60 Aarhus University, Centre for Integrative Sequencing, iSEQ, Aarhus, Denmark. 61 Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark. 62 Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, K. G. Jebsen Center for Genetic Epidemiology, Trondheim, Norway. 63 Division of Clinical Neuroscience, Department of Research, Innovation and Education, Oslo University Hospital, Oslo, Norway. 64 Case Western Reserve University, School of Medicine, Cleveland, OH, USA. 65 Department of Psychiatry, University Hospitals, Cleveland, OH, USA. 66 Chronic Diseases Research Centre (CEDOC), Lisbon Institute of Global Mental Health, Lisbon, Portugal. 67 Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA. 68 University of Toronto, CanPath National Coordinating Center, Toronto, ON, USA. 69 Stony Brook University, Family, Population, and Preventive Medicine, Stony Brook, NY, USA. 70 Stony Brook University, Public Health, Stony Brook, NY, USA. 71 QIMR Berghofer Medical Research Institute, Mental Health & Neuroscience Program, Brisbane, QLD, Australia. 72 Department of Genetics, Instituto Nacional de Psiquiatraía Ramón de la Fuente Muñiz, Mexico City, CDMX, Mexico. 73 Department of Radiology, Department of Neurosciences, University of California San Diego, La Jolla, CA, USA. 74 Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, Western Province, Republic of South Africa. 75 Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, TN, USA. 76 University Hospital of Würzburg, Center of Mental Health, Psychiatry, Psychosomatics and Psychotherapy, Würzburg, Germany. 77 Department of Psychological Sciences, Kent State University, Kent, OH, USA. 78 Minneapolis VA Health Care System, Research Service Line, Minneapolis, MN, USA. 79 Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN, USA. 80 Huntsman Mental Health Institute, Salt Lake City, UT, USA. 81 Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA. 82 University of Freiburg, Faculty of Medicine, Centre for Basics in Neuromodulation, Freiburg, Deutschland. 83 Department of Psychiatry and Psychotherapy, University of Freiburg, Faculty of Medicine, Freiburg, Deutschland. 84 Norwegian Centre for Violence and Traumatic Stress Studies, Oslo, Norway. 85 Department of Psychiatry, University Clinical Center of Sarajevo, Sarajevo, Bosnia-Herzegovina. 86 Indiana University School of Medicine, Biochemistry and Molecular Biology, Indianapolis, IN, USA. 87 Indiana University School of Medicine, Medical and Molecular Genetics, Indianapolis, IN, USA. 88 Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy. 89 Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA. 90 Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA. 91 Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA. 92 Department of Ophthalmology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA. 93 Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA. 94 Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA. 95 Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH, USA. 96 Department of Psychiatry, University of Melbourne, Melbourne, VIC, Australia. 97 Boston University School of Public Health, Boston, MA, USA. 98 Netherlands Ministry of Defence, Brain Research and Innovation Centre, Utrecht, UT, Netherlands. 99 Department of Psychiatry, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, UT, Netherlands. 100 Department of Psychiatry, University Clinical Centre of Kosovo, Prishtina, Kosovo, Republic of Kosovo. 101 National Institutes of Health, National Human Genome Research Institute, Bethesda, MD, USA. 102 Wayne State University School of Medicine, Psychiatry and Behavioral Neurosciences, Detroit, MI, USA. 103 Gallipoli Medical Research Foundation, Greenslopes Private Hospital, Greenslopes, QLD, Australia. 104 Cohen Veterans Bioscience, New York, NY, USA. 105 Department of Genetics, Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA. 106 Department of Psychiatry, Stellenbosch University, Faculty of Medicine and Health Sciences, Cape Town, Western Cape, Republic of South Africa. 107 Stellenbosch University, SAMRC Genomics of Brain Disorders Research Unit, Cape Town, Western Cape, Republic of South Africa. 108 University of Connecticut School of Medicine, Psychiatry, Farmington, CT, USA. 109 University of Sydney, Brain and Mind Centre, Sydney, NSW, Australia. 110 Department of Medicine, Massachusetts General Hospital, Analytic and Translational Genetics Unit, Boston, MA, USA. 111 Department of Psychiatry, Yale University, New Haven, CT, USA. 112 Department of Psychiatry, University Hospital Center of Zagreb, Zagreb, Republika Hrvatska. 113 Icahn School of Medicine at Mount Sinai, Genetics and Genomic Sciences, New York, NY, USA. 114 Cardiff University, National Centre for Mental Health, Cardiff University Centre for Psychiatric Genetics and Genomics, Cardiff, South Glamorgan,Great Britain. 115 Department of Psychology, University of Copenhagen, Copenhagen, Denmark. 116 Centre for Addiction and Mental Health, Neurogenetics Section, Molecular Brain Science Department, Campbell Family Mental Health Research Institute, Toronto, ON, Canada. 117 Centre for Addiction and Mental Health, Tanenbaum Centre for Pharmacogenetics, Toronto, ON, Canada. 118 Department of Psychiatry, University of Toronto, Toronto, ON, Canada. 119 University of Toronto, Institute of Medical Sciences, Toronto, ON, Canada. 120 Department of Health Care Policy, Harvard Medical School, Boston, MA, USA. 121 Durham VA Health Care System, Mental Health Service Line, Durham, NC, USA. 122 The Ohio State University, College of Medicine, Institute for Behavioral Medicine Research, Columbus, OH, USA. 123 Department of Psychiatry & Neuroscience Institute, SA MRC Unit on Risk & Resilience in Mental Disorders, University of Cape Town, Cape Town, Western Province, Republic of South Africa. 124 Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA. 125 Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA. 126 Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA. 127 University of Tartu, Institute of Genomics, Estonian Genome Center, Tartu, Estonia. 128 Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA. 129 Queensland University of Technology, School of Biomedical Sciences, Kelvin Grove, QLD, Australia. 130 Department of Psychiatry and Behavioral Sciences, Texas A&M University College of Medicine, Bryan, TX, USA. 131 Department of Anesthesiology, UNC Institute for Trauma Recovery, Chapel Hill, NC, USA. 132 Boston University School of Medicine, Psychiatry, Biomedical Genetics, Boston, MA, USA. 133 VA Boston Healthcare System, National Center for PTSD, Boston, MA, USA. 134 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet,Stockholm,Sweden. 135 Department of Medicine, Stony Brook University, Stony Brook, NY, USA. 136 Department of Translational Neuroscience, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, UT, Netherlands. 137 Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 138 New York University, Grossman School of Medicine, New York, NY, USA. 139 QIMR Berghofer Medical Research Institute, Genetics, Brisbane, QLD, Australia. 140 University of Adelaide, Discipline of Psychiatry, Adelaide, South Australia, Australia. 141 Department of Psychology, Harvard University, Boston, MA, USA. 142 Department of Emergency Medicine, UNC Institute for Trauma Recovery, Chapel Hill, NC, USA. 143 Queensland University of Technology, Centre for Genomics and Personalised Health, Kelvin Grove, QLD, Australia. 144 Department of Psychiatry and Behavioral Sciences, SUNY Article https://doi.org/10.1038/s41467-025-60487-w Nature Communications | (2025) 16:5631 16 Downstate Health Sciences University, Brooklyn, NY, USA. 145 VA Boston Healthcare System, GRECC/TRACTS, Boston, MA, USA. 146 Duke University School of Medicine, Duke Brain Imaging and Analysis Center, Durham, NC, USA. 147 Aarhus University Hospital - Psychiatry, Psychosis Research Unit, Aarhus, Denmark. 148 Aarhus University, Centre for Integrated Register-based Research, Aarhus, Denmark. 149 Aarhus University, National Centre for Register-Based Research, Aarhus, Denmark. 150 University of Copenhagen, Mental Health Services in the Capital Region of Denmark, Copenhagen, Denmark. 151 Executive Division, National Center for Post Traumatic Stress Disorder, White River Junction, VT, USA. 152 Department of Emergency Medicine, Alpert Brown Medical School, Providence, RI, USA. 153 Department of Pediatrics, Alpert Brown Medical School, Providence, RI, USA. 154 Department of Psychiatry and Human Behavior, Alpert Brown Medical School, Providence, RI, USA. 155 Department of Psychiatry, University of Melbourne, Phoenix Australia, Melbourne, VIC, Australia. 156 Department of Psychology, Northern Illinois University, DeKalb, IL, USA. 157 Universidade Federal de São Paulo, Psychiatry, São Paulo, SP, Brazil. 158 University of Nebraska Medical Center, College of Public Health, Omaha, NE, USA. 159 South Texas Veterans Health Care System, Research and Development Service, San Antonio, TX, USA. 160 Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA. 161 Department of Psychology, University of Washington, Seattle, WA, USA. 162 U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, West Haven, CT, USA. 163 Center for Care Delivery and Outcomes Research (CCDOR), Minneapolis, MN, USA. 164 Duke University, Duke Molecular Physiology Institute, Durham, NC, USA. 165 Department of Epidemiology, Columbia University Mailmain School of Public Health, New York, NY, USA. 166 Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 167 Department of Psychological Sciences, Emory University, Atlanta, GA, USA. 168 Department of Research and Outcomes, Skyland Trail, Atlanta, GA, USA. 169 Department of Psychiatry, University of Washington, Seattle, WA, USA. 170 Department of Nursing and Department of Psychiatry, Medical University of South Carolina, Charleston, SC, USA. 171 School of Public Health and Department of Epidemiology, Louisiana State University Health Sciences Center, New Orleans, LA, USA. 172 Biogen Inc., Research & Development, Cambridge, MA, USA. 173 Department of Psychiatry and Neuropsychology, Maastricht Universitair Medisch Centrum, School for Mental Health and Neuroscience, Maastricht, Limburg, Netherlands. 174 SUNY Downstate Health Sciences University, School of Public Health, Brooklyn, NY, USA. 175 Child Mind Institute, New York, NY, USA. 176 Instituto Nacional de Psiquiatria de Desenvolvimento, São Paulo, SP, Brazil. 177 Department of Medicine, Universidad Peruana de Ciencias Aplicadas, Lima, Lima, Peru. 178 Departamento de Bioquímica - Disciplina de Biologia Molecular, Universidade Federal de São Paulo, São Paulo, SP, Brazil. 179 Stellenbosch University, Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, Western Cape, Republic of South Africa. 180 Stellenbosch University, SAMRC Extramural Genomics of Brain Disorders Research Unit, Cape Town, Western Cape, Republic of South Africa. 181 Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, USA. 182 Department of Women’sand Gender Studies, University of Michigan, Ann Arbor, MI, USA. 183 University of Michigan, Institute for Research on Women and Gender, Ann Arbor, MI, USA. 184 University of Michigan, School of Nursing, Ann Arbor, MI, USA. 185 Department of Psychiatry, University of New South Wales, Sydney, Australia. 186 Department of Gynecology and Obstetrics; Department of Psychiatry and Behavioral Sciences; Department of Human Genetics, Emory University, Atlanta, GA, USA. 187 Massachusetts General Hospital, Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Boston, MA, USA. 188 Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN, USA. 189 Department of Psychology, University of California, Los Angeles, CA, USA. 190 McLean Hospital, Developmental Biopsychiatry Research Program, Belmont, MA, USA. 191 Mental Health Centre Sct. Hans, Institute of Biological Psychiatry, Roskilde, Denmark. 192 University of California San Diego, Herbert Wertheim School of Public Health and Human Longevity Science, La Jolla, CA, USA. 193 University of South Florida College of Public Health, Genomics Program, Tampa, FL, USA. 194 Department of Psychiatry, Uniformed Services University, Bethesda, Maryland, USA. 195 Karolinska Institutet, Unit of Integrative Epidemiology, Institute of Environmental Medicine, Stockholm, Sweden. 196 University of Iceland, Faculty of Medicine, Center of Public Health Sciences, School of Health Sciences, Reykjavik, Iceland. 197 University of Adelaide, Adelaide Medical School, Adelaide, South Australia, Australia. 198 ARQ Nationaal Psychotrauma Centrum, Psychotrauma Reseach Expert Group, Diemen, NH, Netherlands. 199 Department of Psychiatry, Leiden University Medical Center, Leiden, ZH, Netherlands. 200 Department of Psychiatry, New York University School of Medicine, New York, NY, USA. 201 Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, Holland, Netherlands. 202 Department of Anatomy and Neurosciences, Amsterdam UMClocation Vrije Universiteit Amsterdam,Aamsterdam, Holland, Netherlands. 203 Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, Holland, Netherlands. 204 Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA. 205 Department of Mental Health, Ralph H Johnson VA Medical Center, Charleston, SC, USA. 206 Department of Psychology, University of Oslo, Lifespan Changes in Brain and Cognition (LCBC), Oslo, Norway. 207 Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA. 208 Department of Anthropology, University of Toronto, Dalla Lana School of Public Health, Toronto, ON, Canada. 209 Copenhagen University Hospital, Institute of Biological Psychiatry, Mental Health Services, Copenhagen, Denmark. 210 Department of Clinical Medicine and the Globe Institute, University of Copenhagen, LF Center for Geogenetics, Copenhagen, Denmark. 211 Department of Neurology, Oslo University Hospital, Oslo, Norway. 212 Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA. 213 Department of Mental Health, James J. Peters VA Medical Center, Bronx, NY, USA. 214 Queensland University of Technology, School of Clinical Sciences,KelvinGrove,QLD,Australia. 215 University of the Sunshine Coast, The Chancellory, Sippy Downs, QLD, Australia. 216 Central Texas Veterans Health Care System, Research Service, Temple, TX, USA. 217 Department of Psychiatry and Behavioral Sciences, Texas A&M University School of Medicine, Bryan, TX, USA. 218 Department of Laboratory Medicine and Pathology, University of Toronto, Toronto, ON, Canada. 219 General Adult Psychiatry and Health Systems Division, Centre for Addiction and Mental Health, Toronto, ON, Canada. 220 Department of Biostatistics, Yale University, New Haven, CT, USA. Article https://doi.org/10.1038/s41467-025-60487-w Nature Communications | (2025) 16:5631 17