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Guo J, He C, Song H, Gao H, Yao S, Dong SS, Yang TL. Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives. Neurosci Bull 2024; 40:1333-1352. [PMID: 38703276 DOI: 10.1007/s12264-024-01214-1] [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: 07/14/2023] [Accepted: 01/08/2024] [Indexed: 05/06/2024] Open
Abstract
Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.
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Affiliation(s)
- Jing Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Changyi He
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Huimiao Song
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Huiwu Gao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Shi Yao
- Guangdong Key Laboratory of Age-Related Cardiac and Cerebral Diseases, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524000, China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
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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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3
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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: 2] [Impact Index Per Article: 2.0] [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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Chen J, Li T, Zhao B, Chen H, Yuan C, Garden GA, Wu G, Zhu H. The interaction effects of age, APOE and common environmental risk factors on human brain structure. Cereb Cortex 2024; 34:bhad472. [PMID: 38112569 PMCID: PMC10793588 DOI: 10.1093/cercor/bhad472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 10/09/2023] [Accepted: 11/06/2023] [Indexed: 12/21/2023] Open
Abstract
Mounting evidence suggests considerable diversity in brain aging trajectories, primarily arising from the complex interplay between age, genetic, and environmental risk factors, leading to distinct patterns of micro- and macro-cerebral aging. The underlying mechanisms of such effects still remain unclear. We conducted a comprehensive association analysis between cerebral structural measures and prevalent risk factors, using data from 36,969 UK Biobank subjects aged 44-81. Participants were assessed for brain volume, white matter diffusivity, Apolipoprotein E (APOE) genotypes, polygenic risk scores, lifestyles, and socioeconomic status. We examined genetic and environmental effects and their interactions with age and sex, and identified 726 signals, with education, alcohol, and smoking affecting most brain regions. Our analysis revealed negative age-APOE-ε4 and positive age-APOE-ε2 interaction effects, respectively, especially in females on the volume of amygdala, positive age-sex-APOE-ε4 interaction on the cerebellar volume, positive age-excessive-alcohol interaction effect on the mean diffusivity of the splenium of the corpus callosum, positive age-healthy-diet interaction effect on the paracentral volume, and negative APOE-ε4-moderate-alcohol interaction effects on the axial diffusivity of the superior fronto-occipital fasciculus. These findings highlight the need of considering age, sex, genetic, and environmental joint effects in elucidating normal or abnormal brain aging.
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Affiliation(s)
- Jie Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill NC 27514, United States
| | - Tengfei Li
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514, United States
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Chapel Hill, NC 27599, United States
| | - Bingxin Zhao
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, 265 South 37th Street, 3rd & 4th Floors, Philadelphia, PA 19104-1686, United States
| | - Hui Chen
- School of Public Health, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Hangzhou 310058, China
| | - Changzheng Yuan
- School of Public Health, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Hangzhou 310058, China
- Department of Nutrition, Harvard T H Chan School of Public Health, 665 Huntington Avenue Boston, MA, 02115, United States
| | - Gwenn A Garden
- Department of Neurology, School of Medicine, University of North Carolina at Chapel Hill, 170 Manning Drive Chapel Hill, NC 27599-7025, United States
| | - Guorong Wu
- Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514, United States
- Departments of Statistics and Operations Research, University of North Carolina at Chapel Hill, 318 E Cameron Ave #3260, Chapel Hill, NC 27599, United States
- Departments of Computer Science, University of North Carolina at Chapel Hill, 201 South Columbia Street, Chapel Hill, NC 27599, United States
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Dr, Chapel Hill, NC 27599, United States
- Carolina Institute for Developmental Disabilities, 101 Renee Lynne Ct, Carrboro, NC 27510, United States
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill NC 27514, United States
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Chapel Hill, NC 27599, United States
- Departments of Statistics and Operations Research, University of North Carolina at Chapel Hill, 318 E Cameron Ave #3260, Chapel Hill, NC 27599, United States
- Departments of Computer Science, University of North Carolina at Chapel Hill, 201 South Columbia Street, Chapel Hill, NC 27599, United States
- Departments of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27514, United States
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Jameei H, Rakesh D, Zalesky A, Cairns MJ, Reay WR, Wray NR, Di Biase MA. Linking Polygenic Risk of Schizophrenia to Variation in Magnetic Resonance Imaging Brain Measures: A Comprehensive Systematic Review. Schizophr Bull 2024; 50:32-46. [PMID: 37354489 PMCID: PMC10754175 DOI: 10.1093/schbul/sbad087] [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] [Indexed: 06/26/2023]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia is highly heritable, with a polygenic effect of many genes conferring risk. Evidence on whether cumulative risk also predicts alterations in brain morphology and function is inconsistent. This systematic review examined evidence for schizophrenia polygenic risk score (sczPRS) associations with commonly used magnetic resonance imaging (MRI) measures. We expected consistent evidence to emerge for significant sczPRS associations with variation in structure and function, specifically in frontal, temporal, and insula cortices that are commonly implicated in schizophrenia pathophysiology. STUDY DESIGN In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched MEDLINE, Embase, and PsycINFO for peer-reviewed studies published between January 2013 and March 2022. Studies were screened against predetermined criteria and National Institutes of Health (NIH) quality assessment tools. STUDY RESULTS In total, 57 studies of T1-weighted structural, diffusion, and functional MRI were included (age range = 9-80 years, Nrange = 64-76 644). We observed moderate, albeit preliminary, evidence for higher sczPRS predicting global reductions in cortical thickness and widespread variation in functional connectivity, and to a lesser extent, region-specific reductions in frontal and temporal volume and thickness. Conversely, sczPRS does not predict whole-brain surface area or gray/white matter volume. Limited evidence emerged for sczPRS associations with diffusion tensor measures of white matter microstructure in a large community sample and smaller cohorts of children and young adults. These findings were broadly consistent across community and clinical populations. CONCLUSIONS Our review supports the hypothesis that schizophrenia is a disorder of disrupted within and between-region brain connectivity, and points to specific whole-brain and regional MRI metrics that may provide useful intermediate phenotypes.
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Affiliation(s)
- Hadis Jameei
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Divyangana Rakesh
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
- Faculty of Engineering and Information Technology, The University of Melbourne, Parkville, VIC, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Newcastle, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - William R Reay
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Newcastle, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Maria A Di Biase
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
- Department of Anatomy and Physiology, School of Biomedical Sciences, The University of Melbourne, VIC, Australia
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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Stauffer EM, Bethlehem RAI, Dorfschmidt L, Won H, Warrier V, Bullmore ET. The genetic relationships between brain structure and schizophrenia. Nat Commun 2023; 14:7820. [PMID: 38016951 PMCID: PMC10684873 DOI: 10.1038/s41467-023-43567-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 11/14/2023] [Indexed: 11/30/2023] Open
Abstract
Genetic risks for schizophrenia are theoretically mediated by genetic effects on brain structure but it has been unclear which genes are associated with both schizophrenia and cortical phenotypes. We accessed genome-wide association studies (GWAS) of schizophrenia (N = 69,369 cases; 236,642 controls), and of three magnetic resonance imaging (MRI) metrics (surface area, cortical thickness, neurite density index) measured at 180 cortical areas (N = 36,843, UK Biobank). Using Hi-C-coupled MAGMA, 61 genes were significantly associated with both schizophrenia and one or more MRI metrics. Whole genome analysis with partial least squares demonstrated significant genetic covariation between schizophrenia and area or thickness of most cortical regions. Genetic similarity between cortical areas was strongly coupled to their phenotypic covariance, and genetic covariation between schizophrenia and brain phenotypes was strongest in the hubs of structural covariance networks. Pleiotropically associated genes were enriched for neurodevelopmental processes and positionally concentrated in chromosomes 3p21, 17q21 and 11p11. Mendelian randomization analysis indicated that genetically determined variation in a posterior cingulate cortical area could be causal for schizophrenia. Parallel analyses of GWAS on bipolar disorder, Alzheimer's disease and height showed that pleiotropic association with MRI metrics was stronger for schizophrenia compared to other disorders.
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Affiliation(s)
| | - Richard A I Bethlehem
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Lena Dorfschmidt
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Hyejung Won
- Department of Genetics and the Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire & Peterborough NHS Foundation Trust, Cambridge, UK
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Zhai S, Mehrotra DV, Shen J. Applying polygenic risk score methods to pharmacogenomics GWAS: challenges and opportunities. Brief Bioinform 2023; 25:bbad470. [PMID: 38152980 PMCID: PMC10782924 DOI: 10.1093/bib/bbad470] [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: 07/14/2023] [Revised: 11/20/2023] [Accepted: 11/28/2023] [Indexed: 12/29/2023] Open
Abstract
Polygenic risk scores (PRSs) have emerged as promising tools for the prediction of human diseases and complex traits in disease genome-wide association studies (GWAS). Applying PRSs to pharmacogenomics (PGx) studies has begun to show great potential for improving patient stratification and drug response prediction. However, there are unique challenges that arise when applying PRSs to PGx GWAS beyond those typically encountered in disease GWAS (e.g. Eurocentric or trans-ethnic bias). These challenges include: (i) the lack of knowledge about whether PGx or disease GWAS/variants should be used in the base cohort (BC); (ii) the small sample sizes in PGx GWAS with corresponding low power and (iii) the more complex PRS statistical modeling required for handling both prognostic and predictive effects simultaneously. To gain insights in this landscape about the general trends, challenges and possible solutions, we first conduct a systematic review of both PRS applications and PRS method development in PGx GWAS. To further address the challenges, we propose (i) a novel PRS application strategy by leveraging both PGx and disease GWAS summary statistics in the BC for PRS construction and (ii) a new Bayesian method (PRS-PGx-Bayesx) to reduce Eurocentric or cross-population PRS prediction bias. Extensive simulations are conducted to demonstrate their advantages over existing PRS methods applied in PGx GWAS. Our systematic review and methodology research work not only highlights current gaps and key considerations while applying PRS methods to PGx GWAS, but also provides possible solutions for better PGx PRS applications and future research.
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Affiliation(s)
- Song Zhai
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Devan V Mehrotra
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, PA 19454, USA
| | - Judong Shen
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA
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Barbu MC, Viejo-Romero M, Thng G, Adams MJ, Marwick K, Grant SG, McIntosh AM, Lawrie SM, Whalley HC. Pathway-Based Polygenic Risk Scores for Schizophrenia and Associations With Reported Psychotic-like Experiences and Neuroimaging Phenotypes in the UK Biobank. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2023; 3:814-823. [PMID: 37881537 PMCID: PMC10593950 DOI: 10.1016/j.bpsgos.2023.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 03/02/2023] [Accepted: 03/05/2023] [Indexed: 03/28/2023] Open
Abstract
Background Schizophrenia is a heritable psychiatric disorder with a polygenic architecture. Genome-wide association studies have reported that an increasing number of risk-associated variants and polygenic risk scores (PRSs) explain 17% of the variance in the disorder. Substantial heterogeneity exists in the effect of these variants, and aggregating them based on biologically relevant functions may provide mechanistic insight into the disorder. Methods Using the largest schizophrenia genome-wide association study conducted to date, we associated PRSs based on 5 gene sets previously found to contribute to schizophrenia pathophysiology-postsynaptic density of excitatory synapses, postsynaptic membrane, dendritic spine, axon, and histone H3-K4 methylation-along with respective whole-genome PRSs, with neuroimaging (n > 29,000) and reported psychotic-like experiences (n > 119,000) variables in healthy UK Biobank subjects. Results Several variables were significantly associated with the axon gene-set (psychotic-like communications, parahippocampal gyrus volume, fractional anisotropy thalamic radiations, and fractional anisotropy posterior thalamic radiations (β range -0.016 to 0.0916, false discovery rate-corrected p [pFDR] ≤ .05), postsynaptic density gene-set (psychotic-like experiences distress, global surface area, and cingulate lobe surface area [β range -0.014 to 0.0588, pFDR ≤ .05]), and histone gene set (entorhinal surface area: β = -0.016, pFDR = .035). From these, whole-genome PRSs were significantly associated with psychotic-like communications (β = 0.2218, pFDR = 1.34 × 10-7), distress (β = 0.1943, pFDR = 7.28 × 10-16), and fractional anisotropy thalamic radiations (β = -0.0143, pFDR = .036). Permutation analysis revealed that these associations were not due to chance. Conclusions Our results indicate that genetic variation in 3 gene sets relevant to schizophrenia may confer risk for the disorder through effects on previously implicated neuroimaging variables. Because associations were stronger overall for whole-genome PRSs, findings here highlight that selection of biologically relevant variants is not yet sufficient to address the heterogeneity of the disorder.
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Affiliation(s)
- Miruna C. Barbu
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, United Kingdom
| | - Maria Viejo-Romero
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, United Kingdom
| | - Gladi Thng
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, United Kingdom
| | - Mark J. Adams
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, United Kingdom
| | - Katie Marwick
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, United Kingdom
| | - Seth G.N. Grant
- Genes to Cognition Program, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Andrew M. McIntosh
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, United Kingdom
| | - Stephen M. Lawrie
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, United Kingdom
| | - Heather C. Whalley
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, United Kingdom
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Schizophrenia: A Narrative Review of Etiopathogenetic, Diagnostic and Treatment Aspects. J Clin Med 2022; 11:jcm11175040. [PMID: 36078967 PMCID: PMC9457502 DOI: 10.3390/jcm11175040] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/23/2022] [Accepted: 08/24/2022] [Indexed: 11/25/2022] Open
Abstract
Although schizophrenia is currently conceptualized as being characterized as a syndrome that includes a collection of signs and symptoms, there is strong evidence of heterogeneous and complex underpinned etiological, etiopathogenetic, and psychopathological mechanisms, which are still under investigation. Therefore, the present viewpoint review is aimed at providing some insights into the recently investigated schizophrenia research fields in order to discuss the potential future research directions in schizophrenia research. The traditional schizophrenia construct and diagnosis were progressively revised and revisited, based on the recently emerging neurobiological, genetic, and epidemiological research. Moreover, innovative diagnostic and therapeutic approaches are pointed to build a new construct, allowing the development of better clinical and treatment outcomes and characterization for schizophrenic individuals, considering a more patient-centered, personalized, and tailored-based dimensional approach. Further translational studies are needed in order to integrate neurobiological, genetic, and environmental studies into clinical practice and to help clinicians and researchers to understand how to redesign a new schizophrenia construct.
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Cattarinussi G, Delvecchio G, Sambataro F, Brambilla P. The effect of polygenic risk scores for major depressive disorder, bipolar disorder and schizophrenia on morphological brain measures: A systematic review of the evidence. J Affect Disord 2022; 310:213-222. [PMID: 35533776 DOI: 10.1016/j.jad.2022.05.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 04/27/2022] [Accepted: 05/04/2022] [Indexed: 01/26/2023]
Abstract
BACKGROUND Major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia (SCZ) share clinical features and genetic bases. Magnetic Resonance Imaging (MRI) studies assessing the effect of polygenic risk score (PRS) for psychiatric disorders on brain structure show heterogeneous results. Therefore, we provided an overview of the existing evidence on the association between PRS for MDD, BD and SCZ and MRI abnormalities in clinical and healthy populations. METHODS A search on PubMed, Web of Science and Scopus was performed to identify the studies exploring the effect of PRS for MDD, BD and SCZ on MRI measures. A total of 25 studies were included (N = 13 on healthy individuals and N = 12 on clinical populations). RESULTS Both in affected and unaffected individuals, PRS for BD and SCZ showed either positive or negative correlations with cortical thickness (CT), mostly involving fronto-temporal areas, whereas PRS for MDD was associated with cortical alterations in prefrontal regions in healthy subjects. LIMITATIONS The heterogeneity in the methods limits the conclusions of this review. CONCLUSIONS Overall the evidence on the effect of PRS for MDD, BD and SCZ on brain is considerably heterogeneous and far to be conclusive. However, from the results emerged that PRS for MDD, BD and SCZ were associated with widespread cortical abnormalities in all the populations explored, suggesting that genetic risk for MDD, BD and SCZ might affect neurodevelopmental processes, resulting in cortical alterations that transcend diagnostic boundaries and seem to be independent from the clinical status.
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Affiliation(s)
- Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, Padua, Italy; Padua Neuroscience Center, University of Padova, Padua, Italy
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padua, Italy; Padua Neuroscience Center, University of Padova, Padua, Italy
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
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11
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Rodrigue AL, Mathias SR, Knowles EEM, Mollon J, Almasy L, Schultz L, Turner J, Calhoun V, Glahn DC. Specificity of Psychiatric Polygenic Risk Scores and their Effects on Associated Risk Phenotypes. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2022. [PMID: 37519455 PMCID: PMC10382704 DOI: 10.1016/j.bpsgos.2022.05.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background Polygenic risk scores (PRSs) are indices of genetic liability for illness, but their clinical utility for predicting risk for a specific psychiatric disorder is limited. Genetic overlap among disorders and their effects on allied phenotypes may be a possible explanation, but this has been difficult to quantify given focus on singular disorders and/or allied phenotypes. Methods We constructed PRSs for 5 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, attention-deficit/hyperactivity disorder) and 3 nonpsychiatric control traits (height, type II diabetes, irritable bowel disease) in the UK Biobank (N = 31,616) and quantified associations between PRSs and phenotypes allied with mental illness: behavioral (symptoms, cognition, trauma) and brain measures from magnetic resonance imaging. We then evaluated the extent of specificity among PRSs and their effects on these allied phenotypes. Results Correlations among psychiatric PRSs replicated previous work, with overlap between schizophrenia and bipolar disorder, which was distinct from overlap between autism spectrum disorder and attention-deficit/hyperactivity disorder; overlap between psychiatric and control PRSs was minimal. There was, however, substantial overlap of PRS effects on allied phenotypes among psychiatric disorders and among psychiatric disorders and control traits, where the extent and pattern of overlap was phenotype specific. Conclusions Results show that genetic distinctions between psychiatric disorders and between psychiatric disorders and control traits exist, but this does not extend to their effects on allied phenotypes. Although overlap can be informative, work is needed to construct PRSs that will function at the level of specificity needed for clinical application.
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12
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Li Q, Lv X, Jin F, Liao K, Gao L, Xu J. Associations of Polygenic Risk Score for Late-Onset Alzheimer's Disease With Biomarkers. Front Aging Neurosci 2022; 14:849443. [PMID: 35493930 PMCID: PMC9047857 DOI: 10.3389/fnagi.2022.849443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 03/14/2022] [Indexed: 11/25/2022] Open
Abstract
Late-onset Alzheimer's disease (LOAD) is a common irreversible neurodegenerative disease with heterogeneous genetic characteristics. Identifying the biological biomarkers with the potential to predict the conversion from normal controls to LOAD is clinically important for early interventions of LOAD and clinical treatment. The polygenic risk score for LOAD (AD-PRS) has been reported the potential possibility for reliably identifying individuals with risk of developing LOAD recently. To investigate the external phenotype changes resulting from LOAD and the underlying etiology, we summarize the comprehensive associations of AD-PRS with multiple biomarkers, including neuroimaging, cerebrospinal fluid and plasma biomarkers, cardiovascular risk factors, cognitive behavior, and mental health. This systematic review helps improve the understanding of the biomarkers with potential predictive value for LOAD and further optimizing the prediction and accurate treatment of LOAD.
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Affiliation(s)
- Qiaojun Li
- School of Information Engineering, Tianjin University of Commerce, Tianjin, China
| | - Xingping Lv
- School of Sciences, Tianjin University of Commerce, Tianjin, China
| | - Fei Jin
- Department of Molecular Imaging, Qingdao Central Hospital, Qingdao University, Qingdao, China
| | - Kun Liao
- School of Sciences, Tianjin University of Commerce, Tianjin, China
| | - Liyuan Gao
- School of Sciences, Tianjin University of Commerce, Tianjin, China
| | - Jiayuan Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
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13
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de Zwarte SMC, Brouwer RM, Kahn RS, van Haren NEM. Schizophrenia and Bipolar Polygenic Risk Scores in Relation to Intracranial Volume. Genes (Basel) 2022; 13:genes13040695. [PMID: 35456501 PMCID: PMC9026378 DOI: 10.3390/genes13040695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/24/2022] [Accepted: 04/08/2022] [Indexed: 01/27/2023] Open
Abstract
Schizophrenia and bipolar disorder are neurodevelopmental disorders with overlapping symptoms and a shared genetic background. Deviations in intracranial volume (ICV)—a marker for neurodevelopment—differ between schizophrenia and bipolar disorder. Here, we investigated whether genetic risk for schizophrenia and bipolar disorder is related to ICV in the general population by using the UK Biobank data (n = 20,196). Polygenic risk scores for schizophrenia (SZ-PRS) and bipolar disorder (BD-PRS) were computed for 12 genome wide association study P-value thresholds (PT) for each individual and correlations with ICV were investigated. Partial correlations were performed at each PT to investigate whether disease specific genetic risk variants for schizophrenia and bipolar disorder show different relationships with ICV. ICV showed a negative correlation with SZ-PRS at PT ≥ 0.005 (r < −0.02, p < 0.005). ICV was not associated with BD-PRS; however, a positive correlation between BD-PRS and ICV at PT = 0.2 and PT = 0.4 (r = +0.02, p < 0.005) appeared when the genetic overlap between schizophrenia and bipolar disorder was accounted for. Despite small effect sizes, a higher load of schizophrenia risk genes is associated with a smaller ICV in the general population, while risk genes specific for bipolar disorder are correlated with a larger ICV. These findings suggest that schizophrenia and bipolar disorder risk genes, when accounting for the genetic overlap between both disorders, have opposite effects on early brain development.
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Affiliation(s)
- Sonja M. C. de Zwarte
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CG Utrecht, The Netherlands; (R.M.B.); (R.S.K.)
- Correspondence: (S.M.C.d.Z.); (N.E.M.v.H.)
| | - Rachel M. Brouwer
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CG Utrecht, The Netherlands; (R.M.B.); (R.S.K.)
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - René S. Kahn
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CG Utrecht, The Netherlands; (R.M.B.); (R.S.K.)
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- VISN 2 Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Neeltje E. M. van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre Sophia, 3000 CB Rotterdam, The Netherlands
- Correspondence: (S.M.C.d.Z.); (N.E.M.v.H.)
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14
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Nenadić I, Meller T, Schmitt S, Stein F, Brosch K, Mosebach J, Ettinger U, Grant P, Meinert S, Opel N, Lemke H, Fingas S, Förster K, Hahn T, Jansen A, Andlauer TFM, Forstner AJ, Heilmann-Heimbach S, Hall ASM, Awasthi S, Ripke S, Witt SH, Rietschel M, Müller-Myhsok B, Nöthen MM, Dannlowski U, Krug A, Streit F, Kircher T. Polygenic risk for schizophrenia and schizotypal traits in non-clinical subjects. Psychol Med 2022; 52:1069-1079. [PMID: 32758327 DOI: 10.1017/s0033291720002822] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND Schizotypy is a putative risk phenotype for psychosis liability, but the overlap of its genetic architecture with schizophrenia is poorly understood. METHODS We tested the hypothesis that dimensions of schizotypy (assessed with the SPQ-B) are associated with a polygenic risk score (PRS) for schizophrenia in a sample of 623 psychiatrically healthy, non-clinical subjects from the FOR2107 multi-centre study and a second sample of 1133 blood donors. RESULTS We did not find correlations of schizophrenia PRS with either overall SPQ or specific dimension scores, nor with adjusted schizotypy scores derived from the SPQ (addressing inter-scale variance). Also, PRS for affective disorders (bipolar disorder and major depression) were not significantly associated with schizotypy. CONCLUSIONS This important negative finding demonstrates that despite the hypothesised continuum of schizotypy and schizophrenia, schizotypy might share less genetic risk with schizophrenia than previously assumed (and possibly less compared to psychotic-like experiences).
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Affiliation(s)
- Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps-University and University Hospital Marburg, UKGM, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Hans-Meerwein-Str. 6, 35032 Marburg, Germany
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, Philipps-University and University Hospital Marburg, UKGM, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Hans-Meerwein-Str. 6, 35032 Marburg, Germany
| | - Simon Schmitt
- Department of Psychiatry and Psychotherapy, Philipps-University and University Hospital Marburg, UKGM, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Hans-Meerwein-Str. 6, 35032 Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-University and University Hospital Marburg, UKGM, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Hans-Meerwein-Str. 6, 35032 Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-University and University Hospital Marburg, UKGM, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Hans-Meerwein-Str. 6, 35032 Marburg, Germany
| | - Johannes Mosebach
- Department of Psychiatry and Psychotherapy, Philipps-University and University Hospital Marburg, UKGM, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Ulrich Ettinger
- Department of Psychology, Rheinische Friedrich-Wilhelms-Universität Bonn, Kaiser-Karl-Ring 9, 53111 Bonn, Germany
| | - Phillip Grant
- Psychology School, Fresenius University of Applied Sciences, Marienburgstr. 6, 60528 Frankfurt, Germany
- Faculty of Life Science Engineering, Technische Hochschule Mittelhessen University of Applied Sciences, Giessen, Germany
| | - Susanne Meinert
- Department of Psychiatry and Psychotherapy, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Nils Opel
- Department of Psychiatry and Psychotherapy, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Hannah Lemke
- Department of Psychiatry and Psychotherapy, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Stella Fingas
- Department of Psychiatry and Psychotherapy, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Katharina Förster
- Department of Psychiatry and Psychotherapy, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Tim Hahn
- Department of Psychiatry and Psychotherapy, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Philipps-University and University Hospital Marburg, UKGM, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Hans-Meerwein-Str. 6, 35032 Marburg, Germany
| | - Till F M Andlauer
- Max-Planck-Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Andreas J Forstner
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
- Centre for Human Genetics, Philipps-Universität Marburg, Baldingerstraße, 35033 Marburg, Germany
| | - Stefanie Heilmann-Heimbach
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Alisha S M Hall
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, 68159 Mannheim, Germany
| | - Swapnil Awasthi
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Berlin, Germany
| | - Stephan Ripke
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Berlin, Germany
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge MA 02142, USA
| | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, 68159 Mannheim, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, 68159 Mannheim, Germany
| | - Bertram Müller-Myhsok
- Max-Planck-Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Feodor-Lynen-Str. 17, 81377 Munich, Germany
- Institute of Translational Medicine, University of Liverpool, Crown St., Liverpool L69 3BX, UK
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Udo Dannlowski
- Department of Psychiatry and Psychotherapy, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, Philipps-University and University Hospital Marburg, UKGM, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Hans-Meerwein-Str. 6, 35032 Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, 68159 Mannheim, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University and University Hospital Marburg, UKGM, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Hans-Meerwein-Str. 6, 35032 Marburg, Germany
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15
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Quidé Y, Watkeys OJ, Girshkin L, Kaur M, Carr VJ, Cairns MJ, Green MJ. Interactive effects of polygenic risk and cognitive subtype on brain morphology in schizophrenia spectrum and bipolar disorders. Eur Arch Psychiatry Clin Neurosci 2022; 272:1205-1218. [PMID: 35792918 PMCID: PMC9508053 DOI: 10.1007/s00406-022-01450-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 06/12/2022] [Indexed: 11/29/2022]
Abstract
Grey matter volume (GMV) may be associated with polygenic risk for schizophrenia (PRS-SZ) and severe cognitive deficits in people with schizophrenia, schizoaffective disorder (collectively SSD), and bipolar disorder (BD). This study examined the interactive effects of PRS-SZ and cognitive subtypes of SSD and BD in relation to GMV. Two-step cluster analysis was performed on 146 clinical cases (69 SSD and 77 BD) assessed on eight cognitive domains (verbal and visual memory, executive function, processing speed, visual processing, language ability, working memory, and planning). Among them, 55 BD, 51 SSD, and 58 healthy controls (HC), contributed to focal analyses of the relationships between cognitive subtypes, PRS-SZ and their interaction on GMV. Two distinct cognitive subtypes were evident among the combined sample of cases: a 'cognitive deficit' group (CD; N = 31, 20SSD/11BD) showed severe impairment across all cognitive indices, and a 'cognitively spared' (CS; N = 75; 31SSD/44BD) group showed intermediate cognitive performance that was significantly worse than the HC group but better than the CD subgroup. A cognitive subgroup-by-PRS-SZ interaction was significantly associated with GMV in the left precentral gyrus. Moderation analyses revealed a significant negative relationship between PRS-SZ and GMV in the CD group only. At low and average (but not high) PRS-SZ, larger precentral GMV was evident in the CD group compared to both CS and HC groups, and in the CS group compared to HCs. This study provides evidence for a relationship between regional GMV changes and PRS-SZ in psychosis spectrum cases with cognitive deficits, but not in cases cognitively spared.
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Affiliation(s)
- Yann Quidé
- School of Clinical Medicine, Discipline of Psychiatry and Mental Health, University of New South Wales (UNSW), Sydney, NSW Australia ,Neuroscience Research Australia, Randwick, NSW Australia
| | - Oliver J. Watkeys
- School of Clinical Medicine, Discipline of Psychiatry and Mental Health, University of New South Wales (UNSW), Sydney, NSW Australia ,Neuroscience Research Australia, Randwick, NSW Australia
| | - Leah Girshkin
- School of Clinical Medicine, Discipline of Psychiatry and Mental Health, University of New South Wales (UNSW), Sydney, NSW Australia ,Neuroscience Research Australia, Randwick, NSW Australia
| | - Manreena Kaur
- School of Clinical Medicine, Discipline of Psychiatry and Mental Health, University of New South Wales (UNSW), Sydney, NSW Australia ,Neuroscience Research Australia, Randwick, NSW Australia
| | - Vaughan J. Carr
- School of Clinical Medicine, Discipline of Psychiatry and Mental Health, University of New South Wales (UNSW), Sydney, NSW Australia ,Neuroscience Research Australia, Randwick, NSW Australia ,Department of Psychiatry, Monash University, Clayton, VIC Australia
| | - Murray J. Cairns
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW Australia ,Centre for Brain and Mental Health Research, University of Newcastle, Callaghan, NSW Australia ,Hunter Medical Research Institute, New Lambton Heights, NSW Australia
| | - Melissa J. Green
- School of Clinical Medicine, Discipline of Psychiatry and Mental Health, University of New South Wales (UNSW), Sydney, NSW Australia ,Neuroscience Research Australia, Randwick, NSW Australia
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16
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Saarinen A, Lyytikäinen LP, Hietala J, Dobewall H, Lavonius V, Raitakari O, Kähönen M, Sormunen E, Lehtimäki T, Keltikangas-Järvinen L. Magical thinking in individuals with high polygenic risk for schizophrenia but no non-affective psychoses-a general population study. Mol Psychiatry 2022; 27:3286-3293. [PMID: 35505089 PMCID: PMC9708578 DOI: 10.1038/s41380-022-01581-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 04/07/2022] [Accepted: 04/12/2022] [Indexed: 11/09/2022]
Abstract
A strong genetic background for psychoses is well-established. Most individuals with a high genetic risk for schizophrenia, however, do not develop the disorder. We investigated whether individuals, who have a high genetic risk for schizophrenia but no non-affective psychotic disorders, are predisposed to develop milder forms of deviant thinking in terms of magical thinking. Participants came from the population-based Young Finns Study (n = 1292). The polygenic risk score for schizophrenia (PRS) was calculated on the basis of the most recent genome-wide association study (GWAS). Psychiatric diagnoses over the lifespan were collected up to 2017 from the registry of hospital care. Magical thinking was evaluated with the Spiritual Acceptance Scale (e.g., beliefs in telepathy, miracles, mystical events, or sixth sense) of the Temperament and Character Inventory in 1997, 2001, and 2012 (participants were 20-50-year-olds). We found that, among those who did not develop non-affective psychotic disorders, high PRS predicted higher magical thinking in adulthood (p = 0.001). Further, PRS predicted different developmental courses: a low PRS predicted a steady decrease in magical thinking from age 20 to 50 years, while in individuals with high PRS the decrease in magical thinking ceased in middle age so that their level of magical thinking remained higher than expected for that age. These findings remained when controlling for sex, childhood family environment, and adulthood socioeconomic factors. In conclusion, if high PRS does not lead to a non-affective psychotic disorder, it predicts milder forms of deviant thinking such as elevated magical thinking in adulthood, especially in middle age. The finding enhances our understanding of different outcomes of high genetic psychosis risk.
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Affiliation(s)
- Aino Saarinen
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland.
| | - Leo-Pekka Lyytikäinen
- grid.511163.10000 0004 0518 4910Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center, Tampere, Finland ,grid.412330.70000 0004 0628 2985Department of Cardiology, Heart Center, Tampere University Hospital, Tampere, Finland ,grid.502801.e0000 0001 2314 6254Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Jarmo Hietala
- grid.1374.10000 0001 2097 1371Department of Psychiatry, University of Turku and Turku University Hospital, Turku, Finland
| | - Henrik Dobewall
- grid.14758.3f0000 0001 1013 0499National Institute of Health and Welfare, Helsinki, Finland
| | - Veikka Lavonius
- grid.7737.40000 0004 0410 2071Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Olli Raitakari
- grid.1374.10000 0001 2097 1371Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland ,grid.1374.10000 0001 2097 1371Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland ,grid.410552.70000 0004 0628 215XDepartment of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Mika Kähönen
- grid.502801.e0000 0001 2314 6254Department of Clinical Physiology, Tampere University Hospital and Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Elina Sormunen
- grid.1374.10000 0001 2097 1371Department of Psychiatry, University of Turku and Turku University Hospital, Turku, Finland
| | - Terho Lehtimäki
- grid.511163.10000 0004 0518 4910Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center, Tampere, Finland ,grid.502801.e0000 0001 2314 6254Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Liisa Keltikangas-Järvinen
- grid.7737.40000 0004 0410 2071Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
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17
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Johnson D, Wilke MA, Lyle SM, Kowalec K, Jorgensen A, Wright GE, Drögemöller BI. A systematic review and analysis of the use of polygenic scores in pharmacogenomics. Clin Pharmacol Ther 2021; 111:919-930. [PMID: 34953075 DOI: 10.1002/cpt.2520] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 12/18/2021] [Indexed: 11/09/2022]
Abstract
Polygenic scores (PGS) have emerged as promising tools for complex trait risk prediction. The application of these scores to pharmacogenomics provides new opportunities to improve the prediction of treatment outcomes. To gain insight into this area of research, we conducted a systematic review and accompanying analysis. This review uncovered 51 papers examining the use of PGS for drug-related outcomes, with the majority of these papers focusing on the treatment of psychiatric disorders (n=30). Due to difficulties in collecting large cohorts of uniformly treated patients, the majority of pharmacogenomic PGS were derived from large-scale genome-wide association studies of disease phenotypes that were related to the pharmacogenomic phenotypes under investigation (e.g. schizophrenia-derived PGS for antipsychotic response prediction). Examination of the research participants included in these studies revealed that the majority of cohort participants were of European descent (78.4%). These biases were also reflected in research affiliations, which were heavily weighted towards institutions located in Europe and North America, with no first or last authors originating from institutions in Africa or South Asia. There was also substantial variability in the methods used to develop PGS, with between 3 and 6.6 million variants included in the PGS. Finally, we observed significant inconsistencies in the reporting of PGS analyses and results, particularly in terms of risk model development and application, coupled with a lack of data transparency and availability, with only three pharmacogenomics PGS deposited on the PGS Catalog. These findings highlight current gaps and key areas for future pharmacogenomic PGS research.
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Affiliation(s)
- Danielle Johnson
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - MacKenzie Ap Wilke
- Department of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Sarah M Lyle
- Department of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Kaarina Kowalec
- College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Andrea Jorgensen
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Galen Eb Wright
- Department of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada.,Department of Pharmacology and Therapeutics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.,Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Sciences Centre and Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
| | - Britt I Drögemöller
- Department of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada.,CancerCare Manitoba Research Institute, Winnipeg, MB, Canada.,Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada
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18
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Stauffer EM, Bethlehem RAI, Warrier V, Murray GK, Romero-Garcia R, Seidlitz J, Bullmore ET. Grey and white matter microstructure is associated with polygenic risk for schizophrenia. Mol Psychiatry 2021; 26:7709-7718. [PMID: 34462574 PMCID: PMC8872982 DOI: 10.1038/s41380-021-01260-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 07/26/2021] [Accepted: 07/30/2021] [Indexed: 02/07/2023]
Abstract
Recent discovery of approximately 270 common genetic variants associated with schizophrenia has enabled polygenic risk scores (PRS) to be measured in the population. We hypothesized that normal variation in PRS would be associated with magnetic resonance imaging (MRI) phenotypes of brain morphometry and tissue composition. We used the largest extant genome-wide association dataset (N = 69,369 cases and N = 236,642 healthy controls) to measure PRS for schizophrenia in a large sample of adults from the UK Biobank (Nmax = 29,878) who had multiple micro- and macrostructural MRI metrics measured at each of 180 cortical areas, seven subcortical structures, and 15 major white matter tracts. Linear mixed-effect models were used to investigate associations between PRS and brain structure at global and regional scales, controlled for multiple comparisons. Polygenic risk was significantly associated with reduced neurite density index (NDI) at global brain scale, at 149 cortical regions, five subcortical structures, and 14 white matter tracts. Other microstructural parameters, e.g., fractional anisotropy, that were correlated with NDI were also significantly associated with PRS. Genetic effects on multiple MRI phenotypes were co-located in temporal, cingulate, and prefrontal cortical areas, insula, and hippocampus. Post-hoc bidirectional Mendelian randomization analyses provided preliminary evidence in support of a causal relationship between (reduced) thalamic NDI and (increased) risk of schizophrenia. Risk-related reduction in NDI is plausibly indicative of reduced density of myelinated axons and dendritic arborization in large-scale cortico-subcortical networks. Cortical, subcortical, and white matter microstructure may be linked to the genetic mechanisms of schizophrenia.
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Affiliation(s)
- Eva-Maria Stauffer
- Department of Psychiatry, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK.
| | - Richard A I Bethlehem
- Department of Psychiatry, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Varun Warrier
- Department of Psychiatry, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Graham K Murray
- Department of Psychiatry, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Trust, Elizabeth House, Fulbourn Hospital, Cambridge, UK
- Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD, Australia
| | - Rafael Romero-Garcia
- Department of Psychiatry, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Edward T Bullmore
- Department of Psychiatry, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Trust, Elizabeth House, Fulbourn Hospital, Cambridge, UK
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19
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Legge SE, Santoro ML, Periyasamy S, Okewole A, Arsalan A, Kowalec K. Genetic architecture of schizophrenia: a review of major advancements. Psychol Med 2021; 51:2168-2177. [PMID: 33550997 DOI: 10.1017/s0033291720005334] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Schizophrenia is a severe psychiatric disorder with high heritability. Consortia efforts and technological advancements have led to a substantial increase in knowledge of the genetic architecture of schizophrenia over the past decade. In this article, we provide an overview of the current understanding of the genetics of schizophrenia, outline remaining challenges, and summarise future directions of research. World-wide collaborations have resulted in genome-wide association studies (GWAS) in over 56 000 schizophrenia cases and 78 000 controls, which identified 176 distinct genetic loci. The latest GWAS from the Psychiatric Genetics Consortium, available as a pre-print, indicates that 270 distinct common genetic loci have now been associated with schizophrenia. Polygenic risk scores can currently explain around 7.7% of the variance in schizophrenia case-control status. Rare variant studies have implicated eight rare copy-number variants, and an increased burden of loss-of-function variants in SETD1A, as increasing the risk of schizophrenia. The latest exome sequencing study, available as a pre-print, implicates a burden of rare coding variants in a further nine genes. Gene-set analyses have demonstrated significant enrichment of both common and rare genetic variants associated with schizophrenia in synaptic pathways. To address current challenges, future genetic studies of schizophrenia need increased sample sizes from more diverse populations. Continued expansion of international collaboration will likely identify new genetic regions, improve fine-mapping to identify causal variants, and increase our understanding of the biology and mechanisms of schizophrenia.
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Affiliation(s)
- Sophie E Legge
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Marcos L Santoro
- Departamento de Morfologia e Genética, Universidade Federal de Sao Paulo, Sao Paulo, Brazil
- Laboratory of Integrative Neuroscience, Departamento de Psiquiatria, Universidade Federal de Sao Paulo, Sao Paulo, Brazil
| | - Sathish Periyasamy
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia
- Queensland Centre for Mental Health Research, The University of Queensland, Brisbane, QLD, Australia
| | | | - Arsalan Arsalan
- Department of Pharmacy, University of Peshawar, Peshawar, Pakistan
| | - Kaarina Kowalec
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- College of Pharmacy, University of Manitoba, Winnipeg, Canada
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20
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Doucet GE, Lin D, Du Y, Fu Z, Glahn DC, Calhoun VD, Turner J, Frangou S. Personalized estimates of morphometric similarity in bipolar disorder and schizophrenia. NPJ SCHIZOPHRENIA 2020; 6:39. [PMID: 33277498 PMCID: PMC7718905 DOI: 10.1038/s41537-020-00128-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 10/06/2020] [Indexed: 12/29/2022]
Abstract
Bipolar disorder and schizophrenia are associated with brain morphometry alterations. This study investigates inter-individual variability in brain structural profiles, both within diagnostic groups and between patients and healthy individuals. Brain morphometric measures from three independent samples of patients with schizophrenia (n = 168), bipolar disorder (n = 122), and healthy individuals (n = 180) were modeled as single vectors to generated individualized profiles of subcortical volumes and regional cortical thickness. These profiles were then used to compute a person-based similarity index (PBSI) for subcortical volumes and for regional cortical thickness, to quantify the within-group similarity of the morphometric profile of each individual to that of the other participants in the same diagnostic group. There was no effect of diagnosis on the PBSI for subcortical volumes. In contrast, compared to healthy individuals, the PBSI for cortical thickness was lower in patients with schizophrenia (effect size = 0.4, p ≤ 0.0002), but not in patients with bipolar disorder. The results were robust and reproducible across samples. We conclude that disease mechanisms for these disorders produce modest inter-individual variations in brain morphometry that should be considered in future studies attempting to cluster patients in subgroups.
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Affiliation(s)
- Gaelle E Doucet
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Boys Town National Research Hospital, Omaha, NE, USA
| | - Dongdong Lin
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Yuhui Du
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA.,School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - David C Glahn
- Tommy Fuss Center for Neuropsychiatric Disease Research, Boston Children's Hospital, Harvard University, Boston, MA, USA
| | - Vincent D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Jessica Turner
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA.,Department of Psychology, Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. .,Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.
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21
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Passchier RV, Stein DJ, Uhlmann A, van der Merwe C, Dalvie S. Schizophrenia Polygenic Risk and Brain Structural Changes in Methamphetamine-Associated Psychosis in a South African Population. Front Genet 2020; 11:1018. [PMID: 33133134 PMCID: PMC7566162 DOI: 10.3389/fgene.2020.01018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 08/10/2020] [Indexed: 11/13/2022] Open
Abstract
Background The genetic architecture of psychotic disorders is complex, with hundreds of genetic risk loci contributing to a polygenic model of disease. Overlap in the genetics of psychotic disorders and brain measures has been found in European populations, but has not been explored in populations of African ancestry. The aim of this study was to determine whether a relationship exists between a schizophrenia-derived PRS and (i) methamphetamine associated psychosis (MAP), and (ii) brain structural measures, in a South African population. Methods The study sample consisted of three participant groups: 31 individuals with MAP, 48 with apsychotic methamphetamine dependence, and 49 healthy controls. Using PRSice, PRS was generated for each of the participants with GWAS summary statistics from the Psychiatric Genomics Consortium Schizophrenia working group (PGC-SCZ2) as the discovery dataset. Regression analyses were performed to determine associations of PRS, with diagnosis, whole brain, and regional gray and white matter measures. Results Schizophrenia-derived PRS did not significantly predict MAP diagnosis. After correction for multiple testing, no significant associations were found between PRS and brain measures across all groups. Discussion The lack of significant associations here may indicate that the study is underpowered, that brain volumes in MAP are due to factors other than polygenic risk for schizophrenia, or that PRS derived from a largely European discovery set has limited utility in individuals of African ancestry. Larger studies, that include diverse populations, and more nuanced brain measures, may help elucidate the relationship between schizophrenia-PRS, brain structural changes, and psychosis. Conclusion This research presents the first PRS study to investigate shared genetic effects across psychotic disorders and brain structural measures in an African population. Ancestrally comparable discovery datasets may be useful for future African genetic research.
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Affiliation(s)
- Ruth V Passchier
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Dan J Stein
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Anne Uhlmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, TU Dresden, Dresden, Germany
| | - Celia van der Merwe
- The Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, United States
| | - Shareefa Dalvie
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
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22
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Guinart D, Moreno E, Galindo L, Cuenca-Royo A, Barrera-Conde M, Pérez EJ, Fernández-Avilés C, Correll CU, Canela EI, Casadó V, Cordomi A, Pardo L, de la Torre R, Pérez V, Robledo P. Altered Signaling in CB1R-5-HT2AR Heteromers in Olfactory Neuroepithelium Cells of Schizophrenia Patients is Modulated by Cannabis Use. Schizophr Bull 2020; 46:1547-1557. [PMID: 32249318 PMCID: PMC7846100 DOI: 10.1093/schbul/sbaa038] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Schizophrenia (SCZ) has been associated with serotonergic and endocannabinoid systems dysregulation, but difficulty in obtaining in vivo neurological tissue has limited its exploration. We investigated CB1R-5-HT2AR heteromer expression and functionality via intracellular pERK and cAMP quantification in olfactory neuroepithelium (ON) cells of SCZ patients non-cannabis users (SCZ/nc), and evaluated whether cannabis modulated these parameters in patients using cannabis (SCZ/c). Results were compared vs healthy controls non-cannabis users (HC/nc) and healthy controls cannabis users (HC/c). Further, antipsychotic effects on heteromer signaling were tested in vitro in HC/nc and HC/c. Results indicated that heteromer expression was enhanced in both SCZ groups vs HC/nc. Additionally, pooling all 4 groups together, heteromer expression correlated with worse attentional performance and more neurological soft signs (NSS), indicating that these changes may be useful markers for neurocognitive impairment. Remarkably, the previously reported signaling properties of CB1R-5-HT2AR heteromers in ON cells were absent, specifically in SCZ/nc treated with clozapine. These findings were mimicked in cells from HC/nc exposed to clozapine, suggesting a major role of this antipsychotic in altering the quaternary structure of the CB1R-5-HT2AR heteromer in SCZ/nc patients. In contrast, cells from SCZ/c showed enhanced heteromer functionality similar to HC/c. Our data highlight a molecular marker of the interaction between antipsychotic medication and cannabis use in SCZ with relevance for future studies evaluating its association with specific neuropsychiatric alterations.
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Affiliation(s)
- Daniel Guinart
- Neuropsychiatry and Addictions Institute (INAD) of Parc de Salut Mar, Barcelona, Spain,Department of Psychiatry and Legal Medicine, Autonomous University of Barcelona, Barcelona, Spain,Department of Psychiatry, Zucker Hillside Hospital, Northwell Health, New York, NY
| | - Estefanía Moreno
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Institute of Biomedicine of the University of Barcelona, University of Barcelona, Barcelona, Spain,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Instituto de Salud Carlos III, Madrid, Spain
| | - Liliana Galindo
- Neuropsychiatry and Addictions Institute (INAD) of Parc de Salut Mar, Barcelona, Spain,Department of Psychiatry, University of Cambridge, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Aida Cuenca-Royo
- Integrative Pharmacology and Systems Neuroscience, IMIM-Hospital del Mar Research Institute, Barcelona, Spain
| | - Marta Barrera-Conde
- Integrative Pharmacology and Systems Neuroscience, IMIM-Hospital del Mar Research Institute, Barcelona, Spain,Department of Experimental and Health Sciences, University Pompeu Fabra, Barcelona, Spain
| | - Ezequiel J Pérez
- Neuropsychiatry and Addictions Institute (INAD) of Parc de Salut Mar, Barcelona, Spain
| | | | - Christoph U Correll
- Department of Psychiatry, Zucker Hillside Hospital, Northwell Health, New York, NY,Department of Child and Adolescent Psychiatry, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Enric I Canela
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Institute of Biomedicine of the University of Barcelona, University of Barcelona, Barcelona, Spain,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Instituto de Salud Carlos III, Madrid, Spain
| | - Vicent Casadó
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Institute of Biomedicine of the University of Barcelona, University of Barcelona, Barcelona, Spain,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Instituto de Salud Carlos III, Madrid, Spain
| | - Arnau Cordomi
- Laboratori de Medicina Computacional, Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Leonardo Pardo
- Laboratori de Medicina Computacional, Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Rafael de la Torre
- Integrative Pharmacology and Systems Neuroscience, IMIM-Hospital del Mar Research Institute, Barcelona, Spain,Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
| | - Víctor Pérez
- Neuropsychiatry and Addictions Institute (INAD) of Parc de Salut Mar, Barcelona, Spain,Department of Psychiatry and Legal Medicine, Autonomous University of Barcelona, Barcelona, Spain,Centro de Investigación Biomédica en Red de Salud Mental G21, Instituto de Salud Carlos III, Madrid, Spain
| | - Patricia Robledo
- Integrative Pharmacology and Systems Neuroscience, IMIM-Hospital del Mar Research Institute, Barcelona, Spain,Department of Experimental and Health Sciences, University Pompeu Fabra, Barcelona, Spain,To whom correspondence should be addressed; IMIM-Hospital del Mar Research Institute, PRBB, Calle Dr. Aiguader 88, Barcelona 08003, Spain; telephone: +34 93 316 0455; e-mail:
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