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Du J, Baranova A, Zhang F. Bidirectional causal effects between bipolar disorder and immune cell traits. J Affect Disord 2025; 383:179-186. [PMID: 40288451 DOI: 10.1016/j.jad.2025.04.146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2024] [Revised: 04/22/2025] [Accepted: 04/23/2025] [Indexed: 04/29/2025]
Abstract
BACKGROUND The complexity of the pathogenesis hinders the diagnosis and treatment of bipolar disorder (BD). Despite studies finding a correlation between immune function and BD, the causative relationship between the two remains poorly explained. METHODS We investigated the causative relationships between BD (41,917 cases and 371,549 controls) and levels of six types of white blood cells and further evaluated the causative relationships between BD and 731 immune cell traits) using a two-sample Mendelian randomization method, prioritizing the inverse variance weighted approach, based on publicly available GWAS data. Sensitivity analysis was based on MR-Egger intercept method and Cochran's Q test. RESULTS We did not find a significant causative relationship between BD and 6 white blood cell traits (FDR > 0.05). However, we found 38 immune cell traits had a causal effect to BD. Among them, 26 immune cell traits increased the risk of BD (OR: 1.01-1.07), including CD4+/CD28+ T cells and CD20+/CD27+ B cells. The remaining 12 including had a protective effect on BD (OR: 0.92-0.99). The backward MR results showed that BD had negative causal effects on 23 immune cell traits (n = 23, OR: 0.79-0.89), which included monocyte, majority of CD4+ T cells, and CD20+ B cells. BD had Positive causal effects 10 immune cell traits (OR: 1.13-1.19), especially CD19+ B cells. The overall causal effect of BD on immune cell traits was significantly higher than the inverse effect (0.011 ± 0.049 vs. 0.001 ± 0.016, p < 0.001). CONCLUSION A complex network of bidirectional causative relationships exists between BD and various phenotypic features of immune cells. These findings provide new insights into the diagnosis and treatment of BD from an immunotherapeutic perspective.
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Affiliation(s)
- Jianbin Du
- Department of Geriatric Psychiatry, The Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, Jiangsu 214151, China.
| | - Ancha Baranova
- School of Systems Biology, George Mason University, Fairfax, VA 22030, USA; Research Centre for Medical Genetics, Moscow 115478, Russia
| | - Fuquan Zhang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China; Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.
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Li J, Chen J, Li J, Hao M, Ma W. Potential causal association between the oral microbiome and bipolar disorder. J Affect Disord 2025; 382:176-183. [PMID: 40258420 DOI: 10.1016/j.jad.2025.04.058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2024] [Revised: 02/24/2025] [Accepted: 04/10/2025] [Indexed: 04/23/2025]
Abstract
BACKGROUND The oral microbiome can influence the growth, development, and regulation of the nervous system through various pathways; however, its relationship with bipolar disorder (BD) remains ambiguous. This study aims to investigate the causal relationship between the oral microbiome and BD through Mendelian randomization (MR) analysis. METHODS Data regarding single nucleotide polymorphisms (SNPs) in GWAS summary statistics for oral microbiota and BD were obtained from two studies: one reported the association between the oral microbiome and the human genome, encompassing both the dorsum of the tongue and saliva microbiomes. The other study investigated the association between BD and the human genome, categorizing BD into BD I and BD II for separate analyses. Thus, three data components were utilized: BD, BD I, and BD II. In this study, GWAS data for saliva and dorsum of the tongue microbiomes were analyzed separately for BD, BD I, and BD II. The inverse variance weighted (IVW) method was used to assess the causal relationship between the oral microbiome and BD. Analyses were conducted only when the number of instrumental variable SNPs was no less than two. The MR-Egger regression and IVW methods were employed to evaluate heterogeneity, whereas the MR-Egger intercept test was utilized to assess pleiotropy. For MR results exhibiting heterogeneity or pleiotropy, sensitivity analyses were performed using the weighted median, simple mode, weighted mode, MR-Egger test, and leave-one-out methods. Furthermore, funnel plots were employed to evaluate publication bias. Reverse MR analysis was also performed to investigate the potential bidirectional interactions between BD and the oral microbiota. RESULTS A causal relationship exists between the oral microbiome and BD. The effects of the microbiome from different regions of the oral cavity on BD are variable, with a more pronounced impact noted on BD I. This study identified two overlapping causal relationships shared between BD I and BD II, both exhibiting the same directional influence: ①Salivary s Prevotella aurantiaca SGB 2854 (Taxonomy ID: 596085, species); ② Tongue s Prevotella sp000467895 SGB 1817 (Taxonomy ID: 838, genus). Additionally, there are two overlapping bacteria with opposing directional effects: ① Salivary g Eggerthia (Taxonomy ID: 1279384, genus); ② Salivary s unclassified SGB 2636. Three differential bacteria that exclusively regulate one subtype were identified: ① Salivary s Lachnoanaerobaculum sp000296385 SGB 3537 (Taxonomy ID: 1164882, genus); ② Tongue s unclassified SGB 689; ③ Tongue s unclassified SGB 572. Among these, the genus g Eggerthia in saliva inhibits BD I while promoting BD II; conversely, salivary s unclassified SGB 2636 inhibits BD II while promoting BD I. The analysis of tongue s unclassified SGB 489 and s unclassified SGB 1215 demonstrated pleiotropy without causal significance. The reverse MR analysis identified that BD I may influence four microbial species, including f Leptotrichiaceae (Taxonomy ID: 1129771, family), f Streptococcaceae (Taxonomy ID: 1300, family), s unclassified SGB 1210, and s unclassified SGB 1950. There may be a bidirectional causal relationship between s unclassified SGB 1950 and BD I. Additionally, Reverse Mendel suggested that there was no significant causal relationship between BD and salivary and dorsal tongue microbes. CONCLUSION Our Mendelian randomization results indicate a causal relationship between the oral microbiome and the development of BD. However, the microbial profiles associated with the different subtypes, BD I and BD II, differ significantly; even within the same genus, the direction of influence on BD I and BD II varies, suggesting that the underlying mechanisms for the development of BD I and BD II may differ substantially.
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Affiliation(s)
- Jing Li
- Senior Department of Chinese Medicine, the Sixth Medical Center of PLA General Hospital, Beijing 100048, China
| | - Jun Chen
- Department of Acupuncture and Massage, Shaanxi University of Chinese Medicine, Xi'an 712046, China
| | - Jiwen Li
- Senior Department of Otolaryngology-Head & Neck Surgery, the Sixth Medical Center of PLA General Hospital, Beijing 100048, China.
| | - Mingyue Hao
- Senior Department of Otolaryngology-Head & Neck Surgery, the Sixth Medical Center of PLA General Hospital, Beijing 100048, China.
| | - Wei Ma
- Senior Department of Otolaryngology-Head & Neck Surgery, the Sixth Medical Center of PLA General Hospital, Beijing 100048, China.
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Wang LH, Shih MY, Lin YF, Kuo PH, Feng YCA. Polygenic dissection of treatment-resistant depression with proxy phenotypes in the UK Biobank. J Affect Disord 2025; 381:350-359. [PMID: 40187433 DOI: 10.1016/j.jad.2025.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2025] [Revised: 03/31/2025] [Accepted: 04/01/2025] [Indexed: 04/07/2025]
Abstract
BACKGROUND Treatment-resistant depression (TRD) affects one-third of major depressive disorder (MDD) patients. Previous pharmacogenetic studies suggest genetic variation may influence medication response but findings are heterogeneous. We conducted a comprehensive genetic investigation using proxy TRD phenotypes (TRDp) that mirror the treatment options of MDD from UK Biobank primary care records. METHODS Among 15,125 White British MDD patients, we identified TRDp with medication changes (switching or receiving multiple antidepressants [AD]); augmentation therapy (antipsychotics; mood stabilizers; valproate; lithium); or electroconvulsive therapy (ECT). Hospitalized TRDp patients (HOSP-TRDp) were also identified. We conducted genome-wide association analysis, estimated SNP-heritability (hg2), and assessed the genetic burden for nine psychiatric diseases using polygenic risk scores (PRS). RESULTS TRDp patients were more often female, unemployed, less educated, and had higher BMI, with hospitalization rates twice as high as non-TRDp. While no credible risk variants emerged, heritability analysis showed significant genetic influence on TRDp (liability hg2 21-24 %), particularly for HOSP-TRDp (28-31 %). TRDp classified by AD changes and augmentation carried an elevated yet varied polygenic burden for MDD, ADHD, BD, and SCZ. Higher BD PRS increased the likelihood of receiving ECT, lithium, and valproate by 1.27-1.80 fold. Patients in the top 10 % PRS relative to the average had a 12-36 % and 24-51 % higher risk of TRDp and HOSP-TRDp, respectively. CONCLUSIONS Our findings support a significant polygenic basis for TRD, highlighting genetic and phenotypic distinctions from non-TRD. We demonstrate that different TRDp endpoints are enriched with various spectra of psychiatric genetic liability, offering insights into pharmacogenomics and TRD's complex genetic architecture.
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Affiliation(s)
- Ling-Hua Wang
- Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taiwan
| | - Mu-Yi Shih
- Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taiwan; Department of Public Health, College of Public Health, National Taiwan University, Taiwan
| | - Yen-Feng Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan; Department of Public Health & Medical Humanities, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Po-Hsiu Kuo
- Department of Public Health, College of Public Health, National Taiwan University, Taiwan; Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan
| | - Yen-Chen A Feng
- Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taiwan; Department of Public Health, College of Public Health, National Taiwan University, Taiwan; Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
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Lu Y, Sun Y, Feng Z, Jia X, Que J, Cui N, Yu L, Zheng YR, Wei YB, Liu JJ. Genetic insights into the role of mitochondria-related genes in mental disorders: An integrative multi-omics analysis. J Affect Disord 2025; 380:685-695. [PMID: 40180044 DOI: 10.1016/j.jad.2025.03.116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2024] [Revised: 02/16/2025] [Accepted: 03/19/2025] [Indexed: 04/05/2025]
Abstract
BACKGROUND Mitochondrial dysfunction has been implicated in the development of mental disorders, yet the underlying mechanisms remain unclear. In this study, we employed summary-data-based Mendelian randomization (SMR) analysis to explore the associations between mitochondrial-related genes and seven common mental disorders across gene expression, DNA methylation, and protein levels. METHOD Summary statistics from genome-wide association studies were used for seven mental disorders, including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, anxiety, bipolar disorder, major depressive disorder, post-traumatic stress disorder, and schizophrenia (SCZ). Instrumental variables associated with 1136 mitochondria-related genes were derived from summary statistics for DNA methylation, gene expression, and protein quantitative trait loci. SMR analyses and colocalization analyses were then conducted across these three biological levels to explore the associations with each of the seven mental disorders. RESULTS We identified mitochondria-related genes associated with mental disorders with multi-omics evidence: RMDN1 for ADHD, and ACADVL, ETFA, MMAB, and PPA2 for SCZ. Specifically, an increase of one standard deviation in the level of RMDN1 was linked to a 12 % decrease in the risk of developing ADHD (OR = 0.88, 95 % CI: 0.83-0.94). Increased levels of ETFA (OR = 1.79, 95 % CI: 1.24-2.60) and MMAB (OR = 1.10, 95 % CI: 1.05-1.16) were significantly associated with increased risk of SCZ. Conversely, high levels of ACADVL (OR = 0.50, 95 % CI: 0.33-0.77) and PPA2 (OR = 0.68, 95 % CI: 0.55-0.85) were associated with a reduced risk of SCZ. CONCLUSIONS These findings suggested that dysfunction in mitochondria-related genes may underlie the molecular mechanisms of ADHD and SCZ, providing novel biomarkers for diagnosis and therapeutic interventions.
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Affiliation(s)
- Yan'e Lu
- School of Nursing, Peking University, Beijing 100191, China
| | - Yaoyao Sun
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Zhendong Feng
- Beijing Key Laboratory of Drug Dependence Research, National Institute on Drug Dependence, Peking University, Beijing 100191, China
| | - Xinlei Jia
- School of Nursing, Peking University, Beijing 100191, China
| | - Jianyu Que
- Xiamen Xianyue Hospital, Xianyue Hospital Affiliated with Xiamen Medical College, Fujian Psychiatric Center, Fujian Clinical Research Center for Mental Disorders, Xiamen 361012, Fujian, China
| | - Naixue Cui
- School of Nursing and Rehabilitation, Shandong University, Shandong Province 250012, China
| | - Lulu Yu
- Mental Health Center, the First Hospital of Hebei Medical University, Hebei Technical Innovation Center for Mental Health Assessment and Intervention, Shijiazhuang, Hebei Province 050031, China
| | - Yi-Ran Zheng
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
| | - Ya Bin Wei
- Beijing Key Laboratory of Drug Dependence Research, National Institute on Drug Dependence, Peking University, Beijing 100191, China.
| | - Jia Jia Liu
- School of Nursing, Peking University, Beijing 100191, China.
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Zhang N, Dong X. Causal relationship between gut microbiota, lipids, and neuropsychiatric disorders: A Mendelian randomization mediation study. J Affect Disord 2025; 379:19-35. [PMID: 40049531 DOI: 10.1016/j.jad.2025.02.091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2024] [Revised: 02/21/2025] [Accepted: 02/25/2025] [Indexed: 04/12/2025]
Abstract
BACKGROUND Numerous studies have shown an interconnection between the gut microbiota and the brain via the "gut-brain" axis. However, the causal relationships between gut microbiota, lipids, and neuropsychiatric disorders remain unclear. This study aimed to analyze potential associations among gut microbiota, lipids, and neuropsychiatric disorders-including AD, PD, ALS, MS, SCZ, MDD, and BD-using summary data from large-scale GWAS. METHODS Bidirectional Mendelian randomization (MR) with inverse variance weighting (IVW) was the primary method. Supplementary analyses included sensitivity analyses, Steiger tests, and Bayesian weighted MR (BWMR). Mediation analyses used two-step MR (TSMR) and multivariable MR (MVMR). RESULTS The analyses revealed 51 positive correlations (risk factors) (β > 0, P < 0.05) and 47 negative correlations (protective factors) (β < 0, P < 0.05) between gut microbiota and neuropsychiatric disorders. In addition, 35 positive correlations (β > 0, P < 0.05) and 22 negative correlations (β < 0, P < 0.05) between lipids and neuropsychiatric disorders were observed. Assessment of reverse causality with the seven neuropsychiatric disorders as exposures and the identified gut microbiota and lipids as outcomes revealed no evidence of reverse causality (P > 0.05). Mediation analysis indicated that the effect of the species Bacteroides plebeius on MDD is partially mediated through the regulation of phosphatidylcholine (16:0_20:4) levels (mediation proportion = 10.9 % [95 % CI = 0.0110-0.2073]). CONCLUSION This study provides evidence of a causal relationship between gut microbiota and neuropsychiatric disorders, suggesting lipids as mediators. These findings offer new insights into the mechanisms by which gut microbiota may influence neuropsychiatric disorders.
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Affiliation(s)
- Nan Zhang
- Department of Neurology, the Seventh Clinical College of China Medical University, No. 24 Central Street, Xinfu District, Fushun 113000, Liaoning, China
| | - Xiaoyu Dong
- Department of Neurology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang 110000, Liaoning, China.
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Ma H, Wang Y, Yang Y, Chen J, Jin X. Deciphering the shared genetic architecture between bipolar disorder and body mass index. J Affect Disord 2025; 379:127-135. [PMID: 40056998 DOI: 10.1016/j.jad.2025.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2024] [Revised: 02/27/2025] [Accepted: 03/01/2025] [Indexed: 03/14/2025]
Abstract
BACKGROUND The comorbidity between bipolar disorder (BD) and high body mass index (BMI) is well-documented, but their shared genetic architecture remains unclear. Our study aimed to explore this genetic correlation and potential causality. METHODS Utilizing large-scale genome-wide association study (GWAS) summary statistics, we quantified global and local genetic correlations between BD and BMI using linkage disequilibrium score regression (LDSC) and Heritability Estimation from Summary Statistics. Stratified LDSC characterized genetic overlap across functional categories. Cross-trait meta-analyses identified shared risk single nucleotide polymorphisms (SNPs), followed by colocalization analysis using Coloc. Bi-directional Mendelian randomization (MR) assessed causality, while tissue-level SNP heritability enrichment for BD and BMI was evaluated using LDSC-specific expressed genes and Multi-marker Analysis of Genomic Annotation. RESULTS We found a genetic correlation between BD and BMI, especially in localized genomic regions. Cross-trait meta-analysis identified 46 significant SNPs shared between BD and BMI, including three novel shared risk SNPs. Colocalization analysis verified two novel SNPs with shared causal variants linked to ITIH1 and TM6SF2 genes. MR analysis demonstrated a causal effect of BD on BMI, but not the reverse. Gene expression data revealed genetic correlation enrichment in five specific brain regions. CONCLUSION This study comprehensively analyzes the genetic correlation between BD and BMI, uncovering shared genetic architecture and identifying novel risk loci. These findings provide new insights into the interplay between BD and BMI, informing the development of diagnostic tools and therapeutic strategies.
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Affiliation(s)
- Haochuan Ma
- Department of Oncology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong, China; Guangdong Provincial Hospital of Chinese Medicine Postdoctoral Research Workstation, Guangzhou, Guangdong, China
| | - Yongbin Wang
- Department of Laboratory Medicine, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China
| | - Yang Yang
- Department of Laboratory Medicine, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China
| | - Jing Chen
- Department of Laboratory Medicine, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China
| | - Xing Jin
- Department of Laboratory Medicine, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China.
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Askelund AD, Hegemann L, Allegrini AG, Corfield EC, Ask H, Davies NM, Andreassen OA, Havdahl A, Hannigan LJ. The Genetic Architecture of Differentiating Behavioral and Emotional Problems in Early Life. Biol Psychiatry 2025; 97:1163-1174. [PMID: 39793691 DOI: 10.1016/j.biopsych.2024.12.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 11/29/2024] [Accepted: 12/24/2024] [Indexed: 01/13/2025]
Abstract
BACKGROUND Early in life, behavioral and cognitive traits associated with risk for developing a psychiatric condition are broad and undifferentiated. As children develop, these traits differentiate into characteristic clusters of symptoms and behaviors that ultimately form the basis of diagnostic categories. Understanding this differentiation process-in the context of genetic risk for psychiatric conditions, which is highly generalized-can improve early detection and intervention. METHODS We modeled the differentiation of behavioral and emotional problems from age 1.5 to 5 years (behavioral problems - emotional problems = differentiation score) in a preregistered study of ∼79,000 children from the population-based Norwegian Mother, Father, and Child Cohort Study. We used genomic structural equation modeling to identify genetic signal in differentiation and total problems, investigating their links with 11 psychiatric and neurodevelopmental conditions. We examined associations of polygenic scores (PGS) with both outcomes and assessed the relative contributions of direct and indirect genetic effects in ∼33,000 family trios. RESULTS Differentiation was primarily genetically correlated with psychiatric conditions via a neurodevelopmental factor. Total problems were primarily associated with the neurodevelopmental factor and p-factor. PGS analyses revealed an association between liability to attention-deficit/hyperactivity disorder and differentiation (β = 0.11; 95% CI, 0.10 to 0.12) and a weaker association with total problems (β = 0.06; 95% CI, 0.04 to 0.07). Trio-PGS analyses showed predominantly direct genetic effects on both outcomes. CONCLUSIONS We uncovered genomic signal in the differentiation process, mostly related to common variants associated with neurodevelopmental conditions. Investigating the differentiation of early-life behavioral and emotional problems may enhance our understanding of the developmental emergence of different psychiatric and neurodevelopmental conditions.
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Affiliation(s)
- Adrian Dahl Askelund
- PsychGen Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway; Psychiatric Genetic Epidemiology Group, Research Department, Lovisenberg Diaconal Hospital, Oslo, Norway.
| | - Laura Hegemann
- PsychGen Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway; Psychiatric Genetic Epidemiology Group, Research Department, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Andrea G Allegrini
- Division of Psychology and Language Sciences, Department of Clinical, Educational and Health Psychology, University College London, London, United Kingdom; Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Elizabeth C Corfield
- PsychGen Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway; Psychiatric Genetic Epidemiology Group, Research Department, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Helga Ask
- PsychGen Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Neil M Davies
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom; Division of Psychiatry, University College London, London, United Kingdom; Department of Statistical Sciences, University College London, London, United Kingdom; K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Alexandra Havdahl
- PsychGen Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway; Psychiatric Genetic Epidemiology Group, Research Department, Lovisenberg Diaconal Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Laurie J Hannigan
- PsychGen Center for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway; Psychiatric Genetic Epidemiology Group, Research Department, Lovisenberg Diaconal Hospital, Oslo, Norway; Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom.
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Zhang J, Zhang G, Zhao Q, Peng Y. Genetic overlap between household income and psychiatric disorders. Schizophr Res 2025; 282:95-104. [PMID: 40513305 DOI: 10.1016/j.schres.2025.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2025] [Revised: 04/11/2025] [Accepted: 05/12/2025] [Indexed: 06/16/2025]
Abstract
Household income (HI), a significant socioeconomic factor influencing mental health, has seen its association with various mental disorders garnering increasing attention. However, the common genetic basis between HI and psychiatric disorders remains poorly understood. Utilizing genome-wide association study (GWAS) pooled statistics for HI, schizophrenia (SCZ), attention deficit hyperactivity disorder (ADHD), bipolar disorder (BIP), and autism spectrum disorders (ASD), bivariate mixed models of causality (MiXeR) were employed to quantify the shared genetic architecture between HI and psychiatric disorders. The conjunctional false discovery rate (conjFDR) approach was utilized to identify specific shared loci, and the resulting shared genetic loci were analyzed for functional annotation and gene set enrichment. The MiXeR analysis revealed that among the 8.9 K variants affecting HI, 8.7 K were shared with SCZ, 7.8 K with ADHD, 6.2 K with BIP, and 8.7 K with ASD. A total of 344 shared genetic loci were identified between HI and psychiatric disorders using the conjFDR method, with 254 of them being novel. Additionally, the shared loci of HI with SCZ and ADHD mainly demonstrated opposite effect directions, while those with BIP and ASD mainly exhibited mixed effect directions. Functional annotation indicated that the shared genetic loci were predominantly located in intronic and intergenic regions, and enrichment analysis demonstrated that they were involved in nervous system development, multicellular organism development, and neuron differentiation. In conclusion, our study reveals a shared genetic architecture between HI and psychiatric disorders, highlighting common biological processes that may contribute to understanding their complex etiologies and overlapping genetic mechanisms.
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Affiliation(s)
- Jianfei Zhang
- College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161006, Heilongjiang, China
| | - Guangxing Zhang
- College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161006, Heilongjiang, China
| | - Qiyu Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yanmin Peng
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, 300204, China.
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Liu H, Wang S, Cao B, Zhu J, Huang Z, Li P, Zhang S, Liu X, Yu J, Huang Z, Lv L, Cai F, Liu W, Song Z, Liu Y, Pang T, Chang S, Chen Y, Chen J, Chen WX. Unraveling genetic risk contributions to nonverbal status in autism spectrum disorder probands. BEHAVIORAL AND BRAIN FUNCTIONS : BBF 2025; 21:15. [PMID: 40483526 PMCID: PMC12144768 DOI: 10.1186/s12993-025-00278-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2024] [Accepted: 05/16/2025] [Indexed: 06/11/2025]
Abstract
Autism spectrum disorder (ASD) presents a wide range of cognitive and language impairments. In this study, we investigated the genetic basis of non-verbal status in ASD using a comprehensive genomic approach. We identified a novel common variant, rs1944180 in CNTN5, significantly associated with non-verbal status through family-based Transmission Disequilibrium Testing. Polygenic risk score (PRS) analysis further showed that higher ASD PRS was significantly linked to non-verbal status (p = 0.034), specific to ASD and not related to other conditions such as bipolar disorder, schizophrenia and three language-related traits. Using structural equation modeling (SEM), we found two causal SNPs, rs1247761 located in KCNMA1 and rs2524290 in RAB3IL1, linking ASD with language traits. The model indicated a unidirectional effect, with ASD driving language impairments. Additionally, de novo mutations (DNMs) were found to be related with ASD and interaction between common variants and DNMs significantly impacted non-verbal status (p = 0.038). Our findings also identified 5 high-risk ASD genes, and DNMs were enriched in glycosylation-related pathways. These results offer new insights into the genetic mechanisms underlying language deficits in ASD.
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Affiliation(s)
- Huan Liu
- Department of Behavioral Development, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, Guangdong, China
- The Assessment and Intervention Center for Autistic Children, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Shenghan Wang
- Center for Intelligent Medicine, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, Shanghai, China
| | - Binbin Cao
- Department of Neurology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Jijun Zhu
- Center for Intelligent Medicine, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, Shanghai, China
| | - Zhifang Huang
- Department of Behavioral Development, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, Guangdong, China
- The Assessment and Intervention Center for Autistic Children, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Pan Li
- Center for Intelligent Medicine, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, Shanghai, China
| | - Shunjie Zhang
- Center for Intelligent Medicine, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, Shanghai, China
| | - Xian Liu
- Department of Behavioral Development, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, Guangdong, China
- The Assessment and Intervention Center for Autistic Children, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Jing Yu
- Department of Behavioral Development, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, Guangdong, China
- The Assessment and Intervention Center for Autistic Children, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Zhongting Huang
- Center for Intelligent Medicine, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, Shanghai, China
| | - Linzhuo Lv
- Department of Behavioral Development, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, Guangdong, China
- The Assessment and Intervention Center for Autistic Children, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Fuqiang Cai
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Weixin Liu
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Zhijian Song
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Yuxin Liu
- Center for Intelligent Medicine, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, Shanghai, China
| | - Tao Pang
- NHC Key Laboratory of Mental Health (Peking University), Peking University Sixth Hospital, Peking University Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Suhua Chang
- NHC Key Laboratory of Mental Health (Peking University), Peking University Sixth Hospital, Peking University Institute of Mental Health, National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Ying Chen
- Department of Behavioral Development, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, Guangdong, China
- The Assessment and Intervention Center for Autistic Children, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Junfang Chen
- Center for Intelligent Medicine, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, Shanghai, China.
- Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China.
| | - Wen-Xiong Chen
- Department of Behavioral Development, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510623, Guangdong, China.
- The Assessment and Intervention Center for Autistic Children, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
- Department of Neurology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
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10
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Wen J, Skampardoni I, Tian YE, Yang Z, Cui Y, Erus G, Hwang G, Varol E, Boquet-Pujadas A, Chand GB, Nasrallah IM, Satterthwaite TD, Shou H, Shen L, Toga AW, Zalesky A, Davatzikos C. Neuroimaging endophenotypes reveal underlying mechanisms and genetic factors contributing to progression and development of four brain disorders. Nat Biomed Eng 2025:10.1038/s41551-025-01412-w. [PMID: 40481237 DOI: 10.1038/s41551-025-01412-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 04/24/2025] [Indexed: 06/11/2025]
Abstract
Recent work leveraging artificial intelligence has offered promise to dissect disease heterogeneity by identifying complex intermediate brain phenotypes, called dimensional neuroimaging endophenotypes (DNEs). We advance the argument that these DNEs capture the degree of expression of respective neuroanatomical patterns measured, offering a dimensional neuroanatomical representation for studying disease heterogeneity and similarities of neurologic and neuropsychiatric diseases. We investigate the presence of nine DNEs derived from independent yet harmonized studies on Alzheimer's disease, autism spectrum disorder, late-life depression and schizophrenia in the UK Biobank study. Phenome-wide associations align with genome-wide associations, revealing 31 genomic loci (P < 5 × 10-8/9) associated with the nine DNEs. The nine DNEs, along with their polygenic risk scores, significantly enhanced the predictive accuracy for 14 systemic disease categories, particularly for conditions related to mental health and the central nervous system, as well as mortality outcomes. These findings underscore the potential of the nine DNEs to capture the expression of disease-related brain phenotypes in individuals of the general population and to relate such measures with genetics, lifestyle factors and chronic diseases.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA.
- Department of Radiology, Columbia University, New York, NY, USA.
- New York Genome Center (NYGC), New York, NY, USA.
- Department of Biomedical Engineering, Columbia University, New York, NY, USA.
- Data Science Institute (DSI), Columbia University, New York, NY, USA.
- Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID), Department of Radiology, Columbia University, New York, NY, USA.
- Zuckerman Institute, Columbia University, New York, NY, USA.
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ye Ella Tian
- Systems Lab, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gyujoon Hwang
- Department of Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Erdem Varol
- Department of Computer Science and Engineering, New York University, New York, NY, USA
| | - Aleix Boquet-Pujadas
- Laboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA
| | - Ganesh B Chand
- Department of Radiology, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Ilya M Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Andrew Zalesky
- Systems Lab, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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11
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Xia C, Alliey-Rodriguez N, Tamminga CA, Keshavan MS, Pearlson GD, Keedy SK, Clementz B, McDowell JE, Parker D, Lencer R, Hill SK, Bishop JR, Ivleva EI, Wen C, Dai R, Chen C, Liu C, Gershon ES. Genetic analysis of psychosis Biotypes: shared Ancestry-adjusted polygenic risk and unique genomic associations. Mol Psychiatry 2025; 30:2673-2685. [PMID: 39709506 DOI: 10.1038/s41380-024-02876-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 11/22/2024] [Accepted: 12/11/2024] [Indexed: 12/23/2024]
Abstract
The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) created psychosis Biotypes based on neurobiological measurements in a multi-ancestry sample. These Biotypes cut across DSM diagnoses of schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis. Two recently developed post hoc ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Ancestry-Adjusted PRSs (AAPRSs), which allow for PRS analysis of multi-ancestry samples. Applied to schizophrenia PRS, we found the Khera AAPRS method to show superior portability and comparable prediction accuracy as compared with the Ge method. The three Biotypes of psychosis disorders had similar AAPRSs across ancestries. In genomic analysis of Biotypes, 12 genes, and isoforms showed significant genomic associations with specific Biotypes in a Transcriptome-Wide Association Study (TWAS) of genetically regulated expression (GReX) in the adult brain and fetal brain. TWAS inflation was addressed by the inclusion of genotype principal components in the association analyses. Seven of these 12 genes/isoforms satisfied Mendelian Randomization (MR) criteria for putative causality, including four genes TMEM140, ARTN, C1orf115, CYREN, and three transcripts ENSG00000272941, ENSG00000257176, ENSG00000287733. These genes are enriched in the biological pathways of Rearranged during Transfection (RET) signaling, Neural Cell Adhesion Molecule 1 (NCAM1) interactions, and NCAM signaling for neurite out-growth. The specific associations with Biotypes suggest that pharmacological clinical trials and biological investigations might benefit from analyzing Biotypes separately.
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Affiliation(s)
- Cuihua Xia
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Psychiatry and Behavioral Neuroscience, The University of Chicago, Chicago, IL, USA
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA
| | - Ney Alliey-Rodriguez
- Department of Psychiatry and Behavioral Neuroscience, The University of Chicago, Chicago, IL, USA
- Institute of Neuroscience, University of Texas Rio Grande Valley, Harlingen, TX, USA
| | - Carol A Tamminga
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
- Institute of Living, Hartford Healthcare Corp, Hartford, CT, USA
| | - Sarah K Keedy
- Department of Psychiatry and Behavioral Neuroscience, The University of Chicago, Chicago, IL, USA
| | - Brett Clementz
- Departments of Psychology and Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA, USA
| | - Jennifer E McDowell
- Departments of Psychology and Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA, USA
| | - David Parker
- Departments of Psychology and Neuroscience, BioImaging Research Center, University of Georgia, Athens, GA, USA
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Rebekka Lencer
- Institute for Translational Psychiatry, Münster University, Münster, Germany
- Department of Psychiatry and Psychotherapy, Lübeck University, Lübeck, Germany
| | - S Kristian Hill
- Department of Psychology, Rosalind Franklin University of Medicine and Science, Chicago, IL, USA
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology and Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Elena I Ivleva
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
| | - Cindy Wen
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Chao Chen
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
- Furong Laboratory, Changsha, Hunan, China.
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
| | - Chunyu Liu
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA.
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, The University of Chicago, Chicago, IL, USA.
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA.
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12
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Jiang X, Zai CC, Merranko J, Young LT, Birmaher B, Goldstein BI. Psychiatric Polygenic Risk Scores and Week-by-Week Symptomatic Status in Youth with Bipolar Disorder: An Exploratory Study. J Child Adolesc Psychopharmacol 2025; 35:269-276. [PMID: 40059772 DOI: 10.1089/cap.2024.0130] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/31/2025]
Abstract
Introduction: Prior studies have demonstrated that, in both adults and youth, bipolar disorder (BD) is a polygenic illness. However, no studies have examined polygenic risk scores (PRSs) in relation to the longitudinal course of mood symptoms in youth with BD. Methods: This study included 246 youth of European ancestry with BD (7-20 years old at intake) from the Course and Outcome of Bipolar Youth study and Centre for Youth Bipolar Disorder. Mood symptom severity was assessed at intake and, for 168 participants, prospectively for a median of 8.7 years. PRSs for BD, schizophrenia (SCZ), major depressive disorder (MDD), and attention-deficit/hyperactivity disorder (ADHD) were constructed using genome-wide summary statistics from independent adult cohorts. Results: Higher BD-PRS was significantly associated with lower most severe lifetime depression score at intake (β = -0.14, p = 0.03). Higher SCZ-PRS and MDD-PRS were associated with significantly less time spent in euthymia (SCZ-PRS: β = -0.21, p = 0.02; MDD-PRS: β = -0.22, p = 0.01) and more time with any subsyndromal mood symptoms (i.e., any mania, mixed, or depression symptoms; SCZ-PRS: β = 0.15, p = 0.04; MDD-PRS: β = 0.17, p = 0.01) during follow-up. PRSs for BD and ADHD were not significantly associated with any longitudinal mood variable. Conclusions: This exploratory analysis was the first to examine psychiatric PRSs in relation to the prospective course of mood symptoms among youth with BD. Results from the current study can serve to guide future youth BD studies with larger sample sizes on this topic.
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Affiliation(s)
- Xinyue Jiang
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, Canada
| | - Clement C Zai
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada
- Tanenbaum Centre for Pharmacogenetics, Psychiatric Neurogenetics Section, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
- Institute of Medical Science, Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - John Merranko
- Western Psychiatric Hospital, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - L Trevor Young
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
- Centre for Addiction and Mental Health, Toronto, Canada
| | - Boris Birmaher
- Western Psychiatric Hospital, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Benjamin I Goldstein
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
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13
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McQuillin A, Ophoff RA. Genomics of Bipolar Disorder: What the Clinician Needs to Know. Psychiatr Clin North Am 2025; 48:331-341. [PMID: 40348421 DOI: 10.1016/j.psc.2025.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/14/2025]
Abstract
Bipolar disorder (BD) affects approximately 2% of the global population, characterized by alternating episodes of mania or hypomania, and depression. It comprises two main types: bipolar I disorder, marked by severe manic episodes, and bipolar II disorder, defined by milder hypomanic episodes. Individuals often experience rapid cycling and significant comorbidities, leading to decreased productivity and increased mortality rates. Early diagnosis and intervention are crucial for better outcomes. Both genetic and environmental factors contribute to BD's etiology, with genetic research promising improved diagnosis, novel therapeutic targets, and societal understanding that may help destigmatize the disorder.
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Affiliation(s)
- Andrew McQuillin
- Neuroscience Mental Health Department, Division of Psychiatry, University College London, Gower Street, London, WC1E 6BT, UK
| | - Roel A Ophoff
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA.
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14
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Luan D, Li Y, Zhang A, Bai Q, Zhao T, Chen X, Dang X, Wang J, Jiang S, Sun Y, Zhu Y, Kong Y, Luo XJ, Zhang Z. The regulatory variant rs1950834 confers the risk of depressive disorder by reducing LRFN5 expression. BMC Med 2025; 23:316. [PMID: 40442660 PMCID: PMC12123872 DOI: 10.1186/s12916-025-04141-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2025] [Accepted: 05/15/2025] [Indexed: 06/02/2025] Open
Abstract
BACKGROUND Genome-wide association studies have identified 14q21.1 as a robust risk locus for major depressive disorder (MDD). However, the underlying mechanism remains elusive. Here, we aim to explore the regulatory function of rs1950834 on leucine-rich repeat and fibronectin type III domain containing 5 (LRFN5) expression in MDD. METHODS Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9)-mediated genome knockout and single-base editing were used to determine the effects of rs1950834 on the binding of transcriptional factors and the expression of the target gene LRFN5. Meta-analysis of multiple transcriptomic datasets was performed to clarify the brain region responsible for LRFN5 downregulation in MDD patients. Adeno-associated virus (AAV)-mediated Lrfn5 overexpression or knockdown in the nucleus accumbens (NAc) was used to test their effects on depression-like behaviors and sensitivity to chronic unpredictable mild stress (CUMS) in male mice. Synaptic structure and functions were monitored by synaptic protein expression assay, Golgi staining, and electrophysiological analysis. RESULTS The risk allele (A) of rs1950834 reduced the binding affinity to RNA polymerase II subunit A (POLR2A) and the transcription factor RAD21 cohesin complex component (RAD21), leading to decreased expression of LRFN5. LRFN5 expression was downregulated specifically in the NAc of MDD patients as compared to healthy controls. Knockdown of Lrfn5 in NAc neurons induced depression-like behaviors and further exacerbated CUMS-induced phenotypes via synaptic damage, but overexpression of Lrfn5 in mouse NAc induced resilience to CUMS. CONCLUSIONS These findings reveal that the functional risk single nucleotide polymorphism rs1950834 at 14q21.1 regulates LRNN5 expression and function in NAc, providing a novel perspective for molecular diagnosis and targeted interventions of MDD.
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Affiliation(s)
- Di Luan
- Department of Neurology in Affiliated Zhongda Hospital and Jiangsu Provincial Medical Key Discipline, School of Medicine, Research Institute of Neuropsychiatry, Key Laboratory of Developmental Genes and Human Disease of the Ministry of Education, Southeast University, Nanjing, 210096, China
- Shenzhen Key Laboratory of Precision Diagnosis and Treatment of Depression, Department of Mental Health and Public Health in Faculty of Life and Health Sciences of Shenzhen University of Advanced Technology, The Brain Cognition and Brain Disease Institute of Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Yifan Li
- Department of Neurology in Affiliated Zhongda Hospital and Jiangsu Provincial Medical Key Discipline, School of Medicine, Research Institute of Neuropsychiatry, Key Laboratory of Developmental Genes and Human Disease of the Ministry of Education, Southeast University, Nanjing, 210096, China
| | - Aini Zhang
- Department of Neurology in Affiliated Zhongda Hospital and Jiangsu Provincial Medical Key Discipline, School of Medicine, Research Institute of Neuropsychiatry, Key Laboratory of Developmental Genes and Human Disease of the Ministry of Education, Southeast University, Nanjing, 210096, China
| | - Qingqing Bai
- Department of Neurology in Affiliated Zhongda Hospital and Jiangsu Provincial Medical Key Discipline, School of Medicine, Research Institute of Neuropsychiatry, Key Laboratory of Developmental Genes and Human Disease of the Ministry of Education, Southeast University, Nanjing, 210096, China
| | - Te Zhao
- Shenzhen Key Laboratory of Precision Diagnosis and Treatment of Depression, Department of Mental Health and Public Health in Faculty of Life and Health Sciences of Shenzhen University of Advanced Technology, The Brain Cognition and Brain Disease Institute of Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Xi Chen
- The Second Affiliated Hospital of Kunming Medical University, Kunming, 650223, China
| | - Xinglun Dang
- Department of Neurology in Affiliated Zhongda Hospital and Jiangsu Provincial Medical Key Discipline, School of Medicine, Research Institute of Neuropsychiatry, Key Laboratory of Developmental Genes and Human Disease of the Ministry of Education, Southeast University, Nanjing, 210096, China
| | - Junyang Wang
- Department of Human Anatomy, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan, 450001, China
| | - Shaolei Jiang
- Key Laboratory of Optical Technology and Instrument for Medicine, Ministry of Education; School of Optical-Electrical Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Yun Sun
- Department of Neurology in Affiliated Zhongda Hospital and Jiangsu Provincial Medical Key Discipline, School of Medicine, Research Institute of Neuropsychiatry, Key Laboratory of Developmental Genes and Human Disease of the Ministry of Education, Southeast University, Nanjing, 210096, China
| | - Yingjie Zhu
- Shenzhen Key Laboratory of Drug Addiction, Shenzhen Neher Neural Plasticity Laboratory, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Yan Kong
- Department of Biochemistry and Molecular Biology, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China.
| | - Xiong-Jian Luo
- Department of Neurology in Affiliated Zhongda Hospital and Jiangsu Provincial Medical Key Discipline, School of Medicine, Research Institute of Neuropsychiatry, Key Laboratory of Developmental Genes and Human Disease of the Ministry of Education, Southeast University, Nanjing, 210096, China.
| | - Zhijun Zhang
- Department of Neurology in Affiliated Zhongda Hospital and Jiangsu Provincial Medical Key Discipline, School of Medicine, Research Institute of Neuropsychiatry, Key Laboratory of Developmental Genes and Human Disease of the Ministry of Education, Southeast University, Nanjing, 210096, China.
- Shenzhen Key Laboratory of Precision Diagnosis and Treatment of Depression, Department of Mental Health and Public Health in Faculty of Life and Health Sciences of Shenzhen University of Advanced Technology, The Brain Cognition and Brain Disease Institute of Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
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15
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Zhang L, Ivleva EI, Parker DA, Hill SK, Lizano PL, Keefe RSE, Keedy SK, McDowell JE, Pearlson GD, Clementz BA, Keshavan MS, Gershon ES, Tamminga CA, Sweeney JA, Bishop JR. Impact of Polygenic Interactions With Anticholinergic Burden on Cognition and Brain Structure in Psychosis Spectrum Disorders. Am J Psychiatry 2025:appiajp20240709. [PMID: 40432343 DOI: 10.1176/appi.ajp.20240709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/29/2025]
Abstract
OBJECTIVE The authors sought to determine whether genetic predispositions to cognitive ability or psychiatric conditions interact with anticholinergic burden (AChB) to impact cognition and brain structure in individuals with psychotic disorders. METHODS Participants with psychosis spectrum disorders (N=1,704) from the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) consortium, 18-65 years of age and representing diverse ancestries, underwent cognitive assessments, structural neuroimaging, genotyping, and a comprehensive medication review. The primary cognitive outcome was the Brief Assessment of Cognition in Schizophrenia (BACS) composite score, and the primary brain structural phenotype was total gray matter volume. AChB scores for scheduled medications were quantified using the CRIDECO Anticholinergic Load Scale. Polygenic scores (PGSs) for cognition, schizophrenia, bipolar disorder, and depression were constructed, and a composite psychiatric PGS was subsequently generated. Linear regression models were used to examine AChB-PGS interactions and their associations with cognitive and brain structure outcomes, adjusting for clinical covariates and multiple testing with false discovery rate. Hypothesis-driven moderated mediation models were used to explore potential association pathways. RESULTS Higher AChB was significantly associated with lower BACS performance and reduced gray matter volume. Individuals with higher cognitive PGS values exhibited greater adverse effects of AChB on BACS, while those with lower composite psychiatric PGS values showed more pronounced gray matter volume reductions from AChB. AChB associations with cognitive impairment were partially mediated by reduced gray matter volume and were moderated by composite psychiatric PGS. CONCLUSIONS Anticholinergic-polygenic interactions significantly impact cognition and brain structure in individuals with psychotic disorders, highlighting a novel gene-by-environment interaction that advances our mechanistic understanding of cognitive impairments in this population.
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Affiliation(s)
- Lusi Zhang
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis (Zhang, Bishop); Department of Psychiatry, Southwestern Medical Center, University of Texas, Dallas (Ivleva, Tamminga); Department of Psychology and Neuroscience, University of Georgia, Athens (Parker, McDowell, Clementz); Department of Human Genetics, Emory University School of Medicine, Atlanta (Parker); Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago (Hill); Department of Psychiatry (Lizano, Keshavan) and Division of Translational Neuroscience (Lizano), Beth Israel Deaconess Medical Center, Boston; Department of Psychiatry, Harvard Medical School, Boston (Lizano, Keshavan); Departments of Psychiatry, Neuroscience, and Psychology, Duke University, Durham, NC (Keefe); Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago (Keedy, Gershon); Departments of Psychiatry and Neurobiology, School of Medicine, Yale University, New Haven, CT (Pearlson); Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati (Sweeney); Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis (Bishop)
| | - Elena I Ivleva
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis (Zhang, Bishop); Department of Psychiatry, Southwestern Medical Center, University of Texas, Dallas (Ivleva, Tamminga); Department of Psychology and Neuroscience, University of Georgia, Athens (Parker, McDowell, Clementz); Department of Human Genetics, Emory University School of Medicine, Atlanta (Parker); Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago (Hill); Department of Psychiatry (Lizano, Keshavan) and Division of Translational Neuroscience (Lizano), Beth Israel Deaconess Medical Center, Boston; Department of Psychiatry, Harvard Medical School, Boston (Lizano, Keshavan); Departments of Psychiatry, Neuroscience, and Psychology, Duke University, Durham, NC (Keefe); Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago (Keedy, Gershon); Departments of Psychiatry and Neurobiology, School of Medicine, Yale University, New Haven, CT (Pearlson); Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati (Sweeney); Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis (Bishop)
| | - David A Parker
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis (Zhang, Bishop); Department of Psychiatry, Southwestern Medical Center, University of Texas, Dallas (Ivleva, Tamminga); Department of Psychology and Neuroscience, University of Georgia, Athens (Parker, McDowell, Clementz); Department of Human Genetics, Emory University School of Medicine, Atlanta (Parker); Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago (Hill); Department of Psychiatry (Lizano, Keshavan) and Division of Translational Neuroscience (Lizano), Beth Israel Deaconess Medical Center, Boston; Department of Psychiatry, Harvard Medical School, Boston (Lizano, Keshavan); Departments of Psychiatry, Neuroscience, and Psychology, Duke University, Durham, NC (Keefe); Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago (Keedy, Gershon); Departments of Psychiatry and Neurobiology, School of Medicine, Yale University, New Haven, CT (Pearlson); Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati (Sweeney); Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis (Bishop)
| | - Scot K Hill
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis (Zhang, Bishop); Department of Psychiatry, Southwestern Medical Center, University of Texas, Dallas (Ivleva, Tamminga); Department of Psychology and Neuroscience, University of Georgia, Athens (Parker, McDowell, Clementz); Department of Human Genetics, Emory University School of Medicine, Atlanta (Parker); Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago (Hill); Department of Psychiatry (Lizano, Keshavan) and Division of Translational Neuroscience (Lizano), Beth Israel Deaconess Medical Center, Boston; Department of Psychiatry, Harvard Medical School, Boston (Lizano, Keshavan); Departments of Psychiatry, Neuroscience, and Psychology, Duke University, Durham, NC (Keefe); Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago (Keedy, Gershon); Departments of Psychiatry and Neurobiology, School of Medicine, Yale University, New Haven, CT (Pearlson); Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati (Sweeney); Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis (Bishop)
| | - Paulo L Lizano
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis (Zhang, Bishop); Department of Psychiatry, Southwestern Medical Center, University of Texas, Dallas (Ivleva, Tamminga); Department of Psychology and Neuroscience, University of Georgia, Athens (Parker, McDowell, Clementz); Department of Human Genetics, Emory University School of Medicine, Atlanta (Parker); Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago (Hill); Department of Psychiatry (Lizano, Keshavan) and Division of Translational Neuroscience (Lizano), Beth Israel Deaconess Medical Center, Boston; Department of Psychiatry, Harvard Medical School, Boston (Lizano, Keshavan); Departments of Psychiatry, Neuroscience, and Psychology, Duke University, Durham, NC (Keefe); Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago (Keedy, Gershon); Departments of Psychiatry and Neurobiology, School of Medicine, Yale University, New Haven, CT (Pearlson); Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati (Sweeney); Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis (Bishop)
| | - Richard S E Keefe
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis (Zhang, Bishop); Department of Psychiatry, Southwestern Medical Center, University of Texas, Dallas (Ivleva, Tamminga); Department of Psychology and Neuroscience, University of Georgia, Athens (Parker, McDowell, Clementz); Department of Human Genetics, Emory University School of Medicine, Atlanta (Parker); Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago (Hill); Department of Psychiatry (Lizano, Keshavan) and Division of Translational Neuroscience (Lizano), Beth Israel Deaconess Medical Center, Boston; Department of Psychiatry, Harvard Medical School, Boston (Lizano, Keshavan); Departments of Psychiatry, Neuroscience, and Psychology, Duke University, Durham, NC (Keefe); Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago (Keedy, Gershon); Departments of Psychiatry and Neurobiology, School of Medicine, Yale University, New Haven, CT (Pearlson); Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati (Sweeney); Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis (Bishop)
| | - Sarah K Keedy
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis (Zhang, Bishop); Department of Psychiatry, Southwestern Medical Center, University of Texas, Dallas (Ivleva, Tamminga); Department of Psychology and Neuroscience, University of Georgia, Athens (Parker, McDowell, Clementz); Department of Human Genetics, Emory University School of Medicine, Atlanta (Parker); Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago (Hill); Department of Psychiatry (Lizano, Keshavan) and Division of Translational Neuroscience (Lizano), Beth Israel Deaconess Medical Center, Boston; Department of Psychiatry, Harvard Medical School, Boston (Lizano, Keshavan); Departments of Psychiatry, Neuroscience, and Psychology, Duke University, Durham, NC (Keefe); Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago (Keedy, Gershon); Departments of Psychiatry and Neurobiology, School of Medicine, Yale University, New Haven, CT (Pearlson); Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati (Sweeney); Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis (Bishop)
| | - Jennifer E McDowell
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis (Zhang, Bishop); Department of Psychiatry, Southwestern Medical Center, University of Texas, Dallas (Ivleva, Tamminga); Department of Psychology and Neuroscience, University of Georgia, Athens (Parker, McDowell, Clementz); Department of Human Genetics, Emory University School of Medicine, Atlanta (Parker); Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago (Hill); Department of Psychiatry (Lizano, Keshavan) and Division of Translational Neuroscience (Lizano), Beth Israel Deaconess Medical Center, Boston; Department of Psychiatry, Harvard Medical School, Boston (Lizano, Keshavan); Departments of Psychiatry, Neuroscience, and Psychology, Duke University, Durham, NC (Keefe); Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago (Keedy, Gershon); Departments of Psychiatry and Neurobiology, School of Medicine, Yale University, New Haven, CT (Pearlson); Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati (Sweeney); Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis (Bishop)
| | - Godfrey D Pearlson
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis (Zhang, Bishop); Department of Psychiatry, Southwestern Medical Center, University of Texas, Dallas (Ivleva, Tamminga); Department of Psychology and Neuroscience, University of Georgia, Athens (Parker, McDowell, Clementz); Department of Human Genetics, Emory University School of Medicine, Atlanta (Parker); Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago (Hill); Department of Psychiatry (Lizano, Keshavan) and Division of Translational Neuroscience (Lizano), Beth Israel Deaconess Medical Center, Boston; Department of Psychiatry, Harvard Medical School, Boston (Lizano, Keshavan); Departments of Psychiatry, Neuroscience, and Psychology, Duke University, Durham, NC (Keefe); Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago (Keedy, Gershon); Departments of Psychiatry and Neurobiology, School of Medicine, Yale University, New Haven, CT (Pearlson); Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati (Sweeney); Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis (Bishop)
| | - Brett A Clementz
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis (Zhang, Bishop); Department of Psychiatry, Southwestern Medical Center, University of Texas, Dallas (Ivleva, Tamminga); Department of Psychology and Neuroscience, University of Georgia, Athens (Parker, McDowell, Clementz); Department of Human Genetics, Emory University School of Medicine, Atlanta (Parker); Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago (Hill); Department of Psychiatry (Lizano, Keshavan) and Division of Translational Neuroscience (Lizano), Beth Israel Deaconess Medical Center, Boston; Department of Psychiatry, Harvard Medical School, Boston (Lizano, Keshavan); Departments of Psychiatry, Neuroscience, and Psychology, Duke University, Durham, NC (Keefe); Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago (Keedy, Gershon); Departments of Psychiatry and Neurobiology, School of Medicine, Yale University, New Haven, CT (Pearlson); Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati (Sweeney); Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis (Bishop)
| | - Matcheri S Keshavan
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis (Zhang, Bishop); Department of Psychiatry, Southwestern Medical Center, University of Texas, Dallas (Ivleva, Tamminga); Department of Psychology and Neuroscience, University of Georgia, Athens (Parker, McDowell, Clementz); Department of Human Genetics, Emory University School of Medicine, Atlanta (Parker); Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago (Hill); Department of Psychiatry (Lizano, Keshavan) and Division of Translational Neuroscience (Lizano), Beth Israel Deaconess Medical Center, Boston; Department of Psychiatry, Harvard Medical School, Boston (Lizano, Keshavan); Departments of Psychiatry, Neuroscience, and Psychology, Duke University, Durham, NC (Keefe); Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago (Keedy, Gershon); Departments of Psychiatry and Neurobiology, School of Medicine, Yale University, New Haven, CT (Pearlson); Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati (Sweeney); Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis (Bishop)
| | - Elliot S Gershon
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis (Zhang, Bishop); Department of Psychiatry, Southwestern Medical Center, University of Texas, Dallas (Ivleva, Tamminga); Department of Psychology and Neuroscience, University of Georgia, Athens (Parker, McDowell, Clementz); Department of Human Genetics, Emory University School of Medicine, Atlanta (Parker); Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago (Hill); Department of Psychiatry (Lizano, Keshavan) and Division of Translational Neuroscience (Lizano), Beth Israel Deaconess Medical Center, Boston; Department of Psychiatry, Harvard Medical School, Boston (Lizano, Keshavan); Departments of Psychiatry, Neuroscience, and Psychology, Duke University, Durham, NC (Keefe); Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago (Keedy, Gershon); Departments of Psychiatry and Neurobiology, School of Medicine, Yale University, New Haven, CT (Pearlson); Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati (Sweeney); Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis (Bishop)
| | - Carol A Tamminga
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis (Zhang, Bishop); Department of Psychiatry, Southwestern Medical Center, University of Texas, Dallas (Ivleva, Tamminga); Department of Psychology and Neuroscience, University of Georgia, Athens (Parker, McDowell, Clementz); Department of Human Genetics, Emory University School of Medicine, Atlanta (Parker); Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago (Hill); Department of Psychiatry (Lizano, Keshavan) and Division of Translational Neuroscience (Lizano), Beth Israel Deaconess Medical Center, Boston; Department of Psychiatry, Harvard Medical School, Boston (Lizano, Keshavan); Departments of Psychiatry, Neuroscience, and Psychology, Duke University, Durham, NC (Keefe); Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago (Keedy, Gershon); Departments of Psychiatry and Neurobiology, School of Medicine, Yale University, New Haven, CT (Pearlson); Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati (Sweeney); Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis (Bishop)
| | - John A Sweeney
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis (Zhang, Bishop); Department of Psychiatry, Southwestern Medical Center, University of Texas, Dallas (Ivleva, Tamminga); Department of Psychology and Neuroscience, University of Georgia, Athens (Parker, McDowell, Clementz); Department of Human Genetics, Emory University School of Medicine, Atlanta (Parker); Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago (Hill); Department of Psychiatry (Lizano, Keshavan) and Division of Translational Neuroscience (Lizano), Beth Israel Deaconess Medical Center, Boston; Department of Psychiatry, Harvard Medical School, Boston (Lizano, Keshavan); Departments of Psychiatry, Neuroscience, and Psychology, Duke University, Durham, NC (Keefe); Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago (Keedy, Gershon); Departments of Psychiatry and Neurobiology, School of Medicine, Yale University, New Haven, CT (Pearlson); Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati (Sweeney); Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis (Bishop)
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis (Zhang, Bishop); Department of Psychiatry, Southwestern Medical Center, University of Texas, Dallas (Ivleva, Tamminga); Department of Psychology and Neuroscience, University of Georgia, Athens (Parker, McDowell, Clementz); Department of Human Genetics, Emory University School of Medicine, Atlanta (Parker); Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago (Hill); Department of Psychiatry (Lizano, Keshavan) and Division of Translational Neuroscience (Lizano), Beth Israel Deaconess Medical Center, Boston; Department of Psychiatry, Harvard Medical School, Boston (Lizano, Keshavan); Departments of Psychiatry, Neuroscience, and Psychology, Duke University, Durham, NC (Keefe); Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago (Keedy, Gershon); Departments of Psychiatry and Neurobiology, School of Medicine, Yale University, New Haven, CT (Pearlson); Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati (Sweeney); Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis (Bishop)
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16
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Zheng M. Thinking bipolar disorder as a symptom rather than a disease. Asian J Psychiatr 2025; 109:104540. [PMID: 40449413 DOI: 10.1016/j.ajp.2025.104540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2025] [Revised: 05/13/2025] [Accepted: 05/19/2025] [Indexed: 06/03/2025]
Abstract
Bipolar affective disorder (BAD), a major global health challenge, has traditionally been classified as a distinct psychiatric condition characterized by episodes of mania and depression. Recent findings suggest that BAD may not represent a singular disease but rather a manifestation of broader, interconnected pathophysiological processes underlying mood dysregulation. This study proposes a paradigm shift, conceptualizing BAD as a shared symptom reflecting common pathophysiological mechanisms rather than a discrete disorder. Through a systematic disease-wide association study (DWAS) of over 7,000 BAD patients and 337,000 controls, this study identified a wide range of comorbid conditions across multiple organ systems-neurological, metabolic, gastrointestinal, cardiovascular, and immune-related disorders. These comorbidities indicate a deeper etiological connection rather than coincidental associations. This study suggests that BAD is part of a larger, transdiagnostic continuum, supporting the growing emphasis on dimensional models of psychopathology. This reconceptualization offers important clinical advantages, including enhanced diagnostic accuracy, integrated interventions targeting common pathophysiological pathways, and more effective patient management. By transcending traditional diagnostic boundaries, this approach fosters precision psychiatry and lays the groundwork for future research focused on shared biomarkers and therapeutic targets, ultimately improving the clinical care and outcomes for individuals with mood disorders.
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Affiliation(s)
- Ming Zheng
- Beijing Institute of Basic Medical Sciences, 27 Taiping Road, Beijing 100850, China; Academy of Military Medical Sciences, 27 Taiping Road, Beijing 100850, China.
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17
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Benoit-Pilven C, Asteljoki JV, Leinonen JT, Karjalainen J, Daly MJ, Tukiainen T. Early establishment and life course stability of sex biases in the human brain transcriptome. CELL GENOMICS 2025:100890. [PMID: 40425010 DOI: 10.1016/j.xgen.2025.100890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2024] [Revised: 02/07/2025] [Accepted: 04/30/2025] [Indexed: 05/29/2025]
Abstract
To elaborate on the origins of the established male-female differences in several brain-related phenotypes, we assessed the patterns of transcriptomic sex biases in the developing and adult human forebrain. We find an abundance of sex differences in expression (sex-DEs) in the prenatal brain, driven by both hormonal and sex-chromosomal factors, and considerable consistency in the sex effects between the developing and adult brain, with little sex-DE exclusive to the adult forebrain. Sex-DE was not enriched in genes associated with brain disorders, consistent with systematic differences in the characteristics of these genes (e.g., constraint). Yet, the genes with persistent sex-DE across the lifespan were overrepresented in disease gene co-regulation networks, pointing to their potential to mediate sex biases in brain phenotypes. Altogether, our work highlights prenatal development as a crucial time point for the establishment of brain sex differences.
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Affiliation(s)
- Clara Benoit-Pilven
- Institute for Molecular Medicine Finland FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Juho V Asteljoki
- Minerva Foundation Institute for Medical Research, Helsinki, Finland; Department of Internal Medicine, University of Helsinki, Helsinki, Finland; Abdominal Center, Helsinki University Hospital, Helsinki, Finland
| | - Jaakko T Leinonen
- Institute for Molecular Medicine Finland FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Juha Karjalainen
- Institute for Molecular Medicine Finland FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Mark J Daly
- Institute for Molecular Medicine Finland FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Taru Tukiainen
- Institute for Molecular Medicine Finland FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
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18
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Seven E, Kurhan F. Evaluation of retinal layer thickness in patients with bipolar disorder, their relatives, and healthy controls using optical coherence tomography. World J Biol Psychiatry 2025:1-10. [PMID: 40401999 DOI: 10.1080/15622975.2025.2505148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2025] [Revised: 05/03/2025] [Accepted: 05/08/2025] [Indexed: 05/23/2025]
Abstract
BACKGROUND Bipolar disorder (BD) is a chronic psychiatric condition characterised by mood episodes and associated structural changes in the central nervous system. Optical coherence tomography (OCT) offers a non-invasive method to assess retinal layer thickness, potentially serving as an endophenotypic biomarker for neurodegeneration. This study aimed to compare retinal thickness among BD patients, their first-degree relatives, and healthy controls to identify structural markers and assess their alignment with existing literature. METHODS Thirty-six BD patients, 30 first-degree relatives, and 38 healthy controls were recruited from Van Yüzüncü Yıl University. Comprehensive ophthalmologic examinations and retinal layer thickness measurements using Spectralis OCT were performed. Retinal layers were analysed at 1 mm, 3 mm, and 6 mm concentric circles per the ETDRS protocol. Peripapillary retinal nerve fibre layer (RNFL) thickness was evaluated across seven regions. Due to significant age differences among groups (p = 0.002), an ANCOVA analysis was used to control for the age effect. RESULTS Retinal analysis revealed a significant increase in the inferonasal (NI) nerve fibre layer thickness in BD patients and their first-degree relatives compared to healthy controls (p = 0.008). Optic nerve head analyses showed non-significant thinning in the temporal (T), inferotemporal (TI), and superotemporal (TS) nerve fibre layer thicknesses in BD patients and their relatives compared to healthy controls. The thicknesses of the macular retinal layers did not differ significantly among the groups (p > 0.05). CONCLUSIONS The observed increase in NI optic nerve fibre layer thickness in BD patients and their first-degree relatives contrasts with the expected thinning reported in previous literature on neurodegeneration in psychiatric disorders. This finding underscores the complexity of structural changes in BD and raises the possibility of alternative pathophysiological mechanisms or methodological considerations influencing retinal measurements. Further research is needed to elucidate these phenomena and their implications for understanding BD.
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Affiliation(s)
- Erbil Seven
- Department of Ophthalmology, Faculty of Medicine, Yüzüncü Yıl University, Van, Turkey
| | - Faruk Kurhan
- Department of Psychiatry, Faculty of Medicine, Yüzüncü Yıl University, Van, Turkey
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19
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Kanwal A, Zulfiqar R, Cheema HA, Jabbar N, Iftikhar A, Butt AI, Sheikh SA, Pardo JV, Naz S. Rare Homozygous Variants in INSR and NFXL1 Are Associated with Severe Treatment-Resistant Psychosis. Int J Mol Sci 2025; 26:4925. [PMID: 40430072 PMCID: PMC12111829 DOI: 10.3390/ijms26104925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2025] [Revised: 05/09/2025] [Accepted: 05/13/2025] [Indexed: 05/29/2025] Open
Abstract
Psychosis constitutes a cardinal component of schizophrenia and affects nearly fifty percent of those with bipolar disorder. We sought to molecularly characterize psychosis segregating in consanguineous families. Participants from eight multiplex families were evaluated using standardized testing tools. DNA was subjected to exome sequencing followed by Sanger sequencing. Effects of variants were modeled using in-silico tools, while cDNA from a patient's blood sample was analyzed to evaluate the effect of a splice-site variant. Twelve patients in six families were diagnosed with schizophrenia, whereas four patients from two families had psychotic bipolar disorder. Two homozygous rare deleterious variants in INSR (c.2232-7T>G) and NFXL1 (c.1322G>A; p.Cys441Tyr) were identified, which segregated with severe treatment-resistant psychosis/schizophrenia in two families. There were none, or ambiguous findings in the other six families. The predicted deleterious missense variant affected a conserved amino acid, while the intronic variant was predicted to affect splicing. However, cDNA analysis from a patient's blood sample did not reveal an aberrant transcript. Our results indicate that INSR and NFXL1 variants may have a role in psychosis that requires to be investigated further. Lack of molecular diagnosis in some patients suggests the need for genome sequencing to pinpoint the genetic causes.
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Affiliation(s)
- Ambreen Kanwal
- School of Biological Sciences, University of the Punjab, Lahore 54000, Pakistan; (A.K.); (R.Z.); (A.I.B.)
| | - Rimsha Zulfiqar
- School of Biological Sciences, University of the Punjab, Lahore 54000, Pakistan; (A.K.); (R.Z.); (A.I.B.)
| | - Husnain Arshad Cheema
- Punjab Institute of Mental Health, Jail Road, Lahore 54000, Pakistan; (H.A.C.); (N.J.)
| | - Nauman Jabbar
- Punjab Institute of Mental Health, Jail Road, Lahore 54000, Pakistan; (H.A.C.); (N.J.)
| | - Amina Iftikhar
- Rainbow Obesity and Eating Disorders Centre, Shadman, Lahore 54000, Pakistan;
| | - Amina Iftikhar Butt
- School of Biological Sciences, University of the Punjab, Lahore 54000, Pakistan; (A.K.); (R.Z.); (A.I.B.)
| | - Sohail A. Sheikh
- Psychiatry Department, Hawkes Bay DHB, Hastings 4156, New Zealand;
| | - Jose V. Pardo
- Department of Psychiatry, University of Minnesota, Minneapolis, MN 55455, USA
- Minneapolis Veterans Affairs Health Care System, Minneapolis, MN 55417, USA
| | - Sadaf Naz
- School of Biological Sciences, University of the Punjab, Lahore 54000, Pakistan; (A.K.); (R.Z.); (A.I.B.)
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20
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Lerga-Jaso J, Terpolovsky A, Novković B, Osama A, Manson C, Bohn S, De Marino A, Kunitomi M, Yazdi PG. Optimization of multi-ancestry polygenic risk score disease prediction models. Sci Rep 2025; 15:17495. [PMID: 40394127 PMCID: PMC12092622 DOI: 10.1038/s41598-025-02903-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2024] [Accepted: 05/16/2025] [Indexed: 05/22/2025] Open
Abstract
Polygenic risk scores (PRS) have ushered in a new era in genetic epidemiology, offering insights into individual predispositions to a wide range of diseases. However, despite recent marked enhancements in predictive power, PRS-based models still need to overcome several hurdles before they can be broadly applied in the clinic. Chiefly, they need to achieve sufficient accuracy, easy interpretability and portability across diverse populations. Leveraging trans-ancestry genome-wide association study (GWAS) meta-analysis, we generated novel, diverse summary statistics for 30 medically-related traits and benchmarked the performance of six existing PRS algorithms using UK Biobank. We built an ensemble model using logistic regression to combine outputs of top-performing algorithms and validated it on the diverse eMERGE and PAGE MEC cohorts. It surpassed current state-of-the-art PRS models, with minimal performance drops in external cohorts, indicating good calibration. To enhance predictive accuracy for clinical application, we incorporated easily-accessible clinical characteristics such as age, gender, ancestry and risk factors, creating disease prediction models intended as prospective diagnostic tests, with easily interpretable positive or negative outcomes. After adding clinical characteristics, 12 out of 30 models surpassed 80% AUC. Further, 25 traits exceeded the diagnostic odds ratio (DOR) of five, and 19 traits exceeded DOR of 10 for all ancestry groups, indicating high predictive value. Our PRS model for coronary artery disease identified 55-80 times more true coronary events than rare pathogenic variant models, reinforcing its clinical potential. The polygenic component modulated the effect of high-risk rare variants, stressing the need to consider all genetic components in clinical settings. These findings show that newly developed PRS-based disease prediction models have sufficient accuracy and portability to warrant consideration of being used in the clinic.
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Affiliation(s)
| | | | | | - Alex Osama
- Research & Development, Omics Edge, Miami, FL, USA
| | | | - Sandra Bohn
- Research & Development, Omics Edge, Miami, FL, USA
| | | | | | - Puya G Yazdi
- Research & Development, Omics Edge, Miami, FL, USA.
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21
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Murphy KB, Ye Y, Tsalenchuk M, Nott A, Marzi SJ. CHAS infers cell type-specific signatures in bulk brain histone acetylation studies of neurological and psychiatric disorders. CELL REPORTS METHODS 2025; 5:101032. [PMID: 40300607 DOI: 10.1016/j.crmeth.2025.101032] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2024] [Revised: 03/07/2025] [Accepted: 04/04/2025] [Indexed: 05/01/2025]
Abstract
Epigenomic profiling of the brain has largely been done on bulk tissues, limiting our understanding of cell type-specific epigenetic changes in disease states. Here, we introduce cell type-specific histone acetylation score (CHAS), a computational tool for inferring cell type-specific signatures in bulk brain H3K27ac profiles. We applied CHAS to >300 H3K27ac chromatin immunoprecipitation sequencing samples from studies of Alzheimer's disease, Parkinson's disease, autism spectrum disorder, schizophrenia, and bipolar disorder in bulk postmortem brain tissue. In addition to recapitulating known disease-associated shifts in cellular proportions, we identified cell type-specific biological insights into brain-disorder-associated regulatory variation. In most cases, genetic risk and epigenetic dysregulation targeted different cell types, suggesting independent mechanisms. For instance, genetic risk of Alzheimer's disease was exclusively enriched within microglia, while epigenetic dysregulation predominantly fell within oligodendrocyte-specific H3K27ac regions. In addition, reanalysis of the original datasets using CHAS enabled identification of biological pathways associated with each neurological and psychiatric disorder at cellular resolution.
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Affiliation(s)
- Kitty B Murphy
- UK Dementia Research Institute at King's College London, London, UK; Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Brain Sciences, Imperial College London, London, UK.
| | - Yuqian Ye
- Department of Brain Sciences, Imperial College London, London, UK; UK Dementia Research Institute at Imperial College London, London, UK
| | - Maria Tsalenchuk
- UK Dementia Research Institute at King's College London, London, UK; Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Brain Sciences, Imperial College London, London, UK
| | - Alexi Nott
- Department of Brain Sciences, Imperial College London, London, UK; UK Dementia Research Institute at Imperial College London, London, UK
| | - Sarah J Marzi
- UK Dementia Research Institute at King's College London, London, UK; Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Brain Sciences, Imperial College London, London, UK.
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22
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Xue C, Zhou M. Integrating Proteomics and GWAS to Identify Key Tissues and Genes Underlying Human Complex Diseases. BIOLOGY 2025; 14:554. [PMID: 40427743 PMCID: PMC12109507 DOI: 10.3390/biology14050554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2025] [Revised: 05/09/2025] [Accepted: 05/14/2025] [Indexed: 05/29/2025]
Abstract
BACKGROUND The tissues of origin and molecular mechanisms underlying human complex diseases remain incompletely understood. Previous studies have leveraged transcriptomic data to interpret genome-wide association studies (GWASs) for identifying disease-relevant tissues and fine-mapping causal genes. However, according to the central dogma, proteins more directly reflect cellular molecular activities than RNA. Therefore, in this study, we integrated proteomic data with GWAS to identify disease-associated tissues and genes. METHODS We compiled proteomic and paired transcriptomic data for 12,229 genes across 32 human tissues from the GTEx project. Using three tissue inference approaches-S-LDSC, MAGMA, and DESE-we analyzed GWAS data for six representative complex diseases (bipolar disorder, schizophrenia, coronary artery disease, Crohn's disease, rheumatoid arthritis, and type 2 diabetes), with an average sample size of 260 K. We systematically compared disease-associated tissues and genes identified using proteomic versus transcriptomic data. RESULTS Tissue-specific protein abundance showed a moderate correlation with RNA expression (mean correlation coefficient = 0.46, 95% CI: 0.42-0.49). Proteomic data accurately identified disease-relevant tissues, such as the association between brain regions and schizophrenia and between coronary arteries and coronary artery disease. Compared to GWAS-based gene association estimates alone, incorporating proteomic data significantly improved gene association detection (AUC difference test, p = 0.0028). Furthermore, proteomic data revealed unique disease-associated genes that were not identified using transcriptomic data, such as the association between bipolar disorder and CREB1. CONCLUSIONS Integrating proteomic data enables accurate identification of disease-associated tissues and provides irreplaceable advantages in fine-mapping genes for complex diseases.
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Affiliation(s)
- Chao Xue
- Medical College, Jiaying University, Meizhou 514031, China
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
| | - Miao Zhou
- Medical College, Jiaying University, Meizhou 514031, China
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
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Hatoum AS, Gorelik AJ, Blaydon L, Huggett SB, Chi T, Baranger DAA, Miller AP, Johnson EC, Agrawal A, Bogdan R. Psychiatric genome-wide association study enrichment shows promise for future psychopharmaceutical discoveries. COMMUNICATIONS MEDICINE 2025; 5:176. [PMID: 40379965 PMCID: PMC12084526 DOI: 10.1038/s43856-025-00877-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2024] [Accepted: 04/22/2025] [Indexed: 05/19/2025] Open
Abstract
BACKGROUND Innovation in psychiatric therapeutics has stagnated on known mechanisms. Psychiatric genome-wide association studies (GWAS) have identified hundreds of genome-wide significant (GWS) loci that have rapidly advanced our understanding of disease etiology. However, whether these results can be leveraged to improve clinical treatment for specific psychiatric disorders remains poorly understood. METHODS In this proof-of-principal evaluation of GWAS clinical utility, we test whether the targets of drugs used to treat Attention Deficit Hyperactivity Disorder (ADHD), Bipolar Disorder (BiP), Generalized Anxiety Disorder (GAD), Major Depressive Disorder (MDD), Post-Traumatic Stress Disorder (PTSD), Schizophrenia (SCZ), Substance Use Disorders (SUDs), and insomnia (INS), are enriched for GWAS meta-analysis findings. RESULTS The genes coding for treatment targets of medications used to SCZ, BiP, MDD, and SUDs (but not ADHD, PTSD, GAD, or INSOM) are enriched for GWS loci identified in their respective GWAS (ORs: 2.78-27.63; all ps <1.15e-3). Enrichment is largely driven by the presence of a GWS locus or loci within a gene coding for a drug target (i.e., proximity matching). Broadly, additional annotation (i.e., functional: Combined Annotation Dependent Depletion [CADD] scores, regulomeDB scores, eQTL, chromatin loop, and gene region; statistical: effect size of genome-wide significant SNPs; Z-score of SNPs; number of drug targets implicated by GWAS), with the exception of weighting by the largest SNP effect size, does not further improve enrichment across disorders. Evaluation of prior smaller GWAS reveal that more recent larger GWAS improve enrichment. CONCLUSIONS GWAS results may assist in the prioritization of medications for future psychopharmaceutical research.
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Affiliation(s)
- Alexander S Hatoum
- Washington University in St. Louis, Department of Psychological & Brain Sciences, St. Louis, MO, USA.
- Washington University School of Medicine, Department of Psychiatry, St. Louis, MO, USA.
- Washington University School of Medicine, AI and Health Institute, St. Louis, MO, USA.
| | - Aaron J Gorelik
- Washington University in St. Louis, Department of Psychological & Brain Sciences, St. Louis, MO, USA
| | - Lauren Blaydon
- Washington University in St. Louis, Department of Psychological & Brain Sciences, St. Louis, MO, USA
| | | | - Tingying Chi
- St. Louis Behavioral Medicine Institute, St. Louis, WA, USA
| | - David A A Baranger
- Washington University in St. Louis, Department of Psychological & Brain Sciences, St. Louis, MO, USA
- Washington University School of Medicine, Department of Psychiatry, St. Louis, MO, USA
| | - Alex P Miller
- Washington University School of Medicine, Department of Psychiatry, St. Louis, MO, USA
- Indiana University School of Medicine, Department of Psychiatry, Indianapolis, IN, USA
| | - Emma C Johnson
- Washington University School of Medicine, Department of Psychiatry, St. Louis, MO, USA
| | - Arpana Agrawal
- Washington University School of Medicine, Department of Psychiatry, St. Louis, MO, USA
- Washington University School of Medicine, AI and Health Institute, St. Louis, MO, USA
| | - Ryan Bogdan
- Washington University in St. Louis, Department of Psychological & Brain Sciences, St. Louis, MO, USA
- Washington University School of Medicine, AI and Health Institute, St. Louis, MO, USA
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24
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Chen J, Duan W, Liu P, Long C, Li A, Zhang X, Zuo X. Schizophrenia, bipolar disorder and major depressive disorder are probably not risk factors for cardiovascular disease: A Mendelian randomized study. J Affect Disord 2025; 377:184-196. [PMID: 39983779 DOI: 10.1016/j.jad.2025.02.069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 11/28/2024] [Accepted: 02/17/2025] [Indexed: 02/23/2025]
Abstract
BACKGROUND Individuals with severe mental illnesses (SMI) like schizophrenia, bipolar disorder (BD), and major depressive disorder (MDD) have an increased risk for cardiovascular diseases (CVD), but the causal relationship remains unclear. METHODS Mendelian randomization (MR) was used to investigate the potential causal relationship between SMI and CVD and its five subtypes of disease, coronary heart disease, myocardial infarction, stroke, heart failure, and atrial fibrillation. Subsequently, the MR results of SMI with CVD and its subtypes were meta-analyzed separately. To assess the robustness of the findings, Cochran's Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out analysis were used. Select single nucleotide polymorphisms (SNPs) related to SMI and CVD and their five subtypes (coronary heart disease, myocardial infarction, stroke, heart failure, and atrial fibrillation). Use univariable Mendelian randomization (UVMR) and multivariate Mendelian randomization (MVMR) to assess the causal relationship between these conditions. Conduct a meta-analysis of the MR results of SMI and CVD and their subtypes. Use MR mediation analysis to evaluate the mediating effect of BMI between BD and CVD. Use Cochran's Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out analysis to enhance the robustness of the study. RESULTS MR analyses have revealed correlations between schizophrenia and BD with CVD and their subtypes in certain datasets. No significant evidence of an association between MDD and CVD or its subtypes was observed in our MR analyses. After MVMR and MR meta-analysis, no basis for genetically predicted SMI increasing CVD and their subtypes was found. The MR mediation analysis showed that the reduced risk of certain CVDs in BD was partially related to BMI to some extent. CONCLUSION Our MR study did not provide conclusive evidence for a causal association between genetic predisposition to SMI and CVD. Based on the available evidence, it would be more appropriate to consider SMI as potential risk markers for CVD and its subtypes rather than definitive risk factors.
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Affiliation(s)
- Jin Chen
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Department of Psychiatry, The Affiliated Xuzhou Eastern Hospital of Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Wenhuan Duan
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Department of Psychiatry, Pukou Branch of Jiangsu Province Hospital (Nanjing Pukou District Central Hospital), Nanjing 211800, China
| | - Peizi Liu
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Department of Psychiatry, Pukou Branch of Jiangsu Province Hospital (Nanjing Pukou District Central Hospital), Nanjing 211800, China
| | - Cui Long
- Department of Psychiatry, The Affiliated Xuzhou Eastern Hospital of Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Aoyu Li
- Department of Psychiatry, The Affiliated Xuzhou Eastern Hospital of Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Xiangrong Zhang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Department of Psychiatry, The Affiliated Xuzhou Eastern Hospital of Xuzhou Medical University, Xuzhou, Jiangsu 221004, China.
| | - Xiaowei Zuo
- Department of Psychiatry, The Affiliated Xuzhou Eastern Hospital of Xuzhou Medical University, Xuzhou, Jiangsu 221004, China.
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Poggi G, Treccani G, von der Bey M, Tanti A, Schmeisser MJ, Müller M. Canonical and non-canonical roles of oligodendrocyte precursor cells in mental disorders. NPJ MENTAL HEALTH RESEARCH 2025; 4:19. [PMID: 40374740 DOI: 10.1038/s44184-025-00133-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2025] [Accepted: 04/29/2025] [Indexed: 05/18/2025]
Abstract
Psychiatric research has shifted from a neuroncentric view to understanding mental disorders as disturbances of heterogeneous brain networks. Oligodendrocyte precursor cells (OPCs)- actively involved in the modulation of neuronal functions - are altered in psychiatric patients, but the extent and related consequences are unclear. This review explores canonical and non-canonical OPC-related pathways in schizophrenia, bipolar disorder, post-traumatic stress disorder, and depression in humans, highlighting potential mechanisms shared across diagnostic entities.
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Affiliation(s)
- Giulia Poggi
- Institute of Anatomy, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
- Department of Psychiatry and Psychotherapy, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
| | - Giulia Treccani
- Department of Systemic Neuroscience Institute of Anatomy and Cell Biology, Philipps Universität Marburg, Marburg, Germany
| | - Martina von der Bey
- Molecular and Translational Neuroscience, Department of Neurology, University Hospital Ulm, Ulm, Germany
| | - Arnaud Tanti
- Inserm, UMR 1253, iBrain, Université de Tours, Tours, France
| | - Michael J Schmeisser
- Institute of Anatomy, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
- Focus Program Translational Neurosciences, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Marianne Müller
- Department of Psychiatry and Psychotherapy, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
- Leibniz Institute for Resilience Research, Mainz, Germany
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26
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Kendler KS. A history of metaphorical brain talk in psychiatry. Mol Psychiatry 2025:10.1038/s41380-025-03053-6. [PMID: 40360726 DOI: 10.1038/s41380-025-03053-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2025] [Revised: 05/02/2025] [Accepted: 05/07/2025] [Indexed: 05/15/2025]
Abstract
From the very beginnings of our field in the late 18th century, psychiatrists have engaged, often extensively, in "metaphorical brain talk" - rephrasing descriptions of mental processes in unconfirmed brain metaphors (e.g., "diseased working of the brain convolutions"). In the late 19th century, Kraepelin criticized the later developments of such approaches, termed "brain mythology" by the philosopher/psychiatrist Jaspers in 1913. In this essay, I review the history, meaning, and significance of this phenomenon and reach four conclusions. First, this trend has continued to the present day in metaphors such as the "broken brain" and the use of simplistic and empirically poorly supported explanations of psychiatric illness, such as depression being "due to an imbalance of serotonin in the brain." Second, our language stems from the tension in our profession that seeks to be a part of medicine yet declares our main focus as treatment of the mental. We feel more comfortable with the reductionist approach of brain metaphors, which, even though at times self-deceptive, reinforce our commitment to and membership in a brain-based medical specialty. Third, metaphorical brain talk can also be seen as the "promissory note" of our profession, a pledge that the day will come when we can indeed explain accurately to ourselves and to our patients the brain basis of the psychiatric disorders from which they suffer. Finally, moving away from metaphorical brain talk would reflect an increasing maturity of both the research and clinical aspects of our profession.
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Affiliation(s)
- Kenneth S Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA.
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA.
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Friligkou E, Pathak GA, Tylee DS, De Lillo A, Koller D, Cabrera-Mendoza B, Polimanti R. Characterizing pleiotropy among bipolar disorder, schizophrenia, and major depression: a genome-wide cross-disorder meta-analysis. Psychol Med 2025; 55:e145. [PMID: 40357923 PMCID: PMC12094657 DOI: 10.1017/s0033291725001217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2025] [Revised: 04/11/2025] [Accepted: 04/18/2025] [Indexed: 05/15/2025]
Abstract
BACKGROUND To understand the pathogenetic mechanisms shared among schizophrenia (SCZ), bipolar disorder (BP), and major depression (MDD), we investigated the pleiotropic mechanisms using large-scale genome-wide and brain transcriptomic data. METHODS We analyzed SCZ, BP, and MDD genome-wide association datasets available from the Psychiatric Genomics Consortium using the PLEIO framework and characterized the pleiotropic loci identified using pathway and tissue enrichment analyses. Pleiotropic and disorder-specific loci were also assessed. RESULTS Our pleiotropy-informed genome-wide analysis identified 553 variants that included 192 loci not reaching genome-wide significance in input datasets. These were enriched for five molecular pathways: cadherin signaling (p = 2.18 × 10-8), Alzheimer's disease-amyloid secretase (p = 4 × 10-4), oxytocin receptor-mediated signaling (p = 1.47 × 10-3), metabotropic glutamate receptor group III (p = 5.82 × 10-4) and Wnt signaling (p = 1.61 × 10-11). Pleiotropic loci demonstrated the strongest enrichment in the brain cortex (p = 5.8 × 10-28), frontal cortex (p = 3 × 10-31), and cerebellar hemisphere (p = 9.8 × 10-28). SCZ-BP-MDD pleiotropic variants were also enriched for neurodevelopmental brain transcriptomic profiles related to the second-trimester post-conception (week 21, p = 7.35 × 10-5; week 17, p = 6.36 × 10-4) and first year of life (p = 3.25 × 10-5). CONCLUSIONS Genetic mechanisms shared among SCZ, BP, and MDD appear to be related to early neuronal development. Because the genetic architecture of psychopathology transcends diagnostic boundaries, pleiotropy-focused analyses can lead to increased gene discovery and novel insights into relevant pathogenic mechanisms.
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Affiliation(s)
- Eleni Friligkou
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
- Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Gita A. Pathak
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
- VA CT Healthcare Center, West Haven, CT, USA
| | - Daniel S. Tylee
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
- VA CT Healthcare Center, West Haven, CT, USA
| | - Antonella De Lillo
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
- Department of Biology, University of Rome “Tor Vergata”, Rome, Italy
| | - Dora Koller
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
- VA CT Healthcare Center, West Haven, CT, USA
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | - Brenda Cabrera-Mendoza
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
- VA CT Healthcare Center, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
- VA CT Healthcare Center, West Haven, CT, USA
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Alrouh H, Pool R, Middeldorp C, Bartels M. Enduring Mental Health in Childhood and Adolescence: Prevalence, Prediction, and Genetic Architecture. J Am Acad Child Adolesc Psychiatry 2025:S0890-8567(25)00247-3. [PMID: 40378949 DOI: 10.1016/j.jaac.2025.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Revised: 03/20/2025] [Accepted: 05/07/2025] [Indexed: 05/19/2025]
Abstract
OBJECTIVE The concept of Enduring Mental Health (EMH) describes a long-term state in which an individual does not experience mental disorders. As most people encounter mental health issues at some point, this study investigates the prevalence, predictors, and genetic architecture of EMH across childhood. METHOD EMH status was based on longitudinal data from 18,884 Dutch twins assessed at ages 3, 5, 7, 10, and 12 for behavioral and emotional problems. Children were grouped into three categories: EMH, some instances of mental health problems (SIMHP), and many instances of mental health problems (MIMHP). Child and parent level factors including individual polygenic scores were tested for associations with these three categories. A twin model was used to assess the contribution of genetic and environmental factors to EMH. RESULTS Thirty-seven percent of the sample had EMH. EMH was associated with parental education (OR(low) =0.77[0.70-0.86]; OR(middle) = 0.88[0.82-0.95]), child academic achievement (OR=1.07[1.03,1.12]), and child wellbeing (OR=1.44[1.35,1.54]), and was weakly associated with some polygenic scores. The twin model estimated that 54% of the variance in EMH was due to genetic factors. CONCLUSION EMH was observed in just over a third of children. Individual differences in EMH were influenced by various sociodemographic factors, mental health-related variables, and genetic predispositions, suggesting that strategies to support EMH will likely require a comprehensive, multifaceted approach.
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Affiliation(s)
- Hekmat Alrouh
- Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Amsterdam, the Netherlands; Erasmus University Rotterdam, Rotterdam, the Netherlands.
| | - René Pool
- Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Christel Middeldorp
- Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Arkin Institute for Mental Health, Amsterdam, the Netherlands; Levvel Academic Centre for Child and Adolescent Psychiatry, Amsterdam, the Netherlands; University of Queensland, Brisbane, Australia; Children's Health Queensland Hospital and Health Service, Brisbane, Australia
| | - Meike Bartels
- Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
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29
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Saitoh Y, Motofuji S, Kamijo A, Suzuki T, Yoshizawa T, Sakamoto T, Kametani K, Terada N. Involvement of membrane palmitoylated protein 6 (MPP6) in synapses of mouse cerebrum. Histochem Cell Biol 2025; 163:50. [PMID: 40360818 PMCID: PMC12075274 DOI: 10.1007/s00418-025-02378-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/02/2025] [Indexed: 05/15/2025]
Abstract
Membrane palmitoylated protein 6 (MPP6), a membrane skeletal protein, is expressed not only in the peripheral nervous system (PNS) but also in the central nervous system (CNS). In this study, we investigated the localization of MPP6 and its associated protein complexes in the mouse cerebrum, as well as its effects on behavior using MPP6 protein-deficient (Mpp6 -/-) mice. MPP6 was detected in mouse cerebral lysates and synaptic membrane fractions, where it formed protein complexes with other MPP family members, including MPP1, MPP2, and calcium/calmodulin-dependent serine protein kinase (CASK). However, the amounts of these complexes did not differ between Mpp6 -/- and wild-type (Mpp6 +/+) mice. Immunohistochemistry revealed that MPP6 was localized at synapses throughout the cerebrum, particularly in the postsynaptic regions. Ultrastructural analysis showed that synaptic cleft distances and postsynaptic density thickness were slightly reduced in Mpp6 -/- mice compared with Mpp6 +/+ mice. In the elevated plus-maze test, a Mpp6 -/- mouse exhibited unusual behavior not observed in Mpp6 +/+ mice, although there was no statistically significant difference in the time spent in the open and closed arms between the two groups. Locomotor activity measurements revealed that MPP6 -/- mice were more active at midnight and less active from morning to noon than Mpp6 +/+ mice, implying alterations in sleep-wake regulation. These findings suggest that MPP6 plays a role in synaptic function by forming protein complexes with other MPP family members and signaling proteins.
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Affiliation(s)
- Yurika Saitoh
- Center for Medical Education, Teikyo University of Science, 2-2-1 Senjusakuragi, Adachi-Ku, Tokyo, 120-0045, Japan.
- Division of Biosciences, Teikyo University of Science Graduate School of Science & Engineering, Adachi-ku, Tokyo, Japan.
- Health Science Division, Department of Medical Sciences, Shinshu University Graduate School of Medicine, Science and Technology, 3-1-1 Asahi, Matsumoto City, Nagano, 390-8621, Japan.
| | - Sayaka Motofuji
- Division of Biosciences, Teikyo University of Science Graduate School of Science & Engineering, Adachi-ku, Tokyo, Japan
| | - Akio Kamijo
- Division of Basic & Clinical Medicine, Nagano College of Nursing, Komagane City, Nagano, Japan
- Health Science Division, Department of Medical Sciences, Shinshu University Graduate School of Medicine, Science and Technology, 3-1-1 Asahi, Matsumoto City, Nagano, 390-8621, Japan
| | - Tatsuo Suzuki
- Department of Molecular and Cellular Physiology, Shinshu University School of Medicine, Matsumoto City, Nagano, Japan
| | - Takahiro Yoshizawa
- Division of Animal Research, Research Center for Advanced Science and Technology, Shinshu University, Matsumoto City, Nagano, Japan
| | - Takeharu Sakamoto
- Department of Cancer Biology, Institute of Biomedical Science, Kansai Medical University, Hirakata City, Osaka, Japan
| | - Kiyokazu Kametani
- Health Science Division, Department of Medical Sciences, Shinshu University Graduate School of Medicine, Science and Technology, 3-1-1 Asahi, Matsumoto City, Nagano, 390-8621, Japan
| | - Nobuo Terada
- Health Science Division, Department of Medical Sciences, Shinshu University Graduate School of Medicine, Science and Technology, 3-1-1 Asahi, Matsumoto City, Nagano, 390-8621, Japan.
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30
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Owen MJ, Bray NJ, Walters JTR, O'Donovan MC. Genomics of schizophrenia, bipolar disorder and major depressive disorder. Nat Rev Genet 2025:10.1038/s41576-025-00843-0. [PMID: 40355602 DOI: 10.1038/s41576-025-00843-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/10/2025] [Indexed: 05/14/2025]
Abstract
Schizophrenia, bipolar disorder and major depressive disorder - which are the most common adult disorders requiring psychiatric care - contribute substantially to premature mortality and morbidity globally. Treatments for these disorders are suboptimal, there are no diagnostic pathologies or biomarkers and their pathophysiologies are poorly understood. Novel therapeutic and diagnostic approaches are thus badly needed. Given the high heritability of psychiatric disorders, psychiatry has potentially much to gain from the application of genomics to identify molecular risk mechanisms and to improve diagnosis. Recent large-scale, genome-wide association studies and sequencing studies, together with advances in functional genomics, have begun to illuminate the genetic architectures of schizophrenia, bipolar disorder and major depressive disorder and to identify potential biological mechanisms. Genomic findings also point to the aetiological relationships between different diagnoses and to the relationships between adult psychiatric disorders and childhood neurodevelopmental conditions.
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Affiliation(s)
- Michael J Owen
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK.
| | - Nicholas J Bray
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - James T R Walters
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Michael C O'Donovan
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
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Katzourou IK, LINC Consortium, Barroso I, Benger L, Ingason A, Stow D, Tsang R, Wood M, Kirov G, Walters J, Owen MJ, Holmans P, van den Bree MBM. Contributions of common and rare genetic variation to different measures of mood and anxiety disorder in the UK Biobank. BJPsych Open 2025; 11:e97. [PMID: 40341140 PMCID: PMC12089803 DOI: 10.1192/bjo.2025.43] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Revised: 01/08/2025] [Accepted: 02/21/2025] [Indexed: 05/10/2025] Open
Abstract
BACKGROUND Mood and anxiety disorders co-occur and share symptoms, treatments and genetic risk, but it is unclear whether combining them into a single phenotype would better capture genetic variation. The contribution of common genetic variation to these disorders has been investigated using a range of measures; however, the differences in their ability to capture variation remain unclear, while the impact of rare variation is mostly unexplored. AIMS We aimed to explore the contributions of common genetic variation and copy number variations associated with risk of psychiatric morbidity (P-CNVs) to different measures of internalising disorders. METHOD We investigated eight definitions of mood and anxiety disorder, and a combined internalising disorder, derived from self-report questionnaires, diagnostic assessments and electronic healthcare records (EHRs). Association of these definitions with polygenic risk scores (PRSs) of major depressive disorder and anxiety disorder, as well as presence of a P-CNV, was assessed. RESULTS The effect sizes of both PRSs and P-CNVs were similar for mood and anxiety disorder. Compared to mood and anxiety disorder, internalising disorder resulted in higher prediction accuracy for PRSs, and increased significance of associations with P-CNVs for most definitions. Comparison across the eight definitions showed that PRSs had higher prediction accuracy and effect sizes for stricter definitions, whereas P-CNVs were more strongly associated with EHR- and self-report-based definitions. CONCLUSIONS Future studies may benefit from using a combined internalising disorder phenotype, and may need to consider that different phenotype definitions may be more informative depending on whether common or rare variation is studied.
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Affiliation(s)
- Ioanna K. Katzourou
- Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | | | - Inês Barroso
- Medical School, University of Exeter, Exeter, UK
| | - Lauren Benger
- Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | | | - Daniel Stow
- Wolfson Institute for Population Health, Queen Mary University of London, London, UK
| | - Ruby Tsang
- Bristol Medical School, University of Bristol, Bristol, UK
| | - Megan Wood
- School of Psychology, University of Leeds, Leeds, UK
| | - George Kirov
- Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - James Walters
- Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Michael J. Owen
- Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
- Neuroscience and Mental Health Innovation Institute Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Peter Holmans
- Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Marianne B. M. van den Bree
- Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
- Neuroscience and Mental Health Innovation Institute Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
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32
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Nielsen TT, Bali P, Grove J, Mohr-Jensen C, Werge T, Dalsgaard S, Børglum AD, Sonuga-Barke E, Minnis H, Demontis D, and the Autism Spectrum Working Group of the Psychiatric Genomics Consortium. Genetic Architecture and Risk of Childhood Maltreatment Across 5 Psychiatric Diagnoses. JAMA Psychiatry 2025:2833167. [PMID: 40341348 PMCID: PMC12065082 DOI: 10.1001/jamapsychiatry.2025.0828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2024] [Accepted: 03/12/2025] [Indexed: 05/10/2025]
Abstract
Importance Childhood maltreatment (CM) is associated with psychiatric disorders. The underlying mechanisms are complex and involve genetics. Objective To investigate the polygenic architecture of CM-exposed individuals across psychiatric conditions and if genetics modulates absolute CM risk in the presence of high-impact risk factors such as parental psychiatric diagnoses. Design, Setting, and Participants The population-based case-cohort iPSYCH was used to analyze 13 polygenic scores (PGS) in CM-exposed individuals across 5 psychiatric International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnoses benchmarked against controls. Individuals were stratified into PGS quantiles, and absolute CM risk was calculated using Cox regression. Sex-specific analyses were also performed. Data were analyzed from June 2022 to December 2024. Exposures PGS of phenotypes of psychiatric disorders, CM, educational attainment, and substance use. Main Outcomes and Measures PGSs were generated using summary statistics from genome-wide association studies of phenotypes representing psychiatric disorders, CM, educational attainment, and substance use and tested for their association with CM across psychiatric disorders. Results This study included 102 856 individuals (mean [SD] age, 22.6 [7.1] years; 54 918 male [53.4%]) 8 to 35 years old. A total of 2179 CM-exposed individuals were analyzed across individuals with attention-deficit/hyperactivity disorder (ADHD; n = 22 674), autism (n = 18 941), schizophrenia (n = 6103), bipolar disorder (n = 3061), depression (n = 28 896), and controls (n = 34 689). PGSs for ADHD and educational attainment were associated with CM across all psychiatric diagnoses. The absolute CM risk was increased in the highest PGS groups, eg, for ADHD, the absolute CM risk was 5.6% in the highest ADHD-PGS quartile whereas it was only 3.3% in the lowest ADHD-PGS quartile (hazard rate ratio quantile 4 vs quantile 1 = 1.81; 95% CI, 1.47-2.22). CM risk was more than twice as high for children with parents with a psychiatric diagnosis (5.7%) than for children with parents without a psychiatric diagnosis (2.5%), but even in the presence of this risk factor, individuals could still be stratified into risk groups based on their genetics. No genetic differences between CM-exposed males and females were observed, but there were striking sex differences in absolute CM risk, which reached 5.6% for females in the highest ADHD-PGS quartile and 2.0% for males. Conclusions and Relevance Results of this case-control study suggest that individuals with high ADHD-PRS and/or low educational attainment-PRS had an associated elevated risk of CM. Extra attention should be given to individuals at high risk for CM across all 5 psychiatric diagnoses, ie, females with a high ADHD-PGS and/or a parent diagnosed with a psychiatric disorder.
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Affiliation(s)
- Trine Tollerup Nielsen
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine,, Aarhus, Denmark
| | - Paraskevi Bali
- School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Jakob Grove
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine,, Aarhus, Denmark
- Bioinformatics Research Centre, BiRC, Aarhus University, Aarhus, Denmark
| | | | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Mental Health Centre Sct Hans, Capital Region of Denmark, Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
| | - Søren Dalsgaard
- Child and Adolescent Mental Health Center, Copenhagen University Hospital – Mental Health Services CPH, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Anders D. Børglum
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine,, Aarhus, Denmark
| | - Edmund Sonuga-Barke
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
- Center for Child and Adolescent Psychiatry, Aarhus University Hospital, Aarhus, Denmark
- Department of Psychology, Hong Kong University, Hong Kong
| | - Helen Minnis
- School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Ditte Demontis
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine,, Aarhus, Denmark
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
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33
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Chen Y, Liu P, Sabo A, Guan D. Human genetic variation determines 24-hour rhythmic gene expression and disease risk. Nat Commun 2025; 16:4270. [PMID: 40341583 PMCID: PMC12062405 DOI: 10.1038/s41467-025-59524-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Accepted: 04/24/2025] [Indexed: 05/10/2025] Open
Abstract
24-hour biological rhythms are essential to maintain physiological homeostasis. Disruption of these rhythms increases the risks of multiple diseases. Biological rhythms are known to have a genetic basis formed by core clock genes, but how individual genetic variation shapes the oscillating transcriptome and contributes to human chronophysiology and disease risk is largely unknown. Here, we mapped interactions between temporal gene expression and genotype to identify quantitative trait loci (QTLs) contributing to rhythmic gene expression. These newly identified QTLs were termed as rhythmic QTLs (rhyQTLs), which determine previously unappreciated rhythmic genes in human subpopulations with specific genotypes. Functionally, rhyQTLs and their associated rhythmic genes contribute extensively to essential chronophysiological processes, including bile acid and lipid metabolism. The identification of rhyQTLs sheds light on the genetic mechanisms of gene rhythmicity, offers mechanistic insights into variations in human disease risk, and enables precision chronotherapeutic approaches for patients.
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Affiliation(s)
- Ying Chen
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Panpan Liu
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Aniko Sabo
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Dongyin Guan
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
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34
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Gui A, Hollowell A, Wigdor EM, Morgan MJ, Hannigan LJ, Corfield EC, Odintsova V, Hottenga JJ, Wong A, Pool R, Cullen H, Wilson S, Warrier V, Eilertsen EM, Andreassen OA, Middeldorp CM, St Pourcain B, Bartels M, Boomsma DI, Hartman CA, Robinson EB, Arichi T, Edwards AD, Johnson MH, Dudbridge F, Sanders SJ, Havdahl A, Ronald A. Genome-wide association meta-analysis of age at onset of walking in over 70,000 infants of European ancestry. Nat Hum Behav 2025:10.1038/s41562-025-02145-1. [PMID: 40335706 DOI: 10.1038/s41562-025-02145-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2024] [Accepted: 02/21/2025] [Indexed: 05/09/2025]
Abstract
Age at onset of walking is an important early childhood milestone which is used clinically and in public health screening. In this genome-wide association study meta-analysis of age at onset of walking (N = 70,560 European-ancestry infants), we identified 11 independent genome-wide significant loci. SNP-based heritability was 24.13% (95% confidence intervals = 21.86-26.40) with ~11,900 variants accounting for about 90% of it, suggesting high polygenicity. One of these loci, in gene RBL2, co-localized with an expression quantitative trait locus (eQTL) in the brain. Age at onset of walking (in months) was negatively genetically correlated with ADHD and body-mass index, and positively genetically correlated with brain gyrification in both infant and adult brains. The polygenic score showed out-of-sample prediction of 3-5.6%, confirmed as largely due to direct effects in sib-pair analyses, and was separately associated with volume of neonatal brain structures involved in motor control. This study offers biological insights into a key behavioural marker of neurodevelopment.
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Affiliation(s)
- Anna Gui
- Department of Psychology, University of Essex, Wivenhoe Park, Colchester, UK
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck University of London, London, UK
| | - Anja Hollowell
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck University of London, London, UK
| | - Emilie M Wigdor
- Institute of Developmental and Regenerative Medicine, Department of Paediatrics, University of Oxford, Oxford, UK
| | - Morgan J Morgan
- School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, UK
| | - Laurie J Hannigan
- Research Department, Lovisenberg Diaconal Hospital, Oslo, Norway
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Elizabeth C Corfield
- Research Department, Lovisenberg Diaconal Hospital, Oslo, Norway
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Veronika Odintsova
- Department of Biological Psychology, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Psychiatry, University Medical Center of Groningen, University of Groningen, Groningen, the Netherlands
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - René Pool
- Department of Biological Psychology, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Harriet Cullen
- Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King's College London, London, UK
| | - Siân Wilson
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
- Division of Newborn Medicine, Harvard Medical School, Boston, MA, USA
| | - Varun Warrier
- Department of Psychiatry and Psychology, University of Cambridge, Cambridge, UK
| | | | - Ole A Andreassen
- Centre for Precision Psychiatry, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Christel M Middeldorp
- Department of Child and Youth Psychiatry and Psychology, Amsterdam Reproduction and Development Research Institute, Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands
- Arkin Mental Health Care, Amsterdam, the Netherlands
- Levvel, Academic Center for Child and Adolescent Psychiatry, Amsterdam, the Netherlands
- Child Health Research Centre, University of Queensland, Brisbane, Australia
- Child and Youth Mental Health Service, Children's Health Queensland Hospital and Health Service, Brisbane, Australia
| | - Beate St Pourcain
- Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Meike Bartels
- Department of Biological Psychology, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit, Amsterdam, the Netherlands
| | - Catharina A Hartman
- University Medical Center Psychopathology and Emotion Regulation (ICPE), Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | | | - Tomoki Arichi
- Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Anthony D Edwards
- Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Mark H Johnson
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck University of London, London, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Frank Dudbridge
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Stephan J Sanders
- Institute of Developmental and Regenerative Medicine, Department of Paediatrics, University of Oxford, Oxford, UK
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Alexandra Havdahl
- Research Department, Lovisenberg Diaconal Hospital, Oslo, Norway
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA Research Center, University of Oslo, Oslo, Norway
| | - Angelica Ronald
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck University of London, London, UK.
- School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, UK.
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Khan Y, Davis CN, Jinwala Z, Feuer KL, Toikumo S, Hartwell EE, Sanchez-Roige S, Peterson RE, Hatoum AS, Kranzler HR, Kember RL. Transdiagnostic and Disorder-Level GWAS Enhance Precision of Substance Use and Psychiatric Genetic Risk Profiles in African and European Ancestries. Biol Psychiatry 2025:S0006-3223(25)01180-1. [PMID: 40345609 DOI: 10.1016/j.biopsych.2025.04.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Revised: 02/20/2025] [Accepted: 04/21/2025] [Indexed: 05/11/2025]
Abstract
BACKGROUND Substance use disorders (SUDs) and psychiatric disorders frequently co-occur, and their etiology likely reflects both transdiagnostic (i.e., common/shared) and disorder-level (i.e., independent/nonshared) genetic influences. Understanding the genetic influences that are shared and those that operate independently of the shared risk could enhance precision in diagnosis, prevention, and treatment, but this remains underexplored, particularly in non-European ancestry groups. METHODS We applied genomic structural equation modeling to examine the common and independent genetic architecture among SUDs and psychotic, mood, and anxiety disorders using summary statistics from genome-wide association studies (GWAS) conducted in European- (EUR) and African-ancestry (AFR) individuals. To characterize the biological and phenotypic associations, we used FUMA, conducted genetic correlations, and performed phenome-wide association studies (PheWAS). RESULTS In EUR individuals, transdiagnostic genetic factors represented SUDs, psychotic, and mood/anxiety disorders, with GWAS identifying two novel lead single-nucleotide polymorphisms (SNPs) for the mood factor. In AFR individuals, genetic factors represented SUDs and psychiatric disorders, and GWAS identified one novel lead SNP for the SUD factor. In EUR individuals, second-order factor models showed phenotypic and genotypic associations with a broad range of physical and mental health traits. Finally, genetic correlations and PheWAS highlighted how common and independent genetic factors for SUD and psychotic disorders were differentially associated with psychiatric, sociodemographic, and medical phenotypes. CONCLUSIONS Combining transdiagnostic and disorder-level genetic approaches can improve our understanding of co-occurring conditions and increase the specificity of genetic discovery, which is critical for identifying more effective prevention and treatment strategies to reduce the burden of these disorders.
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Affiliation(s)
- Yousef Khan
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Christal N Davis
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104; Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Zeal Jinwala
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Kyra L Feuer
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Sylvanus Toikumo
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104; Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Emily E Hartwell
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104; Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, United States; Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37235, United States; Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Roseann E Peterson
- Institute for Department of Psychiatry and Behavioral Sciences, Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, United States
| | - Alexander S Hatoum
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, United States
| | - Henry R Kranzler
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104; Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Rachel L Kember
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104; Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104.
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36
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Ahn Y, Kim J, Jung K, Lee DJ, Jung JY, Eom Y, Park S, Kim J, Kim H, Jo H, Hong S, O'Connell KS, Andreassen OA, Myung W, Won HH. Relationship Between Problematic Alcohol Use and Various Psychiatric Disorders: A Genetically Informed Study. Am J Psychiatry 2025:appiajp20240095. [PMID: 40329641 DOI: 10.1176/appi.ajp.20240095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/08/2025]
Abstract
OBJECTIVE Problematic alcohol use (PAU) adversely affects the clinical course of psychiatric disorders. Genetic studies have suggested that genetic factors underlie the co-occurrence of PAU with psychiatric disorders. This study aimed to elucidate shared genetic architectures, prioritizing genes that disorders may have in common. METHODS Using genome-wide association data of PAU including 435,563 samples from people of European ancestry, this study investigated the genetic relationship between PAU and 11 psychiatric disorders using a bivariate causal mixture model (MiXeR). Local genetic correlation and colocalization analyses were conducted to identify the genomic regions significantly associated with PAU and each psychiatric disorder. Postanalysis included the false discovery rate (FDR) and transcriptome-wide association studies (TWASs), as well as summary-data-based Mendelian randomization to prioritize shared genes by integrating brain transcriptome data. RESULTS MiXeR analysis revealed a substantial polygenic overlap (39%-73%) between PAU and psychiatric disorders. Four bivariate genomic regions with high correlations suggest shared causal variants of PAU with major depression and schizophrenia. Within these regions, four and six genes for the PAU-major depression and PAU-schizophrenia pairs, respectively, were mapped by conjunctional FDR analysis. Furthermore, TTC12 and ANKK1 were identified as potential causal genes for PAU and these disorders. The findings were replicated in multi-ancestry analyses of colocalization and TWASs. CONCLUSIONS Despite the varying degrees of genetic overlap and directions of shared genetic effect correlations, these results imply the presence of shared genetic factors influencing the comorbidity of PAU and psychiatric disorders. Additionally, TTC12 and ANKK1, located near DRD2, may be causally associated with comorbid conditions.
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Affiliation(s)
- Yeeun Ahn
- Department of Digital Health (Ahn, K. Jung, J.Y. Jung, Park, Jaeyoung Kim, H. Kim, Jo, Hong, Won) and Department of Health Sciences and Technology (Lee), Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea (Ahn, K. Jung, Park, Jaeyoung Kim, Myung); Department of Clinical Medical Sciences (Jaehyun Kim) and Department of Psychiatry (Eom, Myung), Seoul National University College of Medicine, Seoul, South Korea; Department of Medicine, Central Force for National Defense, Republic of Korea Army Personnel Command, Yongin, South Korea (Jaehyun Kim); Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea (Jaehyun Kim); Department of Psychiatry (J.Y. Jung) and Samsung Genome Institute (Won), Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, South Korea; Norwegian Center for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo (O'Connell, Andreassen); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (O'Connell, Andreassen)
| | - Jaehyun Kim
- Department of Digital Health (Ahn, K. Jung, J.Y. Jung, Park, Jaeyoung Kim, H. Kim, Jo, Hong, Won) and Department of Health Sciences and Technology (Lee), Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea (Ahn, K. Jung, Park, Jaeyoung Kim, Myung); Department of Clinical Medical Sciences (Jaehyun Kim) and Department of Psychiatry (Eom, Myung), Seoul National University College of Medicine, Seoul, South Korea; Department of Medicine, Central Force for National Defense, Republic of Korea Army Personnel Command, Yongin, South Korea (Jaehyun Kim); Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea (Jaehyun Kim); Department of Psychiatry (J.Y. Jung) and Samsung Genome Institute (Won), Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, South Korea; Norwegian Center for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo (O'Connell, Andreassen); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (O'Connell, Andreassen)
| | - Kyeongmin Jung
- Department of Digital Health (Ahn, K. Jung, J.Y. Jung, Park, Jaeyoung Kim, H. Kim, Jo, Hong, Won) and Department of Health Sciences and Technology (Lee), Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea (Ahn, K. Jung, Park, Jaeyoung Kim, Myung); Department of Clinical Medical Sciences (Jaehyun Kim) and Department of Psychiatry (Eom, Myung), Seoul National University College of Medicine, Seoul, South Korea; Department of Medicine, Central Force for National Defense, Republic of Korea Army Personnel Command, Yongin, South Korea (Jaehyun Kim); Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea (Jaehyun Kim); Department of Psychiatry (J.Y. Jung) and Samsung Genome Institute (Won), Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, South Korea; Norwegian Center for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo (O'Connell, Andreassen); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (O'Connell, Andreassen)
| | - Dong June Lee
- Department of Digital Health (Ahn, K. Jung, J.Y. Jung, Park, Jaeyoung Kim, H. Kim, Jo, Hong, Won) and Department of Health Sciences and Technology (Lee), Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea (Ahn, K. Jung, Park, Jaeyoung Kim, Myung); Department of Clinical Medical Sciences (Jaehyun Kim) and Department of Psychiatry (Eom, Myung), Seoul National University College of Medicine, Seoul, South Korea; Department of Medicine, Central Force for National Defense, Republic of Korea Army Personnel Command, Yongin, South Korea (Jaehyun Kim); Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea (Jaehyun Kim); Department of Psychiatry (J.Y. Jung) and Samsung Genome Institute (Won), Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, South Korea; Norwegian Center for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo (O'Connell, Andreassen); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (O'Connell, Andreassen)
| | - Jin Young Jung
- Department of Digital Health (Ahn, K. Jung, J.Y. Jung, Park, Jaeyoung Kim, H. Kim, Jo, Hong, Won) and Department of Health Sciences and Technology (Lee), Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea (Ahn, K. Jung, Park, Jaeyoung Kim, Myung); Department of Clinical Medical Sciences (Jaehyun Kim) and Department of Psychiatry (Eom, Myung), Seoul National University College of Medicine, Seoul, South Korea; Department of Medicine, Central Force for National Defense, Republic of Korea Army Personnel Command, Yongin, South Korea (Jaehyun Kim); Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea (Jaehyun Kim); Department of Psychiatry (J.Y. Jung) and Samsung Genome Institute (Won), Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, South Korea; Norwegian Center for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo (O'Connell, Andreassen); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (O'Connell, Andreassen)
| | - Yewon Eom
- Department of Digital Health (Ahn, K. Jung, J.Y. Jung, Park, Jaeyoung Kim, H. Kim, Jo, Hong, Won) and Department of Health Sciences and Technology (Lee), Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea (Ahn, K. Jung, Park, Jaeyoung Kim, Myung); Department of Clinical Medical Sciences (Jaehyun Kim) and Department of Psychiatry (Eom, Myung), Seoul National University College of Medicine, Seoul, South Korea; Department of Medicine, Central Force for National Defense, Republic of Korea Army Personnel Command, Yongin, South Korea (Jaehyun Kim); Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea (Jaehyun Kim); Department of Psychiatry (J.Y. Jung) and Samsung Genome Institute (Won), Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, South Korea; Norwegian Center for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo (O'Connell, Andreassen); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (O'Connell, Andreassen)
| | - Sanghyeon Park
- Department of Digital Health (Ahn, K. Jung, J.Y. Jung, Park, Jaeyoung Kim, H. Kim, Jo, Hong, Won) and Department of Health Sciences and Technology (Lee), Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea (Ahn, K. Jung, Park, Jaeyoung Kim, Myung); Department of Clinical Medical Sciences (Jaehyun Kim) and Department of Psychiatry (Eom, Myung), Seoul National University College of Medicine, Seoul, South Korea; Department of Medicine, Central Force for National Defense, Republic of Korea Army Personnel Command, Yongin, South Korea (Jaehyun Kim); Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea (Jaehyun Kim); Department of Psychiatry (J.Y. Jung) and Samsung Genome Institute (Won), Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, South Korea; Norwegian Center for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo (O'Connell, Andreassen); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (O'Connell, Andreassen)
| | - Jaeyoung Kim
- Department of Digital Health (Ahn, K. Jung, J.Y. Jung, Park, Jaeyoung Kim, H. Kim, Jo, Hong, Won) and Department of Health Sciences and Technology (Lee), Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea (Ahn, K. Jung, Park, Jaeyoung Kim, Myung); Department of Clinical Medical Sciences (Jaehyun Kim) and Department of Psychiatry (Eom, Myung), Seoul National University College of Medicine, Seoul, South Korea; Department of Medicine, Central Force for National Defense, Republic of Korea Army Personnel Command, Yongin, South Korea (Jaehyun Kim); Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea (Jaehyun Kim); Department of Psychiatry (J.Y. Jung) and Samsung Genome Institute (Won), Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, South Korea; Norwegian Center for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo (O'Connell, Andreassen); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (O'Connell, Andreassen)
| | - Hyejin Kim
- Department of Digital Health (Ahn, K. Jung, J.Y. Jung, Park, Jaeyoung Kim, H. Kim, Jo, Hong, Won) and Department of Health Sciences and Technology (Lee), Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea (Ahn, K. Jung, Park, Jaeyoung Kim, Myung); Department of Clinical Medical Sciences (Jaehyun Kim) and Department of Psychiatry (Eom, Myung), Seoul National University College of Medicine, Seoul, South Korea; Department of Medicine, Central Force for National Defense, Republic of Korea Army Personnel Command, Yongin, South Korea (Jaehyun Kim); Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea (Jaehyun Kim); Department of Psychiatry (J.Y. Jung) and Samsung Genome Institute (Won), Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, South Korea; Norwegian Center for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo (O'Connell, Andreassen); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (O'Connell, Andreassen)
| | - Hyeonbin Jo
- Department of Digital Health (Ahn, K. Jung, J.Y. Jung, Park, Jaeyoung Kim, H. Kim, Jo, Hong, Won) and Department of Health Sciences and Technology (Lee), Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea (Ahn, K. Jung, Park, Jaeyoung Kim, Myung); Department of Clinical Medical Sciences (Jaehyun Kim) and Department of Psychiatry (Eom, Myung), Seoul National University College of Medicine, Seoul, South Korea; Department of Medicine, Central Force for National Defense, Republic of Korea Army Personnel Command, Yongin, South Korea (Jaehyun Kim); Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea (Jaehyun Kim); Department of Psychiatry (J.Y. Jung) and Samsung Genome Institute (Won), Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, South Korea; Norwegian Center for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo (O'Connell, Andreassen); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (O'Connell, Andreassen)
| | - Sanghoon Hong
- Department of Digital Health (Ahn, K. Jung, J.Y. Jung, Park, Jaeyoung Kim, H. Kim, Jo, Hong, Won) and Department of Health Sciences and Technology (Lee), Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea (Ahn, K. Jung, Park, Jaeyoung Kim, Myung); Department of Clinical Medical Sciences (Jaehyun Kim) and Department of Psychiatry (Eom, Myung), Seoul National University College of Medicine, Seoul, South Korea; Department of Medicine, Central Force for National Defense, Republic of Korea Army Personnel Command, Yongin, South Korea (Jaehyun Kim); Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea (Jaehyun Kim); Department of Psychiatry (J.Y. Jung) and Samsung Genome Institute (Won), Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, South Korea; Norwegian Center for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo (O'Connell, Andreassen); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (O'Connell, Andreassen)
| | - Kevin S O'Connell
- Department of Digital Health (Ahn, K. Jung, J.Y. Jung, Park, Jaeyoung Kim, H. Kim, Jo, Hong, Won) and Department of Health Sciences and Technology (Lee), Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea (Ahn, K. Jung, Park, Jaeyoung Kim, Myung); Department of Clinical Medical Sciences (Jaehyun Kim) and Department of Psychiatry (Eom, Myung), Seoul National University College of Medicine, Seoul, South Korea; Department of Medicine, Central Force for National Defense, Republic of Korea Army Personnel Command, Yongin, South Korea (Jaehyun Kim); Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea (Jaehyun Kim); Department of Psychiatry (J.Y. Jung) and Samsung Genome Institute (Won), Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, South Korea; Norwegian Center for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo (O'Connell, Andreassen); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (O'Connell, Andreassen)
| | - Ole A Andreassen
- Department of Digital Health (Ahn, K. Jung, J.Y. Jung, Park, Jaeyoung Kim, H. Kim, Jo, Hong, Won) and Department of Health Sciences and Technology (Lee), Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea (Ahn, K. Jung, Park, Jaeyoung Kim, Myung); Department of Clinical Medical Sciences (Jaehyun Kim) and Department of Psychiatry (Eom, Myung), Seoul National University College of Medicine, Seoul, South Korea; Department of Medicine, Central Force for National Defense, Republic of Korea Army Personnel Command, Yongin, South Korea (Jaehyun Kim); Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea (Jaehyun Kim); Department of Psychiatry (J.Y. Jung) and Samsung Genome Institute (Won), Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, South Korea; Norwegian Center for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo (O'Connell, Andreassen); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (O'Connell, Andreassen)
| | - Woojae Myung
- Department of Digital Health (Ahn, K. Jung, J.Y. Jung, Park, Jaeyoung Kim, H. Kim, Jo, Hong, Won) and Department of Health Sciences and Technology (Lee), Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea (Ahn, K. Jung, Park, Jaeyoung Kim, Myung); Department of Clinical Medical Sciences (Jaehyun Kim) and Department of Psychiatry (Eom, Myung), Seoul National University College of Medicine, Seoul, South Korea; Department of Medicine, Central Force for National Defense, Republic of Korea Army Personnel Command, Yongin, South Korea (Jaehyun Kim); Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea (Jaehyun Kim); Department of Psychiatry (J.Y. Jung) and Samsung Genome Institute (Won), Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, South Korea; Norwegian Center for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo (O'Connell, Andreassen); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (O'Connell, Andreassen)
| | - Hong-Hee Won
- Department of Digital Health (Ahn, K. Jung, J.Y. Jung, Park, Jaeyoung Kim, H. Kim, Jo, Hong, Won) and Department of Health Sciences and Technology (Lee), Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea; Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea (Ahn, K. Jung, Park, Jaeyoung Kim, Myung); Department of Clinical Medical Sciences (Jaehyun Kim) and Department of Psychiatry (Eom, Myung), Seoul National University College of Medicine, Seoul, South Korea; Department of Medicine, Central Force for National Defense, Republic of Korea Army Personnel Command, Yongin, South Korea (Jaehyun Kim); Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea (Jaehyun Kim); Department of Psychiatry (J.Y. Jung) and Samsung Genome Institute (Won), Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, South Korea; Norwegian Center for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo (O'Connell, Andreassen); Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway (O'Connell, Andreassen)
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Pintacuda G, Hsu YHH, Páleníková P, Dubonyte U, Fornelos N, Chen M, Mena D, Biagini JC, Botts T, Martorana M, Rebelo D, Ching JKT, Crouse E, Gebre H, Adiconis X, Haywood N, Simmons S, Weïwer M, Hawes D, Pietilainen O, Werge T, Li KW, Smit AB, Kirkeby A, Levin JZ, Nehme R, Lage K. A foundational neuronal protein network model unifying multimodal genetic, transcriptional, and proteomic perturbations in schizophrenia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2025:2025.05.02.25326757. [PMID: 40385394 PMCID: PMC12083573 DOI: 10.1101/2025.05.02.25326757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2025]
Abstract
Schizophrenia (SCZ) is a complex psychiatric disorder with a diverse genetic landscape, involving common regulatory variants, rare protein-coding mutations, structural genomic rearrangements, and transcriptional dysregulation. A critical challenge in developing rationally designed therapeutics is understanding how these various factors converge to disrupt cellular networks in the human brain, ultimately contributing to SCZ. Towards this aim, we generated multimodal data, including SCZ-specific protein-protein interactions in stem-cell-derived neuronal models and adult postmortem cortex, integrated with genetic and transcriptomic datasets from individuals with psychiatric disorders. We identified three distinct neuron-specific SCZ protein networks, or modules, significantly enriched for genetic and transcriptional perturbations associated with SCZ. The relevance of these modules was validated through whole-cell proteomics in patient-derived neurons, revealing their disruption in 22q11.2 deletion carriers diagnosed with SCZ. We demonstrated their therapeutic potential by showing that these modules are targets of GSK3 inhibition using phosphoproteomics. Our findings present a foundational model that integrates genetic, transcriptional, and proteomic perturbations in SCZ. This model provides a cohesive framework for understanding how polygenic and multimodal perturbations affect neuronal pathways in the human brain, as well as a data-driven pathway resource for identifying potential drug targets to reverse disruptions observed in these neuronal networks.
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Coon H, Shabalin AA, Monson ET, DiBlasi E, Han S, Baird LM, Kaufman EA, Tharp D, Staley MJ, Yu Z, Li QS, Colbert SM, Bakian AV, Docherty AR, McIntosh AM, Whalley HC, Amaro D, Crockett DK, Mullins N, Keeshin BR. Different genetic liabilities to neuropsychiatric conditions in suicides with no prior suicidality. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2025:2025.05.02.25326877. [PMID: 40385453 PMCID: PMC12083568 DOI: 10.1101/2025.05.02.25326877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2025]
Abstract
Importance Though suicide attempt is the most robust predictor of suicide death, few who attempt go on to die by suicide (<10%), and ∼50% of all suicide deaths occur in the absence of evidence of prior attempts. Risks in this latter group are particularly poorly understood. Objective Data from the Utah Suicide Mortality Risk Study (USMRS) were used to study underlying polygenic liabilities among suicide deaths without evidence of prior nonfatal suicidal thoughts or behaviors (SD-N) compared to suicide deaths with prior nonfatal suicidality (SD-S). Design We used an analysis of covariance design, comparing SD-N to SD-S and to population controls with similar genetic ancestry from the United Kingdom. Setting We selected 12 source studies to generate descriptive quantitative polygenic scores (PGS) reflecting neuropsychiatric conditions. Analysis of covariance was used to evaluate suicide mortality subsets and controls adjusted for sex, age, and genetic ancestry effects. Participants Suicide deaths were population-ascertained through a 25-year collaboration with the Utah State Office of the Medical Examiner. Evidence of suicidality was determined from diagnoses and clinical notes, yielding 1,364 SD-N and 1,467 SD-S deaths, compared to 20,368 controls. Main Outcomes The tested PGS spanned 12 psychiatric, neurodevelopmental, and neurodegenerative conditions. Results SD-N were significantly more male (82.33% vs. 67.76%) and older at death (47.26 years vs. 41.36 years) than SD-S. Controls were significantly less male than both suicide subsets (43.71%). Genetic ancestry was similar across suicide subsets and controls (% European: 96.77%, 96.81%, and 97.38%). Comparing SD-N to SD-S revealed significantly lower PGS in SD-N for: MDD (p=0.0015), neuroticism (p=0.0016), anxiety (p=0.0048), Alzheimer's (p=0.011), depressed affect (p=0.015), schizophrenia (p=0.020), PTSD (p=0.023), and bipolar disorder (p=0.028). This attenuation in SD-N was particularly pronounced for depressed affect, neuroticism, and Alzheimer's, where PGS were not different from controls. Sex-specific analyses suggested attenuation of PGS in SD-N was driven by males for MDD, anxiety, and PTSD, and by females for bipolar disorder, neuroticism, and Alzheimer's. Conclusions and Relevance SD-N have significantly different genetic liabilities from SD-S, particularly regarding neuropsychiatric conditions. Results have far-reaching implications both for future research and for preventions for those at highest risk of mortality. KEY POINTS Question What are underlying genetic liabilities related to neuropsychiatric conditions in the roughly half of suicide deaths with no evidence of prior nonfatal suicidal thoughts or behaviors (SD-N), a group that has not previously been accessible for study? Findings These suicide deaths with no prior nonfatal suicidality showed significantly attenuated underlying polygenic liabilities associated with mental health traditionally thought to be core features of suicide mortality risk, and justifies additional studies of underlying risks associated with non-psychiatric conditions and behaviors. Meaning These differences in underlying liabilities between suicide deaths with and without prior suicidality suggest departure from the traditional mental health risks that have been the focus of suicide risk discovery, and impel new directions for future research and prevention efforts.
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Zhang R, Luo J, Wang T, Wang W, Sun J, Zhang D. Identifying novel protein biomarkers with cross-psychiatric disorders effects and potential intervention targets: Evidence from proteomic-Mendelian randomization. Prog Neuropsychopharmacol Biol Psychiatry 2025; 139:111396. [PMID: 40334965 DOI: 10.1016/j.pnpbp.2025.111396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2024] [Revised: 05/02/2025] [Accepted: 05/03/2025] [Indexed: 05/09/2025]
Abstract
Plasma proteins are the potential therapeutic targets for psychiatric disorders due to their important roles in signal transduction. We aimed to explore the plasma protein biomarkers with cross-psychiatric disorders effects. Proteome-wide Mendelian randomization (MR) and colocalization analyses were performed to investigate the potential causal relationship between plasma protein biomarkers and 12 psychiatric disorders and further identify the potential proteins with cross-effects. To assess the directionality and exclude potential reverse causation, Steiger directionality tests and reverse MR analyses were additionally conducted. Then, validation analysis was performed by employing summary data from cross-psychiatric disorder GWAS to validate the cross-psychiatric effects of proteins. Protein-protein interactions were conducted to evaluate the interaction between candidate proteins and druggability assessment was used to prioritize potential drug targets for psychiatric disorders. We identified novel plasma proteins that possessed cross-psychiatric disorder effects, especially BTN2A1 and BTN3A2 associated with major depressive disorder (MDD), schizophrenia (SCZ), and bipolar disorder (BIP); ITIH1, ITIH3, ITIH4 and FES associated with SCZ and BIP, and the cross-effects of these proteins on SCZ and BIP were confirmed by validation analyses. Steiger tests and reverse MR supported causal directionality. Besides, the protein-protein interactions (PPI) analysis indicated cross-effects proteins had significant interaction, especially ITIH1-ITIH3. The druggability assessment prioritized eight proteins, two of which (ITIH3 and NCAM1) has been targeted by antipsychotic drugs. Our findings provided insights into shared biological mechanisms underlying these conditions.
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Affiliation(s)
- Ronghui Zhang
- Department of Epidemiology and Health Statistics, The School of Public Health of Qingdao University, Qingdao, Shandong Province, China
| | - Jia Luo
- Department of Epidemiology and Health Statistics, The School of Public Health of Qingdao University, Qingdao, Shandong Province, China
| | - Tong Wang
- Department of Epidemiology and Health Statistics, The School of Public Health of Qingdao University, Qingdao, Shandong Province, China
| | - Weijing Wang
- Department of Epidemiology and Health Statistics, The School of Public Health of Qingdao University, Qingdao, Shandong Province, China
| | - Jing Sun
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Dongfeng Zhang
- Department of Epidemiology and Health Statistics, The School of Public Health of Qingdao University, Qingdao, Shandong Province, China.
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Bhattacharyya U, John J, Lam M, Fisher J, Sun B, Baird D, Burgess S, Chen CY, Lencz T. Circulating Blood-Based Proteins in Psychopathology and Cognition: A Mendelian Randomization Study. JAMA Psychiatry 2025; 82:481-491. [PMID: 40072421 PMCID: PMC11904806 DOI: 10.1001/jamapsychiatry.2025.0033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2024] [Accepted: 12/11/2024] [Indexed: 03/15/2025]
Abstract
Importance Peripheral (blood-based) biomarkers for psychiatric illness could benefit diagnosis and treatment, but research to date has typically been low throughput, and traditional case-control studies are subject to potential confounds of treatment and other exposures. Large-scale 2-sample mendelian randomization (MR) can examine the potentially causal impact of circulating proteins on neuropsychiatric phenotypes without these confounds. Objective To identify circulating proteins associated with risk for schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) as well as cognitive task performance (CTP). Design, Setting, and Participants In a 2-sample MR design, significant proteomic quantitative trait loci were used as candidate instruments, obtained from 2 large-scale plasma proteomics datasets: the UK Biobank Pharma Proteomics Project (2923 proteins per 34 557 UK individuals) and deCODE Genetics (4719 proteins per 35 559 Icelandic individuals). Data analysis was performed from November 2023 to November 2024. Exposure Genetic influence on circulating levels of proteins in plasma. Main Outcomes and Measures Outcome measures were summary statistics drawn from recent large-scale genome-wide association studies for SCZ (67 323 cases and 93 456 controls), BD (40 463 cases and 313 436 controls), MDD (166 773 cases and 507 679 controls), and CTP (215 333 individuals). MR was carried out for each phenotype, and proteins that showed statistically significant (Bonferroni-corrected P < .05) associations from MR analysis were used for pathway, protein-protein interaction, drug target enrichment, and potential druggability analysis for each outcome phenotype separately. Results MR analysis revealed 113 Bonferroni-corrected associations (46 novel) involving 91 proteins across the 4 outcome phenotypes. Immune-related proteins, such as interleukins and complement factors, showed pleiotropic effects across multiple outcome phenotypes. Drug target enrichment analysis provided support for repurposing of anti-inflammatory agents for SCZ, amantadine for BD, retinoic acid for MDD, and duloxetine for CTP. Conclusions and Relevance Identifying potentially causal effects of circulating proteins on neuropsychiatric phenotypes suggests potential biomarkers and offers insights for the development of innovative therapeutic strategies. The study also reveals pleiotropic effects of many proteins across different phenotypes, indicating shared etiology among serious psychiatric conditions and cognition.
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Affiliation(s)
- Upasana Bhattacharyya
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, New York
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York
| | - Jibin John
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, New York
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York
| | - Max Lam
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, New York
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York
- Institute of Mental Health, Hougang, Singapore
- Lee Kong Chian School of Medicine, Population and Global Health, Nanyang Technological University, Singapore, Singapore
| | - Jonah Fisher
- Biogen Inc, Cambridge, Massachusetts
- Harvard T.H. Chan School of Public Health, Cambridge, Massachusetts
| | - Benjamin Sun
- Biogen Inc, Cambridge, Massachusetts
- now with Bristol Myers Squibb, Princeton, New Jersey
| | | | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | | | - Todd Lencz
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, New York
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
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Deng Y, Hao Z, Chen W, Zhang J, Zou Y, Zhang J, Xi Y, Xu J. Causal relationship between graves' disease and mental disorders: A bidirectional Mendelian randomization study. J Psychosom Res 2025; 192:112124. [PMID: 40209607 DOI: 10.1016/j.jpsychores.2025.112124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 03/24/2025] [Accepted: 03/31/2025] [Indexed: 04/12/2025]
Abstract
OBJECTIVE Many patients with Graves' disease (GD) also suffer from mental disorders in clinical practice, but their causal relationship remains unclear. This study aims to investigate the causal relationship between GD and common mental disorders using a bidirectional Mendelian randomization (MR)approach. METHODS We derived genome-wide association study (GWAS) data for common mental disorders, including major depressive disorder (MDD), anxiety disorders, bipolar disorder, and attention-deficit/hyperactivity disorder (ADHD), from the Psychiatric Genomics Consortium consortium. GWAS data for GD were obtained from the FinnGen consortium. Subsequently, a bidirectional MR analysis was conducted, with the inverse-variance weighted (IVW) methods as the primary MR analysis method. Sensitivity analysis used Cochran's Q test, MR-Egger intercept test, and leave-one-out method. RESULTS IVW results in MR demonstrated a positive association between genetic susceptibility to GD and bipolar disorder (OR = 1.073, 95 % CI: 1.042-1.105, p = 2.882 × 10-6). Similar causal estimates were obtained through MR-Egger regression and the weighted median method. Additionally, both Cochran's Q test and MR-Egger intercept test indicated no evidence of heterogeneity or pleiotropy. However, no causal associations were demonstrated between GD and MDD, anxiety disorders, or ADHD. Furthermore, a causal relationship between genetic susceptibility to common mental disorders and GD was not evidenced. CONCLUSIONS This bidirectional MR study supports the role of GD in the causal association with an increased risk of bipolar disorder, which guides us to pay attention to the mental diseases of GD patients in the clinic.
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Affiliation(s)
- Yuanyuan Deng
- Department of Endocrinology and Metabolism, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang,Jiangxi 330006, PR China; Jiangxi Clinical Research Center for Endocrine and Metabolic Disease, Nanchang 330006, PR China; Jiangxi Branch of National Clinical Research Center for Metabolic Disease, Nanchang 330006, PR China
| | - Zejin Hao
- Department of Endocrinology and Metabolism, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang,Jiangxi 330006, PR China; Jiangxi Clinical Research Center for Endocrine and Metabolic Disease, Nanchang 330006, PR China; Jiangxi Branch of National Clinical Research Center for Metabolic Disease, Nanchang 330006, PR China
| | - Wen Chen
- Department of Endocrinology and Metabolism, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang,Jiangxi 330006, PR China; Jiangxi Clinical Research Center for Endocrine and Metabolic Disease, Nanchang 330006, PR China; Jiangxi Branch of National Clinical Research Center for Metabolic Disease, Nanchang 330006, PR China
| | - Junping Zhang
- Department of Endocrinology and Metabolism, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang,Jiangxi 330006, PR China; Jiangxi Clinical Research Center for Endocrine and Metabolic Disease, Nanchang 330006, PR China; Jiangxi Branch of National Clinical Research Center for Metabolic Disease, Nanchang 330006, PR China
| | - Yun Zou
- Department of Endocrinology and Metabolism, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang,Jiangxi 330006, PR China; Jiangxi Clinical Research Center for Endocrine and Metabolic Disease, Nanchang 330006, PR China; Jiangxi Branch of National Clinical Research Center for Metabolic Disease, Nanchang 330006, PR China
| | - Jie Zhang
- Department of Endocrinology and Metabolism, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang,Jiangxi 330006, PR China; Jiangxi Clinical Research Center for Endocrine and Metabolic Disease, Nanchang 330006, PR China; Jiangxi Branch of National Clinical Research Center for Metabolic Disease, Nanchang 330006, PR China
| | - Yanhua Xi
- Department of Endocrinology and Metabolism, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang,Jiangxi 330006, PR China; Jiangxi Clinical Research Center for Endocrine and Metabolic Disease, Nanchang 330006, PR China; Jiangxi Branch of National Clinical Research Center for Metabolic Disease, Nanchang 330006, PR China
| | - Jixiong Xu
- Department of Endocrinology and Metabolism, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang,Jiangxi 330006, PR China; Jiangxi Clinical Research Center for Endocrine and Metabolic Disease, Nanchang 330006, PR China; Jiangxi Branch of National Clinical Research Center for Metabolic Disease, Nanchang 330006, PR China.
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Chan II. Blunted cortisol as a biomarker of depression based on the attenuation hypothesis: A Mendelian randomization analysis using depression as exposure. J Affect Disord 2025; 376:398-409. [PMID: 39961449 DOI: 10.1016/j.jad.2025.02.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Revised: 02/02/2025] [Accepted: 02/12/2025] [Indexed: 02/21/2025]
Abstract
BACKGROUND Both elevated and blunted cortisol responses have been associated with depression. Previous Mendelian randomization (MR) studies have largely ruled out cortisol as a cause of depression. Based on the attenuation hypothesis, this MR study used depression as exposure to assess whether cortisol might be a consequence and therefore a biomarker of depression. METHODS Strong (P < 5 × 10-8) and independent (r2 < 0.001) single nucleotide polymorphisms (SNPs) associated with broadly defined depression (294,322 cases, 741,438 controls) were used as instruments. These were applied to genetic associations with morning, fasting, and random plasma cortisol in the CORtisol NETwork (CORNET) consortium (n = 25,314), METabolic Syndrome in Men (METSIM) study (n = 6667), and Canadian Longitudinal Study on Aging (CLSA) cohort (n = 8299). Multivariable MR, adjusting for childhood maltreatment and major mental disorders, was conducted to address potential horizontal pleiotropy from dichotomous depression. Instruments were also selected by evidence of colocalization with major depressive disorder to address non-specificity. RESULTS Using 133 SNPs as instruments, depression was inversely associated with morning plasma cortisol (β per log-odds of genetic liability to depression = -0.107 [95 % CI, -0.181 to -0.032]) in the CORNET consortium. Replication in the METSIM study (β = -0.203 [95 % CI, -0.367 to -0.040]) and CLSA cohort (β = -0.091 [95 % CI, -0.220 to 0.039]) showed consistent but not always significant associations. Multivariable MR and follow-up analysis incorporating colocalization supported these findings. CONCLUSIONS Consistent with the attenuation hypothesis, blunted cortisol response appeared to be a consequence and potentially a biomarker of depression. Future studies are needed to provide more interpretable effect sizes and validate other biomarker measures.
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Affiliation(s)
- Io Ieong Chan
- Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Macao SAR, China.
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Wang J, Liu Y, Li H, Nguyen TP, Soto-Vargas JL, Wilson R, Wang W, Lam TT, Zhang C, Lin C, Lewis DA, Glausier J, Holtzheimer PE, Friedman MJ, Williams KR, Picciotto MR, Nairn AC, Krystal JH, Duman RS, Young KA, Zhao H, Girgenti MJ. A multi-omic approach implicates novel protein dysregulation in post-traumatic stress disorder. Genome Med 2025; 17:43. [PMID: 40301990 PMCID: PMC12042318 DOI: 10.1186/s13073-025-01473-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Accepted: 04/14/2025] [Indexed: 05/01/2025] Open
Abstract
BACKGROUND Post-traumatic stress disorder (PTSD) is a common and disabling psychiatric disorder. PTSD involves multiple brain regions and is often comorbid with other psychiatric disorders, such as major depressive disorder (MDD). Recent genome-wide association studies (GWASs) have identified many PTSD risk loci and transcriptomics studies of postmortem brain have found differentially expressed genes associated with PTSD cases. In this study, we integrated genome-wide measures across modalities to identify convergent molecular effects in the PTSD brain. METHODS We performed tandem mass spectrometry (MS/MS) on a large cohort of donors (N = 66) in two prefrontal cortical areas, dorsolateral prefrontal cortex (DLPFC), and subgenual prefrontal cortex (sgPFC). We also coupled the proteomics data with transcriptomics and microRNA (miRNA) profiling from RNA-seq and small-RNA sequencing, respectively for the same cohort. Additionally, we utilized published GWAS results of multiple psychiatric disorders for integrative analysis. RESULTS We found differentially expressed proteins and co-expression protein modules disrupted by PTSD. Integrative analysis with transcriptomics and miRNA data from the same cohort pointed to hsa-mir-589 as a regulatory miRNA responsible for dysregulation of neuronal protein networks for PTSD, including the gamma-aminobutyric acid (GABA) vesicular transporter, SLC32A1. In addition, we identified significant enrichment of risk genes for other psychiatric disorders, such as autism spectrum disorder (ASD) and major depressive disorder (MDD) within PTSD protein co-expression modules, suggesting shared molecular pathology. CONCLUSIONS We integrated genome-wide measures of mRNA and miRNA expression and proteomics profiling from PTSD, MDD, and control (CON) brains to identify convergent and divergent molecular processes across genomic modalities. We substantially expand the number of differentially expressed genes and proteins in PTSD and identify downregulation of GABAergic processes in the PTSD proteome. This provides a novel framework for future studies integrating proteomic profiling with transcriptomics and non-coding RNAs in the human brain studies.
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Affiliation(s)
- Jiawei Wang
- Program of Computational Biology & Bioinformatics, Yale University, New Haven, CT, 06511, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06511, USA
| | - Yujing Liu
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06511, USA
| | - Hongyu Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06510, USA
| | - Tuan P Nguyen
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06511, USA
| | | | - Rashaun Wilson
- NIDA Neuroproteomics Center, Yale School of Medicine, New Haven, CT, 06511, USA
| | - Weiwei Wang
- Department of Molecular Biophysics and Biochemistry, Yale School of Medicine, New Haven, CT, 06511, USA
| | - TuKiet T Lam
- NIDA Neuroproteomics Center, Yale School of Medicine, New Haven, CT, 06511, USA
- Department of Molecular Biophysics and Biochemistry, Yale School of Medicine, New Haven, CT, 06511, USA
- Keck MS & Proteomics Resource, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Chi Zhang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06510, USA
| | - Chen Lin
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06510, USA
| | - David A Lewis
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Jill Glausier
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Paul E Holtzheimer
- National Center for PTSD, United States Department of Veterans Affairs, White River Junction, VT, 05009, USA
- Department of Psychiatry, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03756, USA
| | - Matthew J Friedman
- National Center for PTSD, United States Department of Veterans Affairs, White River Junction, VT, 05009, USA
- Department of Psychiatry, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03756, USA
| | - Kenneth R Williams
- NIDA Neuroproteomics Center, Yale School of Medicine, New Haven, CT, 06511, USA
- Department of Molecular Biophysics and Biochemistry, Yale School of Medicine, New Haven, CT, 06511, USA
| | - Marina R Picciotto
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06511, USA
| | - Angus C Nairn
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06511, USA
- NIDA Neuroproteomics Center, Yale School of Medicine, New Haven, CT, 06511, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06511, USA
- National Center for PTSD, United States Department of Veterans Affairs, White River Junction, VT, 05009, USA
| | - Ronald S Duman
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06511, USA
- National Center for PTSD, United States Department of Veterans Affairs, White River Junction, VT, 05009, USA
| | - Keith A Young
- Central Texas Veterans Health Care System, Research Service, Temple, TX, 76504, USA
- Department of Psychiatry and Behavioral Sciences, Texas A&M University School of Medicine, Bryan, TX, 77807, USA
| | - Hongyu Zhao
- Program of Computational Biology & Bioinformatics, Yale University, New Haven, CT, 06511, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06510, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06511, USA.
- National Center for PTSD, United States Department of Veterans Affairs, White River Junction, VT, 05009, USA.
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Ohi K, Fujikane D, Takai K, Kuramitsu A, Muto Y, Sugiyama S, Shioiri T. Methylation Risk Scores in Psychiatric Disorders: Advancing Epigenetic Research in Mental Health. JMA J 2025; 8:363-370. [PMID: 40416010 PMCID: PMC12095519 DOI: 10.31662/jmaj.2024-0329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2024] [Accepted: 12/02/2024] [Indexed: 05/27/2025] Open
Abstract
DNA methylation is an epigenetic modification implicated in psychiatric disorders influenced by both genetic and environmental factors. Methylation risk scores (MRSs) have emerged as a tool for quantifying accumulated epigenetic modifications and assessing the predisposed risk for certain common disorders. This narrative review introduces the MRS application in psychiatric disorders, including schizophrenia (SCZ), bipolar disorder (BD), social anxiety disorder (SAD), and panic disorder (PD), while also discussing the current limitations and ethical considerations in psychiatric research. MRSs are calculated from epigenome-wide association studies (EWASs) for psychiatric disorders in various tissues from blood and brain and reflect methylation patterns associated with the psychiatric disorder risk. MRSs provide a perspective of how the cumulative methylation patterns at specific CpG sites may contribute to the onset of psychiatric disorders. In SCZ and BD, MRSs derived from both blood and brain tissues have shown distinct methylation profiles that differentiate these disorders, particularly in patients with a high genetic SCZ risk. MRSs are also used to assess the impact of environmental factors, such as early-life adversity and chronic stress, on psychiatric outcomes. In SAD and PD, where epigenetic studies are relatively limited, MRSs revealed both shared and distinct epigenetic features between anxiety disorders, with specific methylation changes associated with social avoidance in SAD patients. MRSs can serve as biomarkers, providing a valuable understanding of both genetic predispositions and environmental influences on gene regulation. However, the lack of large-scale EWAS datasets and standardized summary statistics remains as a limitation. To address this issue, this review provides a list of publicly available raw intensity data (IDAT) files from psychiatric epigenetic studies that can help facilitate future research by providing the raw data necessary for conducting independent EWASs and MRS calculations. As the field advances, careful consideration must be given to the ethical implications of MRS applications, particularly in clinical intervention and prevention. While MRSs hold promise for future personalized medicine applications, informing treatment decisions based on an individual's methylation profile, caution is warranted regarding their predictive utility and effect size limitations. This review emphasizes the importance of MRSs in advancing psychiatric research, bridging the gap between genetic risk and environmental factors.
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Affiliation(s)
- Kazutaka Ohi
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
| | - Daisuke Fujikane
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
| | - Kentaro Takai
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
| | - Ayumi Kuramitsu
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
| | - Yukimasa Muto
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
| | - Shunsuke Sugiyama
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
| | - Toshiki Shioiri
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
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45
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Goes FS, Collado-Torres L, Zandi PP, Huuki-Myers L, Tao R, Jaffe AE, Pertea G, Shin JH, Weinberger DR, Kleinman JE, Hyde TM. Large-scale transcriptomic analyses of major depressive disorder reveal convergent dysregulation of synaptic pathways in excitatory neurons. Nat Commun 2025; 16:3981. [PMID: 40295477 PMCID: PMC12037741 DOI: 10.1038/s41467-025-59115-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2025] [Accepted: 04/10/2025] [Indexed: 04/30/2025] Open
Abstract
Major Depressive Disorder (MDD) is a common, complex disorder that is a leading cause of disability worldwide and a significant risk factor for suicide. In this study, we have performed the largest molecular analysis of MDD in postmortem human brains (846 samples across 458 individuals) in the subgenual Anterior Cingulate Cortex (sACC) and the Amygdala, two regions central to mood regulation and the pathophysiology of MDD. We found extensive expression differences, particularly at the level of specific transcripts, with prominent enrichment for genes associated with the vesicular functioning, the postsynaptic density, GTPase signaling, and gene splicing. We find associated transcriptional features in 107 of 243 genome-wide significant loci for MDD and, through integrative analyses, highlight convergence of genetic risk, gene expression, and network-based analyses on dysregulated glutamatergic signaling and synaptic vesicular functioning. Together, these results provide an initial mechanistic understanding of MDD and highlight potential targets for novel drug discovery.
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Affiliation(s)
- Fernando S Goes
- Department of Psychiatry and Behavioral Sciences, Stanley and Elizabeth Star Precision Medicine Center of Excellence in Mood Disorders, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | - Leonardo Collado-Torres
- The Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Peter P Zandi
- Department of Psychiatry and Behavioral Sciences, Stanley and Elizabeth Star Precision Medicine Center of Excellence in Mood Disorders, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Ran Tao
- The Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Andrew E Jaffe
- The Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Geo Pertea
- The Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Joo Heon Shin
- The Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Daniel R Weinberger
- The Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joel E Kleinman
- Department of Psychiatry and Behavioral Sciences, Stanley and Elizabeth Star Precision Medicine Center of Excellence in Mood Disorders, Johns Hopkins School of Medicine, Baltimore, MD, USA
- The Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Thomas M Hyde
- The Lieber Institute for Brain Development, Baltimore, MD, USA.
- Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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46
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Debnath M. The expanding spectrum of infectious risk organisms and immunogenetic susceptibility in neuropsychiatric disorders. Neurosci Biobehav Rev 2025; 174:106177. [PMID: 40300705 DOI: 10.1016/j.neubiorev.2025.106177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2025] [Revised: 04/15/2025] [Accepted: 04/22/2025] [Indexed: 05/01/2025]
Abstract
The spectrum of infectious risk organisms showing associations with psychiatric traits is expanding. Infectious agents can modulate the risk of psychiatric disorders at different stages of life, such as gestational, childhood, adolescent, and adult periods. Prenatal infection appears to 'prime' the developing brain, whereas infection during childhood or later periods may act as a 'second hit', and these may have synergistic effects on the risk of developing psychiatric diseases. However, neither all the individuals with antecedent infection develop psychiatric disorders, nor do infectious organisms alone lead to psychiatric phenotypes. This suggests modulatory effects of additional host factors. The host genetic background crucially determines differential susceptibility to infection and serves as an important gateway for immune activation and signalling, as well as homeostatic brain functions. Despite the presence of several immune checkpoints and effectors, the infectious organisms disrupt the balance between immune-activating and immune-compensatory mechanisms and contribute to immune dysregulation. This depends substantially on genetic loci encoding immune molecules such as Toll-like receptors, Major Histocompatibility Complex, cytokines/ chemokines and their receptors, complement proteins, and other molecules and elements such as human endogenous retroviruses and gut microbiome that have distinct roles in immune regulation and immune effector functions. Genetic variations within these loci not only influence differential susceptibility to infection but also confer risk to psychiatric disorders. This article highlights a comprehensive overview of the nexus between infections and immune function-related genes and their impact on psychiatric traits. Understanding such interactions will lead to the identification of genetic markers of susceptibility to infection and psychiatric diseases.
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Affiliation(s)
- Monojit Debnath
- Department of Human Genetics, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India.
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47
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Dardani C, Robinson JW, Jones HJ, Rai D, Stergiakouli E, Grove J, Gardner R, McIntosh AM, Havdahl A, Hemani G, Davey Smith G, Richardson TG, Gaunt TR, Khandaker GM. Immunological drivers and potential novel drug targets for major psychiatric, neurodevelopmental, and neurodegenerative conditions. Mol Psychiatry 2025:10.1038/s41380-025-03032-x. [PMID: 40281223 DOI: 10.1038/s41380-025-03032-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Revised: 03/26/2025] [Accepted: 04/11/2025] [Indexed: 04/29/2025]
Abstract
Immune dysfunction is implicated in the aetiology of psychiatric, neurodevelopmental, and neurodegenerative conditions, but the issue of causality remains unclear impeding attempts to develop new interventions. Using genomic data on protein and gene expression across blood and brain, we assessed evidence of a potential causal role for 736 immune response-related biomarkers on 7 neuropsychiatric conditions by applying Mendelian randomization (MR) and genetic colocalisation analyses. A systematic three-tier approach, grouping biomarkers based on increasingly stringent criteria, was used to appraise evidence of causality (passing MR sensitivity analyses, colocalisation, False Discovery Rate and Bonferroni thresholds). We provide evidence for a potential causal role of 29 biomarkers for 7 conditions. The identified biomarkers suggest a role of both brain specific and systemic immune response in the aetiology of schizophrenia, Alzheimer's disease, depression, and bipolar disorder. Of the identified biomarkers, 20 are therapeutically tractable, including ACE, TNFRSF17, SERPING1, AGER and CD40, with drugs currently approved or in advanced clinical trials. Based on the largest available selection of plasma immune-response related biomarkers, our study provides insight into possible influential biomarkers for the aetiology of neuropsychiatric conditions. These genetically prioritised biomarkers now require examination to further evaluate causality, their role in the aetiological mechanisms underlying the conditions, and therapeutic potential.
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Affiliation(s)
- Christina Dardani
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway.
- Research Department, Lovisenberg Diakonale Hospital, Oslo, Norway.
| | - Jamie W Robinson
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Hannah J Jones
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Centre for Academic Mental Health, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute of Health and Care Research Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Dheeraj Rai
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Centre for Academic Mental Health, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute of Health and Care Research Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK
- Avon and Wiltshire Mental Health Partnership NHS Trust, Bristol, UK
| | - Evie Stergiakouli
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jakob Grove
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Renee Gardner
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
| | - Andrew M McIntosh
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Alexandra Havdahl
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
- Research Department, Lovisenberg Diakonale Hospital, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute of Health and Care Research Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Tom G Richardson
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom R Gaunt
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute of Health and Care Research Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Golam M Khandaker
- Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
- Centre for Academic Mental Health, Bristol Medical School, University of Bristol, Bristol, UK.
- National Institute of Health and Care Research Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK.
- Avon and Wiltshire Mental Health Partnership NHS Trust, Bristol, UK.
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48
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Solomon P, Kaurani L, Budde M, Guiné JB, Krüger DM, Riquin K, Pena T, Burkhardt S, Fourgeux C, Adorjan K, Heilbronner M, Kalman JL, Kohshour MO, Papiol S, Reich-Erkelenz D, Schaupp SK, Schulte EC, Senner F, Vogl T, Anghelescu IG, Arolt V, Baune BT, Dannlowski U, Dietrich DE, Fallgatter AJ, Figge C, Juckel G, Konrad C, Reimer J, Reininghaus EZ, Schmauß M, Spitzer C, Wiltfang J, Zimmermann J, Schütz AL, Sananbenesi F, Sauvaget A, Falkai P, Schulze TG, Fischer A, Heilbronner U, Poschmann J. Integrative analysis of miRNA expression profiles reveals distinct and common molecular mechanisms underlying broad diagnostic groups of severe mental disorders. Mol Psychiatry 2025:10.1038/s41380-025-03018-9. [PMID: 40263528 DOI: 10.1038/s41380-025-03018-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 03/03/2025] [Accepted: 04/04/2025] [Indexed: 04/24/2025]
Abstract
Micro RNAs (miRNAs) play a crucial role as regulators of various biological processes and have been implicated in the pathogenesis of mental disorders such as schizophrenia and bipolar disorders. In this study, we investigate the expression patterns of miRNAs in the PsyCourse Study (n = 1786), contrasting three broad diagnostic groups: Psychotic (Schizophrenia-spectrum disorders), Affective (Bipolar Disorder I, II and recurrent Depression), and neurotypic healthy individuals. Through comprehensive analyses, including differential miRNA expression, miRNA transcriptome-wide association study (TWAS), and predictive modelling, we identified multiple miRNAs unique to Psychotic and Affective groups as well as shared by both. Furthermore, we performed integrative analysis to identify the target genes of the dysregulated miRNAs and elucidate their potential roles in psychosis. Our findings reveal significant alterations of multiple miRNAs such as miR-584-3p and miR-99b-5p across the studied diagnostic groups, highlighting their role as molecular correlates. Additionally, the miRNA TWAS analysis discovered previously known and novel genetically dysregulated miRNAs confirming the relevance in the etiology of the diagnostic groups. Importantly, novel factors and putative molecular mechanisms underlying these groups were uncovered through the integration of miRNA-target gene interactions. This comprehensive investigation provides valuable insights into the molecular underpinnings of severe mental disorders, shedding light on the complex regulatory networks involving miRNAs.
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Affiliation(s)
- Pierre Solomon
- Nantes Université, CHU-Nantes, INSERM, Center for Research in Transplantation and Translational Immunology, UMR 1064, Nantes, France
| | - Lalit Kaurani
- Department for Systems Medicine and Epigenetics, German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
| | - Monika Budde
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, Germany
| | - Jean-Baptiste Guiné
- Nantes Université, CHU-Nantes, INSERM, Center for Research in Transplantation and Translational Immunology, UMR 1064, Nantes, France
| | - Dennis Manfred Krüger
- Department for Systems Medicine and Epigenetics, German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
| | - Kevin Riquin
- Nantes Université, CHU-Nantes, INSERM, Center for Research in Transplantation and Translational Immunology, UMR 1064, Nantes, France
| | - Tonatiuh Pena
- Department for Systems Medicine and Epigenetics, German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
| | - Susanne Burkhardt
- Department for Systems Medicine and Epigenetics, German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
| | - Cynthia Fourgeux
- Nantes Université, CHU-Nantes, INSERM, Center for Research in Transplantation and Translational Immunology, UMR 1064, Nantes, France
| | - Kristina Adorjan
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, Germany
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Maria Heilbronner
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, Germany
| | - Janos L Kalman
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, Germany
| | - Mojtaba Oraki Kohshour
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, Germany
- Department of Immunology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Sergi Papiol
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, Germany
- Max Planck Institute of Psychiatry, Munich, Germany
| | - Daniela Reich-Erkelenz
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, Germany
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
| | - Sabrina K Schaupp
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, Germany
| | - Eva C Schulte
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, Germany
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Bonn, University of Bonn, Bonn, Germany
- German Center for Mental Health (DZPG), partner site Munich/Augsburg, Munich, Germany
| | - Fanny Senner
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, Germany
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- Centers for Psychiatry Suedwuerttemberg, Ravensburg, Ravensburg, Germany
| | - Thomas Vogl
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, Germany
| | - Ion-George Anghelescu
- Department of Psychiatry and Psychotherapy, Mental Health Institute Berlin, Berlin, Germany
| | - Volker Arolt
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Bernhardt T Baune
- Department of Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, University of Melbourne, Melbourne, Australia
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Australia
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Detlef E Dietrich
- AMEOS Clinical Center Hildesheim, Hildesheim, Germany
- Center for Systems Neuroscience Hannover, Hannover, Germany
- Department of Psychiatry, Medical School of Hannover, Hannover, Germany
| | - Andreas J Fallgatter
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health (TüCMH), University of Tübingen, Tübingen, Germany
- German Center for Mental Health (DZPG), partner site Tübingen, Tübingen, Germany
| | - Christian Figge
- Karl-Jaspers Clinic, European Medical School Oldenburg-Groningen, Oldenburg, Germany
| | - Georg Juckel
- Department of Psychiatry, Ruhr University Bochum, LWL University Hospital, Bochum, Germany
| | - Carsten Konrad
- Department of Psychiatry and Psychotherapy, Agaplesion Diakonieklinikum, Rotenburg, Germany
| | - Jens Reimer
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Psychosocial Medicine, Academic Teaching Hospital Itzehoe, Itzehoe, Germany
| | - Eva Z Reininghaus
- Division of Psychiatry and Psychotherapeutic Medicine, Research Unit for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria
| | - Max Schmauß
- Clinic for Psychiatry, Psychotherapy and Psychosomatics, Augsburg University, Medical Faculty, Bezirkskrankenhaus Augsburg, Augsburg, Germany
| | - Carsten Spitzer
- Department of Psychosomatic Medicine and Psychotherapy, University Medical Center Rostock, Rostock, Germany
| | - Jens Wiltfang
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
- German Center for Neurodegenerative Disease (DZNE), Göttingen, Germany
- Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Jörg Zimmermann
- Psychiatrieverbund Oldenburger Land GMBH, Karl-Jaspers-Klinik, Bad Zwischenahn, Germany
| | - Anna-Lena Schütz
- Research Group for Genome Dynamics in Brain Diseases, German Center for Neurodegenerative Diseases, Göttingen, Germany
| | - Farahnaz Sananbenesi
- Research Group for Genome Dynamics in Brain Diseases, German Center for Neurodegenerative Diseases, Göttingen, Germany
| | - Anne Sauvaget
- Nantes Université, CHU Nantes, Movement - Interactions - Performance, MIP, UR 4334, Nantes, France
| | - Peter Falkai
- Max Planck Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich, Munich, Germany
- German Center for Mental Health (DZPG), partner site Munich/Augsburg, Munich, Germany
| | - Thomas G Schulze
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, Germany
- German Center for Mental Health (DZPG), partner site Munich/Augsburg, Munich, Germany
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA
| | - André Fischer
- Department for Systems Medicine and Epigenetics, German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
- Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), University of Göttingen, Göttingen, Germany
| | - Urs Heilbronner
- Institute of Psychiatric Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, Germany
| | - Jeremie Poschmann
- Nantes Université, CHU-Nantes, INSERM, Center for Research in Transplantation and Translational Immunology, UMR 1064, Nantes, France.
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49
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Worf K, Matosin N, Gerstner N, Fröhlich AS, Koller AC, Degenhardt F, Thiele H, Rietschel M, Udawela M, Scarr E, Dean B, Theis FJ, Mueller NS, Knauer-Arloth J. Exon-variant interplay and multi-modal evidence identify endocrine dysregulation in severe psychiatric disorders impacting excitatory neurons. Transl Psychiatry 2025; 15:153. [PMID: 40253403 PMCID: PMC12009313 DOI: 10.1038/s41398-025-03366-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Revised: 03/17/2025] [Accepted: 03/31/2025] [Indexed: 04/21/2025] Open
Abstract
Bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia share genetic architecture, yet their molecular mechanisms remain elusive. Both common and rare genetic variants contribute to neural dysfunction, impacting cognition and behavior. This study investigates the molecular effects of genetic variants on human cortical single-cell types using a single-exon analysis approach. Integrating exon-level eQTLs (common variants influencing exon expression) and joint exon eQT-Scores (combining polygenic risk scores with exon-level gene expression) from a postmortem psychiatric cohort (BD = 15, MDD = 24, schizophrenia = 68, controls = 62) with schizophrenia-focused rare variant data from the SCHEMA consortium, we identified 110 core genes enriched in pathways including circadian entrainment (FDR = 0.02), cortisol synthesis and secretion (FDR = 0.026), and dopaminergic synapse (FDR = 0.038). Additional enriched pathways included hormone signaling (FDRs < 0.0298, including insulin, GnRH, aldosterone, and growth hormone pathways) and, notably, adrenergic signaling in cardiomyocytes (FDR = 0.0028). These pathways highlight shared molecular mechanisms in the three disorders. Single-nuclei RNA sequencing data from three cortical regions revealed that these core set genes are predominantly expressed in excitatory neuron layers 2-6 of the dorsolateral prefrontal cortex, linking molecular changes to cell types involved in cognitive dysfunction. Our results demonstrate the power of integrating multimodal genetic and transcriptomic data at the exon level. This approach moves beyond symptom-based diagnoses toward molecular classifications, identifying potential therapeutic targets for psychiatric disorders.
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Affiliation(s)
- Karolina Worf
- Institute of Computational Biology, Helmholtz Center, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Natalie Matosin
- Department of Gene and Environment, Max Planck Institute of Psychiatry, Munich, Germany
- School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia
| | - Nathalie Gerstner
- Institute of Computational Biology, Helmholtz Center, Munich, Germany
- Department of Gene and Environment, Max Planck Institute of Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Anna S Fröhlich
- Department of Gene and Environment, Max Planck Institute of Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Anna C Koller
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Franziska Degenhardt
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Holger Thiele
- Cologne Center for Genomics, University of Cologne, Cologne, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, University Medical Center Mannheim/University of Heidelberg, Mannheim, Germany
| | - Madhara Udawela
- The Molecular Psychiatry Laboratory, The Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia
| | - Elizabeth Scarr
- The Department of Psychiatry, The University of Melbourne, Parkville, VIC, Australia
| | - Brian Dean
- The Molecular Psychiatry Laboratory, The Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia
- The Department of Florey, The University of Melbourne, Parkville, VIC, Australia
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
- TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Nikola S Mueller
- Institute of Computational Biology, Helmholtz Center, Munich, Germany
| | - Janine Knauer-Arloth
- Institute of Computational Biology, Helmholtz Center, Munich, Germany.
- Department of Gene and Environment, Max Planck Institute of Psychiatry, Munich, Germany.
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50
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Patel RA, Weiß CL, Zhu H, Mostafavi H, Simons YB, Spence JP, Pritchard JK. Characterizing selection on complex traits through conditional frequency spectra. Genetics 2025; 229:iyae210. [PMID: 39691067 PMCID: PMC12005249 DOI: 10.1093/genetics/iyae210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2024] [Revised: 11/18/2024] [Accepted: 12/03/2024] [Indexed: 12/19/2024] Open
Abstract
Natural selection on complex traits is difficult to study in part due to the ascertainment inherent to genome-wide association studies (GWAS). The power to detect a trait-associated variant in GWAS is a function of its frequency and effect size - but for traits under selection, the effect size of a variant determines the strength of selection against it, constraining its frequency. Recognizing the biases inherent to GWAS ascertainment, we propose studying the joint distribution of allele frequencies across populations, conditional on the frequencies in the GWAS cohort. Before considering these conditional frequency spectra, we first characterized the impact of selection and non-equilibrium demography on allele frequency dynamics forwards and backwards in time. We then used these results to understand conditional frequency spectra under realistic human demography. Finally, we investigated empirical conditional frequency spectra for GWAS variants associated with 106 complex traits, finding compelling evidence for either stabilizing or purifying selection. Our results provide insights into polygenic score portability and other properties of variants ascertained with GWAS, highlighting the utility of conditional frequency spectra.
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Affiliation(s)
- Roshni A Patel
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Clemens L Weiß
- Stanford Cancer Institute Core, Stanford University, Stanford, CA 94305, USA
| | - Huisheng Zhu
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Hakhamanesh Mostafavi
- Center for Human Genetics and Genomics, New York University School of Medicine, New York, NY 10016, USA
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, NY 10016, USA
| | - Yuval B Simons
- Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Jeffrey P Spence
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Jonathan K Pritchard
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Biology, Stanford University, Stanford, CA 94305, USA
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