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Federmann LM, David FS, Jockwitz C, Mühleisen TW, Pelzer DI, Nöthen MM, Caspers S, Amunts K, Goltermann J, Andlauer TFM, Stein F, Brosch K, Kircher T, Cichon S, Dannlowski U, Sindermann L, Forstner AJ. Associations between antagonistic SNPs for neuropsychiatric disorders and human brain structure. Transl Psychiatry 2024; 14:406. [PMID: 39358328 PMCID: PMC11446931 DOI: 10.1038/s41398-024-03098-1] [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: 02/29/2024] [Revised: 08/29/2024] [Accepted: 09/13/2024] [Indexed: 10/04/2024] Open
Abstract
A previously published genome-wide association study (GWAS) meta-analysis across eight neuropsychiatric disorders identified antagonistic single-nucleotide polymorphisms (SNPs) at eleven genomic loci where the same allele was protective against one neuropsychiatric disorder and increased the risk for another. Until now, these antagonistic SNPs have not been further investigated regarding their link to brain structural phenotypes. Here, we explored their associations with cortical surface area and cortical thickness (in 34 brain regions and one global measure each) as well as the volumes of eight subcortical structures using summary statistics of large-scale GWAS of brain structural phenotypes. We assessed if significantly associated brain structural phenotypes were previously reported to be associated with major neuropsychiatric disorders in large-scale case-control imaging studies by the ENIGMA consortium. We further characterized the effects of the antagonistic SNPs on gene expression in brain tissue and their association with additional cognitive and behavioral phenotypes, and performed an exploratory voxel-based whole-brain analysis in the FOR2107 study (n = 754 patients with major depressive disorder and n = 847 controls). We found that eight antagonistic SNPs were significantly associated with brain structural phenotypes in regions such as anterior parts of the cingulate cortex, the insula, and the superior temporal gyrus. Case-control differences in implicated brain structural phenotypes have previously been reported for bipolar disorder, major depressive disorder, and schizophrenia. In addition, antagonistic SNPs were associated with gene expression changes in brain tissue and linked to several cognitive-behavioral traits. In our exploratory whole-brain analysis, we observed significant associations of gray matter volume in the left superior temporal pole and left superior parietal region with the variants rs301805 and rs1933802, respectively. Our results suggest that multiple antagonistic SNPs for neuropsychiatric disorders are linked to brain structural phenotypes. However, to further elucidate these findings, future case-control genomic imaging studies are required.
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Affiliation(s)
- Lydia M Federmann
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany.
| | - Friederike S David
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Thomas W Mühleisen
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Cécile and Oskar Vogt Institute for Brain Research, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
- Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Dominique I Pelzer
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Cécile and Oskar Vogt Institute for Brain Research, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Till F M Andlauer
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-University and University Hospital Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-University and University Hospital Marburg, Marburg, Germany
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University and University Hospital Marburg, Marburg, Germany
| | - Sven Cichon
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Lisa Sindermann
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Andreas J Forstner
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany.
- Centre for Human Genetics, Philipps-University Marburg, Marburg, Germany.
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Auvergne A, Traut N, Henches L, Troubat L, Frouin A, Boetto C, Kazem S, Julienne H, Toro R, Aschard H. Multitrait analysis to decipher the intertwined genetic architecture of neuroanatomical phenotypes and psychiatric disorders. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00266-0. [PMID: 39260564 DOI: 10.1016/j.bpsc.2024.08.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 06/28/2024] [Accepted: 08/12/2024] [Indexed: 09/13/2024]
Abstract
BACKGROUND There is increasing evidence of shared genetic factors between psychiatric disorders and brain magnetic resonance imaging (MRI) phenotypes. However, deciphering the joint genetic architecture of these outcomes has proven challenging, and new approaches are needed to infer potential genetic structure underlying those phenotypes. Multivariate analyses is arising as a meaningful approach to reveal links between MRI phenotypes and psychiatric disorders missed by univariate approaches. METHODS We first conducted univariate and multivariate genome-wide association studies (GWAS) for nine MRI-derived brain volume phenotypes in 20K UK Biobank participants. We next performed various complementary enrichment analyses to assess whether and how univariate and multitrait approaches can distinguish disorder-associated and non-disorder-associated variants from six psychiatric disorders: bipolarity, attention-deficit/hyperactivity disorder (ADHD), autism, schizophrenia, obsessive-compulsive disorder, and major depressive disorder. Finally, we conducted a clustering analysis of top associated variants based on their MRI multitrait association using an optimized k-medoids approach. RESULTS Univariate MRI GWAS displayed only negligible genetic correlation with psychiatric disorders, while multitrait GWAS identified multiple new associations and showed significant enrichment for variants related to both ADHD and schizophrenia. Clustering analyses further detected two clusters displaying not only enrichment for association with ADHD and schizophrenia, but also consistent direction of effects. Functional annotation analyses of those clusters pointed to multiple potential mechanisms, suggesting in particular a role of neurotrophins pathways on both MRI and schizophrenia. CONCLUSIONS Our results show that multitrait association signature can be used to infer genetically-driven latent MRI variables associated with psychiatric disorders, opening paths for future biomarker development.
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Affiliation(s)
- Antoine Auvergne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France.
| | - Nicolas Traut
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Léo Henches
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Lucie Troubat
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Arthur Frouin
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Christophe Boetto
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Sayeh Kazem
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Hanna Julienne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Roberto Toro
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA.
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3
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Genkel V, Domozhirova E, Malinina E. Multimorbidity in Severe Mental Illness as Part of the Neurodevelopmental Continuum: Physical Health-Related Endophenotypes of Schizophrenia-A Narrative Review. Brain Sci 2024; 14:725. [PMID: 39061465 PMCID: PMC11274495 DOI: 10.3390/brainsci14070725] [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: 07/02/2024] [Revised: 07/14/2024] [Accepted: 07/17/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND The majority of deaths in patients with schizophrenia and other severe mental illnesses (SMIs) are caused by natural causes, such as cardiovascular diseases (CVDs). The increased risk of CVD and other somatic diseases in SMIs cannot be fully explained by the contribution of traditional risk factors, behavioral risk factors, patients' lifestyle peculiarities, and the influence of antipsychotics. The present review has the following main objectives: (1) to aggregate evidence that neurodevelopmental disorders are the basis of SMIs; (2) to provide a review of studies that have addressed the shared genetic architecture of SMI and cardiovascular disease; and (3) to propose and substantiate the consideration of somatic diseases as independent endophenotypes of SMIs, which will make it possible to place the research of somatic diseases in SMIs within the framework of the concepts of the "neurodevelopmental continuum and gradient" and "endophenotype". METHODS A comprehensive literature search was performed on 1 July 2024. The search was performed using PubMed and Google Scholar databases up to June 2024. RESULTS The current literature reveals considerable overlap between the genetic susceptibility loci for SMIs and CVDs. We propose that somatic diseases observed in SMIs that have a shared genetic architecture with SMIs can be considered distinct physical health-related endophenotypes. CONCLUSIONS In this narrative review, the results of recent studies of CVDs in SMIs are summarized. Reframing schizophrenia as a multisystem disease should contribute to the activation of new research on somatic diseases in SMIs.
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Affiliation(s)
- Vadim Genkel
- Department of Internal Medicine, South-Ural State Medical University, Chelyabinsk 454092, Russia
| | - Elena Domozhirova
- Department of Psychiatry, South-Ural State Medical University, Chelyabinsk 454092, Russia; (E.D.); (E.M.)
| | - Elena Malinina
- Department of Psychiatry, South-Ural State Medical University, Chelyabinsk 454092, Russia; (E.D.); (E.M.)
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4
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Korbmacher M, van der Meer D, Beck D, Askeland-Gjerde DE, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Distinct Longitudinal Brain White Matter Microstructure Changes and Associated Polygenic Risk of Common Psychiatric Disorders and Alzheimer's Disease in the UK Biobank. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100323. [PMID: 39132576 PMCID: PMC11313202 DOI: 10.1016/j.bpsgos.2024.100323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 03/24/2024] [Accepted: 04/16/2024] [Indexed: 08/13/2024] Open
Abstract
Background During the course of adulthood and aging, white matter (WM) structure and organization are characterized by slow degradation processes such as demyelination and shrinkage. An acceleration of such aging processes has been linked to the development of a range of diseases. Thus, an accurate description of healthy brain maturation, particularly in terms of WM features, is fundamental to the understanding of aging. Methods We used longitudinal diffusion magnetic resonance imaging to provide an overview of WM changes at different spatial and temporal scales in the UK Biobank (UKB) (n = 2678; agescan 1 = 62.38 ± 7.23 years; agescan 2 = 64.81 ± 7.1 years). To examine the genetic overlap between WM structure and common clinical conditions, we tested the associations between WM structure and polygenic risk scores for the most common neurodegenerative disorder, Alzheimer's disease, and common psychiatric disorders (unipolar and bipolar depression, anxiety, obsessive-compulsive disorder, autism, schizophrenia, attention-deficit/hyperactivity disorder) in longitudinal (n = 2329) and cross-sectional (n = 31,056) UKB validation data. Results Our findings indicate spatially distributed WM changes across the brain, as well as distributed associations of polygenic risk scores with WM. Importantly, brain longitudinal changes reflected genetic risk for disorder development better than the utilized cross-sectional measures, with regional differences giving more specific insights into gene-brain change associations than global averages. Conclusions We extend recent findings by providing a detailed overview of WM microstructure degeneration on different spatial levels, helping to understand fundamental brain aging processes. Further longitudinal research is warranted to examine aging-related gene-brain associations.
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Affiliation(s)
- Max Korbmacher
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
| | - Dennis van der Meer
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Dani Beck
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Daniel E. Askeland-Gjerde
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Eli Eikefjord
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
| | - Arvid Lundervold
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ole A. Andreassen
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychology, 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
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
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5
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Nothdurfter D, Jawinski P, Markett S. White Matter Tract Integrity Is Reduced in Depression and in Individuals With Genetic Liability to Depression. Biol Psychiatry 2024; 95:1063-1071. [PMID: 38103877 DOI: 10.1016/j.biopsych.2023.11.028] [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: 02/20/2023] [Revised: 11/06/2023] [Accepted: 11/26/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND While major depression has been linked to changes in white matter architecture, it remains unclear whether risk factors for depression are directly associated with these alterations. We reexamined white matter fiber tracts in individuals with depressive symptoms and investigated the connection between genetic and environmental risk for depression and structural changes in the brain. METHODS We included 19,183 participants from the UK Biobank imaging cohort, with depression status and adverse life experience based on questionnaire data and genetic liability for depression quantified by polygenic scores. The integrity of 27 white matter tracts was assessed using mean fractional anisotropy derived from diffusion magnetic resonance imaging. RESULTS White matter integrity was reduced, particularly in thalamic and intracortical fiber tracts, in individuals with depressive symptoms, independent of current symptom status. In a group of healthy individuals without depression, increasing genetic risk and increasing environmental risk were associated with reduced integrity in relevant fiber tracts, particularly in thalamic radiations. This association was stronger than expected based on statistical dependencies between samples, as confirmed by subsequent in silico simulations and permutation tests. CONCLUSIONS White matter alterations in thalamic and association tracts are associated with depressive symptoms and genetic risk for depression in unaffected individuals, suggesting an intermediate phenotype at the brain level.
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Affiliation(s)
- David Nothdurfter
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Philippe Jawinski
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sebastian Markett
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany.
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6
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Tissink EP, Shadrin AA, van der Meer D, Parker N, Hindley G, Roelfs D, Frei O, Fan CC, Nagel M, Nærland T, Budisteanu M, Djurovic S, Westlye LT, van den Heuvel MP, Posthuma D, Kaufmann T, Dale AM, Andreassen OA. Abundant pleiotropy across neuroimaging modalities identified through a multivariate genome-wide association study. Nat Commun 2024; 15:2655. [PMID: 38531894 DOI: 10.1038/s41467-024-46817-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 03/12/2024] [Indexed: 03/28/2024] Open
Abstract
Genetic pleiotropy is abundant across spatially distributed brain characteristics derived from one neuroimaging modality (e.g. structural, functional or diffusion magnetic resonance imaging [MRI]). A better understanding of pleiotropy across modalities could inform us on the integration of brain function, micro- and macrostructure. Here we show extensive genetic overlap across neuroimaging modalities at a locus and gene level in the UK Biobank (N = 34,029) and ABCD Study (N = 8607). When jointly analysing phenotypes derived from structural, functional and diffusion MRI in a genome-wide association study (GWAS) with the Multivariate Omnibus Statistical Test (MOSTest), we boost the discovery of loci and genes beyond previously identified effects for each modality individually. Cross-modality genes are involved in fundamental biological processes and predominantly expressed during prenatal brain development. We additionally boost prediction of psychiatric disorders by conditioning independent GWAS on our multimodal multivariate GWAS. These findings shed light on the shared genetic mechanisms underlying variation in brain morphology, functional connectivity, and tissue composition.
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Affiliation(s)
- E P Tissink
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands.
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands.
| | - A A Shadrin
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
| | - D van der Meer
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - N Parker
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
| | - G Hindley
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
- Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, 16 De Crespigny Park, London, SE5 8AB, United Kingdom
| | - D Roelfs
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
| | - O Frei
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
| | - C C Fan
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Department of Radiology, University of California San Diego, La Jolla, CA, 92037, USA
| | - M Nagel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
| | - T Nærland
- K.G. Jebsen Centre for Neurodevelopmental disorders, Division of Paediatric Medicine, Institute of Clinical Medicine, University of Oslo, Building 31, Oslo, Norway
| | - M Budisteanu
- Prof. Dr. Alex Obregia Clinical Hospital of Psychiatry, Bucharest, Romania
- "Victor Babes" National Institute of Pathology, Bucharest, Romania
| | - S Djurovic
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, Division of Paediatric Medicine, Institute of Clinical Medicine, University of Oslo, Building 31, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - L T Westlye
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, Division of Paediatric Medicine, Institute of Clinical Medicine, University of Oslo, Building 31, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - M P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
- Department of Child and Adolescent Psychology and Psychiatry, section Complex Trait Genetics, Amsterdam Neuroscience, VU University Medical Centre, Amsterdam, The Netherlands
| | - D Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
- Department of Child and Adolescent Psychology and Psychiatry, section Complex Trait Genetics, Amsterdam Neuroscience, VU University Medical Centre, Amsterdam, The Netherlands
| | - T Kaufmann
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
| | - A M Dale
- Department of Radiology, University of California San Diego, La Jolla, CA, 92037, USA
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, 92037, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, 92037, USA
| | - O A Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Building 48, Oslo, Norway.
- K.G. Jebsen Centre for Neurodevelopmental disorders, Division of Paediatric Medicine, Institute of Clinical Medicine, University of Oslo, Building 31, Oslo, Norway.
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Wang M, Zhao SW, Wu D, Zhang YH, Han YK, Zhao K, Qi T, Liu Y, Cui LB, Wei Y. Transcriptomic and neuroimaging data integration enhances machine learning classification of schizophrenia. PSYCHORADIOLOGY 2024; 4:kkae005. [PMID: 38694267 PMCID: PMC11061866 DOI: 10.1093/psyrad/kkae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 02/26/2024] [Accepted: 03/25/2024] [Indexed: 05/04/2024]
Abstract
Background Schizophrenia is a polygenic disorder associated with changes in brain structure and function. Integrating macroscale brain features with microscale genetic data may provide a more complete overview of the disease etiology and may serve as potential diagnostic markers for schizophrenia. Objective We aim to systematically evaluate the impact of multi-scale neuroimaging and transcriptomic data fusion in schizophrenia classification models. Methods We collected brain imaging data and blood RNA sequencing data from 43 patients with schizophrenia and 60 age- and gender-matched healthy controls, and we extracted multi-omics features of macroscale brain morphology, brain structural and functional connectivity, and gene transcription of schizophrenia risk genes. Multi-scale data fusion was performed using a machine learning integration framework, together with several conventional machine learning methods and neural networks for patient classification. Results We found that multi-omics data fusion in conventional machine learning models achieved the highest accuracy (AUC ~0.76-0.92) in contrast to the single-modality models, with AUC improvements of 8.88 to 22.64%. Similar findings were observed for the neural network, showing an increase of 16.57% for the multimodal classification model (accuracy 71.43%) compared to the single-modal average. In addition, we identified several brain regions in the left posterior cingulate and right frontal pole that made a major contribution to disease classification. Conclusion We provide empirical evidence for the increased accuracy achieved by imaging genetic data integration in schizophrenia classification. Multi-scale data fusion holds promise for enhancing diagnostic precision, facilitating early detection and personalizing treatment regimens in schizophrenia.
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Affiliation(s)
- Mengya Wang
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Shu-Wan Zhao
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
- Schizophrenia Imaging Lab, Xijing 986 Hospital, Fourth Military Medical University, Xi'an, 710054, China
| | - Di Wu
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, China
| | - Ya-Hong Zhang
- Department of Psychiatry, Xi'an Gaoxin Hospital, Xi'an, 710075, China
| | - Yan-Kun Han
- Schizophrenia Imaging Lab, Xijing 986 Hospital, Fourth Military Medical University, Xi'an, 710054, China
| | - Kun Zhao
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Ting Qi
- Department of Neurology, School of Medicine, University of California San Francisco, San Francisco, 94143, California
| | - Yong Liu
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Long-Biao Cui
- Schizophrenia Imaging Lab, Xijing 986 Hospital, Fourth Military Medical University, Xi'an, 710054, China
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
- Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, 100853, China
- Shaanxi Provincial Key Laboratory of Clinic Genetics, Fourth Military Medical University, Xi'an, 710032, China
| | - Yongbin Wei
- Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China
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8
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van der Meer D, Cheng W, Rokicki J, Fernandez-Cabello S, Shadrin A, Smeland OB, Ehrhart F, Gülöksüz S, Pries LK, Lin B, Rutten BPF, van Os J, O’Donovan M, Richards AL, Steen NE, Djurovic S, Westlye LT, Andreassen OA, Kaufmann T. Clustering Schizophrenia Genes by Their Temporal Expression Patterns Aids Functional Interpretation. Schizophr Bull 2024; 50:327-338. [PMID: 37824720 PMCID: PMC10919784 DOI: 10.1093/schbul/sbad140] [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] [Indexed: 10/14/2023]
Abstract
BACKGROUND Schizophrenia is a highly heritable brain disorder with a typical symptom onset in early adulthood. The 2-hit hypothesis posits that schizophrenia results from differential early neurodevelopment, predisposing an individual, followed by a disruption of later brain maturational processes that trigger the onset of symptoms. STUDY DESIGN We applied hierarchical clustering to transcription levels of 345 genes previously linked to schizophrenia, derived from cortical tissue samples from 56 donors across the lifespan. We subsequently calculated clustered-specific polygenic risk scores for 743 individuals with schizophrenia and 743 sex- and age-matched healthy controls. STUDY RESULTS Clustering revealed a set of 183 genes that was significantly upregulated prenatally and downregulated postnatally and 162 genes that showed the opposite pattern. The prenatally upregulated set of genes was functionally annotated to fundamental cell cycle processes, while the postnatally upregulated set was associated with the immune system and neuronal communication. We found an interaction between the 2 scores; higher prenatal polygenic risk showed a stronger association with schizophrenia diagnosis at higher levels of postnatal polygenic risk. Importantly, this finding was replicated in an independent clinical cohort of 3233 individuals. CONCLUSIONS We provide genetics-based evidence that schizophrenia is shaped by disruptions of separable biological processes acting at distinct phases of neurodevelopment. The modeling of genetic risk factors that moderate each other's effect, informed by the timing of their expression, will aid in a better understanding of the development of schizophrenia.
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Affiliation(s)
- Dennis van der Meer
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Faculty of Health, Medicine and Life Sciences, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Weiqiu Cheng
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Jaroslav Rokicki
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Sara Fernandez-Cabello
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alexey Shadrin
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Olav B Smeland
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Friederike Ehrhart
- Faculty of Health, Medicine and Life Sciences, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Sinan Gülöksüz
- Faculty of Health, Medicine and Life Sciences, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
| | - Lotta-Katrin Pries
- Faculty of Health, Medicine and Life Sciences, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Bochao Lin
- Faculty of Health, Medicine and Life Sciences, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Bart P F Rutten
- Faculty of Health, Medicine and Life Sciences, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Jim van Os
- Faculty of Health, Medicine and Life Sciences, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Department of Psychiatry, Utrecht University Medical Centre, Utrecht, The Netherlands
| | - Michael O’Donovan
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, UK
| | - Alexander L Richards
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, UK
| | - Nils Eiel Steen
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- Department of Clinical Science, Norwegian Centre for Mental Disorders Research, University of Bergen, Bergen, Norway
| | - Lars T Westlye
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), 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
| | - Ole A Andreassen
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Tobias Kaufmann
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany
- German Center for Mental Health (DZPG), Tübingen, Germany
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9
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Han J, Li‐Gao R, de Mutsert R, Rosendaal FR, van Hylckama Vlieg A. Association between venous thromboembolism-associated genetic variants, coagulation factor levels, and thrombin generation potential. EJHAEM 2024; 5:47-54. [PMID: 38406509 PMCID: PMC10887265 DOI: 10.1002/jha2.863] [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] [Received: 10/26/2023] [Revised: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 02/27/2024]
Abstract
Recently three large meta-analyses of genome-wide association studies for venous thromboembolism (VTE) identified over 130 genetic variants. However, mechanisms by which newly identified and therefore underexplored VTE-associated genetic variants influence VTE remain unclear. To elucidate the mechanism, we investigated the association between 61 newly identified VTE-associated genetic variants and the levels of coagulation factor (F) VIII, FIX, FXI, and fibrinogen as well as thrombin generation parameters (lag time, peak, endogenous thrombin potential, time-to-peak, and velocity), which are well-known biological traits associated with VTE. This study was conducted on 5341 participants of the Netherlands Epidemiology of Obesity study. The associations between VTE-associated genetic variants and coagulation factor levels and thrombin generation parameters were examined using linear regression analyses, adjusted for age, sex, body mass index, oral contraceptive use, hormone replacement therapy, and menopausal status. Of 61 genetic variants, 33 were associated with one or more of the coagulation factor levels and thrombin generation parameters. Following multiple testing corrections, five genetic variants remained significant, of which MAP1A rs55707100 exhibited the most robust association with thrombin generation parameters and FXI levels (β = -5.33%, 95% confidence interval: -8.44, -2.22). Our findings shed light on the underlying mechanisms by which these genetic variants influence the risk of VTE.
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Affiliation(s)
- Jihee Han
- Department of Clinical EpidemiologyLeiden University Medical CenterLeidenthe Netherlands
| | - Ruifang Li‐Gao
- Department of Clinical EpidemiologyLeiden University Medical CenterLeidenthe Netherlands
| | - Renée de Mutsert
- Department of Clinical EpidemiologyLeiden University Medical CenterLeidenthe Netherlands
| | - Frits R. Rosendaal
- Department of Clinical EpidemiologyLeiden University Medical CenterLeidenthe Netherlands
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10
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Tandon R, Nasrallah H, Akbarian S, Carpenter WT, DeLisi LE, Gaebel W, Green MF, Gur RE, Heckers S, Kane JM, Malaspina D, Meyer-Lindenberg A, Murray R, Owen M, Smoller JW, Yassin W, Keshavan M. The schizophrenia syndrome, circa 2024: What we know and how that informs its nature. Schizophr Res 2024; 264:1-28. [PMID: 38086109 DOI: 10.1016/j.schres.2023.11.015] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/23/2023] [Accepted: 11/29/2023] [Indexed: 03/01/2024]
Abstract
With new data about different aspects of schizophrenia being continually generated, it becomes necessary to periodically revisit exactly what we know. Along with a need to review what we currently know about schizophrenia, there is an equal imperative to evaluate the construct itself. With these objectives, we undertook an iterative, multi-phase process involving fifty international experts in the field, with each step building on learnings from the prior one. This review assembles currently established findings about schizophrenia (construct, etiology, pathophysiology, clinical expression, treatment) and posits what they reveal about its nature. Schizophrenia is a heritable, complex, multi-dimensional syndrome with varying degrees of psychotic, negative, cognitive, mood, and motor manifestations. The illness exhibits a remitting and relapsing course, with varying degrees of recovery among affected individuals with most experiencing significant social and functional impairment. Genetic risk factors likely include thousands of common genetic variants that each have a small impact on an individual's risk and a plethora of rare gene variants that have a larger individual impact on risk. Their biological effects are concentrated in the brain and many of the same variants also increase the risk of other psychiatric disorders such as bipolar disorder, autism, and other neurodevelopmental conditions. Environmental risk factors include but are not limited to urban residence in childhood, migration, older paternal age at birth, cannabis use, childhood trauma, antenatal maternal infection, and perinatal hypoxia. Structural, functional, and neurochemical brain alterations implicate multiple regions and functional circuits. Dopamine D-2 receptor antagonists and partial agonists improve psychotic symptoms and reduce risk of relapse. Certain psychological and psychosocial interventions are beneficial. Early intervention can reduce treatment delay and improve outcomes. Schizophrenia is increasingly considered to be a heterogeneous syndrome and not a singular disease entity. There is no necessary or sufficient etiology, pathology, set of clinical features, or treatment that fully circumscribes this syndrome. A single, common pathophysiological pathway appears unlikely. The boundaries of schizophrenia remain fuzzy, suggesting the absence of a categorical fit and need to reconceptualize it as a broader, multi-dimensional and/or spectrum construct.
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Affiliation(s)
- Rajiv Tandon
- Department of Psychiatry, WMU Homer Stryker School of Medicine, Kalamazoo, MI 49008, United States of America.
| | - Henry Nasrallah
- Department of Psychiatry, University of Cincinnati College of Medicine Cincinnati, OH 45267, United States of America
| | - Schahram Akbarian
- Department of Psychiatry, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, United States of America
| | - William T Carpenter
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD 21201, United States of America
| | - Lynn E DeLisi
- Department of Psychiatry, Cambridge Health Alliance and Harvard Medical School, Cambridge, MA 02139, United States of America
| | - Wolfgang Gaebel
- Department of Psychiatry and Psychotherapy, LVR-Klinikum Dusseldorf, Heinrich-Heine University, Dusseldorf, Germany
| | - Michael F Green
- Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute of Neuroscience and Human Behavior, UCLA, Los Angeles, CA 90024, United States of America; Greater Los Angeles Veterans' Administration Healthcare System, United States of America
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States of America
| | - Stephan Heckers
- Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN 37232, United States of America
| | - John M Kane
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Glen Oaks, NY 11004, United States of America
| | - Dolores Malaspina
- Department of Psychiatry, Neuroscience, Genetics, and Genomics, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, United States of America
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannhein/Heidelberg University, Mannheim, Germany
| | - Robin Murray
- Institute of Psychiatry, Psychology, and Neuroscience, Kings College, London, UK
| | - Michael Owen
- Centre for Neuropsychiatric Genetics and Genomics, and Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Jordan W Smoller
- Center for Precision Psychiatry, Department of Psychiatry, Psychiatric and Neurodevelopmental Unit, Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States of America
| | - Walid Yassin
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, United States of America
| | - Matcheri Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, United States of America
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11
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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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12
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Drake DM, Zhen D, Kerrebijn I, Or B, Gao S, Afsharian K, Tran J, Bhatia S, Cheng A, Wells PG. Breast cancer 1 (BRCA1) protection in altered gene expression and neurodevelopmental disorders due to physiological and ethanol-enhanced reactive oxygen species formation. Free Radic Biol Med 2023; 208:272-284. [PMID: 37541454 DOI: 10.1016/j.freeradbiomed.2023.08.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 06/08/2023] [Accepted: 08/02/2023] [Indexed: 08/06/2023]
Abstract
The breast cancer 1 (Brca1) susceptibility gene regulates the repair of reactive oxygen species (ROS)-mediated DNA damage, which is implicated in neurodevelopmental disorders. Alcohol (ethanol, EtOH) exposure during pregnancy causes fetal alcohol spectrum disorders (FASD), including abnormal brain function, associated with enhanced ROS-initiated DNA damage. Herein, oxidative DNA damage in fetal brains and neurodevelopmental disorders were enhanced in saline-exposed +/- vs. +/+ Brca1 littermates. A single EtOH exposure during gestation further enhanced oxidative DNA damage, altered the expression of developmental/DNA damage response genes in fetal brains, and resulted in neurodevelopmental disorders, all of which were BRCA1-dependent. Pretreatment with the ROS inhibitor phenylbutylnitrone (PBN) blocked DNA damage and some neurodevelopmental disorders in both saline- and EtOH-exposed progeny, corroborating a ROS-dependent mechanism. Fetal BRCA1 protects against altered gene expression and neurodevelopmental disorders caused by both physiological and EtOH-enhanced levels of ROS formation. BRCA1 deficiencies may enhance the risk for FASD.
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Affiliation(s)
- Danielle M Drake
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada; Centre for Pharmaceutical Oncology, Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada
| | - Danlin Zhen
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada
| | - Isabel Kerrebijn
- Department of Pharmacology and Toxicology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin Or
- Department of Pharmacology and Toxicology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Sophie Gao
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada
| | - Kian Afsharian
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada; Centre for Pharmaceutical Oncology, Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada
| | - Jason Tran
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada
| | - Shama Bhatia
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada; Centre for Pharmaceutical Oncology, Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada
| | - Ashley Cheng
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada; Centre for Pharmaceutical Oncology, Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada
| | - Peter G Wells
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada; Centre for Pharmaceutical Oncology, Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada; Department of Pharmacology and Toxicology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
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13
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Hindley G, Shadrin AA, van der Meer D, Parker N, Cheng W, O'Connell KS, Bahrami S, Lin A, Karadag N, Holen B, Bjella T, Deary IJ, Davies G, Hill WD, Bressler J, Seshadri S, Fan CC, Ueland T, Djurovic S, Smeland OB, Frei O, Dale AM, Andreassen OA. Multivariate genetic analysis of personality and cognitive traits reveals abundant pleiotropy. Nat Hum Behav 2023; 7:1584-1600. [PMID: 37365406 PMCID: PMC10824266 DOI: 10.1038/s41562-023-01630-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 05/16/2023] [Indexed: 06/28/2023]
Abstract
Personality and cognitive function are heritable mental traits whose genetic foundations may be distributed across interconnected brain functions. Previous studies have typically treated these complex mental traits as distinct constructs. We applied the 'pleiotropy-informed' multivariate omnibus statistical test to genome-wide association studies of 35 measures of neuroticism and cognitive function from the UK Biobank (n = 336,993). We identified 431 significantly associated genetic loci with evidence of abundant shared genetic associations, across personality and cognitive function domains. Functional characterization implicated genes with significant tissue-specific expression in all tested brain tissues and brain-specific gene sets. We conditioned independent genome-wide association studies of the Big 5 personality traits and cognitive function on our multivariate findings, boosting genetic discovery in other personality traits and improving polygenic prediction. These findings advance our understanding of the polygenic architecture of these complex mental traits, indicating a prominence of pleiotropic genetic effects across higher order domains of mental function such as personality and cognitive function.
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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.
- Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK.
| | - 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.
| | - Dennis van der Meer
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Nadine Parker
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and 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
| | - 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
| | - Shahram Bahrami
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Aihua Lin
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and 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
| | - Børge Holen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Thomas Bjella
- 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
| | - Ian J Deary
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Gail Davies
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - W David Hill
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Jan Bressler
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Chun Chieh Fan
- Department of Radiology, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Torill Ueland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Srdjan Djurovic
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT Centre, Department of Clinical Science, University of Bergen, Bergen, 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
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, Blindern, Oslo, Norway
| | - Anders M Dale
- Department of Radiology, School of Medicine, 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 Cognitive Science, University of California, San Diego, La Jolla, CA, USA
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, 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.
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway.
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14
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O'Connell KS, Koch E, Lenk HÇ, Akkouh IA, Hindley G, Jaholkowski P, Smith RL, Holen B, Shadrin AA, Frei O, Smeland OB, Steen NE, Dale AM, Molden E, Djurovic S, Andreassen OA. Polygenic overlap with body-mass index improves prediction of treatment-resistant schizophrenia. Psychiatry Res 2023; 325:115217. [PMID: 37146461 PMCID: PMC10788293 DOI: 10.1016/j.psychres.2023.115217] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/03/2023] [Accepted: 04/21/2023] [Indexed: 05/07/2023]
Abstract
Treatment resistant schizophrenia (TRS) is characterized by repeated treatment failure with antipsychotics. A recent genome-wide association study (GWAS) of TRS showed a polygenic architecture, but no significant loci were identified. Clozapine is shown to be the superior drug in terms of clinical effect in TRS; at the same time it has a serious side effect profile, including weight gain. Here, we sought to increase power for genetic discovery and improve polygenic prediction of TRS, by leveraging genetic overlap with Body Mass Index (BMI). We analysed GWAS summary statistics for TRS and BMI applying the conditional false discovery rate (cFDR) framework. We observed cross-trait polygenic enrichment for TRS conditioned on associations with BMI. Leveraging this cross-trait enrichment, we identified 2 novel loci for TRS at cFDR <0.01, suggesting a role of MAP2K1 and ZDBF2. Further, polygenic prediction based on the cFDR analysis explained more variance in TRS when compared to the standard TRS GWAS. These findings highlight putative molecular pathways which may distinguish TRS patients from treatment responsive patients. Moreover, these findings confirm that shared genetic mechanisms influence both TRS and BMI and provide new insights into the biological underpinnings of metabolic dysfunction and antipsychotic treatment.
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Affiliation(s)
- Kevin S O'Connell
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Elise Koch
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Hasan Çağın Lenk
- Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway; Section for Pharmacology and Pharmaceutical Biosciences, Department of Pharmacy, University of Oslo, Oslo, Norway
| | - Ibrahim A Akkouh
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Guy Hindley
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, United Kingdom
| | - Piotr Jaholkowski
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Robert Løvsletten Smith
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway
| | - Børge Holen
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alexey A Shadrin
- NORMENT, Centre for Mental Disorders Research, 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
| | - Oleksandr Frei
- NORMENT, Centre for Mental Disorders Research, 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, 0316 Oslo, Norway
| | - Olav B Smeland
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Nils Eiel Steen
- NORMENT, Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Anders M Dale
- Department of Radiology, University of California, San Diego, La Jolla, CA 92093, USA; Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA 92093, USA; Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Espen Molden
- Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway; Section for Pharmacology and Pharmaceutical Biosciences, Department of Pharmacy, University of Oslo, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; NORMENT Centre, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Ole A Andreassen
- NORMENT, Centre for Mental Disorders Research, 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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15
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Han Y, Yang Y, Zhou Z, Jin X, Shi H, Shao M, Song M, Su X, Wang Q, Liu Q, Li W, Lv L. Cortical anatomical variations, gene expression profiles, and clinical phenotypes in patients with schizophrenia. Neuroimage Clin 2023; 39:103451. [PMID: 37315484 PMCID: PMC10509526 DOI: 10.1016/j.nicl.2023.103451] [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: 02/12/2023] [Revised: 06/01/2023] [Accepted: 06/05/2023] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia (SZ) patients display significant structural brain abnormalities; nevertheless, the genetic mechanisms regulating cortical anatomical variations and their correlation with the disease phenotype are still ambiguous. STUDY DESIGN We characterized anatomical variation using a surface-based method derived from structural magnetic resonance imaging of patients with SZ and age- and sex-matched healthy controls (HCs). Partial least-squares regression was performed across cortex regions between anatomical variation and average transcriptional profiles of SZ risk genes and all qualified genes from the Allen Human Brain Atlas. The morphological features of each brain region were correlated to symptomology variables in patients with SZ using partial correlation analysis. STUDY RESULTS A total of 203 SZ and 201 HCs were included in the final analysis. We observed significant variation of 55 regions of cortical thickness, 23 regions of volume, 7 regions of area, and 55 regions of local gyrification index (LGI) between SZ and HC groups. Expression profiles of 4 SZ risk genes and 96 genes from all qualified genes showed a correlation to anatomical variability, however, after multiple comparisons, the correlations were no longer significant. LGI variability in multiple frontal subregions was associated with specific symptoms of SZ, whereas cognitive function involving attention/vigilance was linked to LGI variability across nine brain regions. CONCLUSIONS Cortical anatomical variation of patients with schizophrenia is associated with gene transcriptome profiles as well as clinical phenotypes.
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Affiliation(s)
- Yong Han
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Zhilu Zhou
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Xueyan Jin
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Han Shi
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Minglong Shao
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Meng Song
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Xi Su
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Qi Wang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Qing Liu
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China
| | - Wenqiang Li
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China.
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, China.
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16
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Muntané G, Vázquez-Bourgon J, Sada E, Martorell L, Papiol S, Bosch E, Navarro A, Crespo-Facorro B, Vilella E. Polygenic risk scores enhance prediction of body mass index increase in individuals with a first episode of psychosis. Eur Psychiatry 2023; 66:e28. [PMID: 36852609 PMCID: PMC10044301 DOI: 10.1192/j.eurpsy.2023.9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND Individuals with a first episode of psychosis (FEP) show rapid weight gain during the first months of treatment, which is associated with a reduction in general physical health. Although genetics is assumed to be a significant contributor to weight gain, its exact role is unknown. METHODS We assembled a population-based FEP cohort of 381 individuals that was split into a Training (n = 224) set and a Validation (n = 157) set to calculate the polygenic risk score (PRS) in a two-step process. In parallel, we obtained reference genome-wide association studies for body mass index (BMI) and schizophrenia (SCZ) to examine the pleiotropic landscape between the two traits. BMI PRSs were added to linear models that included sociodemographic and clinical variables to predict BMI increase (∆BMI) in the Validation set. RESULTS The results confirmed considerable shared genetic susceptibility for the two traits involving 449 near-independent genomic loci. The inclusion of BMI PRSs significantly improved the prediction of ∆BMI at 12 months after the onset of antipsychotic treatment by 49.4% compared to a clinical model. In addition, we demonstrated that the PRS containing pleiotropic information between BMI and SCZ predicted ∆BMI better at 3 (12.2%) and 12 months (53.2%). CONCLUSIONS We prove for the first time that genetic factors play a key role in determining ∆BMI during the FEP. This finding has important clinical implications for the early identification of individuals most vulnerable to weight gain and highlights the importance of examining genetic pleiotropy in the context of medically important comorbidities for predicting future outcomes.
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Affiliation(s)
- Gerard Muntané
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Institut de Biologia Evolutiva (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain
| | - Javier Vázquez-Bourgon
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Department of Psychiatry, University Hospital Marqués de Valdecilla, Instituto de Investigación Marqués de Valdecilla (IDIVAL), Santander, Spain.,Departamento de Medicina y Psiquiatría, Facultad de Medicina, Universidad de Cantabria, Santander, Spain
| | - Ester Sada
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - Lourdes Martorell
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - Sergi Papiol
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Department of Psychiatry, Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University, Munich, Germany
| | - Elena Bosch
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Institut de Biologia Evolutiva (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain
| | - Arcadi Navarro
- Institut de Biologia Evolutiva (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain.,Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.,Barcelonaβeta Brain Research Center, Fundació Pasqual Maragall, Barcelona, Spain
| | - Benedicto Crespo-Facorro
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Department of Psychiatry, Instituto de Biomedicina de Sevilla (IBiS), University Hospital Virgen del Rocío, Seville, Spain
| | - Elisabet Vilella
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
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17
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Andreassen OA, Hindley GFL, Frei O, Smeland OB. New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications. World Psychiatry 2023; 22:4-24. [PMID: 36640404 PMCID: PMC9840515 DOI: 10.1002/wps.21034] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 01/15/2023] Open
Abstract
Psychiatric genetics has made substantial progress in the last decade, providing new insights into the genetic etiology of psychiatric disorders, and paving the way for precision psychiatry, in which individual genetic profiles may be used to personalize risk assessment and inform clinical decision-making. Long recognized to be heritable, recent evidence shows that psychiatric disorders are influenced by thousands of genetic variants acting together. Most of these variants are commonly occurring, meaning that every individual has a genetic risk to each psychiatric disorder, from low to high. A series of large-scale genetic studies have discovered an increasing number of common and rare genetic variants robustly associated with major psychiatric disorders. The most convincing biological interpretation of the genetic findings implicates altered synaptic function in autism spectrum disorder and schizophrenia. However, the mechanistic understanding is still incomplete. In line with their extensive clinical and epidemiological overlap, psychiatric disorders appear to exist on genetic continua and share a large degree of genetic risk with one another. This provides further support to the notion that current psychiatric diagnoses do not represent distinct pathogenic entities, which may inform ongoing attempts to reconceptualize psychiatric nosology. Psychiatric disorders also share genetic influences with a range of behavioral and somatic traits and diseases, including brain structures, cognitive function, immunological phenotypes and cardiovascular disease, suggesting shared genetic etiology of potential clinical importance. Current polygenic risk score tools, which predict individual genetic susceptibility to illness, do not yet provide clinically actionable information. However, their precision is likely to improve in the coming years, and they may eventually become part of clinical practice, stressing the need to educate clinicians and patients about their potential use and misuse. This review discusses key recent insights from psychiatric genetics and their possible clinical applications, and suggests future directions.
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Affiliation(s)
- Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Guy F L Hindley
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Olav B Smeland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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18
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Cheng W, van der Meer D, Parker N, Hindley G, O'Connell KS, Wang Y, Shadrin AA, Alnæs D, Bahrami S, Lin A, Karadag N, Holen B, Fernandez-Cabello S, Fan CC, Dale AM, Djurovic S, Westlye LT, Frei O, Smeland OB, Andreassen OA. Shared genetic architecture between schizophrenia and subcortical brain volumes implicates early neurodevelopmental processes and brain development in childhood. Mol Psychiatry 2022; 27:5167-5176. [PMID: 36100668 DOI: 10.1038/s41380-022-01751-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 08/10/2022] [Accepted: 08/12/2022] [Indexed: 01/14/2023]
Abstract
Patients with schizophrenia have consistently shown brain volumetric abnormalities, implicating both etiological and pathological processes. However, the genetic relationship between schizophrenia and brain volumetric abnormalities remains poorly understood. Here, we applied novel statistical genetic approaches (MiXeR and conjunctional false discovery rate analysis) to investigate genetic overlap with mixed effect directions using independent genome-wide association studies of schizophrenia (n = 130,644) and brain volumetric phenotypes, including subcortical brain and intracranial volumes (n = 33,735). We found brain volumetric phenotypes share substantial genetic variants (74-96%) with schizophrenia, and observed 107 distinct shared loci with sign consistency in independent samples. Genes mapped by shared loci revealed (1) significant enrichment in neurodevelopmental biological processes, (2) three co-expression clusters with peak expression at the prenatal stage, and (3) genetically imputed thalamic expression of CRHR1 and ARL17A was associated with the thalamic volume as early as in childhood. Together, our findings provide evidence of shared genetic architecture between schizophrenia and brain volumetric phenotypes and suggest that altered early neurodevelopmental processes and brain development in childhood may be involved in schizophrenia development.
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Affiliation(s)
- Weiqiu Cheng
- 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
| | - Nadine Parker
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guy 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
| | - Kevin S O'Connell
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Yunpeng Wang
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Centre for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, 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
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Aihua Lin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Naz Karadag
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Børge Holen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sara Fernandez-Cabello
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Chun-Chieh Fan
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, 92093, USA.,Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA.,Department of Neurosciences, University of California San Diego, La Jolla, CA, 92093, USA
| | - Anders M Dale
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, 92093, USA.,Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA.,Department of Neurosciences, University of California San Diego, La Jolla, CA, 92093, USA.,Department of Radiology, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway.,NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - 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
| | - Oleksandr Frei
- 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
| | - 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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19
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Wei Y, de Lange SC, Savage JE, Tissink E, Qi T, Repple J, Gruber M, Kircher T, Dannlowski U, Posthuma D, van den Heuvel MP. Associated Genetics and Connectomic Circuitry in Schizophrenia and Bipolar Disorder. Biol Psychiatry 2022:S0006-3223(22)01719-X. [PMID: 36803976 DOI: 10.1016/j.biopsych.2022.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 10/15/2022] [Accepted: 11/02/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND Schizophrenia (SCZ) and bipolar disorder (BD) are severe psychiatric conditions that can involve symptoms of psychosis and cognitive dysfunction. The 2 conditions share symptomatology and genetic etiology and are regularly hypothesized to share underlying neuropathology. Here, we examined how genetic liability to SCZ and BD shapes normative variations in brain connectivity. METHODS We examined the effect of the combined genetic liability for SCZ and BD on brain connectivity from two perspectives. First, we examined the association between polygenic scores for SCZ and BD for 19,778 healthy subjects from the UK Biobank and individual variation in brain structural connectivity reconstructed by means of diffusion weighted imaging data. Second, we conducted genome-wide association studies using genotypic and imaging data from the UK Biobank, taking SCZ-/BD-involved brain circuits as phenotypes of interest. RESULTS Our findings showed brain circuits of superior parietal and posterior cingulate regions to be associated with polygenic liability for SCZ and BD, circuitry that overlaps with brain networks involved in disease conditions (r = 0.239, p < .001). Genome-wide association study analysis showed 9 significant genomic loci associated with SCZ-involved circuits and 14 loci associated with BD-involved circuits. Genes related to SCZ-/BD-involved circuits were significantly enriched in gene sets previously reported in genome-wide association studies for SCZ and BD. CONCLUSIONS Our findings suggest that polygenic liability of SCZ and BD is associated with normative individual variation in brain circuitry.
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Affiliation(s)
- Yongbin Wei
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China; Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
| | - Siemon C de Lange
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, the Netherlands
| | - Jeanne E Savage
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Elleke Tissink
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Ting Qi
- Department of Neurology, School of Medicine, University of California San Francisco, San Francisco, California
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Child and Adolescent Psychiatry and Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, the Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Child and Adolescent Psychiatry and Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, the Netherlands.
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20
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Schizophrenia and Macroscale Brain Structure: Genes in Context. Biol Psychiatry 2022; 92:258-260. [PMID: 35902137 DOI: 10.1016/j.biopsych.2022.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 06/09/2022] [Indexed: 11/22/2022]
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