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Bhatt RR, Gadewar SP, Shetty A, Ba Gari I, Haddad E, Javid S, Ramesh A, Nourollahimoghadam E, Zhu AH, de Leeuw C, Thompson PM, Medland SE, Jahanshad N. The Genetic Architecture of the Human Corpus Callosum and its Subregions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.22.603147. [PMID: 39091796 PMCID: PMC11291056 DOI: 10.1101/2024.07.22.603147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
The corpus callosum (CC) is the largest set of white matter fibers connecting the two hemispheres of the brain. In humans, it is essential for coordinating sensorimotor responses, performing associative/executive functions, and representing information in multiple dimensions. Understanding which genetic variants underpin corpus callosum morphometry, and their shared influence on cortical structure and susceptibility to neuropsychiatric disorders, can provide molecular insights into the CC's role in mediating cortical development and its contribution to neuropsychiatric disease. To characterize the morphometry of the midsagittal corpus callosum, we developed a publicly available artificial intelligence based tool to extract, parcellate, and calculate its total and regional area and thickness. Using the UK Biobank (UKB) and the Adolescent Brain Cognitive Development study (ABCD), we extracted measures of midsagittal corpus callosum morphometry and performed a genome-wide association study (GWAS) meta-analysis of European participants (combined N = 46,685). We then examined evidence for generalization to the non-European participants of the UKB and ABCD cohorts (combined N = 7,040). Post-GWAS analyses implicate prenatal intracellular organization and cell growth patterns, and high heritability in regions of open chromatin, suggesting transcriptional activity regulation in early development. Results suggest programmed cell death mediated by the immune system drives the thinning of the posterior body and isthmus. Global and local genetic overlap, along with causal genetic liability, between the corpus callosum, cerebral cortex, and neuropsychiatric disorders such as attention-deficit/hyperactivity and bipolar disorders were identified. These results provide insight into variability of corpus callosum development, its genetic influence on the cerebral cortex, and biological mechanisms related to neuropsychiatric dysfunction.
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Song J, Gao N, Chen Z, Xu G, Kong M, Wei D, Sun Q, Dong A. Shared genetic etiology of vessel diseases: A genome-wide multi-traits association analysis. Thromb Res 2024; 241:109102. [PMID: 39059088 DOI: 10.1016/j.thromres.2024.109102] [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: 09/24/2023] [Revised: 07/15/2024] [Accepted: 07/18/2024] [Indexed: 07/28/2024]
Abstract
BACKGROUND The comorbidity among vascular diseases has been widely reported, however, the contribution of shared genetic components remains ambiguous. METHODS Based on genome-wide association study summary statistics, we employed statistical genetics methodologies to explore the shared genetic basis of eight vascular diseases: coronary artery disease, abdominal aortic aneurysm, ischemic stroke, peripheral artery disease, thoracic aortic aneurysm, phlebitis, varicose veins, and venous thromboembolism. We assessed global and local genetic correlations among these disorders by linkage disequilibrium score regression, high-definition likelihood, and local analysis of variant association. Cross-trait analyses conducted with CPASSOC identified pleiotropic variants and loci. Further, biological pathways at the multi-omics level were explored using multimarker analysis of genomic annotation, transcriptome-wide and proteome-wide association studies. Causal associations among the vascular diseases were evaluated by mendelian randomization and latent causal variable to assess vertical pleiotropic effects. RESULTS We found significant global genetic associations in 18 pairs of vascular diseases. Additionally, we discovered 317 unique genomic regions where at least one pair of traits demonstrated significant correlation. Multi-trait association analysis identified 19,361 significant potential pleiotropic variants in 274 independent pleiotropic loci. Multi-trait colocalization analysis revealed 56 colocalized loci in specific disease sets. Gene-based analysis identified 700 potential pleiotropic genes, which were subsequently validated at both transcriptome and protein levels. Gene-set enrichment analysis supports the role of biological pathways such as vessel wall structure, coagulation and lipid transport in vascular disease. Additionally, 7 pairs of vascular diseases have a causal relationship. CONCLUSIONS Our study indicates a shared genetic basis and the presence of common risk genes among vascular diseases. These findings offer novel insights into potential mechanisms underlying the association between vascular diseases, as well as provide guidance for interventions and treatments of multi-vascular conditions.
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Affiliation(s)
- Jiangwei Song
- Department of Cardiovascular Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Ning Gao
- Department of Cardiovascular Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhe Chen
- Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha 410011, Hunan, China
| | - Guocong Xu
- Department of Cardiovascular Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Minjian Kong
- Department of Cardiovascular Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Dongdong Wei
- Department of Cardiovascular Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Qi Sun
- Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, First Affiliated Hospital, School of Medicine, Zhejiang University, Qingchun Road 79, Hangzhou 310003, China; Key Laboratory of Combined Multi-organ Transplantation, Ministry of Public Health, First Affiliated Hospital, School of Medicine, Zhejiang University, Qingchun Road 79, Hangzhou 310003, China
| | - Aiqiang Dong
- Department of Cardiovascular Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
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Bouttle K, Ingold N, O’Mara TA. Using Genetics to Investigate Relationships between Phenotypes: Application to Endometrial Cancer. Genes (Basel) 2024; 15:939. [PMID: 39062718 PMCID: PMC11276418 DOI: 10.3390/genes15070939] [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: 06/25/2024] [Revised: 07/14/2024] [Accepted: 07/16/2024] [Indexed: 07/28/2024] Open
Abstract
Genome-wide association studies (GWAS) have accelerated the exploration of genotype-phenotype associations, facilitating the discovery of replicable genetic markers associated with specific traits or complex diseases. This narrative review explores the statistical methodologies developed using GWAS data to investigate relationships between various phenotypes, focusing on endometrial cancer, the most prevalent gynecological malignancy in developed nations. Advancements in analytical techniques such as genetic correlation, colocalization, cross-trait locus identification, and causal inference analyses have enabled deeper exploration of associations between different phenotypes, enhancing statistical power to uncover novel genetic risk regions. These analyses have unveiled shared genetic associations between endometrial cancer and many phenotypes, enabling identification of novel endometrial cancer risk loci and furthering our understanding of risk factors and biological processes underlying this disease. The current status of research in endometrial cancer is robust; however, this review demonstrates that further opportunities exist in statistical genetics that hold promise for advancing the understanding of endometrial cancer and other complex diseases.
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Affiliation(s)
| | | | - Tracy A. O’Mara
- Cancer Research Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia (N.I.)
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Suzuki Y, Ménager H, Brancotte B, Vernet R, Nerin C, Boetto C, Auvergne A, Linhard C, Torchet R, Lechat P, Troubat L, Cho MH, Bouzigon E, Aschard H, Julienne H. Trait selection strategy in multi-trait GWAS: Boosting SNP discoverability. HGG ADVANCES 2024; 5:100319. [PMID: 38872309 PMCID: PMC11260573 DOI: 10.1016/j.xhgg.2024.100319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 06/11/2024] [Accepted: 06/11/2024] [Indexed: 06/15/2024] Open
Abstract
Since the first genome-wide association studies (GWASs), thousands of variant-trait associations have been discovered. However, comprehensively mapping the genetic determinant of complex traits through univariate testing can require prohibitive sample sizes. Multi-trait GWAS can circumvent this issue and improve statistical power by leveraging the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been solved, the strategy to select traits has been overlooked. In this study, we conducted multi-trait GWAS on approximately 20,000 combinations of 72 traits using an omnibus test as implemented in the Joint Analysis of Summary Statistics. We assessed which genetic features of the sets of traits analyzed were associated with an increased detection of variants compared with univariate screening. Several features of the set of traits, including the heritability, the number of traits, and the genetic correlation, drive the multi-trait test gain. Using these features jointly in predictive models captures a large fraction of the power gain of the multi-trait test (Pearson's r between the observed and predicted gain equals 0.43, p < 1.6 × 10-60). Applying an alternative multi-trait approach (Multi-Trait Analysis of GWAS), we identified similar features of interest, but with an overall 70% lower number of new associations. Finally, selecting sets based on our data-driven models systematically outperformed the common strategy of selecting clinically similar traits. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outlines practical strategies for multi-trait testing.
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Affiliation(s)
- Yuka Suzuki
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France.
| | - Hervé Ménager
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Bryan Brancotte
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Raphaël Vernet
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Cyril Nerin
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Christophe Boetto
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Antoine Auvergne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Christophe Linhard
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Rachel Torchet
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Pierre Lechat
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Lucie Troubat
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Emmanuelle Bouzigon
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France.
| | - Hanna Julienne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France; Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France.
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Tsai YT, Hrytsenko Y, Elgart M, Tahir UA, Chen ZZ, Wilson JG, Gerszten RE, Sofer T. A parametric bootstrap approach for computing confidence intervals for genetic correlations with application to genetically determined protein-protein networks. HGG ADVANCES 2024; 5:100304. [PMID: 38720460 PMCID: PMC11140211 DOI: 10.1016/j.xhgg.2024.100304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 05/04/2024] [Accepted: 05/04/2024] [Indexed: 05/21/2024] Open
Abstract
Genetic correlation refers to the correlation between genetic determinants of a pair of traits. When using individual-level data, it is typically estimated based on a bivariate model specification where the correlation between the two variables is identifiable and can be estimated from a covariance model that incorporates the genetic relationship between individuals, e.g., using a pre-specified kinship matrix. Inference relying on asymptotic normality of the genetic correlation parameter estimates may be inaccurate when the sample size is low, when the genetic correlation is close to the boundary of the parameter space, and when the heritability of at least one of the traits is low. We address this problem by developing a parametric bootstrap procedure to construct confidence intervals for genetic correlation estimates. The procedure simulates paired traits under a range of heritability and genetic correlation parameters, and it uses the population structure encapsulated by the kinship matrix. Heritabilities and genetic correlations are estimated using the close-form, method of moment, Haseman-Elston regression estimators. The proposed parametric bootstrap procedure is especially useful when genetic correlations are computed on pairs of thousands of traits measured on the same exact set of individuals. We demonstrate the parametric bootstrap approach on a proteomics dataset from the Jackson Heart Study.
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Affiliation(s)
- Yi-Ting Tsai
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yana Hrytsenko
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michael Elgart
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Usman A Tahir
- Department of Medicine, Harvard Medical School, Boston, MA, USA; CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Zsu-Zsu Chen
- Department of Medicine, Harvard Medical School, Boston, MA, USA; Department of Internal Medicine, Division of Endocrinology, Diabetes, and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - James G Wilson
- Department of Medicine, Harvard Medical School, Boston, MA, USA; CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Robert E Gerszten
- Department of Medicine, Harvard Medical School, Boston, MA, USA; CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Tamar Sofer
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA.
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Xie Y, Zhao Y, Zhou Y, Jiang Y, Zhang Y, Du J, Cai M, Fu J, Liu H. Shared Genetic Architecture Among Gastrointestinal Diseases, Schizophrenia, and Brain Subcortical Volumes. Schizophr Bull 2024:sbae099. [PMID: 38973257 DOI: 10.1093/schbul/sbae099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/09/2024]
Abstract
BACKGROUND AND HYPOTHESIS The gut-brain axis plays important roles in both gastrointestinal diseases (GI diseases) and schizophrenia (SCZ). Moreover, both GI diseases and SCZ exhibit notable abnormalities in brain subcortical volumes. However, the genetic mechanisms underlying the comorbidity of these diseases and the shared alterations in brain subcortical volumes remain unclear. STUDY DESIGN Using the genome-wide association studies data of SCZ, 14 brain subcortical volumes, and 8 GI diseases, the global polygenic overlap and local genetic correlations were identified, as well as the shared genetic variants among those phenotypes. Furthermore, we conducted multi-trait colocalization analyses to bolster our findings. Functional annotations, cell-type enrichment, and protein-protein interaction (PPI) analyses were carried out to reveal the critical etiology and pathology mechanisms. STUDY RESULTS The global polygenic overlap and local genetic correlations informed the close relationships between SCZ and both GI diseases and brain subcortical volumes. Moreover, 84 unique lead-shared variants were identified. The associated genes were linked to vital biological processes within the immune system. Additionally, significant correlations were observed with key immune cells and the PPI analysis identified several histone-associated hub genes. These findings highlighted the pivotal roles played by the immune system for both SCZ and GI diseases, along with the shared alterations in brain subcortical volumes. CONCLUSIONS These findings revealed the shared genetic architecture contributing to SCZ and GI diseases, as well as their shared alterations in brain subcortical volumes. These insights have substantial implications for the concurrent development of intervention and therapy targets for these diseases.
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Affiliation(s)
- Yingying Xie
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yao Zhao
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yujing Zhou
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yurong Jiang
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yujie Zhang
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Jiaojiao Du
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Mengjing Cai
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Jilian Fu
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Huaigui Liu
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, China
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Wootton O, Shadrin AA, Bjella T, Smeland OB, van der Meer D, Frei O, O'Connell KS, Ueland T, Andreassen OA, Stein DJ, Dalvie S. Genomic insights into the shared and distinct genetic architecture of cognitive function and schizophrenia. Sci Rep 2024; 14:15356. [PMID: 38961113 PMCID: PMC11222449 DOI: 10.1038/s41598-024-66085-y] [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: 01/04/2024] [Accepted: 06/26/2024] [Indexed: 07/05/2024] Open
Abstract
Cognitive impairment is a major determinant of functional outcomes in schizophrenia, however, understanding of the biological mechanisms underpinning cognitive dysfunction in the disorder remains incomplete. Here, we apply Genomic Structural Equation Modelling to identify latent cognitive factors capturing genetic liabilities to 12 cognitive traits measured in the UK Biobank. We identified three broad factors that underly the genetic correlations between the cognitive tests. We explore the overlap between latent cognitive factors, schizophrenia, and schizophrenia symptom dimensions using a complementary set of statistical approaches, applied to data from the latest schizophrenia genome-wide association study (Ncase = 53,386, Ncontrol = 77,258) and the Thematically Organised Psychosis study (Ncase = 306, Ncontrol = 1060). Global genetic correlations showed a significant moderate negative genetic correlation between each cognitive factor and schizophrenia. Local genetic correlations implicated unique genomic regions underlying the overlap between schizophrenia and each cognitive factor. We found substantial polygenic overlap between each cognitive factor and schizophrenia and biological annotation of the shared loci implicated gene-sets related to neurodevelopment and neuronal function. Lastly, we show that the common genetic determinants of the latent cognitive factors are not predictive of schizophrenia symptoms in the Norwegian Thematically Organized Psychosis cohort. Overall, these findings inform our understanding of cognitive function in schizophrenia by demonstrating important differences in the shared genetic architecture of schizophrenia and cognitive abilities.
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Affiliation(s)
- Olivia Wootton
- Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa.
| | - Alexey A Shadrin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Thomas Bjella
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Olav B Smeland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, Blindern, Oslo, Norway
| | - Kevin S O'Connell
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Torill Ueland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dan J Stein
- Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- SAMRC Unit on Risk & Resilience in Mental Disorders, Cape Town, South Africa
| | - Shareefa Dalvie
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
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Chen Y, Liu P, Yi S, Fan C, Zhao W, Liu J. Investigating the shared genetic architecture between attention-deficit/hyperactivity disorder and risk taking behavior: A large-scale genomewide cross-trait analysis. J Affect Disord 2024; 356:22-31. [PMID: 38565336 DOI: 10.1016/j.jad.2024.03.107] [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: 12/29/2023] [Revised: 02/20/2024] [Accepted: 03/22/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND This study aims to explore the genetic architecture shared between Attention-Deficit/Hyperactivity Disorder (ADHD) and risk behavior. METHODS Based on the latest large-scale Genome-wide association studies (GWAS), we firstly employed Linkage disequilibrium score regression (LDSC) and Local Analysis of Variant Association (LAVA) to investigate the genetic correlation between risk behavior and ADHD. Then, we conducted cross-trait analysis to identified the Pleiotropic loci. Finally, bidirectional Mendelian randomization analysis (MR) was applied to examine the causal relationship. RESULTS We found a significant positive genetic correlation between ADHD and risk-taking behavior (rg = 0.351, p = 6.50E-37). The cross-trait meta-analysis identified 27 significant SNPs shared between ADHD and risk behavior. The most significant locus, located near the CADM2 gene on chromosome 3, had been identified associated with this two trait (pADHD = 3.07E-05 and prisk-taking behavior = 2.47E-30). The same situation can also be observed near the FOXP2 gene on chromosome 7 (rs8180817, pmeta = 5.72E-21). We found CCDC171 gene and other genes played a significant role in ADHD and risk behavior in mRNA level. Bidirectional MR analysis found a causal relationship between them. LIMITATION The majority of our data sources were of European origin, which may limit the generalizability of our findings to other ethnic populations. CONCLUSION This article reveals in depth the shared genetic structure between ADHD and risk-taking behavior, finding a significant positive genetic correlation between ADHD and risk-taking behavior. Providing insights for the future treatment and management of these two traits.
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Affiliation(s)
- Yanjing Chen
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan Province 410011, People's Republic of China.
| | - Ping Liu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan Province 410011, People's Republic of China.
| | - Sijie Yi
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan Province 410011, People's Republic of China
| | - Chunhua Fan
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan Province 410011, People's Republic of China
| | - Wei Zhao
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan Province 410011, People's Republic of China; Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan Province 410011, People's Republic of China.
| | - Jun Liu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan Province 410011, People's Republic of China; Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan Province 410011, People's Republic of China.
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Bergstedt J, Pasman JA, Ma Z, Harder A, Yao S, Parker N, Treur JL, Smit DJA, Frei O, Shadrin AA, Meijsen JJ, Shen Q, Hägg S, Tornvall P, Buil A, Werge T, Hjerling-Leffler J, Als TD, Børglum AD, Lewis CM, McIntosh AM, Valdimarsdóttir UA, Andreassen OA, Sullivan PF, Lu Y, Fang F. Distinct biological signature and modifiable risk factors underlie the comorbidity between major depressive disorder and cardiovascular disease. NATURE CARDIOVASCULAR RESEARCH 2024; 3:754-769. [PMID: 38898929 PMCID: PMC11182748 DOI: 10.1038/s44161-024-00488-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 05/08/2024] [Indexed: 06/21/2024]
Abstract
Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Here we show that CVDs share most of their genetic risk factors with MDD. Multivariate genome-wide association analysis of shared genetic liability between MDD and atherosclerotic CVD revealed seven loci and distinct patterns of tissue and brain cell-type enrichments, suggesting the involvement of the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic and psychosocial or lifestyle risk factors. Our data indicated causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and showed that the causal effects were partly explained by metabolic and psychosocial or lifestyle factors. The distinct signature of MDD-atherosclerotic CVD comorbidity suggests an immunometabolic subtype of MDD that is more strongly associated with CVD than overall MDD. In summary, we identified biological mechanisms underlying MDD-CVD comorbidity and modifiable risk factors for prevention of CVD in individuals with MDD.
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Affiliation(s)
- Jacob Bergstedt
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Joëlle A. Pasman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ziyan Ma
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Arvid Harder
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Shuyang Yao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Nadine Parker
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Jorien L. Treur
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Dirk J. A. Smit
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Oleksandr Frei
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Alexey A. Shadrin
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Joeri J. Meijsen
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
| | - Qing Shen
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, China
- Institute for Advanced Study, Tongji University, Shanghai, China
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Per Tornvall
- Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Alfonso Buil
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
| | - Thomas Werge
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jens Hjerling-Leffler
- Department Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Thomas D. Als
- Department of Molecular Medicine (MOMA), Molecular Diagnostic Laboratory, Aarhus University Hospital, Aarhus, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Anders D. Børglum
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Cathryn M. Lewis
- Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK
- Department of Medical and Molecular Genetics, King’s College London, London, UK
| | - Andrew M. McIntosh
- Centre for Clinical Brain Sciences, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
- Centre for Genomics and Experimental Medicine, University of Edinburgh, Edinburgh, UK
| | - Unnur A. Valdimarsdóttir
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Centre of Public Health Sciences, Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Department of Epidemiology, Harvard TH Chan School of Public Health, Harvard University, Boston, MA USA
| | - Ole A. Andreassen
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Patrick F. Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Fang Fang
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
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10
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Chen Z, Gao N, Wang X, Chen X, Zeng Y, Li C, Yang X, Cai Q, Wang X. Shared genetic aetiology of respiratory diseases: a genome-wide multitraits association analysis. BMJ Open Respir Res 2024; 11:e002148. [PMID: 38834332 PMCID: PMC11163672 DOI: 10.1136/bmjresp-2023-002148] [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: 10/22/2023] [Accepted: 05/10/2024] [Indexed: 06/06/2024] Open
Abstract
OBJECTIVE This study aims to explore the common genetic basis between respiratory diseases and to identify shared molecular and biological mechanisms. METHODS This genome-wide pleiotropic association study uses multiple statistical methods to systematically analyse the shared genetic basis between five respiratory diseases (asthma, chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, lung cancer and snoring) using the largest publicly available genome wide association studies summary statistics. The missions of this study are to evaluate global and local genetic correlations, to identify pleiotropic loci, to elucidate biological pathways at the multiomics level and to explore causal relationships between respiratory diseases. Data were collected from 27 November 2022 to 30 March 2023 and analysed from 14 April 2023 to 13 July 2023. MAIN OUTCOMES AND MEASURES The primary outcomes are shared genetic loci, pleiotropic genes, biological pathways and estimates of genetic correlations and causal effects. RESULTS Significant genetic correlations were found for 10 paired traits in 5 respiratory diseases. Cross-Phenotype Association identified 12 400 significant potential pleiotropic single-nucleotide polymorphism at 156 independent pleiotropic loci. In addition, multitrait colocalisation analysis identified 15 colocalised loci and a subset of colocalised traits. Gene-based analyses identified 432 potential pleiotropic genes and were further validated at the transcriptome and protein levels. Both pathway enrichment and single-cell enrichment analyses supported the role of the immune system in respiratory diseases. Additionally, five pairs of respiratory diseases have a causal relationship. CONCLUSIONS AND RELEVANCE This study reveals the common genetic basis and pleiotropic genes among respiratory diseases. It provides strong evidence for further therapeutic strategies and risk prediction for the phenomenon of respiratory disease comorbidity.
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Affiliation(s)
- Zhe Chen
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University Department of Thoracic Surgery, Changsha, Hunan, China
| | - Ning Gao
- Department of Cardiovascular Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xuanye Wang
- Department of Oncology, Xi'an Jiaotong University Second Affiliated Hospital Department of Oncology, Xi'an, Shaanxi, China
| | - Xiangming Chen
- Department of Orthopaedics, Xiangya Hospital Central South University, Changsha, Hunan, China
| | - YaQi Zeng
- Department of Psychiatry, Brain Hospital of Hunan Province, Changsha, Hunan, China
| | - Cong Li
- Department of Radiology, The Second Xiangya Hospital of Central South University Department of Radiology, Changsha, Hunan, China
| | - Xiahong Yang
- Department of Anesthesiology, The Second Xiangya Hospital of Central South University Department of Anesthesiology, Changsha, Hunan, China
| | - Qidong Cai
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University Department of Thoracic Surgery, Changsha, Hunan, China
| | - Xiang Wang
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University Department of Thoracic Surgery, Changsha, Hunan, China
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11
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Li X, Zhou Z, Ma Y, Ding K, Xiao H, Chen D, Liu N. Shared Genetic Architectures between Coronary Artery Disease and Type 2 Diabetes Mellitus in East Asian and European Populations. Biomedicines 2024; 12:1243. [PMID: 38927450 PMCID: PMC11201280 DOI: 10.3390/biomedicines12061243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/23/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024] Open
Abstract
Coronary artery disease (CAD) is a common comorbidity of type 2 diabetes mellitus (T2DM). However, the pathophysiology connecting these two phenotypes remains to be further understood. Combined analysis in multi-ethnic populations can help contribute to deepening our understanding of biological mechanisms caused by shared genetic loci. We applied genetic correlation analysis and then performed conditional and joint association analyses in Chinese, Japanese, and European populations to identify the genetic variants jointly associated with CAD and T2DM. Next, the associations between genes and the two traits were also explored. Finally, fine-mapping and functional enrichment analysis were employed to identify the potential causal variants and pathways. Genetic correlation results indicated significant genetic overlap between CAD and T2DM in the three populations. Over 10,000 shared signals were identified, and 587 were shared by East Asian and European populations. Fifty-six novel shared genes were found to have significant effects on both CAD and T2DM. Most loci were fine-mapped to plausible causal variant sets. Several similarities and differences of the involved genes in GO terms and KEGG pathways were revealed across East Asian and European populations. These findings highlight the importance of immunoregulation, neuroregulation, heart development, and the regulation of glucose metabolism in shared etiological mechanisms between CAD and T2DM.
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Affiliation(s)
- Xiaoyi Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Zechen Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Yujia Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Kexin Ding
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Han Xiao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Dafang Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (X.L.); (Z.Z.); (Y.M.); (K.D.); (H.X.)
| | - Na Liu
- Department of Neurology, Peking University Third Hospital, Beijing 100191, China
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12
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Deiana G, He J, Cabrera-Mendoza B, Ciccocioppo R, Napolioni V, Polimanti R. Brain-wide pleiotropy investigation of alcohol drinking and tobacco smoking behaviors. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.27.24307989. [PMID: 38854122 PMCID: PMC11160805 DOI: 10.1101/2024.05.27.24307989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
To investigate the pleiotropic mechanisms linking brain structure and function to alcohol drinking and tobacco smoking, we integrated genome-wide data generated by the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN; up to 805,431 participants) with information related to 3,935 brain imaging-derived phenotypes (IDPs) available from UK Biobank (N=33,224). We observed global genetic correlation of smoking behaviors with white matter hyperintensities, the morphology of the superior longitudinal fasciculus, and the mean thickness of pole-occipital. With respect to the latter brain IDP, we identified a local genetic correlation with age at which the individual began smoking regularly (hg38 chr2:35,895,678-36,640,246: rho=1, p=1.01×10 -5 ). This region has been previously associated with smoking initiation, educational attainment, chronotype, and cortical thickness. Our genetically informed causal inference analysis using both latent causal variable approach and Mendelian randomization linked the activity of prefrontal and premotor cortex and that of superior and inferior precentral sulci, and cingulate sulci to the number of alcoholic drinks per week (genetic causality proportion, gcp=0.38, p=8.9×10 -4 , rho=-0.18±0.07; inverse variance weighting, IVW beta=-0.04, 95%CI=-0.07 - -0.01). This relationship could be related to the role of these brain regions in the modulation of reward-seeking motivation and the processing of social cues. Overall, our brain-wide investigation highlighted that different pleiotropic mechanisms likely contribute to the relationship of brain structure and function with alcohol drinking and tobacco smoking, suggesting decision-making activities and chemosensory processing as modulators of propensity towards alcohol and tobacco consumption.
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13
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Adewuyi EO, Porter T, O'Brien EK, Olaniru O, Verdile G, Laws SM. Genome-wide cross-disease analyses highlight causality and shared biological pathways of type 2 diabetes with gastrointestinal disorders. Commun Biol 2024; 7:643. [PMID: 38802514 PMCID: PMC11130317 DOI: 10.1038/s42003-024-06333-z] [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: 11/09/2023] [Accepted: 05/15/2024] [Indexed: 05/29/2024] Open
Abstract
Studies suggest links between diabetes and gastrointestinal (GI) traits; however, their underlying biological mechanisms remain unclear. Here, we comprehensively assess the genetic relationship between type 2 diabetes (T2D) and GI disorders. Our study demonstrates a significant positive global genetic correlation of T2D with peptic ulcer disease (PUD), irritable bowel syndrome (IBS), gastritis-duodenitis, gastroesophageal reflux disease (GERD), and diverticular disease, but not inflammatory bowel disease (IBD). We identify several positive local genetic correlations (negative for T2D - IBD) contributing to T2D's relationship with GI disorders. Univariable and multivariable Mendelian randomisation analyses suggest causal effects of T2D on PUD and gastritis-duodenitis and bidirectionally with GERD. Gene-based analyses reveal a gene-level genetic overlap between T2D and GI disorders and identify several shared genes reaching genome-wide significance. Pathway-based study implicates leptin (T2D - IBD), thyroid, interferon, and notch signalling (T2D - IBS), abnormal circulating calcium (T2D - PUD), cardiovascular, viral, proinflammatory and (auto)immune-mediated mechanisms in T2D and GI disorders. These findings support a risk-increasing genetic overlap between T2D and GI disorders (except IBD), implicate shared biological pathways with putative causality for certain T2D - GI pairs, and identify targets for further investigation.
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Affiliation(s)
- Emmanuel O Adewuyi
- Centre for Precision Health, Edith Cowan University, Joondalup, 6027, Western, Australia.
- Collaborative Genomics and Translation Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, 6027, Western, Australia.
| | - Tenielle Porter
- Centre for Precision Health, Edith Cowan University, Joondalup, 6027, Western, Australia
- Collaborative Genomics and Translation Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, 6027, Western, Australia
- Curtin Medical School, Curtin University, Bentley, 6102, Western, Australia
| | - Eleanor K O'Brien
- Centre for Precision Health, Edith Cowan University, Joondalup, 6027, Western, Australia
- Collaborative Genomics and Translation Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, 6027, Western, Australia
| | - Oladapo Olaniru
- Department of Diabetes, School of Cardiovascular and Metabolic Medicine & Sciences, King's College London, London, UK
| | - Giuseppe Verdile
- Curtin Medical School, Curtin University, Bentley, 6102, Western, Australia
- Curtin Health Innovation Research Institute, Curtin University, Bentley, 6102, Western, Australia
| | - Simon M Laws
- Centre for Precision Health, Edith Cowan University, Joondalup, 6027, Western, Australia.
- Collaborative Genomics and Translation Group, School of Medical and Health Sciences, Edith Cowan University, Joondalup, 6027, Western, Australia.
- Curtin Medical School, Curtin University, Bentley, 6102, Western, Australia.
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14
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Daskalakis NP, Iatrou A, Chatzinakos C, Jajoo A, Snijders C, Wylie D, DiPietro CP, Tsatsani I, Chen CY, Pernia CD, Soliva-Estruch M, Arasappan D, Bharadwaj RA, Collado-Torres L, Wuchty S, Alvarez VE, Dammer EB, Deep-Soboslay A, Duong DM, Eagles N, Huber BR, Huuki L, Holstein VL, Logue ΜW, Lugenbühl JF, Maihofer AX, Miller MW, Nievergelt CM, Pertea G, Ross D, Sendi MSE, Sun BB, Tao R, Tooke J, Wolf EJ, Zeier Z, Berretta S, Champagne FA, Hyde T, Seyfried NT, Shin JH, Weinberger DR, Nemeroff CB, Kleinman JE, Ressler KJ. Systems biology dissection of PTSD and MDD across brain regions, cell types, and blood. Science 2024; 384:eadh3707. [PMID: 38781393 PMCID: PMC11203158 DOI: 10.1126/science.adh3707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 04/05/2024] [Indexed: 05/25/2024]
Abstract
The molecular pathology of stress-related disorders remains elusive. Our brain multiregion, multiomic study of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) included the central nucleus of the amygdala, hippocampal dentate gyrus, and medial prefrontal cortex (mPFC). Genes and exons within the mPFC carried most disease signals replicated across two independent cohorts. Pathways pointed to immune function, neuronal and synaptic regulation, and stress hormones. Multiomic factor and gene network analyses provided the underlying genomic structure. Single nucleus RNA sequencing in dorsolateral PFC revealed dysregulated (stress-related) signals in neuronal and non-neuronal cell types. Analyses of brain-blood intersections in >50,000 UK Biobank participants were conducted along with fine-mapping of the results of PTSD and MDD genome-wide association studies to distinguish risk from disease processes. Our data suggest shared and distinct molecular pathology in both disorders and propose potential therapeutic targets and biomarkers.
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Affiliation(s)
- Nikolaos P. Daskalakis
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Artemis Iatrou
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Chris Chatzinakos
- McLean Hospital; Belmont, MA, 02478, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, 11203, USA
- VA New York Harbor Healthcare System, Brooklyn, NY, 11209, USA
| | - Aarti Jajoo
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Clara Snijders
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Dennis Wylie
- Center for Biomedical Research Support, The University of Texas at Austin; Austin, TX, 78712, USA
| | - Christopher P. DiPietro
- McLean Hospital; Belmont, MA, 02478, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Ioulia Tsatsani
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
- Department of Psychiatry and Neuropsychology, School for Mental Health, and Neuroscience (MHeNs), Maastricht University, Maastricht, 6229 ER, The Netherlands
| | | | - Cameron D. Pernia
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Marina Soliva-Estruch
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
- Department of Psychiatry and Neuropsychology, School for Mental Health, and Neuroscience (MHeNs), Maastricht University, Maastricht, 6229 ER, The Netherlands
| | - Dhivya Arasappan
- Center for Biomedical Research Support, The University of Texas at Austin; Austin, TX, 78712, USA
| | - Rahul A. Bharadwaj
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Stefan Wuchty
- Departments of Computer Science, University of Miami, Miami, FL, 33146, USA
- Department of Biology, University of Miami, Miami, FL, 33146, USA
| | - Victor E. Alvarez
- Department of Neurology, Boston University, Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
- VA Bedford Healthcare System, Bedford, MA, 01730, USA
- National Posttraumatic Stress Disorder Brain Bank, VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Eric B Dammer
- Department of Biochemistry, Center for Neurodegenerative Disease, Emory School of Medicine; Atlanta GA, 30329, USA
| | - Amy Deep-Soboslay
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Duc M. Duong
- Department of Biochemistry, Center for Neurodegenerative Disease, Emory School of Medicine; Atlanta GA, 30329, USA
| | - Nick Eagles
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Bertrand R. Huber
- Department of Neurology, Boston University, Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
- National Posttraumatic Stress Disorder Brain Bank, VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Louise Huuki
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Vincent L Holstein
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | - Μark W. Logue
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, 02130, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
- Department of Biomedical Genetics, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Justina F. Lugenbühl
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
- Department of Psychiatry and Neuropsychology, School for Mental Health, and Neuroscience (MHeNs), Maastricht University, Maastricht, 6229 ER, The Netherlands
| | - Adam X. Maihofer
- Department of Psychiatry, University of California San Diego; La Jolla, CA, 92093, USA
- Center for Excellence in Stress and Mental Health, Veterans Affairs San Diego Healthcare System; San Diego, CA, 92161, USA
- Research Service, Veterans Affairs San Diego Healthcare System; San Diego, CA, 92161, USA
| | - Mark W. Miller
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, 02130, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
| | - Caroline M Nievergelt
- Department of Psychiatry, University of California San Diego; La Jolla, CA, 92093, USA
- Center for Excellence in Stress and Mental Health, Veterans Affairs San Diego Healthcare System; San Diego, CA, 92161, USA
- Research Service, Veterans Affairs San Diego Healthcare System; San Diego, CA, 92161, USA
| | - Geo Pertea
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Deanna Ross
- Department of Psychology, University of Texas at Austin; Austin, TX, 78712, USA
| | - Mohammad S. E Sendi
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | | | - Ran Tao
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - James Tooke
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Erika J. Wolf
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, 02130, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
| | - Zane Zeier
- Department of Psychiatry & Behavioral Sciences, Center for Therapeutic Innovation, University of Miami Miller School of Medicine; Miami, FL, 33136, USA
| | | | - Sabina Berretta
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA
| | | | - Thomas Hyde
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
| | - Nicholas T. Seyfried
- Department of Biochemistry, Center for Neurodegenerative Disease, Emory School of Medicine; Atlanta GA, 30329, USA
| | - Joo Heon Shin
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
| | - Daniel R. Weinberger
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
| | - Charles B. Nemeroff
- Department of Psychology, University of Texas at Austin; Austin, TX, 78712, USA
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin; Austin, TX, 78712, USA
| | - Joel E. Kleinman
- Lieber Institute for Brain Development; Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA
| | - Kerry J. Ressler
- McLean Hospital; Belmont, MA, 02478, USA
- Department of Psychiatry, Harvard Medical School; Boston, MA, 02115, USA
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15
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Teng J, Zhai T, Zhang X, Zhao C, Wang W, Tang H, Wang D, Shang Y, Ning C, Zhang Q. Improving multi-population genomic prediction accuracy using multi-trait GBLUP models which incorporate global or local genetic correlation information. Brief Bioinform 2024; 25:bbae276. [PMID: 38856170 PMCID: PMC11163384 DOI: 10.1093/bib/bbae276] [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: 02/23/2024] [Revised: 05/05/2024] [Accepted: 05/24/2024] [Indexed: 06/11/2024] Open
Abstract
In the application of genomic prediction, a situation often faced is that there are multiple populations in which genomic prediction (GP) need to be conducted. A common way to handle the multi-population GP is simply to combine the multiple populations into a single population. However, since these populations may be subject to different environments, there may exist genotype-environment interactions which may affect the accuracy of genomic prediction. In this study, we demonstrated that multi-trait genomic best linear unbiased prediction (MTGBLUP) can be used for multi-population genomic prediction, whereby the performances of a trait in different populations are regarded as different traits, and thus multi-population prediction is regarded as multi-trait prediction by employing the between-population genetic correlation. Using real datasets, we proved that MTGBLUP outperformed the conventional multi-population model that simply combines different populations together. We further proposed that MTGBLUP can be improved by partitioning the global between-population genetic correlation into local genetic correlations (LGC). We suggested two LGC models, LGC-model-1 and LGC-model-2, which partition the genome into regions with and without significant LGC (LGC-model-1) or regions with and without strong LGC (LGC-model-2). In analysis of real datasets, we demonstrated that the LGC models could increase universally the prediction accuracy and the relative improvement over MTGBLUP reached up to 163.86% (25.64% on average).
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Affiliation(s)
- Jun Teng
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
- Shandong Futeng Food Co. Ltd., Zaozhuang 277500, Shandong, China
| | - Tingting Zhai
- National Key Laboratory of Wheat Improvement, College of Life Science, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Xinyi Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Changheng Zhao
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Wenwen Wang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Hui Tang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Dan Wang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Yingli Shang
- College of Veterinary Medicine, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Chao Ning
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
| | - Qin Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, Shandong, China
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16
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Iakunchykova O, Pan M, Amlien IK, Roe JM, Walhovd KB, Fjell AM, Chen CH, Benros ME, Wang Y. Genetic evidence for the causal effects of C-reactive protein on self-reported habitual sleep duration. Brain Behav Immun Health 2024; 37:100754. [PMID: 38511149 PMCID: PMC10950822 DOI: 10.1016/j.bbih.2024.100754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 02/13/2024] [Accepted: 03/08/2024] [Indexed: 03/22/2024] Open
Abstract
Inflammatory responses to acute stimuli are proposed to regulate sleep, but the relationship between chronic inflammation and habitual sleep duration is elusive. Here, we study this relation using genetically predicted level of chronic inflammation, indexed by CRP and IL6 signaling, and self-reported sleep duration. By Mendelian randomization analysis, we show that elevated CRP level within <10 mg/L has a homeostatic effect that facilitates maintaining 7-8 h sleep duration per day - making short-sleepers sleep longer (p = 2.42 × 10-2) and long-sleepers sleep shorter (1.87 × 10-7); but it is not associated with the overall sleep duration (p = 0.17). This homeostatic effect replicated in an independent CRP dataset. We observed causal effects of the soluble interleukin 6 receptor and gp130 on overall sleep duration (p = 1.62 × 10-8, p = 2.61 × 10-58, respectively), but these effects disappeared when CRP effects were accounted for in the model. Using polygenic score analysis, we found that the homeostatic effect of CRP on sleep duration stems primarily from the genetic variants within the CRP gene region: when genetic variants outside of this region were used to predict CRP levels, the opposite direction of effect was observed. In conclusion, we show that elevated CRP level may causally facilitate maintaining an optimal sleep duration that is beneficial to health, thus updating our current knowledge of immune regulation on sleep.
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Affiliation(s)
- Olena Iakunchykova
- Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - Mengyu Pan
- Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - Inge K. Amlien
- Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - James M. Roe
- Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - Kristine B. Walhovd
- Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Rikshospitalet, POB 4950, Nydalen, 0424, Oslo, Norway
| | - Anders M. Fjell
- Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Rikshospitalet, POB 4950, Nydalen, 0424, Oslo, Norway
| | - Chi-Hua Chen
- Department of Radiology, University of California in San Diego, Gilman Drive 9500, 92093, La Jolla, CA, USA
| | - Michael E. Benros
- Copenhagen Research Centre for Mental Health, Mental Health Center Copenhagen, Copenhagen University Hospital, Gentofte Hospitalsvej 15, 2900, Hellerup, Denmark
| | - Yunpeng Wang
- Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
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17
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Nievergelt CM, Maihofer AX, Atkinson EG, Chen CY, Choi KW, Coleman JRI, Daskalakis NP, Duncan LE, Polimanti R, Aaronson C, Amstadter AB, Andersen SB, Andreassen OA, Arbisi PA, Ashley-Koch AE, Austin SB, Avdibegoviç E, Babić D, Bacanu SA, Baker DG, Batzler A, Beckham JC, Belangero S, Benjet C, Bergner C, Bierer LM, Biernacka JM, Bierut LJ, Bisson JI, Boks MP, Bolger EA, Brandolino A, Breen G, Bressan RA, Bryant RA, Bustamante AC, Bybjerg-Grauholm J, Bækvad-Hansen M, Børglum AD, Børte S, Cahn L, Calabrese JR, Caldas-de-Almeida JM, Chatzinakos C, Cheema S, Clouston SAP, Colodro-Conde L, Coombes BJ, Cruz-Fuentes CS, Dale AM, Dalvie S, Davis LK, Deckert J, Delahanty DL, Dennis MF, Desarnaud F, DiPietro CP, Disner SG, Docherty AR, Domschke K, Dyb G, Kulenović AD, Edenberg HJ, Evans A, Fabbri C, Fani N, Farrer LA, Feder A, Feeny NC, Flory JD, Forbes D, Franz CE, Galea S, Garrett ME, Gelaye B, Gelernter J, Geuze E, Gillespie CF, Goleva SB, Gordon SD, Goçi A, Grasser LR, Guindalini C, Haas M, Hagenaars S, Hauser MA, Heath AC, Hemmings SMJ, Hesselbrock V, Hickie IB, Hogan K, Hougaard DM, Huang H, Huckins LM, Hveem K, Jakovljević M, Javanbakht A, Jenkins GD, Johnson J, Jones I, Jovanovic T, Karstoft KI, Kaufman ML, Kennedy JL, Kessler RC, Khan A, Kimbrel NA, King AP, Koen N, Kotov R, Kranzler HR, Krebs K, Kremen WS, Kuan PF, Lawford BR, Lebois LAM, Lehto K, Levey DF, Lewis C, Liberzon I, Linnstaedt SD, Logue MW, Lori A, Lu Y, Luft BJ, Lupton MK, Luykx JJ, Makotkine I, Maples-Keller JL, Marchese S, Marmar C, Martin NG, Martínez-Levy GA, McAloney K, McFarlane A, McLaughlin KA, McLean SA, Medland SE, Mehta D, Meyers J, Michopoulos V, Mikita EA, Milani L, Milberg W, Miller MW, Morey RA, Morris CP, Mors O, Mortensen PB, Mufford MS, Nelson EC, Nordentoft M, Norman SB, Nugent NR, O'Donnell M, Orcutt HK, Pan PM, Panizzon MS, Pathak GA, Peters ES, Peterson AL, Peverill M, Pietrzak RH, Polusny MA, Porjesz B, Powers A, Qin XJ, Ratanatharathorn A, Risbrough VB, Roberts AL, Rothbaum AO, Rothbaum BO, Roy-Byrne P, Ruggiero KJ, Rung A, Runz H, Rutten BPF, de Viteri SS, Salum GA, Sampson L, Sanchez SE, Santoro M, Seah C, Seedat S, Seng JS, Shabalin A, Sheerin CM, Silove D, Smith AK, Smoller JW, Sponheim SR, Stein DJ, Stensland S, Stevens JS, Sumner JA, Teicher MH, Thompson WK, Tiwari AK, Trapido E, Uddin M, Ursano RJ, Valdimarsdóttir U, Van Hooff M, Vermetten E, Vinkers CH, Voisey J, Wang Y, Wang Z, Waszczuk M, Weber H, Wendt FR, Werge T, Williams MA, Williamson DE, Winsvold BS, Winternitz S, Wolf C, Wolf EJ, Xia Y, Xiong Y, Yehuda R, Young KA, Young RM, Zai CC, Zai GC, Zervas M, Zhao H, Zoellner LA, Zwart JA, deRoon-Cassini T, van Rooij SJH, van den Heuvel LL, Stein MB, Ressler KJ, Koenen KC. Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder. Nat Genet 2024; 56:792-808. [PMID: 38637617 DOI: 10.1038/s41588-024-01707-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 03/05/2024] [Indexed: 04/20/2024]
Abstract
Post-traumatic stress disorder (PTSD) genetics are characterized by lower discoverability than most other psychiatric disorders. The contribution to biological understanding from previous genetic studies has thus been limited. We performed a multi-ancestry meta-analysis of genome-wide association studies across 1,222,882 individuals of European ancestry (137,136 cases) and 58,051 admixed individuals with African and Native American ancestry (13,624 cases). We identified 95 genome-wide significant loci (80 new). Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators (for example, GRIA1, GRM8 and CACNA1E), developmental, axon guidance and transcription factors (for example, FOXP2, EFNA5 and DCC), synaptic structure and function genes (for example, PCLO, NCAM1 and PDE4B) and endocrine or immune regulators (for example, ESR1, TRAF3 and TANK). Additional top genes influence stress, immune, fear and threat-related processes, previously hypothesized to underlie PTSD neurobiology. These findings strengthen our understanding of neurobiological systems relevant to PTSD pathophysiology, while also opening new areas for investigation.
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Affiliation(s)
- Caroline M Nievergelt
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA.
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA.
| | - Adam X Maihofer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
| | - Elizabeth G Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Chia-Yen Chen
- Biogen Inc.,Translational Sciences, Cambridge, MA, USA
| | - Karmel W Choi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Jonathan R I Coleman
- King's College London, National Institute for Health and Care Research Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
- King's College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - Nikolaos P Daskalakis
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Center of Excellence in Depression and Anxiety Disorders, Belmont, MA, USA
| | - Laramie E Duncan
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Renato Polimanti
- VA Connecticut Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Cindy Aaronson
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Ananda B Amstadter
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
| | - Soren B Andersen
- The Danish Veteran Centre, Research and Knowledge Centre, Ringsted, Denmark
| | - Ole A Andreassen
- Oslo University Hospital, Division of Mental Health and Addiction, Oslo, Norway
- University of Oslo, Institute of Clinical Medicine, Oslo, Norway
| | - Paul A Arbisi
- Minneapolis VA Health Care System, Mental Health Service Line, Minneapolis, MN, USA
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA
| | | | - S Bryn Austin
- Boston Children's Hospital, Division of Adolescent and Young Adult Medicine, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Esmina Avdibegoviç
- Department of Psychiatry, University Clinical Center of Tuzla, Tuzla, Bosnia and Herzegovina
| | - Dragan Babić
- Department of Psychiatry, University Clinical Center of Mostar, Mostar, Bosnia and Herzegovina
| | - Silviu-Alin Bacanu
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Dewleen G Baker
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Psychiatry Service, San Diego, CA, USA
| | - Anthony Batzler
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Jean C Beckham
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Research, Durham VA Health Care System, Durham, NC, USA
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center (MIRECC), Genetics Research Laboratory, Durham, NC, USA
| | - Sintia Belangero
- Department of Morphology and Genetics, Universidade Federal de São Paulo, São Paulo, Brazil
- Department of Psychiatry, Universidade Federal de São Paulo, Laboratory of Integrative Neuroscience, São Paulo, Brazil
| | - Corina Benjet
- Instituto Nacional de Psiquiatraía Ramón de la Fuente Muñiz, Center for Global Mental Health, Mexico City, Mexico
| | - Carisa Bergner
- Medical College of Wisconsin, Comprehensive Injury Center, Milwaukee, WI, USA
| | - Linda M Bierer
- Department of Psychiatry, James J. Peters VA Medical Center, Bronx, NY, USA
| | - Joanna M Biernacka
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Laura J Bierut
- Department of Psychiatry, Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Jonathan I Bisson
- Cardiff University, National Centre for Mental Health, MRC Centre for Psychiatric Genetics and Genomics, Cardiff, UK
| | - Marco P Boks
- Department of Psychiatry, Brain Center University Medical Center Utrecht, Utrecht, The Netherlands
| | - Elizabeth A Bolger
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Amber Brandolino
- Department of Surgery, Division of Trauma & Acute Care Surgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Gerome Breen
- King's College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- King's College London, NIHR Maudsley BRC, London, UK
| | - Rodrigo Affonseca Bressan
- Department of Psychiatry, Universidade Federal de São Paulo, Laboratory of Integrative Neuroscience, São Paulo, Brazil
- Department of Psychiatry, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Richard A Bryant
- University of New South Wales, School of Psychology, Sydney, New South Wales, Australia
| | - Angela C Bustamante
- Department of Internal Medicine, University of Michigan Medical School, Division of Pulmonary and Critical Care Medicine, Ann Arbor, MI, USA
| | - Jonas Bybjerg-Grauholm
- Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
| | - Marie Bækvad-Hansen
- Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
| | - Anders D Børglum
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Aarhus University, Centre for Integrative Sequencing, iSEQ, Aarhus, Denmark
- Department of Biomedicine-Human Genetics, Aarhus University, Aarhus, Denmark
| | - Sigrid Børte
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, K. G. Jebsen Center for Genetic Epidemiology, Trondheim, Norway
- Oslo University Hospital, Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo, Norway
| | - Leah Cahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Joseph R Calabrese
- Case Western Reserve University, School of Medicine, Cleveland, OH, USA
- Department of Psychiatry, University Hospitals, Cleveland, OH, USA
| | | | - Chris Chatzinakos
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Division of Depression and Anxiety Disorders, Belmont, MA, USA
| | - Sheraz Cheema
- University of Toronto, CanPath National Coordinating Center, Toronto, Ontario, Canada
| | - Sean A P Clouston
- Stony Brook University, Family, Population, and Preventive Medicine, Stony Brook, NY, USA
- Stony Brook University, Public Health, Stony Brook, NY, USA
| | - Lucía Colodro-Conde
- QIMR Berghofer Medical Research Institute, Mental Health & Neuroscience Program, Brisbane, Queensland, Australia
| | - Brandon J Coombes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Carlos S Cruz-Fuentes
- Department of Genetics, Instituto Nacional de Psiquiatraía Ramón de la Fuente Muñiz, Mexico City, Mexico
| | - Anders M Dale
- Department of Radiology, Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Shareefa Dalvie
- Department of Pathology, University of Cape Town, Division of Human Genetics, Cape Town, South Africa
| | - Lea K Davis
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, TN, USA
| | - Jürgen Deckert
- University Hospital of Würzburg, Center of Mental Health, Psychiatry, Psychosomatics and Psychotherapy, Würzburg, Denmark
| | | | - Michelle F Dennis
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Research, Durham VA Health Care System, Durham, NC, USA
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center (MIRECC), Genetics Research Laboratory, Durham, NC, USA
| | - Frank Desarnaud
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Christopher P DiPietro
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- McLean Hospital, Division of Depression and Anxiety Disorders, Belmont, MA, USA
| | - Seth G Disner
- Minneapolis VA Health Care System, Research Service Line, Minneapolis, MN, USA
- Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Anna R Docherty
- Huntsman Mental Health Institute, Salt Lake City, UT, USA
- Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Katharina Domschke
- University of Freiburg, Faculty of Medicine, Centre for Basics in Neuromodulation, Freiburg, Denmark
- Department of Psychiatry and Psychotherapy, University of Freiburg, Faculty of Medicine, Freiburg, Denmark
| | - Grete Dyb
- University of Oslo, Institute of Clinical Medicine, Oslo, Norway
- Norwegian Centre for Violence and Traumatic Stress Studies, Oslo, Norway
| | - Alma Džubur Kulenović
- Department of Psychiatry, University Clinical Center of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Howard J Edenberg
- Indiana University School of Medicine, Biochemistry and Molecular Biology, Indianapolis, IN, USA
- Indiana University School of Medicine, Medical and Molecular Genetics, Indianapolis, IN, USA
| | - Alexandra Evans
- Cardiff University, National Centre for Mental Health, MRC Centre for Psychiatric Genetics and Genomics, Cardiff, UK
| | - Chiara Fabbri
- King's College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Negar Fani
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Lindsay A Farrer
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Ophthalmology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Adriana Feder
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Norah C Feeny
- Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Janine D Flory
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - David Forbes
- Department of Psychiatry, University of Melbourne, Melbourne, Victoria, Australia
| | - Carol E Franz
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Sandro Galea
- Boston University School of Public Health, Boston, MA, USA
| | - Melanie E Garrett
- Duke University, Duke Molecular Physiology Institute, Durham, NC, USA
| | - Bizu Gelaye
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Joel Gelernter
- VA Connecticut Healthcare Center, Psychiatry Service, West Haven, CT, USA
- Department of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Elbert Geuze
- Netherlands Ministry of Defence, Brain Research and Innovation Centre, Utrecht, The Netherlands
- Department of Psychiatry, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, The Netherlands
| | - Charles F Gillespie
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Slavina B Goleva
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, TN, USA
- National Institutes of Health, National Human Genome Research Institute, Bethesda, MD, USA
| | - Scott D Gordon
- QIMR Berghofer Medical Research Institute, Mental Health & Neuroscience Program, Brisbane, Queensland, Australia
| | - Aferdita Goçi
- Department of Psychiatry, University Clinical Centre of Kosovo, Prishtina, Kosovo
| | - Lana Ruvolo Grasser
- Wayne State University School of Medicine, Psychiatry and Behavioral Neurosciencess, Detroit, MI, USA
| | - Camila Guindalini
- Gallipoli Medical Research Foundation, Greenslopes Private Hospital, Greenslopes, Queensland, Australia
| | - Magali Haas
- Cohen Veterans Bioscience, New York City, NY, USA
| | - Saskia Hagenaars
- King's College London, National Institute for Health and Care Research Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
- King's College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - Michael A Hauser
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Andrew C Heath
- Department of Genetics, Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Sian M J Hemmings
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- SAMRC Genomics of Brain Disorders Research Unit, Stellenbosch University, Cape Town, South Africa
| | - Victor Hesselbrock
- University of Connecticut School of Medicine, Psychiatry, Farmington, CT, USA
| | - Ian B Hickie
- University of Sydney, Brain and Mind Centre, Sydney, New South Wales, Australia
| | - Kelleigh Hogan
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
| | - David Michael Hougaard
- Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
| | - Hailiang Huang
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Analytic and Translational Genetics Unit, Boston, MA, USA
| | - Laura M Huckins
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Kristian Hveem
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, K. G. Jebsen Center for Genetic Epidemiology, Trondheim, Norway
| | - Miro Jakovljević
- Department of Psychiatry, University Hospital Center of Zagreb, Zagreb, Croatia
| | - Arash Javanbakht
- Wayne State University School of Medicine, Psychiatry and Behavioral Neurosciencess, Detroit, MI, USA
| | - Gregory D Jenkins
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Jessica Johnson
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Ian Jones
- Cardiff University, National Centre for Mental Health, Cardiff University Centre for Psychiatric Genetics and Genomics, Cardiff, UK
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Karen-Inge Karstoft
- The Danish Veteran Centre, Research and Knowledge Centre, Ringsted, Denmark
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Milissa L Kaufman
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - James L Kennedy
- Centre for Addiction and Mental Health, Neurogenetics Section, Molecular Brain Science Department, Campbell Family Mental Health Research Institute, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, Tanenbaum Centre for Pharmacogenetics, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Alaptagin Khan
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Nathan A Kimbrel
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center (MIRECC), Genetics Research Laboratory, Durham, NC, USA
- Durham VA Health Care System, Mental Health Service Line, Durham, NC, USA
| | - Anthony P King
- The Ohio State University, College of Medicine, Institute for Behavioral Medicine Research, Columbus, OH, USA
| | - Nastassja Koen
- University of Cape Town, Department of Psychiatry & Neuroscience Institute, SA MRC Unit on Risk & Resilience in Mental Disorders, Cape Town, South Africa
| | - Roman Kotov
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
| | - Henry R Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kristi Krebs
- University of Tartu, Institute of Genomics, Estonian Genome Center, Tartu, Estonia
| | - William S Kremen
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Pei-Fen Kuan
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Bruce R Lawford
- Queensland University of Technology, School of Biomedical Sciences, Kelvin Grove, Queensland, Australia
| | - Lauren A M Lebois
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Center of Excellence in Depression and Anxiety Disorders, Belmont, MA, USA
| | - Kelli Lehto
- University of Tartu, Institute of Genomics, Estonian Genome Center, Tartu, Estonia
| | - Daniel F Levey
- VA Connecticut Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Catrin Lewis
- Cardiff University, National Centre for Mental Health, MRC Centre for Psychiatric Genetics and Genomics, Cardiff, UK
| | - Israel Liberzon
- Department of Psychiatry and Behavioral Sciences, Texas A&M University College of Medicine, Bryan, TX, USA
| | - Sarah D Linnstaedt
- Department of Anesthesiology, UNC Institute for Trauma Recovery, Chapel Hill, NC, USA
| | - Mark W Logue
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Boston University School of Medicine, Psychiatry, Biomedical Genetics, Boston, MA, USA
- VA Boston Healthcare System, National Center for PTSD, Boston, MA, USA
| | - Adriana Lori
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Benjamin J Luft
- Department of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Michelle K Lupton
- QIMR Berghofer Medical Research Institute, Mental Health & Neuroscience Program, Brisbane, Queensland, Australia
| | - Jurjen J Luykx
- Department of Psychiatry, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, The Netherlands
- Department of Translational Neuroscience, UMC Utrecht Brain Center Rudolf Magnus, Utrecht, The Netherlands
| | - Iouri Makotkine
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | | | - Shelby Marchese
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Charles Marmar
- New York University, Grossman School of Medicine, New York City, NY, USA
| | - Nicholas G Martin
- QIMR Berghofer Medical Research Institute, Genetics, Brisbane, Queensland, Australia
| | - Gabriela A Martínez-Levy
- Department of Genetics, Instituto Nacional de Psiquiatraía Ramón de la Fuente Muñiz, Mexico City, Mexico
| | - Kerrie McAloney
- QIMR Berghofer Medical Research Institute, Mental Health & Neuroscience Program, Brisbane, Queensland, Australia
| | - Alexander McFarlane
- University of Adelaide, Discipline of Psychiatry, Adelaide, South Australia, Australia
| | | | - Samuel A McLean
- Department of Anesthesiology, UNC Institute for Trauma Recovery, Chapel Hill, NC, USA
- Department of Emergency Medicine, UNC Institute for Trauma Recovery, Chapel Hill, NC, USA
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Mental Health & Neuroscience Program, Brisbane, Queensland, Australia
| | - Divya Mehta
- Queensland University of Technology, School of Biomedical Sciences, Kelvin Grove, Queensland, Australia
- Queensland University of Technology, Centre for Genomics and Personalised Health, Kelvin Grove, Queensland, Australia
| | - Jacquelyn Meyers
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Vasiliki Michopoulos
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Elizabeth A Mikita
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
| | - Lili Milani
- University of Tartu, Institute of Genomics, Estonian Genome Center, Tartu, Estonia
| | | | - Mark W Miller
- Boston University School of Medicine, Psychiatry, Biomedical Genetics, Boston, MA, USA
- VA Boston Healthcare System, National Center for PTSD, Boston, MA, USA
| | - Rajendra A Morey
- Duke University School of Medicine, Duke Brain Imaging and Analysis Center, Durham, NC, USA
| | - Charles Phillip Morris
- Queensland University of Technology, School of Biomedical Sciences, Kelvin Grove, Queensland, Australia
| | - Ole Mors
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Aarhus University Hospital-Psychiatry, Psychosis Research Unit, Aarhus, Denmark
| | - Preben Bo Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Aarhus University, Centre for Integrative Sequencing, iSEQ, Aarhus, Denmark
- Aarhus University, Centre for Integrated Register-Based Research, Aarhus, Denmark
- Aarhus University, National Centre for Register-Based Research, Aarhus, Denmark
| | - Mary S Mufford
- Department of Pathology, University of Cape Town, Division of Human Genetics, Cape Town, South Africa
| | - Elliot C Nelson
- Department of Psychiatry, Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- University of Copenhagen, Mental Health Services in the Capital Region of Denmark, Copenhagen, Denmark
| | - Sonya B Norman
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- National Center for Post Traumatic Stress Disorder, Executive Division, White River Junction, VT, USA
| | - Nicole R Nugent
- Department of Emergency Medicine, Alpert Brown Medical School, Providence, RI, USA
- Department of Pediatrics, Alpert Brown Medical School, Providence, RI, USA
- Department of Psychiatry and Human Behavior, Alpert Brown Medical School, Providence, RI, USA
| | - Meaghan O'Donnell
- Department of Psychiatry, University of Melbourne, Phoenix Australia, Melbourne, Victoria, Australia
| | - Holly K Orcutt
- Department of Psychology, Northern Illinois University, DeKalb, IL, USA
| | - Pedro M Pan
- Universidade Federal de São Paulo, Psychiatry, São Paulo, Brazil
| | - Matthew S Panizzon
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Gita A Pathak
- VA Connecticut Healthcare Center, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Edward S Peters
- University of Nebraska Medical Center, College of Public Health, Omaha, NE, USA
| | - Alan L Peterson
- South Texas Veterans Health Care System, Research and Development Service, San Antonio, TX, USA
- Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Matthew Peverill
- Department of Psychology, University of Washington, Seattle, WA, USA
| | - Robert H Pietrzak
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- U.S. Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, West Haven, CT, USA
| | - Melissa A Polusny
- Minneapolis VA Health Care System, Mental Health Service Line, Minneapolis, MN, USA
- Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN, USA
- Center for Care Delivery and Outcomes Research (CCDOR), Minneapolis, MN, USA
| | - Bernice Porjesz
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Abigail Powers
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Xue-Jun Qin
- Duke University, Duke Molecular Physiology Institute, Durham, NC, USA
| | - Andrew Ratanatharathorn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Columbia University Mailmain School of Public Health, New York City, NY, USA
| | - Victoria B Risbrough
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA
- Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA
| | - Andrea L Roberts
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alex O Rothbaum
- Department of Psychological Sciences, Emory University, Atlanta, GA, USA
- Department of Research and Outcomes, Skyland Trail, Atlanta, GA, USA
| | - Barbara O Rothbaum
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Peter Roy-Byrne
- Department of Psychiatry, University of Washington, Seattle, WA, USA
| | - Kenneth J Ruggiero
- Department of Nursing, Department of Psychiatry, Medical University of South Carolina, Charleston, SC, USA
| | - Ariane Rung
- Department of Epidemiology, Louisiana State University Health Sciences Center, School of Public Health, New Orleans, LA, USA
| | - Heiko Runz
- Biogen Inc., Research & Development, Cambridge, MA, USA
| | - Bart P F Rutten
- Department of Psychiatry and Neuropsychology, Maastricht Universitair Medisch Centrum, School for Mental Health and Neuroscience, Maastricht, The Netherlands
| | | | - Giovanni Abrahão Salum
- Child Mind Institute, New York City, NY, USA
- Instituto Nacional de Psiquiatria de Desenvolvimento, São Paulo, Brazil
| | - Laura Sampson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Sixto E Sanchez
- Department of Medicine, Universidad Peruana de Ciencias Aplicadas, Lima, Peru
| | - Marcos Santoro
- Universidade Federal de São Paulo, Departamento de Bioquímica-Disciplina de Biologia Molecular, São Paulo, Brazil
| | - Carina Seah
- Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Soraya Seedat
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- Stellenbosch University, SAMRC Extramural Genomics of Brain Disorders Research Unit, Cape Town, South Africa
| | - Julia S Seng
- Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, USA
- Department of Women's and Gender Studies, University of Michigan, Ann Arbor, MI, USA
- University of Michigan, Institute for Research on Women and Gender, Ann Arbor, MI, USA
- University of Michigan, School of Nursing, Ann Arbor, MI, USA
| | - Andrey Shabalin
- Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Christina M Sheerin
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, USA
| | - Derrick Silove
- Department of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia
| | - Alicia K Smith
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
- Department of Gynecology and Obstetrics, Department of Psychiatry and Behavioral Sciences, Department of Human Genetics, Emory University, Atlanta, GA, USA
| | - Jordan W Smoller
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Massachusetts General Hospital, Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Boston, MA, USA
| | - Scott R Sponheim
- Minneapolis VA Health Care System, Mental Health Service Line, Minneapolis, MN, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Dan J Stein
- University of Cape Town, Department of Psychiatry & Neuroscience Institute, SA MRC Unit on Risk & Resilience in Mental Disorders, Cape Town, South Africa
| | - Synne Stensland
- Oslo University Hospital, Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo, Norway
- Norwegian Centre for Violence and Traumatic Stress Studies, Oslo, Norway
| | - Jennifer S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Jennifer A Sumner
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Martin H Teicher
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Developmental Biopsychiatry Research Program, Belmont, MA, USA
| | - Wesley K Thompson
- Mental Health Centre Sct. Hans, Institute of Biological Psychiatry, Roskilde, Denmark
- University of California San Diego, Herbert Wertheim School of Public Health and Human Longevity Science, La Jolla, CA, USA
| | - Arun K Tiwari
- Centre for Addiction and Mental Health, Neurogenetics Section, Molecular Brain Science Department, Campbell Family Mental Health Research Institute, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, Tanenbaum Centre for Pharmacogenetics, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Edward Trapido
- Department of Epidemiology, Louisiana State University Health Sciences Center, School of Public Health, New Orleans, LA, USA
| | - Monica Uddin
- University of South Florida College of Public Health, Genomics Program, Tampa, FL, USA
| | - Robert J Ursano
- Department of Psychiatry, Uniformed Services University, Bethesda, MD, USA
| | - Unnur Valdimarsdóttir
- Karolinska Institutet, Unit of Integrative Epidemiology, Institute of Environmental Medicine, Stockholm, Sweden
- University of Iceland, Faculty of Medicine, Center of Public Health Sciences, School of Health Sciences, Reykjavik, Iceland
| | - Miranda Van Hooff
- University of Adelaide, Adelaide Medical School, Adelaide, South Australia, Australia
| | - Eric Vermetten
- ARQ Nationaal Psychotrauma Centrum, Psychotrauma Research Expert Group, Diemen, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
- Department of Psychiatry, New York University School of Medicine, New York City, NY, USA
| | - Christiaan H Vinkers
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, The Netherlands
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Joanne Voisey
- Queensland University of Technology, School of Biomedical Sciences, Kelvin Grove, Queensland, Australia
- Queensland University of Technology, Centre for Genomics and Personalised Health, Kelvin Grove, Queensland, Australia
| | - Yunpeng Wang
- Department of Psychology, University of Oslo, Lifespan Changes in Brain and Cognition (LCBC), Oslo, Norway
| | - Zhewu Wang
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
- Department of Mental Health, Ralph H Johnson VA Medical Center, Charleston, SC, USA
| | - Monika Waszczuk
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA
| | - Heike Weber
- University Hospital of Würzburg, Center of Mental Health, Psychiatry, Psychosomatics and Psychotherapy, Würzburg, Denmark
| | - Frank R Wendt
- Department of Anthropology, University of Toronto, Dalla Lana School of Public Health, Toronto, Ontario, Canada
| | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Copenhagen University Hospital, Institute of Biological Psychiatry, Mental Health Services, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- University of Copenhagen, The Globe Institute, Lundbeck Foundation Center for Geogenetics, Copenhagen, Denmark
| | - Michelle A Williams
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Douglas E Williamson
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
- Research, Durham VA Health Care System, Durham, NC, USA
| | - Bendik S Winsvold
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, K. G. Jebsen Center for Genetic Epidemiology, Trondheim, Norway
- Oslo University Hospital, Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Sherry Winternitz
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Christiane Wolf
- University Hospital of Würzburg, Center of Mental Health, Psychiatry, Psychosomatics and Psychotherapy, Würzburg, Denmark
| | - Erika J Wolf
- VA Boston Healthcare System, National Center for PTSD, Boston, MA, USA
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Yan Xia
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Analytic and Translational Genetics Unit, Boston, MA, USA
| | - Ying Xiong
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Rachel Yehuda
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Mental Health, James J. Peters VA Medical Center, Bronx, NY, USA
| | - Keith A Young
- Central Texas Veterans Health Care System, Research Service, Temple, TX, USA
- Department of Psychiatry and Behavioral Sciences, Texas A&M University School of Medicine, Bryan, TX, USA
| | - Ross McD Young
- Queensland University of Technology, School of Clinical Sciences, Kelvin Grove, Queensland, Australia
- University of the Sunshine Coast, The Chancellory, Sippy Downs, Queensland, Australia
| | - Clement C Zai
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Centre for Addiction and Mental Health, Neurogenetics Section, Molecular Brain Science Department, Campbell Family Mental Health Research Institute, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, Tanenbaum Centre for Pharmacogenetics, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathology, University of Toronto, Toronto, Ontario, Canada
| | - Gwyneth C Zai
- Centre for Addiction and Mental Health, Neurogenetics Section, Molecular Brain Science Department, Campbell Family Mental Health Research Institute, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, Tanenbaum Centre for Pharmacogenetics, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, General Adult Psychiatry and Health Systems Division, Toronto, Ontario, Canada
| | - Mark Zervas
- Cohen Veterans Bioscience, New York City, NY, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT, USA
| | - Lori A Zoellner
- Department of Psychology, University of Washington, Seattle, WA, USA
| | - John-Anker Zwart
- University of Oslo, Institute of Clinical Medicine, Oslo, Norway
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, K. G. Jebsen Center for Genetic Epidemiology, Trondheim, Norway
- Oslo University Hospital, Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo, Norway
| | - Terri deRoon-Cassini
- Department of Surgery, Division of Trauma & Acute Care Surgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Sanne J H van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Leigh L van den Heuvel
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- SAMRC Genomics of Brain Disorders Research Unit, Stellenbosch University, Cape Town, South Africa
| | - Murray B Stein
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Veterans Affairs San Diego Healthcare System, Psychiatry Service, San Diego, CA, USA
- University of California San Diego, School of Public Health, La Jolla, CA, USA
| | - Kerry J Ressler
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA
| | - Karestan C Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA
- Massachusetts General Hospital, Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Boston, MA, USA
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18
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Asgel Z, Kouakou MR, Koller D, Pathak GA, Cabrera-Mendoza B, Polimanti R. Unraveling COVID-19 relationship with anxiety disorders and symptoms using genome-wide data. J Affect Disord 2024; 352:333-341. [PMID: 38382819 PMCID: PMC10939738 DOI: 10.1016/j.jad.2024.02.061] [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: 08/07/2023] [Revised: 02/08/2024] [Accepted: 02/16/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND There is still a limited understanding of the dynamics contributing to the comorbidity of COVID-19 and anxiety outcomes. METHODS To dissect the pleiotropic mechanisms contributing to COVID-19/anxiety comorbidity, we used genome-wide data from UK Biobank (up to 420,531 participants), FinnGen Project (up to 329,077 participants), Million Veteran Program (175,163 participants), and COVID-19 Host Genetics Initiative (up to 122,616 cases and 2,475,240 controls). Specifically, we assessed global and local genetic correlation and genetically inferred effects linking COVID-19 outcomes (infection, hospitalization, and severe respiratory symptoms) to anxiety disorders and symptoms. RESULTS We observed a strong genetic correlation of anxiety disorder with COVID-19 positive status (rg = 0.35, p = 2×10-4) and COVID-19 hospitalization (rg = 0.31, p = 7.2×10-4). Among anxiety symptoms, "Tense, sore, or aching muscles during worst period of anxiety" was genetically correlated with COVID-19 positive status (rg = 0.33, p = 0.001), while "Frequent trouble falling or staying asleep during worst period of anxiety" was genetically correlated with COVID-19 hospitalization (rg = 0.24, p = 0.004). Through a latent causal variable analysis, we observed that COVID-19 outcomes have statistically significant genetic causality proportion (gcp) on anxiety symptoms (e.g., COVID-19 positive status→"Recent easy annoyance or irritability" │gcp│ = 0.18, p = 6.72×10-17). Conversely, anxiety disorders appear to have a possible causal effect on COVID-19 (│gcp│ = 0.38, p = 3.17×10-9). Additionally, we also identified multiple loci with evidence of local genetic correlation between anxiety and COVID-19. These appear to be related to genetic effects shared with lung function, brain morphology, alcohol and tobacco use, and hematologic parameters. CONCLUSIONS This study provided insights into the pleiotropic mechanisms linking COVID-19 and anxiety outcomes, suggesting differences between dynamics related to anxiety disorders and those related to anxiety symptoms.
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Affiliation(s)
- Zeynep Asgel
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Manuela R Kouakou
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Dora Koller
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Department of Genetics, Microbiology, and Statistics, Faculty of Biology, University of Barcelona, Catalonia, Spain
| | - Gita A Pathak
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; VA Connecticut Healthcare System, West Haven, CT, USA
| | - Brenda Cabrera-Mendoza
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; VA Connecticut Healthcare System, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; VA Connecticut Healthcare System, West Haven, CT, USA; Wu Tsai Institute, Yale University, New Haven, CT, USA.
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19
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Carrión-Castillo A, Boeckx C. Insights into the genetic architecture of cerebellar lobules derived from the UK Biobank. Sci Rep 2024; 14:9488. [PMID: 38664414 DOI: 10.1038/s41598-024-59699-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 04/15/2024] [Indexed: 06/19/2024] Open
Abstract
In this work we endeavor to further understand the genetic architecture of the cerebellum by examining the genetic underpinnings of the different cerebellar lob(ul)es, identifying their genetic relation to cortical and subcortical regions, as well as to psychiatric disorders, as well as traces of their evolutionary trajectories. We confirm the moderate heritability of cerebellar volumes, and reveal genetic clustering and variability across their different substructures, which warranted a detailed analysis using this higher structural resolution. We replicated known genetic correlations with several subcortical volumes, and report new cortico-cerebellar genetic correlations, including negative genetic correlations between anterior cerebellar lobules and cingulate, and positive ones between lateral Crus I and lobule VI with cortical measures in the fusiform region. Heritability partitioning for evolutionary annotations highlighted that the vermis of Crus II has depleted heritability in genomic regions of "archaic introgression deserts", but no enrichment/depletion of heritability in any other cerebellar regions. Taken together, these findings reveal novel insights into the genetic underpinnings of the different cerebellar lobules.
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Affiliation(s)
- Amaia Carrión-Castillo
- Basque Center on Cognition, Brain and Language (BCBL), Donostia-San Sebastián, Spain.
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain.
| | - Cedric Boeckx
- Universitat de Barcelona, Barcelona, Spain.
- Universitat de Barcelona Institute of Complex Systems, Barcelona, Spain.
- Universitat de Barcelona Institute of Neurosciences, Barcelona, Spain.
- Catalan Institute for Research and Advanced Studies (ICREA), Barcelona, Spain.
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20
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Caseras X, Simmonds E, Pardiñas AF, Anney R, Legge SE, Walters JTR, Harrison NA, O'Donovan MC, Escott-Price V. Common risk alleles for schizophrenia within the major histocompatibility complex predict white matter microstructure. Transl Psychiatry 2024; 14:194. [PMID: 38649377 PMCID: PMC11035599 DOI: 10.1038/s41398-024-02910-2] [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: 02/23/2024] [Revised: 03/15/2024] [Accepted: 04/12/2024] [Indexed: 04/25/2024] Open
Abstract
Recent research has highlighted the role of complement genes in shaping the microstructure of the brain during early development, and in contributing to common allele risk for Schizophrenia. We hypothesised that common risk variants for schizophrenia within complement genes will associate with structural changes in white matter microstructure within tracts innervating the frontal lobe. Results showed that risk alleles within the complement gene set, but also intergenic alleles, significantly predict axonal density in white matter tracts connecting frontal cortex with parietal, temporal and occipital cortices. Specifically, risk alleles within the Major Histocompatibility Complex region in chromosome 6 appeared to drive these associations. No significant associations were found for the orientation dispersion index. These results suggest that changes in axonal packing - but not in axonal coherence - determined by common risk alleles within the MHC genomic region - including variants related to the Complement system - appear as a potential neurobiological mechanism for schizophrenia.
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Affiliation(s)
- Xavier Caseras
- Centre for Neuropsychiatric Genetics and Genomics, Department of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK.
| | - Emily Simmonds
- Centre for Neuropsychiatric Genetics and Genomics, Department of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- Dementia Research Institute, London, UK
| | - Antonio F Pardiñas
- Centre for Neuropsychiatric Genetics and Genomics, Department of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Richard Anney
- Centre for Neuropsychiatric Genetics and Genomics, Department of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Sophie E Legge
- Centre for Neuropsychiatric Genetics and Genomics, Department of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - James T R Walters
- Centre for Neuropsychiatric Genetics and Genomics, Department of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Neil A Harrison
- Centre for Neuropsychiatric Genetics and Genomics, Department of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK
| | - Michael C O'Donovan
- Centre for Neuropsychiatric Genetics and Genomics, Department of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Valentina Escott-Price
- Centre for Neuropsychiatric Genetics and Genomics, Department of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- Dementia Research Institute, London, UK
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21
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Sun X, Qian Y, Cheng W, Ye D, Liu B, Zhou D, Wen C, Andreassen OA, Mao Y. Characterizing the polygenic overlap and shared loci between rheumatoid arthritis and cardiovascular diseases. BMC Med 2024; 22:152. [PMID: 38589871 PMCID: PMC11003061 DOI: 10.1186/s12916-024-03376-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 03/26/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Despite substantial research revealing that patients with rheumatoid arthritis (RA) have excessive morbidity and mortality of cardiovascular disease (CVD), the mechanism underlying this association has not been fully known. This study aims to systematically investigate the phenotypic and genetic correlation between RA and CVD. METHODS Based on UK Biobank, we conducted two cohort studies to evaluate the phenotypic relationships between RA and CVD, including atrial fibrillation (AF), coronary artery disease (CAD), heart failure (HF), and stroke. Next, we used linkage disequilibrium score regression, Local Analysis of [co]Variant Association, and bivariate causal mixture model (MiXeR) methods to examine the genetic correlation and polygenic overlap between RA and CVD, using genome-wide association summary statistics. Furthermore, we explored specific shared genetic loci by conjunctional false discovery rate analysis and association analysis based on subsets. RESULTS Compared with the general population, RA patients showed a higher incidence of CVD (hazard ratio [HR] = 1.21, 95% confidence interval [CI]: 1.15-1.28). We observed positive genetic correlations of RA with AF and stroke, and a mixture of negative and positive local genetic correlations underlying the global genetic correlation for CAD and HF, with 13 ~ 33% of shared genetic variants for these trait pairs. We further identified 23 pleiotropic loci associated with RA and at least one CVD, including one novel locus (rs7098414, TSPAN14, 10q23.1). Genes mapped to these shared loci were enriched in immune and inflammatory-related pathways, and modifiable risk factors, such as high diastolic blood pressure. CONCLUSIONS This study revealed the shared genetic architecture of RA and CVD, which may facilitate drug target identification and improved clinical management.
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Affiliation(s)
- Xiaohui Sun
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Yu Qian
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
- School of Life Sciences, Westlake University, Hangzhou, 310024, China
| | - Weiqiu Cheng
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, 0407, Norway
| | - Ding Ye
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Bin Liu
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Dan Zhou
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chengping Wen
- College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, 0407, Norway.
| | - Yingying Mao
- Department of Epidemiology, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China.
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22
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Tesfaye M, Jaholkowski P, Shadrin AA, van der Meer D, Hindley GF, Holen B, Parker N, Parekh P, Birkenæs V, Rahman Z, Bahrami S, Kutrolli G, Frei O, Djurovic S, Dale AM, Smeland OB, O’Connell KS, Andreassen OA. Identification of Novel Genomic Loci for Anxiety and Extensive Genetic Overlap with Psychiatric Disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.09.01.23294920. [PMID: 37693403 PMCID: PMC10491354 DOI: 10.1101/2023.09.01.23294920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Background Anxiety disorders are prevalent and anxiety symptoms co-occur with many psychiatric disorders. We aimed to identify genomic risk loci associated with anxiety, characterize its genetic architecture, and genetic overlap with psychiatric disorders. Methods We used the GWAS of anxiety symptoms, schizophrenia, bipolar disorder, major depression, and attention deficit hyperactivity disorder (ADHD). We employed MiXeR and LAVA to characterize the genetic architecture and genetic overlap between the phenotypes. Conditional and conjunctional false discovery rate analyses were performed to boost the identification of genomic loci associated with anxiety and those shared with psychiatric disorders. Gene annotation and gene set analyses were conducted using OpenTargets and FUMA, respectively. Results Anxiety was polygenic with 12.9k estimated genetic risk variants and overlapped extensively with psychiatric disorders (4.1-11.4k variants). MiXeR and LAVA revealed predominantly positive genetic correlations between anxiety and psychiatric disorders. We identified 114 novel loci for anxiety by conditioning on the psychiatric disorders. We also identified loci shared between anxiety and major depression (n = 47), bipolar disorder (n = 33), schizophrenia (n = 71), and ADHD (n = 20). Genes annotated to anxiety loci exhibit enrichment for a broader range of biological pathways and differential tissue expression in more diverse tissues than those annotated to the shared loci. Conclusions Anxiety is a highly polygenic phenotype with extensive genetic overlap with psychiatric disorders. These genetic overlaps enabled the identification of novel loci for anxiety. The shared genetic architecture may underlie the extensive cross-disorder comorbidity of anxiety, and the identified genetic loci implicate molecular pathways that may lead to potential drug targets.
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Affiliation(s)
- Markos Tesfaye
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Piotr Jaholkowski
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alexey A. Shadrin
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Dennis van der Meer
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guy F.L. Hindley
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Børge Holen
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Nadine Parker
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Pravesh Parekh
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Viktoria Birkenæs
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Zillur Rahman
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Gleda Kutrolli
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Srdjan Djurovic
- Department of Clinical Science, University of Bergen, Bergen, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Anders M. Dale
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Olav B. Smeland
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kevin S. O’Connell
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A. Andreassen
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
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23
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Galimberti M, Levey DF, Deak JD, Zhou H, Stein MB, Gelernter J. Genetic influences and causal pathways shared between cannabis use disorder and other substance use traits. Mol Psychiatry 2024:10.1038/s41380-024-02548-y. [PMID: 38580809 DOI: 10.1038/s41380-024-02548-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/25/2024] [Accepted: 03/28/2024] [Indexed: 04/07/2024]
Abstract
Cannabis use disorder (CanUD) has increased with the legalization of the use of cannabis. Around 20% of individuals using cannabis develop CanUD, and the number of users has grown with increasing ease of access. CanUD and other substance use disorders (SUDs) are associated phenotypically and genetically. We leveraged new CanUD genomics data to undertake genetically-informed analyses with unprecedented power, to investigate the genetic architecture and causal relationships between CanUD and lifetime cannabis use with risk for developing SUDs and substance use traits. Analyses included calculating local and global genetic correlations, genomic structural equation modeling (genomicSEM), and Mendelian Randomization (MR). Results from the genetic correlation and genomicSEM analyses demonstrated that CanUD and cannabis use differ in their relationships with SUDs and substance use traits. We found significant causal effects of CanUD influencing all the analyzed traits: opioid use disorder (OUD) (Inverse variant weighted, IVW β = 0.925 ± 0.082), problematic alcohol use (PAU) (IVW β = 0.443 ± 0.030), drinks per week (DPW) (IVW β = 0.182 ± 0.025), Fagerström Test for Nicotine Dependence (FTND) (IVW β = 0.183 ± 0.052), cigarettes per day (IVW β = 0.150 ± 0.045), current versus former smokers (IVW β = 0.178 ± 0.052), and smoking initiation (IVW β = 0.405 ± 0.042). We also found evidence of bidirectionality showing that OUD, PAU, smoking initiation, smoking cessation, and DPW all increase risk of developing CanUD. For cannabis use, bidirectional relationships were inferred with PAU, smoking initiation, and DPW; cannabis use was also associated with a higher risk of developing OUD (IVW β = 0.785 ± 0.266). GenomicSEM confirmed that CanUD and cannabis use load onto different genetic factors. We conclude that CanUD and cannabis use can increase the risk of developing other SUDs. This has substantial public health implications; the move towards legalization of cannabis use may be expected to increase other kinds of problematic substance use. These harmful outcomes are in addition to the medical harms associated directly with CanUD.
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Affiliation(s)
- Marco Galimberti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Daniel F Levey
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Joseph D Deak
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Hang Zhou
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Murray B Stein
- Department of Psychiatry and School of Public Health, University of California San Diego, La Jolla, CA, USA
- VA San Diego Healthcare System, San Diego, CA, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA.
- Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT, USA.
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24
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Enduru N, Fernandes BS, Bahrami S, Dai Y, Andreassen OA, Zhao Z. Genetic overlap between Alzheimer's disease and immune-mediated diseases: an atlas of shared genetic determinants and biological convergence. Mol Psychiatry 2024:10.1038/s41380-024-02510-y. [PMID: 38499654 DOI: 10.1038/s41380-024-02510-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 02/27/2024] [Accepted: 02/29/2024] [Indexed: 03/20/2024]
Abstract
The occurrence of immune disease comorbidities in Alzheimer's disease (AD) has been observed in both epidemiological and molecular studies, suggesting a neuroinflammatory basis in AD. However, their shared genetic components have not been systematically studied. Here, we composed an atlas of the shared genetic associations between 11 immune-mediated diseases and AD by analyzing genome-wide association studies (GWAS) summary statistics. Our results unveiled a significant genetic overlap between AD and 11 individual immune-mediated diseases despite negligible genetic correlations, suggesting a complex shared genetic architecture distributed across the genome. The shared loci between AD and immune-mediated diseases implicated several genes, including GRAMD1B, FUT2, ADAMTS4, HBEGF, WNT3, TSPAN14, DHODH, ABCB9, and TNIP1, all of which are protein-coding genes and thus potential drug targets. Top biological pathways enriched with these identified shared genes were related to the immune system and cell adhesion. In addition, in silico single-cell analyses showed enrichment of immune and brain cells, including neurons and microglia. In summary, our results suggest a genetic relationship between AD and the 11 immune-mediated diseases, pinpointing the existence of a shared however non-causal genetic basis. These identified protein-coding genes have the potential to serve as a novel path to therapeutic interventions for both AD and immune-mediated diseases and their comorbidities.
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Affiliation(s)
- Nitesh Enduru
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Brisa S Fernandes
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Shahram Bahrami
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Yulin Dai
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
- Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.
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25
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Li Y, Xue F, Li B, Yang Y, Fan Z, Shu J, Yang X, Wang X, Lin J, Copana C, Zhao B. Analyzing bivariate cross-trait genetic architecture in GWAS summary statistics with the BIGA cloud computing platform. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.28.538585. [PMID: 38559152 PMCID: PMC10979906 DOI: 10.1101/2023.04.28.538585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
As large-scale biobanks provide increasing access to deep phenotyping and genomic data, genome-wide association studies (GWAS) are rapidly uncovering the genetic architecture behind various complex traits and diseases. GWAS publications typically make their summary-level data (GWAS summary statistics) publicly available, enabling further exploration of genetic overlaps between phenotypes gathered from different studies and cohorts. However, systematically analyzing high-dimensional GWAS summary statistics for thousands of phenotypes can be both logistically challenging and computationally demanding. In this paper, we introduce BIGA (https://bigagwas.org/), a website that aims to offer unified data analysis pipelines and processed data resources for cross-trait genetic architecture analyses using GWAS summary statistics. We have developed a framework to implement statistical genetics tools on a cloud computing platform, combined with extensive curated GWAS data resources. Through BIGA, users can upload data, submit jobs, and share results, providing the research community with a convenient tool for consolidating GWAS data and generating new insights.
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Affiliation(s)
- Yujue Li
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Fei Xue
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Bingxuan Li
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Yilin Yang
- Department of Computer and Information Science and Electrical and Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Xiyao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Jinjie Lin
- Yale School of Management, Yale University, New Haven, CT 06511, USA
| | - Carlos Copana
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Applied Mathematics and Computational Science Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Population Aging Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
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26
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Zhang Z, Chen L, Zhang H, Xiao W, Yang J, Huang J, Hu Q, Jin K, Hong Y. Genetic correlations and causal relationships between cardio-metabolic traits and sepsis. Sci Rep 2024; 14:5718. [PMID: 38459230 PMCID: PMC10923865 DOI: 10.1038/s41598-024-56467-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 03/06/2024] [Indexed: 03/10/2024] Open
Abstract
Cardio-metabolic traits have been reported to be associated with the development of sepsis. It is, however, unclear whether these co-morbidities reflect causal associations, shared genetic heritability, or are confounded by environmental factors. We performed three analyses to explore the relationships between cardio-metabolic traits and sepsis. Mendelian randomization (MR) study to evaluate the causal effects of multiple cardio-metabolic traits on sepsis. Global genetic correlation analysis to explore the correlations between cardio-metabolic traits and sepsis. Local genetic correlation (GC) analysis to explore shared genetic heritability between cardio-metabolic traits and sepsis. Some loci were further examined for related genes responsible for the causal relationships. Genetic associations were obtained from the UK Biobank data or published large-scale genome-wide association studies with sample sizes between 200,000 to 750,000. In MR, we found causality between BMI and sepsis (OR: 1.53 [1.4-1.67]; p < 0.001). Body mass index (BMI), which is confirmed by sensitivity analyses and multivariable MR adjusting for confounding factors. Global GC analysis showed a significant correlation between BMI and sepsis (rg = 0.55, p < 0.001). More cardio-metabolic traits were identified to be correlated to the sepsis onset such as CRP (rg = 0.37, p = 0.035), type 2 diabetes (rg = 0.33, p < 0.001), HDL (rg = - 0.41, p < 0.001), and coronary artery disease (rg = 0.43, p < 0.001). Local GC revealed some shared genetic loci responsible for the causality. The top locus 1126 was located at chromosome 7 and comprised genes HIBADH, JAZF1, and CREB5. The present study provides evidence for an independent causal effect of BMI on sepsis. Further detailed analysis of the shared genetic heritability between cardio-metabolic traits and sepsis provides the opportunity to improve the preventive strategies for sepsis.
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Affiliation(s)
- Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China.
| | - Lin Chen
- Neurological Intensive Care Unit, Department of Neurosurgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
| | - Haoyang Zhang
- School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Wei Xiao
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China
| | - Jie Yang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China
| | - Jiajie Huang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China
| | - Qichao Hu
- Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province, Dian Diagnostics Group Co., Ltd., Hangzhou, Zhejiang, China
| | - Ketao Jin
- Department of Gastrointestinal, Colorectal and Anal Surgery, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, 310006, Zhejiang, China
| | - Yucai Hong
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China
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27
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Fanelli G, Franke B, Fabbri C, Werme J, Erdogan I, De Witte W, Poelmans G, Ruisch IH, Reus LM, van Gils V, Jansen WJ, Vos SJ, Alam KA, Martinez A, Haavik J, Wimberley T, Dalsgaard S, Fóthi Á, Barta C, Fernandez-Aranda F, Jimenez-Murcia S, Berkel S, Matura S, Salas-Salvadó J, Arenella M, Serretti A, Mota NR, Bralten J. Local patterns of genetic sharing challenge the boundaries between neuropsychiatric and insulin resistance-related conditions. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.07.24303921. [PMID: 38496672 PMCID: PMC10942494 DOI: 10.1101/2024.03.07.24303921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The co-occurrence of insulin resistance (IR)-related metabolic conditions with neuropsychiatric disorders is a complex public health challenge. Evidence of the genetic links between these phenotypes is emerging, but little is currently known about the genomic regions and biological functions that are involved. To address this, we performed Local Analysis of [co]Variant Association (LAVA) using large-scale (N=9,725-933,970) genome-wide association studies (GWASs) results for three IR-related conditions (type 2 diabetes mellitus, obesity, and metabolic syndrome) and nine neuropsychiatric disorders. Subsequently, positional and expression quantitative trait locus (eQTL)-based gene mapping and downstream functional genomic analyses were performed on the significant loci. Patterns of negative and positive local genetic correlations (|rg|=0.21-1, pFDR<0.05) were identified at 109 unique genomic regions across all phenotype pairs. Local correlations emerged even in the absence of global genetic correlations between IR-related conditions and Alzheimer's disease, bipolar disorder, and Tourette's syndrome. Genes mapped to the correlated regions showed enrichment in biological pathways integral to immune-inflammatory function, vesicle trafficking, insulin signalling, oxygen transport, and lipid metabolism. Colocalisation analyses further prioritised 10 genetically correlated regions for likely harbouring shared causal variants, displaying high deleterious or regulatory potential. These variants were found within or in close proximity to genes, such as SLC39A8 and HLA-DRB1, that can be targeted by supplements and already known drugs, including omega-3/6 fatty acids, immunomodulatory, antihypertensive, and cholesterol-lowering drugs. Overall, our findings underscore the complex genetic landscape of IR-neuropsychiatric multimorbidity, advocating for an integrated disease model and offering novel insights for research and treatment strategies in this domain.
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Affiliation(s)
- Giuseppe Fanelli
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Barbara Franke
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Chiara Fabbri
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Josefin Werme
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Izel Erdogan
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Ward De Witte
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Geert Poelmans
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
| | - I. Hyun Ruisch
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Lianne Maria Reus
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Center for Neurobehavioral Genetics, University of California, Los Angeles, Los Angeles, California, United States
| | - Veerle van Gils
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, Maastricht, The Netherlands
| | - Willemijn J. Jansen
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, Maastricht, The Netherlands
| | - Stephanie J.B. Vos
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, Maastricht, The Netherlands
| | | | - Aurora Martinez
- Department of Biomedicine, University of Bergen, Norway
- K.G. Jebsen Center for Translational Research in Parkinson’s Disease, University of Bergen, Norway
| | - Jan Haavik
- Department of Biomedicine, University of Bergen, Norway
- Division of Psychiatry, Haukeland University Hospital, Norway
| | - Theresa Wimberley
- National Centre for Register-based Research, School of Business and Social Sciences, Aarhus University, Aarhus, Denmark
- iPSYCH - The Lundbeck Foundation Initiative for Integrated Psychiatric Research, Aarhus, Denmark
| | - Søren Dalsgaard
- National Centre for Register-based Research, School of Business and Social Sciences, Aarhus University, Aarhus, Denmark
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Child and Adolescent Psychiatry Glostrup, Mental Health Services of the Capital Region, Hellerup, Denmark
| | - Ábel Fóthi
- Department of Molecular Biology, Institute of Biochemistry and Molecular Biology, Semmelweis University, Budapest, Hungary
| | - Csaba Barta
- Department of Molecular Biology, Institute of Biochemistry and Molecular Biology, Semmelweis University, Budapest, Hungary
| | - Fernando Fernandez-Aranda
- Clinical Psychology Department, University Hospital of Bellvitge, Barcelona, Spain
- Psychoneurobiology of Eating and Addictive Behaviors Group, Neurosciences Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
- CIBER Fisiopatología Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Barcelona, Spain
- Department of Clinical Sciences, School of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Susana Jimenez-Murcia
- Clinical Psychology Department, University Hospital of Bellvitge, Barcelona, Spain
- Psychoneurobiology of Eating and Addictive Behaviors Group, Neurosciences Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
- CIBER Fisiopatología Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Barcelona, Spain
- Department of Clinical Sciences, School of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
- Psychological Services, University of Barcelona, Spain
| | - Simone Berkel
- Institute of Human Genetics, Heidelberg University, Heidelberg, Germany
| | - Silke Matura
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Jordi Salas-Salvadó
- Universitat Rovira i Virgili, Biochemistry and biotechnology Department, Grup Alimentació, Nutrició, Desenvolupament i Salut Mental, Unitat de Nutrició Humana, Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Martina Arenella
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, UK
| | | | - Nina Roth Mota
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Janita Bralten
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
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28
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Zhao T, Wang F, Mott R, Dekkers J, Cheng H. Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality. Genetics 2024; 226:iyad210. [PMID: 38085098 PMCID: PMC11090459 DOI: 10.1093/genetics/iyad210] [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: 10/09/2023] [Accepted: 11/13/2023] [Indexed: 03/08/2024] Open
Abstract
To adhere to and capitalize on the benefits of the FAIR (findable, accessible, interoperable, and reusable) principles in agricultural genome-to-phenome studies, it is crucial to address privacy and intellectual property issues that prevent sharing and reuse of data in research and industry. Direct sharing of genotype and phenotype data is often prohibited due to intellectual property and privacy concerns. Thus, there is a pressing need for encryption methods that obscure confidential aspects of the data, without affecting the outcomes of certain statistical analyses. A homomorphic encryption method for genotypes and phenotypes (HEGP) has been proposed for single-marker regression in genome-wide association studies (GWAS) using linear mixed models with Gaussian errors. This methodology permits frequentist likelihood-based parameter estimation and inference. In this paper, we extend HEGP to broader applications in genome-to-phenome analyses. We show that HEGP is suited to commonly used linear mixed models for genetic analyses of quantitative traits including genomic best linear unbiased prediction (GBLUP) and ridge-regression best linear unbiased prediction (RR-BLUP), as well as Bayesian variable selection methods (e.g. those in Bayesian Alphabet), for genetic parameter estimation, genomic prediction, and GWAS. By advancing the capabilities of HEGP, we offer researchers and industry professionals a secure and efficient approach for collaborative genomic analyses while preserving data confidentiality.
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Affiliation(s)
- Tianjing Zhao
- Department of Animal Science, University of California, Davis, CA 95616, USA
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Fangyi Wang
- Department of Plant Sciences, University of California, Davis, CA 95616, USA
| | - Richard Mott
- Genetics Institute, University College London, London, WC1E 6BT, UK
| | - Jack Dekkers
- Department of Animal Science, Iowa State University, Ames, IA 50011, USA
| | - Hao Cheng
- Department of Animal Science, University of California, Davis, CA 95616, USA
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Lu HF, Chou CH, Lin YJ, Uchiyama S, Terao C, Wang YW, Yang JS, Liu TY, Wong HSC, Chen SCC, Tsai FJ. The genome-wide association study of serum IgE levels demonstrated a shared genetic background in allergic diseases. Clin Immunol 2024; 260:109897. [PMID: 38199299 DOI: 10.1016/j.clim.2024.109897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 12/12/2023] [Accepted: 01/03/2024] [Indexed: 01/12/2024]
Abstract
Immunoglobulin E (IgE) synthessis is highly related to a variety of atopic diseases, and several genome-wide association studies (GWASs) have demonstrated the association between genes and IgE level. In this study, we conducted the largest genome-wide association study of IgE involving a Taiwanese Han population. Eight independent variants exhibited genome-wide significance. Among them, an intronic SNP of CD28, rs1181388, and an intergenic SNP, rs1002957030, on 11q23.2 were identified as novel signals for IgE. Seven of the loci were replicated successfully in a meta-analysis using data on Japanese population. Among all the human leukocyte antigen (HLA) regions, HLA-DQA1*03:02 - HLA-DQB1*03:03 was the most significant haplotype (OR = 1.25, SE = 0.02, FDR = 1.6 × 10-14), corresponding to HLA-DQA1 Asp160 and HLA-DQB1 Leu87 amino acid residues. The genetic correlation showed significance between IgE and allergic diseases including asthma, atopic dermatitis, and pollinosis. IgE PRS was significantly correlated with total IgE levels. Furthermore, the top decile IgE polygenic risk score (PRS) group had the highest risk of asthma for the Taiwan Biobank and Biobank Japan cohorts. IgE PRS may be used to aid in predicting the occurrence of allergic reactions before symptoms occur and biomarkers are detectable. Our study provided a more comprehensive understanding of the impact of genomic variants, including complex HLA alleles, on serum IgE levels.
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Affiliation(s)
- Hsing-Fang Lu
- Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan; Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Chen-Hsing Chou
- PhD Program for Health Science and Industry, College of Health Care, China Medical University, Taichung, Taiwan; Department of Health Services Administration, China Medical University, Taichung, Taiwan
| | - Ying-Ju Lin
- Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan; School of Chinese Medicine, China Medical University, Taichung, Taiwan
| | - Shunsuke Uchiyama
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan; The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Yu-Wen Wang
- Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan
| | - Jai-Sing Yang
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, 404327, Taiwan
| | - Ting-Yuan Liu
- Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan
| | - Henry Sung-Ching Wong
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Sean Chun-Chang Chen
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Fuu-Jen Tsai
- Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan; School of Chinese Medicine, China Medical University, Taichung, Taiwan; Department of Biotechnology and Bioinformatics, Asia University, Taichung, Taiwan.
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30
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Chen TT, Kim J, Lam M, Chuang YF, Chiu YL, Lin SC, Jung SH, Kim B, Kim S, Cho C, Shim I, Park S, Ahn Y, Okbay A, Jang H, Kim HJ, Seo SW, Park WY, Ge T, Huang H, Feng YCA, Lin YF, Myung W, Chen CY, Won HH. Shared genetic architectures of educational attainment in East Asian and European populations. Nat Hum Behav 2024; 8:562-575. [PMID: 38182883 PMCID: PMC10963262 DOI: 10.1038/s41562-023-01781-9] [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: 03/19/2023] [Accepted: 11/09/2023] [Indexed: 01/07/2024]
Abstract
Educational attainment (EduYears), a heritable trait often used as a proxy for cognitive ability, is associated with various health and social outcomes. Previous genome-wide association studies (GWASs) on EduYears have been focused on samples of European (EUR) genetic ancestries. Here we present the first large-scale GWAS of EduYears in people of East Asian (EAS) ancestry (n = 176,400) and conduct a cross-ancestry meta-analysis with EduYears GWAS in people of EUR ancestry (n = 766,345). EduYears showed a high genetic correlation and power-adjusted transferability ratio between EAS and EUR. We also found similar functional enrichment, gene expression enrichment and cross-trait genetic correlations between two populations. Cross-ancestry fine-mapping identified refined credible sets with a higher posterior inclusion probability than single population fine-mapping. Polygenic prediction analysis in four independent EAS and EUR cohorts demonstrated transferability between populations. Our study supports the need for further research on diverse ancestries to increase our understanding of the genetic basis of educational attainment.
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Affiliation(s)
- Tzu-Ting Chen
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Jaeyoung Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Max Lam
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Human Genetics, Genome Institute of Singapore, Singapore, Singapore
- Division of Psychiatry Research, the Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Research Division Institute of Mental Health Singapore, Singapore, Singapore
| | - Yi-Fang Chuang
- Institute of Public Health and International Health Program, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yen-Ling Chiu
- Graduate Institute of Medicine, Yuan Ze University, Taoyuan City, Taiwan
- Department of Medical Research, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Shu-Chin Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Beomsu Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Soyeon Kim
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Chamlee Cho
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Injeong Shim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Sanghyeon Park
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Yeeun Ahn
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Aysu Okbay
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Hyemin Jang
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, South Korea
| | - Hee Jin Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, South Korea
| | - Sang Won Seo
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, South Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Tian Ge
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yen-Chen Anne Feng
- Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei City, Taiwan
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei City, Taiwan
| | - Yen-Feng Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan.
- Department of Public Health and 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.
| | - Woojae Myung
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea.
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea.
| | | | - Hong-Hee Won
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
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Waller C, Ho A, Batzler A, Geske J, Karpyak V, Biernacka J, Winham S. Genetic correlations of alcohol consumption and alcohol use disorder with sex hormone levels in females and males. RESEARCH SQUARE 2024:rs.3.rs-3944066. [PMID: 38464231 PMCID: PMC10925434 DOI: 10.21203/rs.3.rs-3944066/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Background Alcohol consumption behaviors and alcohol use disorder risk and presentation differ by sex, and these complex traits are associated with blood concentrations of the steroid sex hormones, testosterone and estradiol, and their regulatory binding proteins, sex hormone binding globulin (SHBG) and albumin. Genetic variation is associated with alcohol consumption and alcohol use disorder, as well as levels of steroid sex hormones and their binding proteins. Methods To assess the contribution of genetic factors to previously described phenotypic associations between alcohol-use traits and sex-hormone levels, we estimated genetic correlations (rg) using summary statistics from prior published, large sample size genome-wide association studies (GWAS) of alcohol consumption, alcohol dependence, testosterone, estradiol, SHBG, and albumin. Results For alcohol consumption, we observed positive genetic correlation (i.e. genetic effects in the same direction) with total testosterone in males (rg = 0.084, p = 0.007) and trends toward positive genetic correlation with bioavailable testosterone (rg = 0.060, p = 0.084) and SHBG in males (rg = 0.056, p = 0.086) and with albumin in a sex-combined cohort (rg = 0.082, p = 0.015); however in females, we observed positive genetic correlation with SHBG (rg = 0.089, p = 0.004) and a trend toward negative genetic correlation (i.e. genetic effects in opposite directions) with bioavailable testosterone (rg = -0.064, p = 0.032). For alcohol dependence, we observed a trend toward negative genetic correlation with total testosterone in females (rg = -0.106, p = 0.024) and positive genetic correlation with BMI-adjusted SHBG in males (rg = 0.119, p = 0.017). Several of these genetic correlations differed between females and males and were not in the same direction as the corresponding phenotypic associations. Conclusions Findings suggest that shared genetic effects may contribute to positive associations of alcohol consumption with albumin in both sexes, as well as positive associations between alcohol consumption and bioavailable testosterone and between alcohol dependence and SHBG in males. However, relative contributions of heritable and environmental factors to associations between alcohol-use traits and sex-hormone levels may differ by sex, with genetic factors contributing more in males and environmental factors contributing more in females.
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Friligkou E, Løkhammer S, Cabrera-Mendoza B, Shen J, He J, Deiana G, Zanoaga MD, Asgel Z, Pilcher A, Di Lascio L, Makharashvili A, Koller D, Tylee DS, Pathak GA, Polimanti R. Gene Discovery and Biological Insights into Anxiety Disorders from a Multi-Ancestry Genome-wide Association Study of >1.2 Million Participants. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.14.24302836. [PMID: 38405718 PMCID: PMC10889004 DOI: 10.1101/2024.02.14.24302836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
We leveraged information from more than 1.2 million participants to investigate the genetics of anxiety disorders across five continental ancestral groups. Ancestry-specific and cross-ancestry genome-wide association studies identified 51 anxiety-associated loci, 39 of which are novel. Additionally, polygenic risk scores derived from individuals of European descent were associated with anxiety in African, Admixed-American, and East Asian groups. The heritability of anxiety was enriched for genes expressed in the limbic system, the cerebral cortex, the cerebellum, the metencephalon, the entorhinal cortex, and the brain stem. Transcriptome- and proteome-wide analyses highlighted 115 genes associated with anxiety through brain-specific and cross-tissue regulation. We also observed global and local genetic correlations with depression, schizophrenia, and bipolar disorder and putative causal relationships with several physical health conditions. Overall, this study expands the knowledge regarding the genetic risk and pathogenesis of anxiety disorders, highlighting the importance of investigating diverse populations and integrating multi-omics information.
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33
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Cade BE, Redline S. Heritability and genetic correlations for sleep apnea, insomnia, and hypersomnia in a large clinical biobank. Sleep Health 2024; 10:S157-S160. [PMID: 38101993 PMCID: PMC11031312 DOI: 10.1016/j.sleh.2023.11.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 10/31/2023] [Accepted: 11/01/2023] [Indexed: 12/17/2023]
Abstract
RATIONALE Comorbid insomnia and sleep apnea is reported to have worse outcomes than either condition alone. The local genetic correlations of these disorders are unknown. OBJECTIVES To identify local genomic regions with heritability for clinically diagnosed sleep apnea and insomnia, and to identify local genetic correlations between these disorders and/or hypersomnia. METHODS Fifty thousand two hundred seventeen patients of European ancestry were examined. Global and local heritability and genetic correlations for independent regions were calculated, adjusting for obesity and other covariates. RESULTS Sleep apnea and insomnia were significantly globally heritable and had 118 and 168 genetic regions with local heritability p-values <.05, respectively. One region had a significant genetic correlation for sleep apnea and hypersomnia (p-value = 9.85 × 10-4). CONCLUSIONS Clinically diagnosed sleep apnea and insomnia have minimal shared genetic architecture, supporting genetically distinct comorbid insomnia and sleep apnea components. However, additional correlated regions may be identified with additional sample size and methodological improvements.
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Affiliation(s)
- Brian E Cade
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts, USA; Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA.
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts, USA; Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
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34
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Chen Y, Fan C, Liu J. Investigating the shared genetic architecture between COVID-19 and obesity: a large-scale genome wide cross-trait analysis. Front Endocrinol (Lausanne) 2024; 15:1325939. [PMID: 38352709 PMCID: PMC10862482 DOI: 10.3389/fendo.2024.1325939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 01/11/2024] [Indexed: 02/16/2024] Open
Abstract
Observational studies have reported high comorbidity between obesity and severe COVID-19. The aim of this study is to explore whether genetic factors are involved in the co-occurrence of the two traits. Based on the available genome-wide association studies (GWAS) summary statistics, we explored the genetic correlation and performed cross-trait meta-analysis (CPASSOC) and colocalization analysis (COLOC) to detect pleiotropic single nucleotide polymorphisms (SNPs). At the genetic level, we obtained genes detected by Functional mapping and annotation (FUMA) and the Multi-marker Analysis of GenoMic Annotation (MAGMA). Potential functional genes were further investigated by summary-data-based Mendelian randomization (SMR). Finally, the casualty was identiied using the latent causal variable model (LCV). A significant positive genetic correlation was revealed between obesity and COVID-19. We found 331 shared genetic SNPs by CPASSOC and 13 shared risk loci by COLOC. At the genetic level, We obtained 3546 pleiotropic genes, among which 107 genes were found to be significantly expressed by SMR. Lastly, we observed these genes were mainly enriched in immune pathways and signaling transduction. These indings could provide new insights into the etiology of comorbidity and have implications for future therapeutic trial.
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Affiliation(s)
- Yanjing Chen
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Chunhua Fan
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jun Liu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan, China
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Bergstedt J, Pasman JA, Ma Z, Harder A, Yao S, Parker N, Treur JL, Smit DJA, Frei O, Shadrin A, Meijsen JJ, Shen Q, Hägg S, Tornvall P, Buil A, Werge T, Hjerling-Leffler J, Als TD, Børglum AD, Lewis CM, McIntosh AM, Valdimarsdóttir UA, Andreassen OA, Sullivan PF, Lu Y, Fang F. Distinct genomic signatures and modifiable risk factors underly the comorbidity between major depressive disorder and cardiovascular disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.09.01.23294931. [PMID: 37693619 PMCID: PMC10491387 DOI: 10.1101/2023.09.01.23294931] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Using genomic data, this study elucidates biological mechanisms, key risk factors, and causal pathways underlying their comorbidity. We show that CVDs share a large proportion of their genetic risk factors with MDD. Multivariate genome-wide association analysis of the shared genetic liability between MDD and atherosclerotic CVD (ASCVD) revealed seven novel loci and distinct patterns of tissue and brain cell-type enrichments, suggesting a role for the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic, and psychosocial/lifestyle risk factors. Finally, we found support for causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and demonstrated that the causal effects were partly explained by metabolic and psychosocial/lifestyle factors. The distinct signature of MDD-ASCVD comorbidity aligns with the idea of an immunometabolic sub-type of MDD more strongly associated with CVD than overall MDD. In summary, we identify plausible biological mechanisms underlying MDD-CVD comorbidity, as well as key modifiable risk factors for prevention of CVD in individuals with MDD.
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Affiliation(s)
- Jacob Bergstedt
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Joëlle A Pasman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ziyan Ma
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Arvid Harder
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Shuyang Yao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department Medical Biochemistry and Biophysics, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Nadine Parker
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Jorien L Treur
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Dirk J A Smit
- Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Alexey Shadrin
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Joeri J Meijsen
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
| | - Qing Shen
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, China
- Institute for Advanced Study, Tongji University, Shanghai, China
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Per Tornvall
- Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Alfonso Buil
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
| | - Thomas Werge
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Copenhagen, Denmark
- Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jens Hjerling-Leffler
- Department Medical Biochemistry and Biophysics, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Thomas D Als
- Department of Molecular Medicine (MOMA), Molecular Diagnostic Laboratory, Aarhus University Hospital, Aarhus, 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
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
- Department of Medical and Molecular Genetics, King's College London, London, UK
| | - Andrew M McIntosh
- Centre for Clinical Brain Sciences, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
- Centre for Genomics and Experimental Medicine, University of Edinburgh, Edinburgh, UK
| | - Unnur A Valdimarsdóttir
- Centre of Public Health Sciences, Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Epidemiology, Harvard TH Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
| | - Ole A Andreassen
- NORMENT Centre, 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 and Oslo University Hospital, Oslo, Norway
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Departments of Genetics and Psychiatry, University of North Carolina at Chapel Hill, NC, USA
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Fang Fang
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
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Gupta P, Galimberti M, Liu Y, Beck S, Wingo A, Wingo T, Adhikari K, Stein MB, Gelernter J, Levey DF. A genome-wide investigation into the underlying genetic architecture of personality traits and overlap with psychopathology. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.17.24301428. [PMID: 38293137 PMCID: PMC10827244 DOI: 10.1101/2024.01.17.24301428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Personality is influenced by both genetic and environmental factors and is associated with other psychiatric traits such as anxiety and depression. The "Big Five" personality traits, which include neuroticism, extraversion, agreeableness, conscientiousness, and openness, are a widely accepted and influential framework for understanding and describing human personality. Of the big five personality traits, neuroticism has most often been the focus of genetic studies and is linked to various mental illnesses including depression, anxiety, and schizophrenia. Our knowledge of the genetic architecture of the other four personality traits is more limited. Utilizing the Million Veteran Program (MVP) cohort we conducted a genome-wide association study (GWAS) in individuals of European and African ancestry. Adding other published data, we performed GWAS meta-analysis for each of the five personality traits with sample sizes ranging from 237,390 to 682,688. We identified 158, 14, 3, 2, and 7 independent genome-wide significant (GWS) loci associated with neuroticism, extraversion, agreeableness, conscientiousness, and openness, respectively. These findings represent 55 novel loci for neuroticism, as well as the first GWS loci discovered for extraversion and agreeableness. Gene-based association testing revealed 254 genes showing significant association with at least one of the five personality traits. Transcriptome-wide and proteome-wide analysis identified altered expression of genes and proteins such as CRHR1, SLC12A5, MAPT, and STX4. Pathway enrichment and drug perturbation analyses identified complex biology underlying human personality traits. We also studied the inter-relationship of personality traits with 1,437 other traits in a phenome-wide genetic correlation analysis, identifying new associations. Mendelian randomization showed positive bidirectional effects between neuroticism and depression and anxiety while a negative bidirectional effect was observed for agreeableness and these psychiatric traits. This study improves our comprehensive understanding of the genetic architecture underlying personality traits and their relationship to other complex human traits.
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Affiliation(s)
- Priya Gupta
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Marco Galimberti
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Yue Liu
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, USA
| | - Sarah Beck
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Aliza Wingo
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, USA
- Atlanta Veterans Affairs Medical Center, USA
| | - Thomas Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, USA
| | - Keyrun Adhikari
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Murray B Stein
- Psychiatry Service, VA San Diego Healthcare System, San Diego, CA
- Departments of Psychiatry, School of Medicine, and Herbert Wertheim School of Public Health, University of California San Diego, La Jolla, CA
| | - Joel Gelernter
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
| | - Daniel F Levey
- Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare Center, West Haven, CT, USA
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Olasege BS, Oh ZY, Tahir MS, Porto-Neto LR, Hayes BJ, Fortes MRS. Genomic regions and biological pathways associated with sex-limited reproductive traits in bovine species. J Anim Sci 2024; 102:skae085. [PMID: 38545844 PMCID: PMC11135212 DOI: 10.1093/jas/skae085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 03/25/2024] [Indexed: 05/30/2024] Open
Abstract
Many animal species exhibit sex-limited traits, where certain phenotypes are exclusively expressed in one sex. Yet, the genomic regions that contribute to these sex-limited traits in males and females remain a subject of debate. Reproductive traits are ideal phenotypes to study sexual differences since they are mostly expressed in a sex-limited way. Therefore, this study aims to use local correlation analyses to identify genomic regions and biological pathways significantly associated with male and female sex-limited traits in two distinct cattle breeds (Brahman [BB] and Tropical Composite [TC]). We used the Correlation Scan method to perform local correlation analysis on 42 trait pairs consisting of six female and seven male reproductive traits recorded on ~1,000 animals for each sex in each breed. To pinpoint a specific region associated with these sex-limited reproductive traits, we investigated the genomic region(s) consistently identified as significant across the 42 trait pairs in each breed. The genes found in the identified regions were subjected to Quantitative Trait Loci (QTL) colocalization, QTL enrichment analyses, and functional analyses to gain biological insight into sexual differences. We found that the genomic regions associated with the sex-limited reproductive phenotypes are widely distributed across all the chromosomes. However, no single region across the genome was associated with all the 42 reproductive trait pairs in the two breeds. Nevertheless, we found a region on the X-chromosome to be most significant for 80% to 90% (BB: 33 and TC: 38) of the total 42 trait pairs. A considerable number of the genes in this region were regulatory genes. By considering only genomic regions that were significant for at least 50% of the 42 trait pairs, we observed more regions spread across the autosomes and the X-chromosome. All genomic regions identified were highly enriched for trait-specific QTL linked to sex-limited traits (percentage of normal sperm, metabolic weight, average daily gain, carcass weight, age at puberty, etc.). The gene list created from these identified regions was enriched for biological pathways that contribute to the observed differences between sexes. Our results demonstrate that genomic regions associated with male and female sex-limited reproductive traits are distributed across the genome. Yet, chromosome X seems to exert a relatively larger effect on the phenotypic variation observed between the sexes.
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Affiliation(s)
- Babatunde S Olasege
- The University of Queensland, School of Chemistry and Molecular Biosciences, Saint Lucia Campus, Brisbane, QLD, 4072, Australia
- Ag and Food, CSIRO Agriculture and Food, Saint Lucia, QLD, 4067, Australia
| | - Zhen Yin Oh
- The University of Queensland, School of Chemistry and Molecular Biosciences, Saint Lucia Campus, Brisbane, QLD, 4072, Australia
| | - Muhammad S Tahir
- The University of Queensland, School of Chemistry and Molecular Biosciences, Saint Lucia Campus, Brisbane, QLD, 4072, Australia
- Ag and Food, CSIRO Agriculture and Food, Saint Lucia, QLD, 4067, Australia
| | | | - Ben J Hayes
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Saint Lucia Campus, Brisbane, QLD, 4072, Australia
| | - Marina R S Fortes
- The University of Queensland, School of Chemistry and Molecular Biosciences, Saint Lucia Campus, Brisbane, QLD, 4072, Australia
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Saint Lucia Campus, Brisbane, QLD, 4072, Australia
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38
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Parker N, Cheng W, Hindley GFL, O'Connell KS, Karthikeyan S, Holen B, Shadrin AA, Rahman Z, Karadag N, Bahrami S, Lin A, Steen NE, Ueland T, Aukrust P, Djurovic S, Dale AM, Smeland OB, Frei O, Andreassen OA. Genetic Overlap Between Global Cortical Brain Structure, C-Reactive Protein, and White Blood Cell Counts. Biol Psychiatry 2024; 95:62-71. [PMID: 37348803 DOI: 10.1016/j.biopsych.2023.06.008] [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: 01/19/2023] [Revised: 06/02/2023] [Accepted: 06/11/2023] [Indexed: 06/24/2023]
Abstract
BACKGROUND For many brain disorders, a subset of patients jointly exhibit alterations in cortical brain structure and elevated levels of circulating immune markers. This may be driven in part by shared genetic architecture. Therefore, we investigated the phenotypic and genetic associations linking global cortical surface area and thickness with blood immune markers (i.e., white blood cell counts and plasma C-reactive protein levels). METHODS Linear regression was used to assess phenotypic associations in 30,823 UK Biobank participants. Genome-wide and local genetic correlations were assessed using linkage disequilibrium score regression and local analysis of covariance annotation. The number of shared trait-influencing genetic variants was estimated using MiXeR. Shared genetic architecture was assessed using a conjunctional false discovery rate framework, and mapped genes were included in gene-set enrichment analyses. RESULTS Cortical structure and blood immune markers exhibited predominantly inverse phenotypic associations. There were modest genome-wide genetic correlations, the strongest of which were for C-reactive protein levels (rg_surface_area = -0.13, false discovery rate-corrected p = 4.17 × 10-3; rg_thickness = -0.13, false discovery rate-corrected p = 4.00 × 10-2). Meanwhile, local genetic correlations showed a mosaic of positive and negative associations. White blood cells shared on average 46.24% and 38.64% of trait-influencing genetic variants with surface area and thickness, respectively. Additionally, surface area shared 55 unique loci with the blood immune markers while thickness shared 15. Overall, monocyte count exhibited the largest genetic overlap with cortical brain structure. A series of gene enrichment analyses implicated neuronal-, astrocytic-, and schizophrenia-associated genes. CONCLUSIONS The findings indicate shared genetic underpinnings for cortical brain structure and blood immune markers, with implications for neurodevelopment and understanding the etiology of brain-related disorders.
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Affiliation(s)
- Nadine Parker
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Weiqiu Cheng
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guy F L Hindley
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom
| | - Kevin S O'Connell
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sandeep Karthikeyan
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Børge Holen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alexey A Shadrin
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Zillur Rahman
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Naz Karadag
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Aihua Lin
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Nils Eiel Steen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Thor Ueland
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Research Institute of Internal Medicine, Oslo University Hospital, Oslo, Norway; KG Jebsen Thrombosis Research and Expertise Centre, University of Tromsø, Tromsø, Norway
| | - Pål Aukrust
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Research Institute of Internal Medicine, Oslo University Hospital Rikshospitalet, Oslo, Norway; Section of Clinical Immunology and Infectious Disease, Oslo University Hospital Rikshospitalet, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; Norwegian Centre for Mental Disorders Research, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, California; Department of Psychiatry, University of California, San Diego, La Jolla, California; Department of Neurosciences, University of California San Diego, La Jolla, California; Department of Radiology, University of California San Diego, La Jolla, California
| | - Olav B Smeland
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
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Schulze A, Streit F, Zillich L, Awasthi S, Hall ASM, Jungkunz M, Kleindienst N, Frank J, Schwarze CE, Dahmen N, Schott BH, Nöthen M, Mobascher A, Rujescu D, Lieb K, Roepke S, Herpertz SC, Schmahl C, Bohus M, Ripke S, Rietschel M, Lis S, Witt S. Evidence for a shared genetic contribution to loneliness and borderline personality disorder. Transl Psychiatry 2023; 13:398. [PMID: 38105248 PMCID: PMC10725864 DOI: 10.1038/s41398-023-02705-x] [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: 03/27/2023] [Revised: 11/30/2023] [Accepted: 12/05/2023] [Indexed: 12/19/2023] Open
Abstract
Loneliness, influenced by genetic and environmental factors such as childhood maltreatment, is one aspect of interpersonal dysfunction in Borderline Personality Disorder (BPD). Numerous studies link loneliness and BPD and twin studies indicate a genetic contribution to this association. The aim of our study was to investigate whether genetic predisposition for loneliness and BPD risk overlap and whether genetic risk for loneliness contributes to higher loneliness reported by BPD patients, using genome-wide genotype data. We assessed the genetic correlation of genome-wide association studies (GWAS) of loneliness and BPD using linkage disequilibrium score regression and tested whether a polygenic score for loneliness (loneliness-PGS) was associated with case-control status in two independent genotyped samples of BPD patients and healthy controls (HC; Witt2017-sample: 998 BPD, 1545 HC; KFO-sample: 187 BPD, 261 HC). In the KFO-sample, we examined associations of loneliness-PGS with reported loneliness, and whether the loneliness-PGS influenced the association between childhood maltreatment and loneliness. We found a genetic correlation between the GWAS of loneliness and BPD in the Witt2017-sample (rg = 0.23, p = 0.015), a positive association of loneliness-PGS with BPD case-control status (Witt2017-sample: NkR² = 2.3%, p = 2.7*10-12; KFO-sample: NkR² = 6.6%, p = 4.4*10-6), and a positive association between loneliness-PGS and loneliness across patient and control groups in the KFO-sample (β = 0.186, p = 0.002). The loneliness-PGS did not moderate the association between childhood maltreatment and loneliness in BPD. Our study is the first to use genome-wide genotype data to show that the genetic factors underlying variation in loneliness in the general population and the risk for BPD overlap. The loneliness-PGS was associated with reported loneliness. Further research is needed to investigate which genetic mechanisms and pathways are involved in this association and whether a genetic predisposition for loneliness contributes to BPD risk.
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Affiliation(s)
- Anna Schulze
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
| | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Lea Zillich
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Swapnil Awasthi
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Alisha S M Hall
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Affective Disorders, Aarhus University Hospital-Psychiatry, Aarhus, Denmark
| | - Martin Jungkunz
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Hospital, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg University, Medical Faculty Heidelberg, Department of Medical Oncology, Section Translational Medical Ethics, Heidelberg, Germany
| | - Nikolaus Kleindienst
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Josef Frank
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Cornelia E Schwarze
- Department of Psychology, Developmental and Biological Psychology Unit, Heidelberg University, Heidelberg, Germany
| | - Norbert Dahmen
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Mainz, Germany
| | - Björn H Schott
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
| | - Markus Nöthen
- Institute of Human Genetics, University Hospital Bonn, Bonn, Germany
| | - Arian Mobascher
- Department of Psychiatry and Psychotherapy, St. Elisabeth Krankenhaus Lahnstein, Lahnstein, Germany
| | - Dan Rujescu
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Klaus Lieb
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Mainz, Germany
| | - Stefan Roepke
- Department of Psychiatry and Neuroscience, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sabine C Herpertz
- Department of General Psychiatry, Center for Psychosocial Medicine, Medical Faculty, Heidelberg University, Heidelberg, Germany
| | - Christian Schmahl
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Martin Bohus
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Clinical Psychology, Ruhr University Bochum, Bochum, Germany
| | - Stephan Ripke
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
- Massachusetts General Hospital and Department of Medicine, Harvard Medical School, Analytic and Translational Genetics Unit, Boston, MA, USA
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stefanie Lis
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stephanie Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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Privé F, Albiñana C, Arbel J, Pasaniuc B, Vilhjálmsson BJ. Inferring disease architecture and predictive ability with LDpred2-auto. Am J Hum Genet 2023; 110:2042-2055. [PMID: 37944514 PMCID: PMC10716363 DOI: 10.1016/j.ajhg.2023.10.010] [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: 06/14/2023] [Revised: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 11/12/2023] Open
Abstract
LDpred2 is a widely used Bayesian method for building polygenic scores (PGSs). LDpred2-auto can infer the two parameters from the LDpred model, the SNP heritability h2 and polygenicity p, so that it does not require an additional validation dataset to choose best-performing parameters. The main aim of this paper is to properly validate the use of LDpred2-auto for inferring multiple genetic parameters. Here, we present a new version of LDpred2-auto that adds an optional third parameter α to its model, for modeling negative selection. We then validate the inference of these three parameters (or two, when using the previous model). We also show that LDpred2-auto provides per-variant probabilities of being causal that are well calibrated and can therefore be used for fine-mapping purposes. We also introduce a formula to infer the out-of-sample predictive performance r2 of the resulting PGS directly from the Gibbs sampler of LDpred2-auto. Finally, we extend the set of HapMap3 variants recommended to use with LDpred2 with 37% more variants to improve the coverage of this set, and we show that this new set of variants captures 12% more heritability and provides 6% more predictive performance, on average, in UK Biobank analyses.
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Affiliation(s)
- Florian Privé
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark.
| | - Clara Albiñana
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
| | - Julyan Arbel
- University Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Bjarni J Vilhjálmsson
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark; Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark; Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA
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41
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Arenella M, Fanelli G, Kiemeney LA, McAlonan G, Murphy DG, Bralten J. Genetic relationship between the immune system and autism. Brain Behav Immun Health 2023; 34:100698. [PMID: 38020478 PMCID: PMC10663755 DOI: 10.1016/j.bbih.2023.100698] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 12/01/2023] Open
Abstract
Autism spectrum disorder (ASD) is a common and complex neurodevelopmental condition. The pathophysiology of ASD is poorly defined; however, it includes a strong genetic component and there is increasing evidence to support a role of immune dysregulation. Nonetheless, it is unclear which immune phenotypes link to ASD through genetics. Hence, we investigated the genetic correlation between ASD and diverse classes of immune conditions and markers; and if these immune-related genetic factors link to specific autistic-like traits in the population. We estimated global and local genetic correlations between ASD (n = 55,420) and 11 immune phenotypes (n = 14,256-755,406) using genome-wide association study summary statistics. Subsequently, polygenic scores (PGS) for these immune phenotypes were calculated in a population-based sample (n = 2487) and associated to five autistic-like traits (i.e., attention to detail, childhood behaviour, imagination, rigidity, social skills), and a total autistic-like traits score. Sex-stratified PGS analyses were also performed. At the genome-wide level, ASD was positively correlated with allergic diseases (ALG), and negatively correlated with lymphocyte count, rheumatoid arthritis (RA), and systemic lupus erythematosus (SLE) (FDR-p = 0.01-0.02). At the local genetic level, ASD was correlated with RA, C-reactive protein, and granulocytes and lymphocyte counts (p = 5.8 × 10-6-0.002). In the general population sample, increased genetic liability for SLE, RA, ALG, and lymphocyte levels, captured by PGS, was associated with the total autistic score and with rigidity and childhood behaviour (FDR-p = 0.03). In conclusion, we demonstrated a genetic relationship between ASD and immunity that depends on the type of immune phenotype considered; some increase likelihood whereas others may potentially help build resilience. Also, this relationship may be restricted to specific genetic loci and link to specific autistic dimensions (e.g., rigidity).
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Affiliation(s)
- Martina Arenella
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands
| | - Giuseppe Fanelli
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Lambertus A. Kiemeney
- Department for Health Evidence, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Grainne McAlonan
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Maudsley and South London NHS Foundation, London, United Kingdom
| | - Declan G. Murphy
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Maudsley and South London NHS Foundation, London, United Kingdom
| | - Janita Bralten
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands
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42
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Stauffer EM, Bethlehem RAI, Dorfschmidt L, Won H, Warrier V, Bullmore ET. The genetic relationships between brain structure and schizophrenia. Nat Commun 2023; 14:7820. [PMID: 38016951 PMCID: PMC10684873 DOI: 10.1038/s41467-023-43567-7] [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: 04/06/2023] [Accepted: 11/14/2023] [Indexed: 11/30/2023] Open
Abstract
Genetic risks for schizophrenia are theoretically mediated by genetic effects on brain structure but it has been unclear which genes are associated with both schizophrenia and cortical phenotypes. We accessed genome-wide association studies (GWAS) of schizophrenia (N = 69,369 cases; 236,642 controls), and of three magnetic resonance imaging (MRI) metrics (surface area, cortical thickness, neurite density index) measured at 180 cortical areas (N = 36,843, UK Biobank). Using Hi-C-coupled MAGMA, 61 genes were significantly associated with both schizophrenia and one or more MRI metrics. Whole genome analysis with partial least squares demonstrated significant genetic covariation between schizophrenia and area or thickness of most cortical regions. Genetic similarity between cortical areas was strongly coupled to their phenotypic covariance, and genetic covariation between schizophrenia and brain phenotypes was strongest in the hubs of structural covariance networks. Pleiotropically associated genes were enriched for neurodevelopmental processes and positionally concentrated in chromosomes 3p21, 17q21 and 11p11. Mendelian randomization analysis indicated that genetically determined variation in a posterior cingulate cortical area could be causal for schizophrenia. Parallel analyses of GWAS on bipolar disorder, Alzheimer's disease and height showed that pleiotropic association with MRI metrics was stronger for schizophrenia compared to other disorders.
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Affiliation(s)
| | - Richard A I Bethlehem
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Lena Dorfschmidt
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Hyejung Won
- Department of Genetics and the Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire & Peterborough NHS Foundation Trust, Cambridge, UK
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43
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Wootton O, Shadrin AA, Bjella T, Smeland OB, van der Meer D, Frei O, O’Connell KS, Ueland T, Andreassen OA, Stein DJ, Dalvie S. Genomic Insights into the Shared and Distinct Genetic Architecture of Cognitive Function and Schizophrenia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.13.23298348. [PMID: 38014326 PMCID: PMC10680895 DOI: 10.1101/2023.11.13.23298348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Cognitive impairment is a major determinant of functional outcomes in schizophrenia, and efforts to understand the biological basis of cognitive dysfunction in the disorder are ongoing. Previous studies have suggested genetic overlap between global cognitive ability and schizophrenia, but further work is needed to delineate the shared genetic architecture. Here, we apply genomic structural equation modelling to identify latent cognitive factors capturing genetic liabilities to 12 cognitive traits measured in the UK Biobank (UKB). We explore the overlap between latent cognitive factors, schizophrenia, and schizophrenia symptom dimensions using a complementary set of statistical approaches, applied to data from the latest schizophrenia genome-wide association study (Ncase = 53,386, Ncontrol = 77,258) and the Thematically Organised Psychosis study (Ncase = 306, Ncontrol = 1060). We identified three broad factors (visuo-spatial, verbal analytic and decision/reaction time) that underly the genetic correlations between the UKB cognitive tests. Global genetic correlations showed a significant but moderate negative genetic correlation between each cognitive factor and schizophrenia. Local genetic correlations implicated unique genomic regions underlying the overlap between schizophrenia and each cognitive factor. We found evidence of substantial polygenic overlap between each cognitive factor and schizophrenia but show that most loci shared between the latent cognitive factors and schizophrenia have unique patterns of association with the cognitive factors. Biological annotation of the shared loci implicated gene-sets related to neurodevelopment and neuronal function. Lastly, we find that the common genetic determinants of the latent cognitive factors are not predictive of schizophrenia symptom dimensions. Overall, these findings inform our understanding of cognitive function in schizophrenia by demonstrating important differences in the shared genetic architecture of schizophrenia and cognitive abilities.
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Affiliation(s)
- Olivia Wootton
- Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Alexey A. Shadrin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Thomas Bjella
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Olav B. Smeland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, Blindern, Oslo, Norway
| | - Kevin S O’Connell
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Torill Ueland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Ole A. Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dan J. Stein
- Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- SAMRC Unit on Risk & Resilience in Mental Disorders, South Africa
| | - Shareefa Dalvie
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
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44
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Tang D, Freudenberg J, Dahl A. Factorizing polygenic epistasis improves prediction and uncovers biological pathways in complex traits. Am J Hum Genet 2023; 110:1875-1887. [PMID: 37922884 PMCID: PMC10645564 DOI: 10.1016/j.ajhg.2023.10.002] [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: 06/05/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Epistasis is central in many domains of biology, but it has not yet been proven useful for understanding the etiology of complex traits. This is partly because complex-trait epistasis involves polygenic interactions that are poorly captured in current models. To address this gap, we developed a model called Epistasis Factor Analysis (EFA). EFA assumes that polygenic epistasis can be factorized into interactions between a few epistasis factors (EFs), which represent latent polygenic components of the observed complex trait. The statistical goals of EFA are to improve polygenic prediction and to increase power to detect epistasis, while the biological goal is to unravel genetic effects into more-homogeneous units. We mathematically characterize EFA and use simulations to show that EFA outperforms current epistasis models when its assumptions approximately hold. Applied to predicting yeast growth rates, EFA outperforms the additive model for several traits with large epistasis heritability and uniformly outperforms the standard epistasis model. We replicate these prediction improvements in a second dataset. We then apply EFA to four previously characterized traits in the UK Biobank and find statistically significant epistasis in all four, including two that are robust to scale transformation. Moreover, we find that the inferred EFs partly recover pre-defined biological pathways for two of the traits. Our results demonstrate that more realistic models can identify biologically and statistically meaningful epistasis in complex traits, indicating that epistasis has potential for precision medicine and characterizing the biology underlying GWAS results.
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Affiliation(s)
- David Tang
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA; Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA.
| | - Jerome Freudenberg
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Andy Dahl
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA.
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45
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Alagöz G, Eising E, Mekki Y, Bignardi G, Fontanillas P, Nivard MG, Luciano M, Cox NJ, Fisher SE, Gordon RL. The shared genetic architecture and evolution of human language and musical rhythm. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.01.564908. [PMID: 37961248 PMCID: PMC10634981 DOI: 10.1101/2023.11.01.564908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Rhythm and language-related traits are phenotypically correlated, but their genetic overlap is largely unknown. Here, we leveraged two large-scale genome-wide association studies performed to shed light on the shared genetics of rhythm (N=606,825) and dyslexia (N=1,138,870). Our results reveal an intricate shared genetic and neurobiological architecture, and lay groundwork for resolving longstanding debates about the potential co-evolution of human language and musical traits.
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Affiliation(s)
- Gökberk Alagöz
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, 6500 AH Nijmegen, The Netherlands
| | - Else Eising
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, 6500 AH Nijmegen, The Netherlands
| | - Yasmina Mekki
- Department of Otolaryngology - Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Giacomo Bignardi
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, 6500 AH Nijmegen, The Netherlands
- Max Planck School of Cognition, Leipzig, Germany
| | | | - Michel G Nivard
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
| | - Michelle Luciano
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Nancy J Cox
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, 6500 AH Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6500 HB Nijmegen, The Netherlands
| | - Reyna L Gordon
- Department of Otolaryngology - Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- The Curb Center, Vanderbilt University, Nashville, TN, USA
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46
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Krainc D, Martin WJ, Casey B, Jensen FE, Tishkoff S, Potter WZ, Hyman SE. Shifting the trajectory of therapeutic development for neurological and psychiatric disorders. Sci Transl Med 2023; 15:eadg4775. [PMID: 38190501 DOI: 10.1126/scitranslmed.adg4775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 10/13/2023] [Indexed: 01/10/2024]
Abstract
Clinical trials for central nervous system disorders often enroll patients with unrecognized heterogeneous diseases, leading to costly trials that have high failure rates. Here, we discuss the potential of emerging technologies and datasets to elucidate disease mechanisms and identify biomarkers to improve patient stratification and monitoring of disease progression in clinical trials for neuropsychiatric disorders. Greater efforts must be centered on rigorously standardizing data collection and sharing of methods, datasets, and analytical tools across sectors. To address health care disparities in clinical trials, diversity of genetic ancestries and environmental exposures of research participants and associated biological samples must be prioritized.
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Affiliation(s)
- Dimitri Krainc
- Davee Department of Neurology, Simpson Querrey Center for Neurogenetics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Bradford Casey
- Michael J. Fox Foundation for Parkinson's Research, New York, NY, USA
| | - Frances E Jensen
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah Tishkoff
- Departments of Genetics and Biology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Steven E Hyman
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
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47
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Parker N, Cheng W, Hindley GFL, Parekh P, Shadrin AA, Maximov II, Smeland OB, Djurovic S, Dale AM, Westlye LT, Frei O, Andreassen OA. Psychiatric disorders and brain white matter exhibit genetic overlap implicating developmental and neural cell biology. Mol Psychiatry 2023; 28:4924-4932. [PMID: 37759039 DOI: 10.1038/s41380-023-02264-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: 05/18/2023] [Revised: 09/06/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023]
Abstract
Improved understanding of the shared genetic architecture between psychiatric disorders and brain white matter may provide mechanistic insights for observed phenotypic associations. Our objective is to characterize the shared genetic architecture of bipolar disorder (BD), major depression (MD), and schizophrenia (SZ) with white matter fractional anisotropy (FA) and identify shared genetic loci to uncover biological underpinnings. We used genome-wide association study (GWAS) summary statistics for BD (n = 413,466), MD (n = 420,359), SZ (n = 320,404), and white matter FA (n = 33,292) to uncover the genetic architecture (i.e., polygenicity and discoverability) of each phenotype and their genetic overlap (i.e., genetic correlations, overlapping trait-influencing variants, and shared loci). This revealed that BD, MD, and SZ are at least 7-times more polygenic and less genetically discoverable than average FA. Even in the presence of weak genetic correlations (range = -0.05 to -0.09), average FA shared an estimated 42.5%, 43.0%, and 90.7% of trait-influencing variants as well as 12, 4, and 28 shared loci with BD, MD, and SZ, respectively. Shared variants were mapped to genes and tested for enrichment among gene-sets which implicated neurodevelopmental expression, neural cell types, myelin, and cell adhesion molecules. For BD and SZ, case vs control tract-level differences in FA associated with genetic correlations between those same tracts and the respective disorder (rBD = 0.83, p = 4.99e-7 and rSZ = 0.65, p = 5.79e-4). Genetic overlap at the tract-level was consistent with average FA results. Overall, these findings suggest a genetic basis for the involvement of brain white matter aberrations in the pathophysiology of psychiatric disorders.
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Affiliation(s)
- Nadine Parker
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Weiqiu Cheng
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guy F L Hindley
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Pravesh Parekh
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alexey A Shadrin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Ivan I Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Olav B Smeland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Anders M Dale
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
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48
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Hindley G, Drange OK, Lin A, Kutrolli G, Shadrin AA, Parker N, O'Connell KS, Rødevand L, Cheng W, Bahrami S, Karadag N, Holen B, Jaholkowski P, Woldeyohannes MT, Djurovic S, Dale AM, Frei O, Ueland T, Smeland OB, Andreassen OA. Cross-trait genome-wide association analysis of C-reactive protein level and psychiatric disorders. Psychoneuroendocrinology 2023; 157:106368. [PMID: 37659117 PMCID: PMC10802833 DOI: 10.1016/j.psyneuen.2023.106368] [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: 03/30/2023] [Revised: 07/10/2023] [Accepted: 08/13/2023] [Indexed: 09/04/2023]
Abstract
C-reactive protein (CRP) tends to be elevated in individuals with psychiatric disorders. Recent findings have suggested a protective effect of the genetic liability to elevated CRP on schizophrenia risk and a causative effect on depression despite weak genetic correlations, while causal relationships with bipolar disorder were inconclusive. We investigated the shared genetic underpinnings of psychiatric disorders and variation in CRP levels. Genome-wide association studies for CRP (n = 575,531), bipolar disorder (n = 413,466), depression (n = 480,359), and schizophrenia (n = 130,644) were used in causal mixture models to compare CRP with psychiatric disorders based on polygenicity, discoverability, and genome-wide genetic overlap. The conjunctional false discovery rate method was used to identify specific shared genetic loci. Shared variants were mapped to putative causal genes, which were tested for overrepresentation among gene ontology gene-sets. CRP was six to ten times less polygenic (n = 1400 vs 8600-14,500 variants) and had a discoverability one to two orders of magnitude higher than psychiatric disorders. Most CRP-associated variants were overlapping with psychiatric disorders. We identified 401 genetic loci jointly associated with CRP and psychiatric disorders with mixed effect directions. Gene-set enrichment analyses identified predominantly CNS-related gene sets for CRP and each of depression and schizophrenia, and basic cellular processes for CRP and bipolar disorder. In conclusion, CRP has a markedly different genetic architecture to psychiatric disorders, but the majority of CRP associated variants are also implicated in psychiatric disorders. Shared genetic loci implicated CNS-related processes to a greater extent than immune processes, which may have implications for how we conceptualise causal relationships between CRP and psychiatric disorders.
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Affiliation(s)
- Guy Hindley
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Akershus University Hospital, Division of Mental Health Services, Department for Special Psychiatry, Lorenskog, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
| | - Ole Kristian Drange
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychiatry, Sørlandet Hospital, Kristiansand, Norway
| | - Aihua Lin
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Gleda Kutrolli
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Alexey A Shadrin
- NORMENT Centre, 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
| | - Nadine Parker
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Kevin S O'Connell
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Linn Rødevand
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Weiqiu Cheng
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Shahram Bahrami
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Naz Karadag
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Børge Holen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Piotr Jaholkowski
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Markos Tesfaye Woldeyohannes
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; NORMENT Centre, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Anders M Dale
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States; Multimodal Imaging Laboratory, University of California, San Diego, La Jolla, CA, United States; Department of Cognitive Science, University of California, San Diego, La Jolla, CA, United States; Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Thor Ueland
- Research Institute of Internal Medicine, Oslo University Hospital and Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Olav B Smeland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychiatry, Sørlandet Hospital, Kristiansand, Norway
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway.
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49
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Suzuki Y, Ménager H, Brancotte B, Vernet R, Nerin C, Boetto C, Auvergne A, Linhard C, Torchet R, Lechat P, Troubat L, Cho MH, Bouzigon E, Aschard H, Julienne H. Trait selection strategy in multi-trait GWAS: Boosting SNPs discoverability. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.27.564319. [PMID: 37961722 PMCID: PMC10634875 DOI: 10.1101/2023.10.27.564319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Since the first Genome-Wide Association Studies (GWAS), thousands of variant-trait associations have been discovered. However, the sample size required to detect additional variants using standard univariate association screening is increasingly prohibitive. Multi-trait GWAS offers a relevant alternative: it can improve statistical power and lead to new insights about gene function and the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been discussed, the strategy to select trait, among overwhelming possibilities, has been overlooked. In this study, we conducted extensive multi-trait tests using JASS (Joint Analysis of Summary Statistics) and assessed which genetic features of the analysed sets were associated with an increased detection of variants as compared to univariate screening. Our analyses identified multiple factors associated with the gain in the association detection in multi-trait tests. Together, these factors of the analysed sets are predictive of the gain of the multi-trait test (Pearson's ρ equal to 0.43 between the observed and predicted gain, P < 1.6 × 10-60). Applying an alternative multi-trait approach (MTAG, multi-trait analysis of GWAS), we found that in most scenarios but particularly those with larger numbers of traits, JASS outperformed MTAG. Finally, we benchmark several strategies to select set of traits including the prevalent strategy of selecting clinically similar traits, which systematically underperformed selecting clinically heterogenous traits or selecting sets that issued from our data-driven models. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outline practical strategies for multi-trait testing.
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Affiliation(s)
- Yuka Suzuki
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Hervé Ménager
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Bryan Brancotte
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Raphaël Vernet
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Cyril Nerin
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Christophe Boetto
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Antoine Auvergne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Christophe Linhard
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Rachel Torchet
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Pierre Lechat
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Lucie Troubat
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, 181 Longwood Ave, Boston, MA, 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Emmanuelle Bouzigon
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Hanna Julienne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
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50
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Tsai YT, Hrytsenko Y, Elgart M, Tahir U, Chen ZZ, Wilson JG, Gerszten R, Sofer T. A parametric bootstrap approach for computing confidence intervals for genetic correlations with application to genetically-determined protein-protein networks. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.24.23297474. [PMID: 37961678 PMCID: PMC10635196 DOI: 10.1101/2023.10.24.23297474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Genetic correlation refers to the correlation between genetic determinants of a pair of traits. When using individual-level data, it is typically estimated based on a bivariate model specification where the correlation between the two variables is identifiable and can be estimated from a covariance model that incorporates the genetic relationship between individuals, e.g., using a pre-specified kinship matrix. Inference relying on asymptotic normality of the genetic correlation parameter estimates may be inaccurate when the sample size is low, when the genetic correlation is close to the boundary of the parameter space, and when the heritability of at least one of the traits is low. We address this problem by developing a parametric bootstrap procedure to construct confidence intervals for genetic correlation estimates. The procedure simulates paired traits under a range of heritability and genetic correlation parameters, and it uses the population structure encapsulated by the kinship matrix. Heritabilities and genetic correlations are estimated using the close-form, method of moment, Haseman-Elston regression estimators. The proposed parametric bootstrap procedure is especially useful when genetic correlations are computed on pairs of thousands of traits measured on the same exact set of individuals. We demonstrate the parametric bootstrap approach on a proteomics dataset from the Jackson Heart Study.
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Affiliation(s)
- Yi-Ting Tsai
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Yana Hrytsenko
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Michael Elgart
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Usman Tahir
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Zsu-Zsu Chen
- Department of Medicine, Harvard Medical School, Boston, MA
- Department of Internal Medicine, Division of Endocrinology, Diabetes, and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA
| | - James G Wilson
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Robert Gerszten
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
| | - Tamar Sofer
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA
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