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Pingault JB, Barkhuizen W, Wang B, Hannigan LJ, Eilertsen EM, Corfield E, Andreassen OA, Ask H, Tesli M, Askeland RB, Davey Smith G, Stoltenberg C, Davies NM, Reichborn-Kjennerud T, Ystrom E, Havdahl A. Genetic nurture versus genetic transmission of risk for ADHD traits in the Norwegian Mother, Father and Child Cohort Study. Mol Psychiatry 2023; 28:1731-1738. [PMID: 36385167 PMCID: PMC10208953 DOI: 10.1038/s41380-022-01863-6] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 10/26/2022] [Accepted: 10/28/2022] [Indexed: 11/17/2022]
Abstract
Identifying mechanisms underlying the intergenerational transmission of risk for attention-deficit/hyperactivity disorder (ADHD) traits can inform interventions and provide insights into the role of parents in shaping their children's outcomes. We investigated whether genetic transmission and genetic nurture (environmentally mediated effects) underlie associations between polygenic scores indexing parental risk and protective factors and their offspring's ADHD traits. This birth cohort study included 19,506 genotyped mother-father-offspring trios from the Norwegian Mother, Father and Child Cohort Study. Polygenic scores were calculated for parental factors previously associated with ADHD, including psychopathology, substance use, neuroticism, educational attainment, and cognitive performance. Mothers reported on their 8-year-old children's ADHD traits (n = 9,454 children) using the Parent/Teacher Rating Scale for Disruptive Behaviour Disorders. We found that associations between ADHD maternal and paternal polygenic scores and child ADHD traits decreased significantly when adjusting for the child polygenic score (pΔβ = 9.95 × 10-17 for maternal and pΔβ = 1.48 × 10-14 for paternal estimates), suggesting genetic transmission of ADHD risk. Similar patterns suggesting genetic transmission of risk were observed for smoking, educational attainment, and cognition. The maternal polygenic score for neuroticism remained associated with children's ADHD ratings even after adjusting for the child polygenic score, indicating genetic nurture. There was no robust evidence of genetic nurture for other parental factors. Our findings indicate that the intergenerational transmission of risk for ADHD traits is largely explained by the transmission of genetic variants from parents to offspring rather than by genetic nurture. Observational associations between parental factors and childhood ADHD outcomes should not be interpreted as evidence for predominantly environmentally mediated effects.
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Affiliation(s)
- Jean-Baptiste Pingault
- Division of Psychology and Language Sciences, University College London, London, United Kingdom
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College, London, United Kingdom
| | - Wikus Barkhuizen
- Division of Psychology and Language Sciences, University College London, London, United Kingdom.
| | - Biyao Wang
- Division of Psychology and Language Sciences, University College London, London, United Kingdom
| | - Laurie J Hannigan
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Espen Moen Eilertsen
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Elizabeth Corfield
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Helga Ask
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Martin Tesli
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ragna Bugge Askeland
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Camilla Stoltenberg
- Norwegian Institute of Public Health, Oslo, Norway
- University of Bergen, Bergen, Norway
| | - Neil M Davies
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ted Reichborn-Kjennerud
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Eivind Ystrom
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- School of Pharmacy, University of Oslo, Oslo, Norway
| | - Alexandra Havdahl
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
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152
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Wu BS, Ge YJ, Zhang W, Chen SD, Xiang ST, Zhang YR, Ou YN, Jiang YC, Tan L, Cheng W, Suckling J, Feng JF, Yu JT, Mao Y. Genome-wide association study of cerebellar white matter microstructure and genetic overlap with common brain disorders. Neuroimage 2023; 269:119928. [PMID: 36740028 DOI: 10.1016/j.neuroimage.2023.119928] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/12/2023] [Accepted: 02/02/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The cerebellum is recognized as being involved in neurocognitive and motor functions with communication with extra-cerebellar regions relying on the white matter integrity of the cerebellar peduncles. However, the genetic determinants of cerebellar white matter integrity remain largely unknown. METHODS We conducted a genome-wide association analysis of cerebellar white matter microstructure using diffusion tensor imaging data from 25,415 individuals from UK Biobank. The integrity of cerebellar white matter microstructure was measured as fractional anisotropy (FA) and mean diffusivity (MD). Identification of independent genomic loci, functional annotation, and tissue and cell-type analysis were conducted with FUMA. The linkage disequilibrium score regression (LDSC) was used to calculate genetic correlations between cerebellar white matter microstructure and regional brain volumes and brain-related traits. Furthermore, the conditional/conjunctional false discovery rate (condFDR/conjFDR) framework was employed to identify the shared genetic basis between cerebellar white matter microstructure and common brain disorders. RESULTS We identified 11 genetic loci (P < 8.3 × 10-9) and 86 genes associated with cerebellar white matter microstructure. Further functional enrichment analysis implicated the involvement of GABAergic neurons and cholinergic pathways. Significant polygenetic overlap between cerebellar white matter tracts and their anatomically connected or adjacent brain regions was detected. In addition, we report the overall genetic correlation and specific loci shared between cerebellar white matter microstructural integrity and brain-related traits, including movement, cognitive, psychiatric, and cerebrovascular categories. CONCLUSIONS Collectively, this study represents a step forward in understanding the genetics of cerebellar white matter microstructure and its shared genetic etiology with common brain disorders.
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Affiliation(s)
- Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Jun Ge
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shi-Tong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Nan Ou
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yu-Chao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China; Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - John Suckling
- Department of Psychiatry, Brain Mapping Unit, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Ying Mao
- Department of Neurosurgery and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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153
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Elkrief L, Lin B, Marchi M, Afzali MH, Banaschewski T, Bokde ALW, Quinlan EB, Desrivières S, Flor H, Garavan H, Gowland P, Heinz A, Ittermann B, Martinot JL, Martinot MLP, Nees F, Orfanos DP, Paus T, Poustka L, Hohmann S, Fröhner JH, Smolka MN, Walter H, Whelan R, Schumann G, Luykx J, Boks MP, Conrod PJ. Independent contribution of polygenic risk for schizophrenia and cannabis use in predicting psychotic-like experiences in young adulthood: testing gene × environment moderation and mediation. Psychol Med 2023; 53:1759-1769. [PMID: 37310336 PMCID: PMC10106286 DOI: 10.1017/s0033291721003378] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 07/20/2021] [Accepted: 07/26/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND It has not yet been determined if the commonly reported cannabis-psychosis association is limited to individuals with pre-existing genetic risk for psychotic disorders. METHODS We examined whether the relationship between polygenic risk score for schizophrenia (PRS-Sz) and psychotic-like experiences (PLEs), as measured by the Community Assessment of Psychic Experiences-42 (CAPE-42) questionnaire, is mediated or moderated by lifetime cannabis use at 16 years of age in 1740 of the individuals of the European IMAGEN cohort. Secondary analysis examined the relationships between lifetime cannabis use, PRS-Sz and the various sub-scales of the CAPE-42. Sensitivity analyses including covariates, including a PRS for cannabis use, were conducted and results were replicated using data from 1223 individuals in the Dutch Utrecht cannabis cohort. RESULTS PRS-Sz significantly predicted cannabis use (p = 0.027) and PLE (p = 0.004) in the IMAGEN cohort. In the full model, considering PRS-Sz and covariates, cannabis use was also significantly associated with PLE in IMAGEN (p = 0.007). Results remained consistent in the Utrecht cohort and through sensitivity analyses. Nevertheless, there was no evidence of a mediation or moderation effects. CONCLUSIONS These results suggest that cannabis use remains a risk factor for PLEs, over and above genetic vulnerability for schizophrenia. This research does not support the notion that the cannabis-psychosis link is limited to individuals who are genetically predisposed to psychosis and suggests a need for research focusing on cannabis-related processes in psychosis that cannot be explained by genetic vulnerability.
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Affiliation(s)
- Laurent Elkrief
- Sainte-Justine Hospital Research Center, Montréal, Québec, Canada
- Département de psychiatrie et d'addictologie, Université de Montréal, Montréal, QC, Canada
| | - Bochao Lin
- Department of Translational Neuroscience, Brain Center University Medical Center, Utrecht University, Utrecht, the Netherlands
| | - Mattia Marchi
- Department Psychiatry, Brain Center University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi, 287–41125 Modena, Italy
| | - Mohammad H Afzali
- Sainte-Justine Hospital Research Center, Montréal, Québec, Canada
- Département de psychiatrie et d'addictologie, Université de Montréal, Montréal, QC, Canada
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Arun L. W. Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - Erin Burke Quinlan
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, United Kingdom
| | - Sylvane Desrivières
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, United Kingdom
| | - Herta Flor
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, Vermont, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
| | - Bernd Ittermann
- Physikalisch-Technische Bundesanstalt (PTB), Abbestr. 2 - 12, Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes - Sorbonne Paris Cité; and Maison de Solenn, Paris, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes; and AP-HP.Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
| | | | - Tomáš Paus
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Juliane H. Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N. Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Gunter Schumann
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, United Kingdom
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
- PONS Research Group, Dept of Psychiatry and Psychotherapy, Campus Charite Mitte, Humboldt University, Berlin and Leibniz Institute for Neurobiology, Magdeburg, Germany, and Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, P.R. China
| | - Jurjen Luykx
- Department Psychiatry, Brain Center University Medical Center Utrecht, Utrecht, the Netherlands
| | - Marco P. Boks
- Department Psychiatry, Brain Center University Medical Center Utrecht, Utrecht, the Netherlands
| | - Patricia J. Conrod
- Sainte-Justine Hospital Research Center, Montréal, Québec, Canada
- Département de psychiatrie et d'addictologie, Université de Montréal, Montréal, QC, Canada
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154
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Hou K, Ding Y, Xu Z, Wu Y, Bhattacharya A, Mester R, Belbin GM, Buyske S, Conti DV, Darst BF, Fornage M, Gignoux C, Guo X, Haiman C, Kenny EE, Kim M, Kooperberg C, Lange L, Manichaikul A, North KE, Peters U, Rasmussen-Torvik LJ, Rich SS, Rotter JI, Wheeler HE, Wojcik GL, Zhou Y, Sankararaman S, Pasaniuc B. Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals. Nat Genet 2023; 55:549-558. [PMID: 36941441 PMCID: PMC11120833 DOI: 10.1038/s41588-023-01338-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 02/16/2023] [Indexed: 03/23/2023]
Abstract
Individuals of admixed ancestries (for example, African Americans) inherit a mosaic of ancestry segments (local ancestry) originating from multiple continental ancestral populations. This offers the unique opportunity of investigating the similarity of genetic effects on traits across ancestries within the same population. Here we introduce an approach to estimate correlation of causal genetic effects (radmix) across local ancestries and analyze 38 complex traits in African-European admixed individuals (N = 53,001) to observe very high correlations (meta-analysis radmix = 0.95, 95% credible interval 0.93-0.97), much higher than correlation of causal effects across continental ancestries. We replicate our results using regression-based methods from marginal genome-wide association study summary statistics. We also report realistic scenarios where regression-based methods yield inflated heterogeneity-by-ancestry due to ancestry-specific tagging of causal effects, and/or polygenicity. Our results motivate genetic analyses that assume minimal heterogeneity in causal effects by ancestry, with implications for the inclusion of ancestry-diverse individuals in studies.
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Affiliation(s)
- Kangcheng Hou
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA.
| | - Yi Ding
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Ziqi Xu
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Yue Wu
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Rachel Mester
- Graduate Program in Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Gillian M Belbin
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steve Buyske
- Department of Statistics, Rutgers University, Piscataway, NJ, USA
| | - David V Conti
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Burcu F Darst
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Myriam Fornage
- Brown Foundation Institute for Molecular Medicine, The University of Texas Health Science Center, Houston, TX, USA
| | - Chris Gignoux
- Division of Biomedical Informatics and Personalized Medicine, University of Colorado, Denver, CO, USA
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Christopher Haiman
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michelle Kim
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Charles Kooperberg
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Leslie Lange
- Department of Medicine, University of Colorado, Aurora, CO, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Kari E North
- Department of Statistics, Rutgers University, Piscataway, NJ, USA
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ulrike Peters
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Heather E Wheeler
- Department of Biology, Loyola University Chicago, Chicago, IL, USA
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, USA
| | - Genevieve L Wojcik
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Ying Zhou
- Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Sriram Sankararaman
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
- Department of Computer Science, UCLA, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA.
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
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Seah C, Huckins LM, Brennand KJ. Stem Cell Models for Context-Specific Modeling in Psychiatric Disorders. Biol Psychiatry 2023; 93:642-650. [PMID: 36658083 DOI: 10.1016/j.biopsych.2022.09.033] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 09/27/2022] [Accepted: 09/27/2022] [Indexed: 01/21/2023]
Abstract
Genome-wide association studies reveal the complex polygenic architecture underlying psychiatric disorder risk, but there is an unmet need to validate causal variants, resolve their target genes(s), and explore their functional impacts on disorder-related mechanisms. Disorder-associated loci regulate transcription of target genes in a cell type- and context-specific manner, which can be measured through expression quantitative trait loci. In this review, we discuss methods and insights from context-specific modeling of genetically and environmentally regulated expression. Human induced pluripotent stem cell-derived cell type and organoid models have uncovered context-specific psychiatric disorder associations by investigating tissue-, cell type-, sex-, age-, and stressor-specific genetic regulation of expression. Techniques such as massively parallel reporter assays and pooled CRISPR (clustered regularly interspaced short palindromic repeats) screens make it possible to functionally fine-map genome-wide association study loci and validate their target genes at scale. Integration of disorder-associated contexts with these patient-specific human induced pluripotent stem cell models makes it possible to uncover gene by environment interactions that mediate disorder risk, which will ultimately improve our ability to diagnose and treat psychiatric disorders.
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Affiliation(s)
- Carina Seah
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York; Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York
| | - Laura M Huckins
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York; Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York; Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.
| | - Kristen J Brennand
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York; Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York; Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.
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156
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Associations between brain gene expression perturbations implicated by COVID-19 and psychiatric disorders. J Psychiatr Res 2023; 162:79-87. [PMID: 37105022 PMCID: PMC10043811 DOI: 10.1016/j.jpsychires.2023.03.033] [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: 11/15/2022] [Revised: 03/13/2023] [Accepted: 03/27/2023] [Indexed: 03/30/2023]
Abstract
Background Currently, there is increasing evidence from clinic, epidemiology, as well as neuroimaging, demonstrating neuropsychiatric abnormalities in COVID-19, however, whether there were associations between brain changes caused by COVID-19 and genetic susceptibility of psychiatric disorders was still unknown. Methods In this study, we performed a meta-analysis to investigate these associations by combing single-cell RNA sequencing datasets of brain tissues of COVID-19 and genome-wide association study summary statistics of psychiatric disorders. Results The analysis demonstrated that among ten psychiatric disorders, gene expression perturbations implicated by COVID-19 in excitatory neurons of choroid plexus were significantly associated with schizophrenia. Conclusions Our analysis might provide insights for the underlying mechanism of the psychiatric consequence of COVID-19.
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157
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Ayorech Z, Cheesman R, Eilertsen EM, Bjørndal LD, Røysamb E, McAdams TA, Havdahl A, Ystrom E. Maternal depression and the polygenic p factor: A family perspective on direct and indirect effects. J Affect Disord 2023; 332:159-167. [PMID: 36963516 DOI: 10.1016/j.jad.2023.03.043] [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: 11/25/2022] [Revised: 03/03/2023] [Accepted: 03/16/2023] [Indexed: 03/26/2023]
Abstract
Within-family studies typically assess indirect genetic effects of parents on children, however social support theory points to a critical role of partners and children on women's depression. To address this research gap and account for the high heterogeneity of depression, we calculated a general psychiatric factor using eleven major psychiatric polygenic scores (polygenic p), in up to 25,000 parent-offspring trios from the Norwegian Mother, Father and Child Cohort Study (MoBa). Multilevel modeling of trio polygenic p was used to distinguish direct and indirect genetic effects on mothers depression during pregnancy (gestational age 17 and 30 weeks), infancy (6 months, 18 months) and early childhood (3 years, 5 years, and 8 years). We found mothers polygenic p predicts their depression symptoms (b = 0.092; 95 % CI [0.087,0.098]), outperforming prediction using a single major depressive disorder polygenic score (b = 0.070, 95 % CI [0.066,0.075]). Jointly modeling trio polygenic p revealed indirect genetic effects of fathers (b = 0.022, 95 % CI [0.014,0.030]) and children (b = 0.021, 95 % CI [0.010,0.037]) on mothers' depression. Our results support the generalizability of polygenic effects across mental health and highlight the role of close family members on women's depression.
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Affiliation(s)
- Ziada Ayorech
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo 0373, Norway.
| | - Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo 0373, Norway
| | - Espen M Eilertsen
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo 0373, Norway; Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway; Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Ludvig Daae Bjørndal
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo 0373, Norway
| | - Espen Røysamb
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo 0373, Norway
| | - Tom A McAdams
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo 0373, Norway; Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Alexandra Havdahl
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo 0373, Norway; Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway; Nic Waals Institute, Spångbergveien 25, 0853 Oslo, Norway
| | - Eivind Ystrom
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo 0373, Norway; Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
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158
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Deans PJM, Seah C, Johnson J, Gonzalez JG, Townsley K, Cao E, Schrode N, Stahl E, O’Reilly P, Huckins LM, Brennand KJ. Non-additive effects of schizophrenia risk genes reflect convergent downstream function. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.20.23287497. [PMID: 36993466 PMCID: PMC10055596 DOI: 10.1101/2023.03.20.23287497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Genetic studies of schizophrenia (SCZ) reveal a complex polygenic risk architecture comprised of hundreds of risk variants, the majority of which are common in the population at-large and confer only modest increases in disorder risk. Precisely how genetic variants with individually small predicted effects on gene expression combine to yield substantial clinical impacts in aggregate is unclear. Towards this, we previously reported that the combinatorial perturbation of four SCZ risk genes ("eGenes", whose expression is regulated by common variants) resulted in gene expression changes that were not predicted by individual perturbations, being most non-additive among genes associated with synaptic function and SCZ risk. Now, across fifteen SCZ eGenes, we demonstrate that non-additive effects are greatest within groups of functionally similar eGenes. Individual eGene perturbations reveal common downstream transcriptomic effects ("convergence"), while combinatorial eGene perturbations result in changes that are smaller than predicted by summing individual eGene effects ("sub-additive effects"). Unexpectedly, these convergent and sub-additive downstream transcriptomic effects overlap and constitute a large proportion of the genome-wide polygenic risk score, suggesting that functional redundancy of eGenes may be a major mechanism underlying non-additivity. Single eGene perturbations likewise fail to predict the magnitude or directionality of cellular phenotypes resulting from combinatorial perturbations. Overall, our results indicate that polygenic risk cannot be extrapolated from experiments testing one risk gene at a time and must instead be empirically measured. By unravelling the interactions between complex risk variants, it may be possible to improve the clinical utility of polygenic risk scores through more powerful prediction of symptom onset, clinical trajectory, and treatment response, or to identify novel targets for therapeutic intervention.
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Affiliation(s)
- PJ Michael Deans
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Carina Seah
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Jessica Johnson
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Judit Garcia Gonzalez
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Kayla Townsley
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Evan Cao
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Nadine Schrode
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Eli Stahl
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Paul O’Reilly
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Laura M. Huckins
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Kristen J. Brennand
- Departments of Psychiatry and Genetics, Division of Molecular Psychiatry, Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
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159
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Jia Y, Hui L, Sun L, Guo D, Shi M, Zhang K, Yang P, Wang Y, Liu F, Shen O, Zhu Z. Association Between Human Blood Metabolome and the Risk of Psychiatric Disorders. Schizophr Bull 2023; 49:428-443. [PMID: 36124769 PMCID: PMC10016401 DOI: 10.1093/schbul/sbac130] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
BACKGROUND AND HYPOTHESIS To identify promising drug targets for psychiatric disorders, we applied Mendelian randomization (MR) design to systematically screen blood metabolome for potential mediators of psychiatric disorders and further predict target-mediated side effects. STUDY DESIGN We selected 92 unique blood metabolites from 3 metabolome genome-wide association studies (GWASs) with totally 147 827 participants. Summary statistics for bipolar disorder (BIP), attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), major depressive disorder (MDD), schizophrenia (SCZ), panic disorder (PD), autistic spectrum disorder (ASD), and anorexia nervosa (AN) originated from the Psychiatric Genomics Consortium, involving 1 143 340 participants. Mendelian randomization (MR) analyses were conducted to estimate associations of blood metabolites with psychiatric disorders. Phenome-wide MR analysis was further performed to predict side effects mediated by metabolite-targeted interventions. RESULTS Eight metabolites were identified associated with psychiatric disorders, including five established mediators: N-acetylornithine (BIP: OR, 0.72 [95% CI, 0.66-0.79]; SCZ: OR, 0.74 [0.64-0.84]), glycine (BIP: OR, 0.62 [0.50-0.77]), docosahexaenoic acid (MDD: OR, 0.96 [0.94-0.97]), 3-Hydroxybutyrate (MDD: OR, 1.14 [1.08-1.21]), butyrylcarnitine (SCZ: OR, 1.22 [1.12-1.32]); and three novel mediators: 1-arachidonoylglycerophosphocholine (1-arachidonoyl-GPC)(BIP: OR, 0.31 [0.23-0.41]), glycoproteins (BIP: OR, 0.94 [0.92-0.97]), sphingomyelins (AN: OR, 1.12 [1.06-1.19]). Phenome-wide MR analysis showed that all identified metabolites except for N-acetylornithine and 3-Hydroxybutyrate had additional effects on nonpsychiatric diseases, while glycine, 3-Hydroxybutyrate, N-acetylornithine, and butyrylcarnitine had no adverse side effects. CONCLUSIONS This MR study identified five established and three novel mediators for psychiatric disorders. N-acetylornithine, glycine, 3-Hydroxybutyrate, and butyrylcarnitine might be promising targets against psychiatric disorders with no predicted adverse side effects.
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Affiliation(s)
- Yiming Jia
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Li Hui
- Research Center of Biological Psychiatry, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Lulu Sun
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Daoxia Guo
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
- School of Nursing, Medical College of Soochow University, Suzhou, China
| | - Mengyao Shi
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Kaixin Zhang
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Pinni Yang
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Yu Wang
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Fanghua Liu
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Ouxi Shen
- Department of Occupational Health, Suzhou Industrial Park Center for Disease Control and Prevention, Suzhou, China
| | - Zhengbao Zhu
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
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160
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Dai R, Chu T, Zhang M, Wang X, Jourdon A, Wu F, Mariani J, Vaccarino FM, Lee D, Fullard JF, Hoffman GE, Roussos P, Wang Y, Wang X, Pinto D, Wang SH, Zhang C, Chen C, Liu C. Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.13.532468. [PMID: 36993743 PMCID: PMC10054947 DOI: 10.1101/2023.03.13.532468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Sample-wise deconvolution methods have been developed to estimate cell-type proportions and gene expressions in bulk-tissue samples. However, the performance of these methods and their biological applications has not been evaluated, particularly on human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk-tissue RNAseq, single-cell/nuclei (sc/sn) RNAseq, and immunohistochemistry. A total of 1,130,767 nuclei/cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expression. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk-tissue or single-cell eQTLs alone. Differential gene expression associated with multiple phenotypes were also examined using the deconvoluted data. Our findings, which were replicated in bulk-tissue RNAseq and sc/snRNAseq data, provided new insights into the biological applications of deconvoluted data.
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Affiliation(s)
- Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Tianyao Chu
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Ming Zhang
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Xuan Wang
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | | | - Feinan Wu
- Child Study Center, Yale University, New Haven, CT, USA
| | | | - Flora M Vaccarino
- Child Study Center, Yale University, New Haven, CT, USA
- Department of Neuroscience, Yale University, New Haven, CT, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John F Fullard
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, VA, USA
| | - Xusheng Wang
- Department of Biology, University of North Dakota, Grand Forks, ND, USA
| | - Dalila Pinto
- Department of Psychiatry, Department of Genetics and Genomic Sciences, Mindich Child Health and Development Institute, and Icahn Genomics Institute for Data Science and Genomic Technology, Seaver Autism Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sidney H Wang
- Center for Human Genetics, The Brown foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Chunling Zhang
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Chao Chen
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
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161
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Santarriaga S, Gerlovin K, Layadi Y, Karmacharya R. Human stem cell-based models to study synaptic dysfunction and cognition in schizophrenia: A narrative review. Schizophr Res 2023:S0920-9964(23)00084-1. [PMID: 36925354 PMCID: PMC10500041 DOI: 10.1016/j.schres.2023.02.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 02/27/2023] [Accepted: 02/28/2023] [Indexed: 03/18/2023]
Abstract
Cognitive impairment is the strongest predictor of functional outcomes in schizophrenia and is hypothesized to result from synaptic dysfunction. However, targeting synaptic plasticity and cognitive deficits in patients remains a significant clinical challenge. A comprehensive understanding of synaptic plasticity and the molecular basis of learning and memory in a disease context can provide specific targets for the development of novel therapeutics targeting cognitive impairments in schizophrenia. Here, we describe the role of synaptic plasticity in cognition, summarize evidence for synaptic dysfunction in schizophrenia and demonstrate the use of patient derived induced-pluripotent stem cells for studying synaptic plasticity in vitro. Lastly, we discuss current advances and future technologies for bridging basic science research of synaptic dysfunction with clinical and translational research that can be used to predict treatment response and develop novel therapeutics.
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Affiliation(s)
- Stephanie Santarriaga
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Chemical Biology and Therapeutic Science Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Kaia Gerlovin
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Chemical Biology and Therapeutic Science Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yasmine Layadi
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Chimie ParisTech, Université Paris Sciences et Lettres, Paris, France
| | - Rakesh Karmacharya
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Chemical Biology and Therapeutic Science Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA; Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA, USA.
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162
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Ou YN, Wu BS, Ge YJ, Zhang Y, Jiang YC, Kuo K, Yang L, Tan L, Feng JF, Cheng W, Yu JT. The genetic architecture of human amygdala volumes and their overlap with common brain disorders. Transl Psychiatry 2023; 13:90. [PMID: 36906575 PMCID: PMC10008562 DOI: 10.1038/s41398-023-02387-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 02/26/2023] [Accepted: 02/28/2023] [Indexed: 03/13/2023] Open
Abstract
The amygdala is a crucial interconnecting structure in the brain that performs several regulatory functions, yet its genetic architectures and involvement in brain disorders remain largely unknown. We carried out the first multivariate genome-wide association study (GWAS) of amygdala subfield volumes in 27,866 UK Biobank individuals. The whole amygdala was segmented into nine nuclei groups using Bayesian amygdala segmentation. The post-GWAS analysis allowed us to identify causal genetic variants in phenotypes at the SNP, locus, and gene levels, as well as genetic overlap with brain health-related traits. We further generalized our GWAS in Adolescent Brain Cognitive Development (ABCD) cohort. The multivariate GWAS identified 98 independent significant variants within 32 genomic loci associated (P < 5 × 10-8) with amygdala volume and its nine nuclei. The univariate GWAS identified significant hits for eight of the ten volumes, tagging 14 independent genomic loci. Overall, 13 of the 14 loci identified in the univariate GWAS were replicated in the multivariate GWAS. The generalization in ABCD cohort supported the GWAS results with the 12q23.2 (RNA gene RP11-210L7.1) being discovered. All of these imaging phenotypes are heritable, with heritability ranging from 15% to 27%. Gene-based analyses revealed pathways relating to cell differentiation/development and ion transporter/homeostasis, with the astrocytes found to be significantly enriched. Pleiotropy analyses revealed shared variants with neurological and psychiatric disorders under the conjFDR threshold of 0.05. These findings advance our understanding of the complex genetic architectures of amygdala and their relevance in neurological and psychiatric disorders.
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Affiliation(s)
- Ya-Nan Ou
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Bang-Sheng Wu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Yi-Jun Ge
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yi Zhang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Yu-Chao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Kevin Kuo
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Liu Yang
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.,Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China. .,Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China. .,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China. .,Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China.
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163
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Bigdeli TB, Barr PB, Rajeevan N, Graham DP, Li Y, Meyers JL, Gorman BR, Peterson RE, Sayward F, Radhakrishnan K, Natarajan S, Nielsen DA, Wilkinson AV, Malhotra AK, Zhao H, Brophy M, Shi Y, O’Leary TJ, Gleason T, Przygodzki R, Pyarajan S, Muralidhar S, Gaziano JM, Huang GD, Concato J, Siever LJ, DeLisi LE, Kimbrel NA, Beckham JC, Swann AC, Kosten TR, Fanous AH, Aslan M, Harvey PD. Correlates of suicidal behaviors and genetic risk among United States veterans with schizophrenia or bipolar I disorder. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.06.23286866. [PMID: 36945597 PMCID: PMC10029042 DOI: 10.1101/2023.03.06.23286866] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Objective Persons diagnosed with schizophrenia (SCZ) or bipolar I disorder (BPI) are at high risk for self-injurious behavior, suicidal ideation, and suicidal behaviors (SB). Characterizing associations between diagnosed mental and physical health problems, prior pharmacological treatments, and aggregate genetic factors has potential to inform risk stratification and mitigation strategies. Methods In this study of 3,942 SCZ and 5,414 BPI patients receiving VA care, self-reported SB and ideation were assessed using the Columbia Suicide Severity Rating Scale (C-SSRS). These cross-sectional data were integrated with electronic health records (EHR), and compared by lifetime diagnoses, treatment histories, follow-up screenings, and mortality data. Polygenic scores (PGS) for traits related to psychiatric disorders, substance use, and cognition were constructed using available genomic data, and exploratory genome-wide association studies were performed to identify and prioritize specific loci. Results Only 20% of veterans who self-reported SB had a corroborating ICD-9/10 code in their EHR; and among those who denied prior behaviors, more than 20% reported new-onset SB at follow-up. SB were associated with a range of psychiatric and non-psychiatric diagnoses, and with treatment with specific classes of psychotropic medications (e.g., antidepressants, antipsychotics, etc.). PGS for externalizing behaviors, smoking, suicide attempt, and major depressive disorder were also associated with attempt and ideation. Conclusions Among individuals with a diagnosed mental illness, a GWAS for SB did not yield any significant loci. Self-reported SB were strongly associated with clinical variables across several EHR domains. Overall, clinical and polygenic analyses point to sequelae of substance-use related behaviors and other psychiatric comorbidities as strong correlates of prior and subsequent SB. Nonetheless, past SB was frequently not documented in clinical settings, underscoring the value of regular screening based on direct, in-person assessments, especially among high-risk individuals.
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Affiliation(s)
- Tim B. Bigdeli
- VA New York Harbor Healthcare System, Brooklyn, NY
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY
| | - Peter B. Barr
- VA New York Harbor Healthcare System, Brooklyn, NY
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY
| | - Nallakkandi Rajeevan
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT
- Yale University School of Medicine, New Haven, CT
| | - David P. Graham
- Michael E. DeBakey VA Medical Center, Houston, TX
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX
| | - Yuli Li
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT
- Yale University School of Medicine, New Haven, CT
| | - Jacquelyn L. Meyers
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY
| | - Bryan R. Gorman
- Massachusetts Area Veterans Epidemiology, Research and Information Center (MAVERIC), Jamaica Plain, MA
| | - Roseann E. Peterson
- VA New York Harbor Healthcare System, Brooklyn, NY
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA
| | - Frederick Sayward
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT
- Yale University School of Medicine, New Haven, CT
| | - Krishnan Radhakrishnan
- National Mental Health and Substance Use Policy Laboratory, Substance Abuse and Mental Health Services Administration, Rockville, MD
| | | | - David A. Nielsen
- Michael E. DeBakey VA Medical Center, Houston, TX
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX
| | - Anna V. Wilkinson
- Michael E. DeBakey VA Medical Center, Houston, TX
- National Mental Health and Substance Use Policy Laboratory, Substance Abuse and Mental Health Services Administration, Rockville, MD
| | - Anil K. Malhotra
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY
- Department of Psychiatry, Hofstra Northwell School of Medicine, Hempstead, NY
| | - Hongyu Zhao
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT
- Yale University School of Medicine, New Haven, CT
| | - Mary Brophy
- Massachusetts Area Veterans Epidemiology, Research and Information Center (MAVERIC), Jamaica Plain, MA
- Boston University School of Medicine, Boston, MA
| | - Yunling Shi
- Massachusetts Area Veterans Epidemiology, Research and Information Center (MAVERIC), Jamaica Plain, MA
| | - Timothy J. O’Leary
- Office of Research and Development, Veterans Health Administration, Washington, DC
| | - Theresa Gleason
- Office of Research and Development, Veterans Health Administration, Washington, DC
| | - Ronald Przygodzki
- Office of Research and Development, Veterans Health Administration, Washington, DC
| | - Saiju Pyarajan
- Massachusetts Area Veterans Epidemiology, Research and Information Center (MAVERIC), Jamaica Plain, MA
| | - Sumitra Muralidhar
- Office of Research and Development, Veterans Health Administration, Washington, DC
| | - J. Michael Gaziano
- Massachusetts Area Veterans Epidemiology, Research and Information Center (MAVERIC), Jamaica Plain, MA
- Harvard University, Boston, MA
| | | | | | - Grant D. Huang
- Office of Research and Development, Veterans Health Administration, Washington, DC
| | - John Concato
- Yale University School of Medicine, New Haven, CT
- Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD
| | - Larry J. Siever
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, NY
- James J. Peters Veterans Affairs Medical Center, Bronx, NY
| | - Lynn E. DeLisi
- Department of Psychiatry, Cambridge Health Alliance, Cambridge, MA
| | - Nathan A. Kimbrel
- Durham VA Health Care System, Durham, NC
- VA Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC
| | - Jean C. Beckham
- Durham VA Health Care System, Durham, NC
- VA Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC
| | - Alan C. Swann
- Michael E. DeBakey VA Medical Center, Houston, TX
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX
| | - Thomas R. Kosten
- Michael E. DeBakey VA Medical Center, Houston, TX
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX
- Departments of Neuroscience, Pharmacology, and Immunology and Rheumatology, Baylor College of Medicine, Houston, TX
| | - Ayman H. Fanous
- VA New York Harbor Healthcare System, Brooklyn, NY
- Department of Psychiatry, University of Arizona College of Medicine Phoenix, Phoenix, AZ
| | - Mihaela Aslan
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, CT
- Yale University School of Medicine, New Haven, CT
| | - Philip D. Harvey
- Bruce W. Carter Miami Veterans Affairs (VA) Medical Center, Miami, FL
- University of Miami School of Medicine, Miami, FL
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164
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Anderson AG, Rogers BB, Loupe JM, Rodriguez-Nunez I, Roberts SC, White LM, Brazell JN, Bunney WE, Bunney BG, Watson SJ, Cochran JN, Myers RM, Rizzardi LF. Single nucleus multiomics identifies ZEB1 and MAFB as candidate regulators of Alzheimer's disease-specific cis-regulatory elements. CELL GENOMICS 2023; 3:100263. [PMID: 36950385 PMCID: PMC10025452 DOI: 10.1016/j.xgen.2023.100263] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 12/06/2022] [Accepted: 01/12/2023] [Indexed: 02/05/2023]
Abstract
Cell type-specific transcriptional differences between brain tissues from donors with Alzheimer's disease (AD) and unaffected controls have been well documented, but few studies have rigorously interrogated the regulatory mechanisms responsible for these alterations. We performed single nucleus multiomics (snRNA-seq plus snATAC-seq) on 105,332 nuclei isolated from cortical tissues from 7 AD and 8 unaffected donors to identify candidate cis-regulatory elements (CREs) involved in AD-associated transcriptional changes. We detected 319,861 significant correlations, or links, between gene expression and cell type-specific transposase accessible regions enriched for active CREs. Among these, 40,831 were unique to AD tissues. Validation experiments confirmed the activity of many regions, including several candidate regulators of APP expression. We identified ZEB1 and MAFB as candidate transcription factors playing important roles in AD-specific gene regulation in neurons and microglia, respectively. Microglia links were globally enriched for heritability of AD risk and previously identified active regulatory regions.
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Affiliation(s)
| | - Brianne B. Rogers
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
- University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jacob M. Loupe
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | | | | | - Lauren M. White
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | | | - William E. Bunney
- Department of Psychiatry and Human Behavior, College of Medicine, University of California, Irvine, Irvine, CA, USA
| | - Blynn G. Bunney
- Department of Psychiatry and Human Behavior, College of Medicine, University of California, Irvine, Irvine, CA, USA
| | - Stanley J. Watson
- Mental Health Research Institute, University of Michigan, Ann Arbor, MI, USA
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165
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Hernandez LM, Kim M, Zhang P, Bethlehem RAI, Hoftman G, Loughnan R, Smith D, Bookheimer SY, Fan CC, Bearden CE, Thompson WK, Gandal MJ. Multi-ancestry phenome-wide association of complement component 4 variation with psychiatric and brain phenotypes in youth. Genome Biol 2023; 24:42. [PMID: 36882872 PMCID: PMC9990244 DOI: 10.1186/s13059-023-02878-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 02/15/2023] [Indexed: 03/09/2023] Open
Abstract
BACKGROUND Increased expression of the complement component 4A (C4A) gene is associated with a greater lifetime risk of schizophrenia. In the brain, C4A is involved in synaptic pruning; yet, it remains unclear the extent to which upregulation of C4A alters brain development or is associated with the risk for psychotic symptoms in childhood. Here, we perform a multi-ancestry phenome-wide association study in 7789 children aged 9-12 years to examine the relationship between genetically regulated expression (GREx) of C4A, childhood brain structure, cognition, and psychiatric symptoms. RESULTS While C4A GREx is not related to childhood psychotic experiences, cognition, or global measures of brain structure, it is associated with a localized reduction in regional surface area (SA) of the entorhinal cortex. Furthermore, we show that reduced entorhinal cortex SA at 9-10 years predicts a greater number and severity of psychosis-like events at 1-year and 2-year follow-up time points. We also demonstrate that the effects of C4A on the entorhinal cortex are independent of genome-wide polygenic risk for schizophrenia. CONCLUSIONS Our results suggest neurodevelopmental effects of C4A on childhood medial temporal lobe structure, which may serve as a biomarker for schizophrenia risk prior to symptom onset.
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Affiliation(s)
- Leanna M. Hernandez
- Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095 USA
| | - Minsoo Kim
- Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095 USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095 USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095 USA
| | - Pan Zhang
- Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095 USA
| | - Richard A. I. Bethlehem
- University of Cambridge, Department of Psychiatry, Cambridge Biomedical Campus, Cambridge, CB2 0SZ UK
| | - Gil Hoftman
- Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095 USA
| | - Robert Loughnan
- Population Neuroscience and Genetics Lab, University of California, San Diego, San Diego, CA 92093 USA
| | - Diana Smith
- Population Neuroscience and Genetics Lab, University of California, San Diego, San Diego, CA 92093 USA
| | - Susan Y. Bookheimer
- Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095 USA
| | - Chun Chieh Fan
- Population Neuroscience and Genetics Lab, University of California, San Diego, San Diego, CA 92093 USA
| | - Carrie E. Bearden
- Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095 USA
| | - Wesley K. Thompson
- Population Neuroscience and Genetics Lab, University of California, San Diego, San Diego, CA 92093 USA
| | - Michael J. Gandal
- Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095 USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095 USA
- Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA USA
- Lifespan Brain Institute at Penn Med and the Children’s Hospital of Philadelphia, Philadelphia, PA USA
- Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA USA
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166
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Al-Soufi L, Costas J. Genetic susceptibility for schizophrenia after adjustment by genetic susceptibility for smoking: implications in identification of risk genes and genetic correlation with related traits. Psychol Med 2023; 53:1-11. [PMID: 36876478 DOI: 10.1017/s0033291723000326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Abstract
BACKGROUND Prevalence of smoking in schizophrenia (SCZ) is larger than in general population. Genetic studies provided some evidence of a causal effect of smoking on SCZ. We aim to characterize the genetic susceptibility to SCZ affected by genetic susceptibility to smoking. METHODS Multi-trait-based conditional and joint analysis was applied to the largest European SCZ genome-wide association studies (GWAS) to remove genetic effects on SCZ driven by smoking, estimated by generalized summary data-based Mendelian randomization. Enrichment analysis was performed to compare original v. conditional GWAS. Change in genetic correlation between SCZ and relevant traits after conditioning was assessed. Colocalization analysis was performed to identify specific loci confirming general findings. RESULTS Conditional analysis identified 19 new risk loci for SCZ and 42 lost loci whose association with SCZ may be partially driven by smoking. These results were strengthened by colocalization analysis. Enrichment analysis indicated a higher association of differentially expressed genes at prenatal brain stages after conditioning. Genetic correlation of SCZ with substance use and dependence, attention deficit-hyperactivity disorder, and several externalizing traits significantly changed after conditioning. Colocalization of association signal between SCZ and these traits was identified for some of the lost loci, such as CHRNA2, CUL3, and PCDH7. CONCLUSIONS Our approach led to identification of potential new SCZ loci, loci partially associated to SCZ through smoking, and a shared genetic susceptibility between SCZ and smoking behavior related to externalizing phenotypes. Application of this approach to other psychiatric disorders and substances may lead to a better understanding of the role of substances on mental health.
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Affiliation(s)
- Laila Al-Soufi
- Psychiatric Genetics group, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, Galicia, Spain
- Red de Investigación en Atención Primaria de Adicciones (RIAPAd), Spain
- Department of Zoology, Genetics and Physical Anthropology, Universidade de Santiago de Compostela (USC), Santiago de Compostela, Galicia, Spain
| | - Javier Costas
- Psychiatric Genetics group, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, Galicia, Spain
- Red de Investigación en Atención Primaria de Adicciones (RIAPAd), Spain
- Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Galicia, Spain
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167
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Chen M, Li S, Zhu Z, Dai C, Hao X. Investigating the shared genetic architecture and causal relationship between pain and neuropsychiatric disorders. Hum Genet 2023; 142:431-443. [PMID: 36445456 DOI: 10.1007/s00439-022-02507-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 11/14/2022] [Indexed: 11/30/2022]
Abstract
Pain often occurs in parallel with neuropsychiatric disorders. However, the underlying mechanisms and potential causality have not been well studied. We collected the genome-wide association study (GWAS) summary statistics of 26 common pain and neuropsychiatric disorders with sample size ranging from 17,310 to 482,730 in European population. The genetic correlation between pair of pain and neuropsychiatric disorders, as well as the relevant cell types were investigated by linkage disequilibrium (LD) score regression analyses. Then, transcriptome-wide association study (TWAS) was applied to identify the potential shared genes by integrating the gene expression information and GWAS. In addition, Mendelian randomization (MR) analyses were conducted to infer the potential causality between pain and neuropsychiatric disorders. Among the 169 pairwise pain and neuropsychiatric disorders, 55 pairs showed positive correlations (median rg = 0.43) and 9 pairs showed negative correlations (median rg = -0.31). Using MR analyses, 26 likely causal associations were identified, including that neuroticism and insomnia were risk factors for most of short-term pain, and multisite chronic pain was risk factor for neuroticism, insomnia, major depressive disorder and attention deficit/hyperactivity disorder, and vice versa. The signals of pain and neuropsychiatric disorders tended to be enriched in the functional regions of cell types from central nervous system (CNS). A total of 19 genes shared in at least one pain and neuropsychiatric disorder pair were identified by TWAS, including AMT, NCOA6, and UNC45A, which involved in glycine degradation, insulin secretion, and cell proliferation, respectively. Our findings provided the evidence of shared genetic structure, causality and potential shared pathogenic mechanisms between pain and neuropsychiatric disorders, and enhanced our understanding of the comorbidities of pain and neuropsychiatric disorders.
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Affiliation(s)
- Mengya Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Si Li
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Ziwei Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Chengguqiu Dai
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Xingjie Hao
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
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168
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Laugesen K, Mengel-From J, Christensen K, Olsen J, Hougaard DM, Boding L, Olsen A, Erikstrup C, Hetland ML, Høgdall E, Kjaergaard AD, Sørensen E, Brügmann A, Petersen ERB, Brandslund I, Nordestgaard BG, Jensen GB, Skajaa N, Troelsen FS, Fuglsang CH, Svingel LS, Sørensen HT. A Review of Major Danish Biobanks: Advantages and Possibilities of Health Research in Denmark. Clin Epidemiol 2023; 15:213-239. [PMID: 36852012 PMCID: PMC9960719 DOI: 10.2147/clep.s392416] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 01/20/2023] [Indexed: 02/23/2023] Open
Abstract
Biobank research may lead to an improved understanding of disease etiology and advance personalized medicine. Denmark (population ~5.9 million) provides a unique setting for population-based health research. The country is a rich source of biobanks and the universal, tax-funded healthcare system delivers routinely collected data to numerous registries and databases. By virtue of the civil registration number (assigned uniquely to all Danish citizens), biological specimens stored in biobanks can be combined with clinical and demographic data from these population-based health registries and databases. In this review, we aim to provide an understanding of advantages and possibilities of biobank research in Denmark. As knowledge about the Danish setting is needed to grasp the full potential, we first introduce the Danish healthcare system, the Civil Registration System, the population-based registries, and the interface with biobanks. We then describe the biobank infrastructures, comprising the Danish National Biobank Initiative, the Bio- and Genome Bank Denmark, and the Danish National Genome Center. Further, we briefly provide an overview of fourteen selected biobanks, including: The Danish Newborn Screening Biobank; The Danish National Birth Cohort; The Danish Twin Registry Biobank; Diet, Cancer and Health; Diet, Cancer and Health - Next generations; Danish Centre for Strategic Research in Type 2 Diabetes; Vejle Diabetes Biobank; The Copenhagen Hospital Biobank; The Copenhagen City Heart Study; The Copenhagen General Population Study; The Danish Cancer Biobank; The Danish Rheumatological Biobank; The Danish Blood Donor Study; and The Danish Pathology Databank. Last, we inform on practical aspects, such as data access, and discuss future implications.
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Affiliation(s)
- Kristina Laugesen
- Department of Clinical Epidemiology, Aarhus University Hospital and Aarhus University, Aarhus, Denmark
| | - Jonas Mengel-From
- Epidemiology, Biostatistics and Biodemography, the Danish Twin Registry, and the Danish Aging Research Center, Department of Public Health, University of Southern Denmark, Odense, Denmark.,Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
| | - Kaare Christensen
- Epidemiology, Biostatistics and Biodemography, the Danish Twin Registry, and the Danish Aging Research Center, Department of Public Health, University of Southern Denmark, Odense, Denmark.,Department of Clinical Genetics, Odense University Hospital, Odense, Denmark.,Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, Denmark
| | - Jørn Olsen
- Department of Clinical Epidemiology, Aarhus University Hospital and Aarhus University, Aarhus, Denmark
| | - David M Hougaard
- iPSYCH, Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark.,Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Lasse Boding
- The Danish National Biobank, Statens Serum Institut, Copenhagen, Denmark
| | - Anja Olsen
- Danish Cancer Society Research Center, Danish Cancer Society, Copenhagen, Denmark.,Department of Public Health, Aarhus University, Aarhus, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Merete Lund Hetland
- The DANBIO Registry and Copenhagen Center for Arthritis Research (COPECARE), Center for Rheumatology and Spine Diseases, Centre of Head and Orthopaedics, Copenhagen University Hospital Rigshospitalet, Glostrup, Denmark.,Department of Clinical Medicine, University of Copenhagen, Faculty of Health and Medical Sciences, Copenhagen, Denmark
| | - Estrid Høgdall
- Department of Clinical Medicine, University of Copenhagen, Faculty of Health and Medical Sciences, Copenhagen, Denmark.,Bio- and GenomeBank Denmark (RBGB), Molecular Unit, Department of Pathology, Herlev Hospital, Herlev, Denmark
| | - Alisa D Kjaergaard
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Erik Sørensen
- Department of Clinical Immunology, Copenhagen University Hospital (Rigshospitalet), Copenhagen, Denmark
| | - Anja Brügmann
- Department of Pathology, Aalborg University Hospital, Aalborg, Denmark
| | | | - Ivan Brandslund
- Department of Clinical Biochemistry and Immunology, Lillebaelt Hospital, Vejle, Denmark.,Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Børge G Nordestgaard
- The Copenhagen General Population Study, Department of Clinical Biochemistry, Copenhagen University Hospital - Herlev Gentofte, University of Copenhagen, Herlev, Denmark
| | - Gorm B Jensen
- The Copenhagen City Heart Study, Frederiksberg and Bispebjerg Hospital, Frederiksberg, Denmark
| | - Nils Skajaa
- Department of Clinical Epidemiology, Aarhus University Hospital and Aarhus University, Aarhus, Denmark
| | | | | | - Lise Skovgaard Svingel
- Department of Clinical Epidemiology, Aarhus University Hospital and Aarhus University, Aarhus, Denmark
| | - Henrik T Sørensen
- Department of Clinical Epidemiology, Aarhus University Hospital and Aarhus University, Aarhus, Denmark
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169
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Ding H, Xie M, Wang J, Ouyang M, Huang Y, Yuan F, Jia Y, Zhang X, Liu N, Zhang N. Shared genetics of psychiatric disorders and type 2 diabetes:a large-scale genome-wide cross-trait analysis. J Psychiatr Res 2023; 159:185-195. [PMID: 36738649 DOI: 10.1016/j.jpsychires.2023.01.037] [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] [Received: 08/20/2022] [Revised: 12/31/2022] [Accepted: 01/26/2023] [Indexed: 01/29/2023]
Abstract
BACKGROUND Individuals with psychiatric disorders have elevated rates of type 2 diabetes comorbidity. Although little is known about the shared genetics and causality of this association. Thus, we aimed to investigate shared genetics and causal link between different type 2 diabetes and psychiatric disorders. METHODS We conducted a large-scale genome-wide cross-trait association study(GWAS) to investigate genetic overlap between type 2 diabetes and anorexia nervosa, attention deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, schizophrenia, anxiety disorders and Tourette syndrome. By post-GWAS functional analysis, we identify variants genes expression in various tissues. Enrichment pathways, potential protein interaction and mendelian randomization also provided to research the relationship between type 2 diabetes and psychiatric disorders. RESULTS We discovered that type 2 diabetes and psychiatric disorders had a significant correlation. We identified 138 related loci, 32 were novel loci. Post-GWAS analysis revealed that 86 differentially expressed genes were located in different brain regions and peripheral blood in type 2 diabetes and related psychiatric disorders. MAPK signaling pathway plays an important role in neural development and insulin signaling. In addition, there is a causal relationship between T2D and mental disorders. In PPI analysis, the central genes of the DEG PPI network were FTO and TCF7L2. CONCLUSION This large-scale genome-wide cross-trait analysis identified shared genetics andpotential causal links between type 2 diabetes and related psychiatric disorders, suggesting potential new biological functions in common among them.
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Affiliation(s)
- Hui Ding
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China
| | - Minyao Xie
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China
| | - Jinyi Wang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China
| | - Mengyuan Ouyang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China
| | - Yanyuan Huang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China
| | - Fangzheng Yuan
- School of Psychology, Nanjing Normal University, Nanjing, 210023, PR China
| | - Yunhan Jia
- School of Psychology, Nanjing Normal University, Nanjing, 210023, PR China
| | - Xuedi Zhang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China
| | - Na Liu
- Department of Medical Psychology, The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, PR China.
| | - Ning Zhang
- The Affiliated Nanjing Brain Hospital of Nanjing Medical Univesity, 264 Guangzhou Road, Nanjing, Jiangsu, 210029, PR China.
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170
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Johnson EC, Kapoor M, Hatoum AS, Zhou H, Polimanti R, Wendt FR, Walters RK, Lai D, Kember RL, Hartz S, Meyers JL, Peterson RE, Ripke S, Bigdeli TB, Fanous AH, Pato CN, Pato MT, Goate AM, Kranzler HR, O'Donovan MC, Walters JTR, Gelernter J, Edenberg HJ, Agrawal A. Investigation of convergent and divergent genetic influences underlying schizophrenia and alcohol use disorder. Psychol Med 2023; 53:1196-1204. [PMID: 34231451 PMCID: PMC8738774 DOI: 10.1017/s003329172100266x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
BACKGROUND Alcohol use disorder (AUD) and schizophrenia (SCZ) frequently co-occur, and large-scale genome-wide association studies (GWAS) have identified significant genetic correlations between these disorders. METHODS We used the largest published GWAS for AUD (total cases = 77 822) and SCZ (total cases = 46 827) to identify genetic variants that influence both disorders (with either the same or opposite direction of effect) and those that are disorder specific. RESULTS We identified 55 independent genome-wide significant single nucleotide polymorphisms with the same direction of effect on AUD and SCZ, 8 with robust effects in opposite directions, and 98 with disorder-specific effects. We also found evidence for 12 genes whose pleiotropic associations with AUD and SCZ are consistent with mediation via gene expression in the prefrontal cortex. The genetic covariance between AUD and SCZ was concentrated in genomic regions functional in brain tissues (p = 0.001). CONCLUSIONS Our findings provide further evidence that SCZ shares meaningful genetic overlap with AUD.
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Affiliation(s)
- Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Manav Kapoor
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexander S Hatoum
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Hang Zhou
- Department of Psychiatry, Division of Human Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Division of Human Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Frank R Wendt
- Department of Psychiatry, Division of Human Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Raymond K Walters
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Rachel L Kember
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- VISN 4 MIRECC, Crescenz VAMC, Philadelphia, PA, USA
| | - Sarah Hartz
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jacquelyn L Meyers
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
- Henri Begleiter Neurodynamics Laboratory, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Roseann E Peterson
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Stephan Ripke
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Tim B Bigdeli
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Ayman H Fanous
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Carlos N Pato
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Michele T Pato
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Alison M Goate
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Henry R Kranzler
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- VISN 4 MIRECC, Crescenz VAMC, Philadelphia, PA, USA
| | - Michael C O'Donovan
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, UK
| | - James T R Walters
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, UK
| | - Joel Gelernter
- Department of Psychiatry, Division of Human Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Howard J Edenberg
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
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171
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Dang X, Liu J, Zhang Z, Luo XJ. Mendelian Randomization Study Using Dopaminergic Neuron-Specific eQTL Identifies Novel Risk Genes for Schizophrenia. Mol Neurobiol 2023; 60:1537-1546. [PMID: 36517655 DOI: 10.1007/s12035-022-03160-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 12/04/2022] [Indexed: 12/23/2022]
Abstract
Multiple integrative studies have been performed to identify the potential target genes of the non-coding schizophrenia (SCZ) risk variants. However, all the integrative studies used expression quantitative trait loci (eQTL) data from bulk tissues. Considering the cell type-specific regulatory effect of many genetic variants, it is important to conduct integrative studies using cell type-specific eQTL data. Here, we conduct a Mendelian randomization (MR) study by integrating genome-wide associations of SCZ (74,776 cases and 101,023 controls) and eQTL data (N = 215) from dopaminergic neurons, which were differentiated from human-induced pluripotent stem cell (iPSC) lines. For eQTL from young post-mitotic dopaminergic neurons (differentiation of iPSC for 30 days, D30), we identified 34 genes whose genetically regulated expression in dopaminergic neurons may have a causal role in SCZ. Among which, ARL3 showed the most significant associations with SCZ. For eQTL from more mature dopaminergic neurons (D52), we identified 37 potential SCZ causal genes, and ARL3 and GNL3 showed the most significant associations. Only 12 genes showed significant associations with SCZ in both D30 and D52 eQTL datasets, indicating the time point-specific genetic regulatory effects in young post-mitotic dopaminergic neurons and more mature dopaminergic neurons. Comparing the results from dopaminergic neurons with bulk brain tissues prioritized 2 high-confidence risk genes, including DDHD2 and GALNT10. Our study identifies multiple risk genes whose genetically regulated expression in dopaminergic neurons may have a causal role in SCZ. Further mechanistic investigation will provide pivotal insights into SCZ pathophysiology.
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Affiliation(s)
- Xinglun Dang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Jiewei Liu
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Zhijun Zhang
- Zhongda Hospital, Advanced Institute for Life and Health, Southeast University, Nanjing, 210096, China
- Department of Neurology, School of Medicine, Affiliated Zhongda Hospital, Research Institution of Neuropsychiatry, Southeast University, Nanjing, 210009, Jiangsu Province, China
| | - Xiong-Jian Luo
- Zhongda Hospital, Advanced Institute for Life and Health, Southeast University, Nanjing, 210096, China.
- Department of Neurology, School of Medicine, Affiliated Zhongda Hospital, Research Institution of Neuropsychiatry, Southeast University, Nanjing, 210009, Jiangsu Province, China.
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172
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Quintero Reis A, Newton BA, Kessler R, Polimanti R, Wendt FR. Functional and molecular characterization of suicidality factors using phenotypic and genome-wide data. Mol Psychiatry 2023; 28:1064-1071. [PMID: 36604601 PMCID: PMC10005939 DOI: 10.1038/s41380-022-01929-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 12/09/2022] [Accepted: 12/14/2022] [Indexed: 01/07/2023]
Abstract
Genome-wide association studies (GWAS) of suicidal thoughts and behaviors support the existence of genetic contributions. Continuous measures of psychiatric disorder symptom severity can sometimes model polygenic risk better than binarized definitions. We compared two severity measures of suicidal thoughts and behaviors at the molecular and functional levels using genome-wide data. We used summary association data from GWAS of four traits analyzed in 122,935 individuals of European ancestry: thought life was not worth living (TLNWL), thoughts of self-harm, actual self-harm, and attempted suicide. A new trait for suicidal thoughts and behaviors was constructed first, phenotypically, by aggregating the previous four traits (termed "suicidality") and second, genetically, by using genomic structural equation modeling (gSEM; termed S-factor). Suicidality and S-factor were compared using SNP-heritability (h2) estimates, genetic correlation (rg), partitioned h2, effect size distribution, transcriptomic correlations (ρGE) in the brain, and cross-population polygenic scoring (PGS). The S-factor had good model fit (χ2 = 0.21, AIC = 16.21, CFI = 1.00, SRMR = 0.024). Suicidality (h2 = 7.6%) had higher h2 than the S-factor (h2 = 2.54, Pdiff = 4.78 × 10-13). Although the S-factor had a larger number of non-null susceptibility loci (πc = 0.010), these loci had small effect sizes compared to those influencing suicidality (πc = 0.005, Pdiff = 0.045). The h2 of both traits was enriched for conserved biological pathways. The rg and ρGE support highly overlapping genetic and transcriptomic features between suicidality and the S-factor. PGS using European-ancestry SNP effect sizes strongly associated with TLNWL in Admixed Americans: Nagelkerke's R2 = 8.56%, P = 0.009 (PGSsuicidality) and Nagelkerke's R2 = 7.48%, P = 0.045 (PGSS-factor). An aggregate suicidality phenotype was statistically more heritable than the S-factor across all analyses and may be more informative for future genetic study designs interested in common genetic factors among different suicide related phenotypes.
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Affiliation(s)
- Andrea Quintero Reis
- American University of Antigua College of Medicine, Osbourn, Antigua and Barbuda
| | - Brendan A Newton
- Forensic Science Program, University of Toronto, Mississauga, ON, Canada
| | - Ronald Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- VA CT Healthcare System, West Haven, CT, USA
| | - Frank R Wendt
- Forensic Science Program, University of Toronto, Mississauga, ON, Canada.
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
- VA CT Healthcare System, West Haven, CT, USA.
- Department of Anthropology, University of Toronto, Mississauga, ON, Canada.
- Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
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173
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Kato H, Kimura H, Kushima I, Takahashi N, Aleksic B, Ozaki N. The genetic architecture of schizophrenia: review of large-scale genetic studies. J Hum Genet 2023; 68:175-182. [PMID: 35821406 DOI: 10.1038/s10038-022-01059-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/12/2022] [Accepted: 06/20/2022] [Indexed: 11/09/2022]
Abstract
Schizophrenia is a complex and often chronic psychiatric disorder with high heritability. Diagnosis of schizophrenia is still made clinically based on psychiatric symptoms; no diagnostic tests or biomarkers are available. Pathophysiology-based diagnostic scheme and treatments are also not available. Elucidation of the pathogenesis is needed for development of pathology-based diagnostics and treatments. In the past few decades, genetic research has made substantial advances in our understanding of the genetic architecture of schizophrenia. Rare copy number variations (CNVs) and rare single-nucleotide variants (SNVs) detected by whole-genome CNV analysis and whole-genome/-exome sequencing analysis have provided the great advances. Common single-nucleotide polymorphisms (SNPs) detected by large-scale genome-wide association studies have also provided important information. Large-scale genetic studies have been revealed that both rare and common genetic variants play crucial roles in this disorder. In this review, we focused on CNVs, SNVs, and SNPs, and discuss the latest research findings on the pathogenesis of schizophrenia based on these genetic variants. Rare variants with large effect sizes can provide mechanistic hypotheses. CRISPR-based genetics approaches and induced pluripotent stem cell technology can facilitate the functional analysis of these variants detected in patients with schizophrenia. Recent advances in long-read sequence technology are expected to detect variants that cannot be detected by short-read sequence technology. Various studies that bring together data from common variant and transcriptomic datasets provide biological insight. These new approaches will provide additional insight into the pathophysiology of schizophrenia and facilitate the development of pathology-based therapeutics.
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Affiliation(s)
- Hidekazu Kato
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Hiroki Kimura
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Itaru Kushima
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan.,Medical Genomics Center, Nagoya University Hospital, Nagoya, Japan
| | - Nagahide Takahashi
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Branko Aleksic
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan.
| | - Norio Ozaki
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan.,Medical Genomics Center, Nagoya University Hospital, Nagoya, Japan.,Institute for Glyco-core Research (iGCORE), Nagoya University, Nagoya, Japan
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174
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Coors A, Imtiaz MA, Boenniger MM, Aziz NA, Breteler MMB, Ettinger U. Polygenic risk scores for schizophrenia are associated with oculomotor endophenotypes. Psychol Med 2023; 53:1611-1619. [PMID: 34412712 PMCID: PMC10009390 DOI: 10.1017/s0033291721003251] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/15/2021] [Accepted: 07/20/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Schizophrenia is a heterogeneous disorder with substantial heritability. The use of endophenotypes may help clarify its aetiology. Measures from the smooth pursuit and antisaccade eye movement tasks have been identified as endophenotypes for schizophrenia in twin and family studies. However, the genetic basis of the overlap between schizophrenia and these oculomotor markers is largely unknown. Here, we tested whether schizophrenia polygenic risk scores (PRS) were associated with oculomotor performance in the general population. METHODS Analyses were based on the data of 2956 participants (aged 30-95) of the Rhineland Study, a community-based cohort study in Bonn, Germany. Genotyping was performed on Omni-2.5 exome arrays. Using summary statistics from a recent meta-analysis based on the two largest schizophrenia genome-wide association studies to date, we quantified genetic risk for schizophrenia by creating PRS at different p value thresholds for genetic markers. We examined associations between PRS and oculomotor performance using multivariable regression models. RESULTS Higher PRS were associated with higher antisaccade error rate and latency, and lower antisaccade amplitude gain. PRS showed inconsistent patterns of association with smooth pursuit velocity gain and were not associated with saccade rate during smooth pursuit or performance on a prosaccade control task. CONCLUSIONS There is an overlap between genetic determinants of schizophrenia and oculomotor endophenotypes. Our findings suggest that the mechanisms that underlie schizophrenia also affect oculomotor function in the general population.
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Affiliation(s)
- Annabell Coors
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Mohammed-Aslam Imtiaz
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Meta M. Boenniger
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - N. Ahmad Aziz
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Monique M. B. Breteler
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany
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175
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Shmakova AA, Semina EV, Neyfeld EA, Tsygankov BD, Karagyaur MN. [An analysis of the relationship between genetic factors and the risk of schizophrenia]. Zh Nevrol Psikhiatr Im S S Korsakova 2023; 123:26-36. [PMID: 36843456 DOI: 10.17116/jnevro202312302126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2023]
Abstract
The etiology and pathogenesis of schizophrenia remain poorly understood, but it has been established that the contribution of heredity to the development of the disease is about 80-85%. Over the past decade, significant progress has been made in the search for specific genetic variants associated with the development of schizophrenia. The review discusses the results of modern large-scale studies aimed at searching for genetic associations with schizophrenia: genome-wide association studies (GWAS) and the search for rare variants (mutations or copy number variations, CNV), including the use of whole exome sequencing. We synthesize data on currently known genes that are significantly associated with schizophrenia and discuss their biological functions in order to identify the main molecular pathways involved in the pathophysiology of schizophrenia.
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Affiliation(s)
- A A Shmakova
- Koltzov Institute of Developmental Biology, Moscow, Russia
| | - E V Semina
- Lomonosov Moscow State University, Moscow, Russia.,Institute for Regenerative Medicine - Lomonosov Moscow State University, Moscow, Russia
| | - E A Neyfeld
- Lomonosov Moscow State University, Moscow, Russia
| | | | - M N Karagyaur
- Lomonosov Moscow State University, Moscow, Russia.,Institute for Regenerative Medicine - Lomonosov Moscow State University, Moscow, Russia
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176
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Identification of Personality-Related Candidate Genes in Thoroughbred Racehorses Using a Bioinformatics-Based Approach Involving Functionally Annotated Human Genes. Animals (Basel) 2023; 13:ani13040769. [PMID: 36830556 PMCID: PMC9951868 DOI: 10.3390/ani13040769] [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: 10/15/2022] [Revised: 02/07/2023] [Accepted: 02/20/2023] [Indexed: 02/25/2023] Open
Abstract
Considering the personality traits of racehorses (e.g., flightiness, anxiety, and affability) is considered essential to improve training efficiency and decrease accident frequency, especially when retraining for a second career that may involve contact with inexperienced personnel after retiring from racing. Studies on human personality-related genes are frequently conducted; however, such studies are rare in horses because a consistent methodology for personality evaluation is lacking. Using the recently published whole genome variant database of 101 Thoroughbred horses, we compared horse genes orthologous to human genes related to the Big Five personality traits, and identified 18 personality-related candidate genes in horses. These genes include 55 variants that involve non-synonymous substitutions that highly impact the encoded protein. Moreover, we evaluated the allele frequencies and functional impact on the proteins in terms of the difference in molecular weights and hydrophobicity levels between reference and altered amino acids. We identified 15 newly discovered genes that may affect equine personality, but their associations with personality are still unclear. Although more studies are required to compare genetic and behavioral information to validate this approach, it may be useful under limited conditions for personality evaluation.
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177
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Zhao B, Li Y, Fan Z, Wu Z, Shu J, Yang X, Yang Y, Wang X, Li B, Wang X, Copana C, Yang Y, Lin J, Li Y, Stein JL, O'Brien JM, Li T, Zhu H. Eye-brain connections revealed by multimodal retinal and brain imaging genetics in the UK Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.16.23286035. [PMID: 36824893 PMCID: PMC9949187 DOI: 10.1101/2023.02.16.23286035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
As an anatomical extension of the brain, the retina of the eye is synaptically connected to the visual cortex, establishing physiological connections between the eye and the brain. Despite the unique opportunity retinal structures offer for assessing brain disorders, less is known about their relationship to brain structure and function. Here we present a systematic cross-organ genetic architecture analysis of eye-brain connections using retina and brain imaging endophenotypes. Novel phenotypic and genetic links were identified between retinal imaging biomarkers and brain structure and function measures derived from multimodal magnetic resonance imaging (MRI), many of which were involved in the visual pathways, including the primary visual cortex. In 65 genomic regions, retinal imaging biomarkers shared genetic influences with brain diseases and complex traits, 18 showing more genetic overlaps with brain MRI traits. Mendelian randomization suggests that retinal structures have bidirectional genetic causal links with neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, cross-organ imaging genetics reveals a genetic basis for eye-brain connections, suggesting that the retinal images can elucidate genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.
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Affiliation(s)
- Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yujue Li
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zhenyi Wu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yilin Yang
- Department of Computer and Information Science and Electrical and Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bingxuan Li
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Xiyao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Carlos Copana
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jinjie Lin
- Yale School of Management, Yale University, New Haven, CT 06511, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jason L. Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joan M. O'Brien
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn Medicine Center for Ophthalmic Genetics in Complex Diseases, PA, 19104, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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178
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Functional connectivity signatures of NMDAR dysfunction in schizophrenia-integrating findings from imaging genetics and pharmaco-fMRI. Transl Psychiatry 2023; 13:59. [PMID: 36797233 PMCID: PMC9935542 DOI: 10.1038/s41398-023-02344-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 01/26/2023] [Accepted: 01/30/2023] [Indexed: 02/18/2023] Open
Abstract
Both, pharmacological and genome-wide association studies suggest N-methyl-D-aspartate receptor (NMDAR) dysfunction and excitatory/inhibitory (E/I)-imbalance as a major pathophysiological mechanism of schizophrenia. The identification of shared fMRI brain signatures of genetically and pharmacologically induced NMDAR dysfunction may help to define biomarkers for patient stratification. NMDAR-related genetic and pharmacological effects on functional connectivity were investigated by integrating three different datasets: (A) resting state fMRI data from 146 patients with schizophrenia genotyped for the disease-associated genetic variant rs7191183 of GRIN2A (encoding the NMDAR 2 A subunit) as well as 142 healthy controls. (B) Pharmacological effects of the NMDAR antagonist ketamine and the GABA-A receptor agonist midazolam were obtained from a double-blind, crossover pharmaco-fMRI study in 28 healthy participants. (C) Regional gene expression profiles were estimated using a postmortem whole-brain microarray dataset from six healthy donors. A strong resemblance was observed between the effect of the genetic variant in schizophrenia and the ketamine versus midazolam contrast of connectivity suggestive for an associated E/I-imbalance. This similarity became more pronounced for regions with high density of NMDARs, glutamatergic neurons, and parvalbumin-positive interneurons. From a functional perspective, increased connectivity emerged between striato-pallido-thalamic regions and cortical regions of the auditory-sensory-motor network, while decreased connectivity was observed between auditory (superior temporal gyrus) and visual processing regions (lateral occipital cortex, fusiform gyrus, cuneus). Importantly, these imaging phenotypes were associated with the genetic variant, the differential effect of ketamine versus midazolam and schizophrenia (as compared to healthy controls). Moreover, the genetic variant was associated with language-related negative symptomatology which correlated with disturbed connectivity between the left posterior superior temporal gyrus and the superior lateral occipital cortex. Shared genetic and pharmacological functional connectivity profiles were suggestive of E/I-imbalance and associated with schizophrenia. The identified brain signatures may help to stratify patients with a common molecular disease pathway providing a basis for personalized psychiatry.
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179
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Li Y, Wen J, Li G, Chen J, Sun Q, Liu W, Guan W, Lai B, Szatkiewicz J, He X, Sullivan P. DeepGWAS: Enhance GWAS Signals for Neuropsychiatric Disorders via Deep Neural Network. RESEARCH SQUARE 2023:rs.3.rs-2399024. [PMID: 36824788 PMCID: PMC9949268 DOI: 10.21203/rs.3.rs-2399024/v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Genetic dissection of neuropsychiatric disorders can potentially reveal novel therapeutic targets. While genome-wide association studies (GWAS) have tremendously advanced our understanding, we approach a sample size bottleneck (i.e., the number of cases needed to identify >90% of all loci is impractical). Therefore, computationally enhancing GWAS on existing samples may be particularly valuable. Here, we describe DeepGWAS, a deep neural network-based method to enhance GWAS by integrating GWAS results with linkage disequilibrium and brain-related functional annotations. DeepGWAS enhanced schizophrenia (SCZ) loci by ~3X when applied to the largest European GWAS, and 21.3% enhanced loci were validated by the latest multi-ancestry GWAS. Importantly, DeepGWAS models can be transferred to other neuropsychiatric disorders. Transferring SCZ-trained models to Alzheimer's disease and major depressive disorder, we observed 1.3-17.6X detected loci compared to standard GWAS, among which 27-40% were validated by other GWAS studies. We anticipate DeepGWAS to be a powerful tool in GWAS studies.
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Affiliation(s)
- Yun Li
- University of North Carolina at Chapel Hill
| | | | | | | | - Quan Sun
- University of North Carolina, USA
| | | | | | - Boqiao Lai
- Toyota Technological Institute at Chicago
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180
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A Systematic Review of the Human Accelerated Regions in Schizophrenia and Related Disorders: Where the Evolutionary and Neurodevelopmental Hypotheses Converge. Int J Mol Sci 2023; 24:ijms24043597. [PMID: 36835010 PMCID: PMC9962562 DOI: 10.3390/ijms24043597] [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: 12/22/2022] [Revised: 02/03/2023] [Accepted: 02/07/2023] [Indexed: 02/15/2023] Open
Abstract
Schizophrenia is a psychiatric disorder that results from genetic and environmental factors interacting and disrupting neurodevelopmental trajectories. Human Accelerated Regions (HARs) are evolutionarily conserved genomic regions that have accumulated human-specific sequence changes. Thus, studies on the impact of HARs in the context of neurodevelopment, as well as with respect to adult brain phenotypes, have increased considerably in the last few years. Through a systematic approach, we aim to offer a comprehensive review of HARs' role in terms of human brain development, configuration, and cognitive abilities, as well as whether HARs modulate the susceptibility to neurodevelopmental psychiatric disorders such as schizophrenia. First, the evidence in this review highlights HARs' molecular functions in the context of the neurodevelopmental regulatory genetic machinery. Second, brain phenotypic analyses indicate that HAR genes' expression spatially correlates with the regions that suffered human-specific cortical expansion, as well as with the regional interactions for synergistic information processing. Lastly, studies based on candidate HAR genes and the global "HARome" variability describe the involvement of these regions in the genetic background of schizophrenia, but also in other neurodevelopmental psychiatric disorders. Overall, the data considered in this review emphasise the crucial role of HARs in human-specific neurodevelopment processes and encourage future research on this evolutionary marker for a better understanding of the genetic basis of schizophrenia and other neurodevelopmental-related psychiatric disorders. Accordingly, HARs emerge as interesting genomic regions that require further study in order to bridge the neurodevelopmental and evolutionary hypotheses in schizophrenia and other related disorders and phenotypes.
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181
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Abumadini MS, Al Ghamdi KS, Alqahtani AH, Almedallah DK, Callans L, Jarad JA, Cyrus C, Koeleman BPC, Keating BJ, Pankratz N, Al-Ali AK. Genome-wide copy number variant screening of Saudi schizophrenia patients reveals larger deletions in cases versus controls. Front Mol Neurosci 2023; 16:1069375. [PMID: 36846569 PMCID: PMC9950097 DOI: 10.3389/fnmol.2023.1069375] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 01/20/2023] [Indexed: 02/12/2023] Open
Abstract
Introduction Genome-wide association studies have discovered common polymorphisms in regions associated with schizophrenia. No genome-wide analyses have been performed in Saudi schizophrenia subjects. Methods Genome-wide genotyping data from 136 Saudi schizophrenia cases and 97 Saudi controls in addition to 4,625 American were examined for copy number variants (CNVs). A hidden Markov model approach was used to call CNVs. Results CNVs in schizophrenia cases were twice as large on average than CNVs in controls (p = 0.04). The analyses focused on extremely large >250 kilobases CNVs or homozygous deletions of any size. One extremely large deletion was noted in a single case (16.5 megabases on chromosome 10). Two cases had an 814 kb duplication of chromosome 7 spanning a cluster of genes, including circadian-related loci, and two other cases had 277 kb deletions of chromosome 9 encompassing an olfactory receptors gene family. CNVs were also seen in loci previously associated with schizophrenia, namely a 16p11 proximal duplication and two 22q11.2 deletions. Discussion Runs of homozygosity (ROHs) were analyzed across the genome to investigate correlation with schizophrenia risk. While rates and sizes of these ROHs were similar in cases and controls, we identified 10 regions where multiple cases had ROHs and controls did not.
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Affiliation(s)
- Mahdi S. Abumadini
- Department of Psychiatry, King Fahd Hospital of the University, Al-Khobar and College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Kholoud S. Al Ghamdi
- Department of Physiology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Abdullah H. Alqahtani
- Department of Psychiatry, King Fahd Hospital of the University, Al-Khobar and College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Dana K. Almedallah
- Department of Psychiatry, King Fahd Hospital of the University, Al-Khobar and College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Lauren Callans
- Department of Surgery, University of Pennsylvania School of Medicine, Philadelphia, PA, United States
| | - Jumanah A. Jarad
- Department of Psychiatry, King Fahd Hospital of the University, Al-Khobar and College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Cyril Cyrus
- Department of Clinical Biochemistry, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Bobby P. C. Koeleman
- Department of Genetics, Division Lab, University Medical Center Utrecht, Utrecht, Netherlands
| | - Brendan J. Keating
- Department of Surgery, University of Pennsylvania School of Medicine, Philadelphia, PA, United States
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, United States
| | - Amein K. Al-Ali
- Department of Clinical Biochemistry, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia,*Correspondence: Amein K. Al-Ali, ✉
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Lynham AJ, Knott S, Underwood JFG, Hubbard L, Agha SS, Bisson JI, van den Bree MBM, Chawner SJRA, Craddock N, O'Donovan M, Jones IR, Kirov G, Langley K, Martin J, Rice F, Roberts NP, Thapar A, Anney R, Owen MJ, Hall J, Pardiñas AF, Walters JTR. DRAGON-Data: a platform and protocol for integrating genomic and phenotypic data across large psychiatric cohorts. BJPsych Open 2023; 9:e32. [PMID: 36752340 PMCID: PMC9970169 DOI: 10.1192/bjo.2022.636] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 11/02/2022] [Accepted: 12/16/2022] [Indexed: 02/09/2023] Open
Abstract
BACKGROUND Current psychiatric diagnoses, although heritable, have not been clearly mapped onto distinct underlying pathogenic processes. The same symptoms often occur in multiple disorders, and a substantial proportion of both genetic and environmental risk factors are shared across disorders. However, the relationship between shared symptoms and shared genetic liability is still poorly understood. AIMS Well-characterised, cross-disorder samples are needed to investigate this matter, but few currently exist. Our aim is to develop procedures to purposely curate and aggregate genotypic and phenotypic data in psychiatric research. METHOD As part of the Cardiff MRC Mental Health Data Pathfinder initiative, we have curated and harmonised phenotypic and genetic information from 15 studies to create a new data repository, DRAGON-Data. To date, DRAGON-Data includes over 45 000 individuals: adults and children with neurodevelopmental or psychiatric diagnoses, affected probands within collected families and individuals who carry a known neurodevelopmental risk copy number variant. RESULTS We have processed the available phenotype information to derive core variables that can be reliably analysed across groups. In addition, all data-sets with genotype information have undergone rigorous quality control, imputation, copy number variant calling and polygenic score generation. CONCLUSIONS DRAGON-Data combines genetic and non-genetic information, and is available as a resource for research across traditional psychiatric diagnostic categories. Algorithms and pipelines used for data harmonisation are currently publicly available for the scientific community, and an appropriate data-sharing protocol will be developed as part of ongoing projects (DATAMIND) in partnership with Health Data Research UK.
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Affiliation(s)
- Amy J. Lynham
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - Sarah Knott
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - Jack F. G. Underwood
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - Leon Hubbard
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - Sharifah S. Agha
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - Jonathan I. Bisson
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - Marianne B. M. van den Bree
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - Samuel J. R. A. Chawner
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - Nicholas Craddock
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - Michael O'Donovan
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - Ian R. Jones
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - George Kirov
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - Kate Langley
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK; and School of Psychology, Cardiff University, UK
| | - Joanna Martin
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - Frances Rice
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - Neil P. Roberts
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK; and Directorate of Psychology and Psychological Therapies, Cardiff & Vale University Health Board, UK
| | - Anita Thapar
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - Richard Anney
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - Michael J. Owen
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - Jeremy Hall
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - Antonio F. Pardiñas
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
| | - James T. R. Walters
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, UK
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Gong W, Guo P, Li Y, Liu L, Yan R, Liu S, Wang S, Xue F, Zhou X, Yuan Z. Role of the Gut-Brain Axis in the Shared Genetic Etiology Between Gastrointestinal Tract Diseases and Psychiatric Disorders: A Genome-Wide Pleiotropic Analysis. JAMA Psychiatry 2023; 80:360-370. [PMID: 36753304 PMCID: PMC9909581 DOI: 10.1001/jamapsychiatry.2022.4974] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
IMPORTANCE Comorbidities and genetic correlations between gastrointestinal tract diseases and psychiatric disorders have been widely reported, with the gut-brain axis (GBA) hypothesized as a potential biological basis. However, the degree to which the shared genetic determinants are involved in these associations underlying the GBA is unclear. OBJECTIVE To investigate the shared genetic etiology between gastrointestinal tract diseases and psychiatric disorders and to identify shared genomic loci, genes, and pathways. DESIGN, SETTING, AND PARTICIPANTS This genome-wide pleiotropic association study using genome-wide association summary statistics from publicly available data sources was performed with various statistical genetic approaches to sequentially investigate the pleiotropic associations from genome-wide single-nucleotide variation (SNV; formerly single-nucleotide polymorphism [SNP]), and gene levels and biological pathways to disentangle the underlying shared genetic etiology between 4 gastrointestinal tract diseases (inflammatory bowel disease, irritable bowel syndrome, peptic ulcer disease, and gastroesophageal reflux disease) and 6 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, attention-deficit/hyperactivity disorder, posttraumatic stress disorder, and anorexia nervosa). Data were collected from March 10, 2021, to August 25, 2021, and analysis was performed from January 8 through May 30, 2022. MAIN OUTCOMES AND MEASURES The primary outcomes consisted of a list of genetic loci, genes, and pathways shared between gastrointestinal tract diseases and psychiatric disorders. RESULTS Extensive genetic correlations and genetic overlaps were found among 22 of 24 trait pairs. Pleiotropic analysis under a composite null hypothesis identified 2910 significant potential pleiotropic SNVs in 19 trait pairs, with 83 pleiotropic loci and 24 colocalized loci detected. Gene-based analysis found 158 unique candidate pleiotropic genes, which were highly enriched in certain GBA-related phenotypes and tissues, whereas pathway enrichment analysis further highlighted biological pathways primarily involving cell adhesion, synaptic structure and function, and immune cell differentiation. Several identified pleiotropic loci also shared causal variants with gut microbiomes. Mendelian randomization analysis further illustrated vertical pleiotropy across 8 pairwise traits. Notably, many pleiotropic loci were identified for multiple pairwise traits, such as 1q32.1 (INAVA), 19q13.33 (FUT2), 11q23.2 (NCAM1), and 1p32.3 (LRP8). CONCLUSIONS AND RELEVANCE These findings suggest that the pleiotropic genetic determinants between gastrointestinal tract diseases and psychiatric disorders are extensively distributed across the genome. These findings not only support the shared genetic basis underlying the GBA but also have important implications for intervention and treatment targets of these diseases simultaneously.
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Affiliation(s)
- Weiming Gong
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Ping Guo
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Yuanming Li
- School of Medicine, Cheeloo College of Medicine, Shandong University Jinan, China
| | - Lu Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Ran Yan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Shuai Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Shukang Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor,Center for Statistical Genetics, University of Michigan, Ann Arbor
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
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184
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Cui Y, Arnold FJ, Peng F, Wang D, Li JS, Michels S, Wagner EJ, La Spada AR, Li W. Alternative polyadenylation transcriptome-wide association study identifies APA-linked susceptibility genes in brain disorders. Nat Commun 2023; 14:583. [PMID: 36737438 PMCID: PMC9898543 DOI: 10.1038/s41467-023-36311-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 01/25/2023] [Indexed: 02/05/2023] Open
Abstract
Alternative polyadenylation (APA) plays an essential role in brain development; however, current transcriptome-wide association studies (TWAS) largely overlook APA in nominating susceptibility genes. Here, we performed a 3' untranslated region (3'UTR) APA TWAS (3'aTWAS) for 11 brain disorders by combining their genome-wide association studies data with 17,300 RNA-seq samples across 2,937 individuals. We identified 354 3'aTWAS-significant genes, including known APA-linked risk genes, such as SNCA in Parkinson's disease. Among these 354 genes, ~57% are not significant in traditional expression- and splicing-TWAS studies, since APA may regulate the translation, localization and protein-protein interaction of the target genes independent of mRNA level expression or splicing. Furthermore, we discovered ATXN3 as a 3'aTWAS-significant gene for amyotrophic lateral sclerosis, and its modulation substantially impacted pathological hallmarks of amyotrophic lateral sclerosis in vitro. Together, 3'aTWAS is a powerful strategy to nominate important APA-linked brain disorder susceptibility genes, most of which are largely overlooked by conventional expression and splicing analyses.
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Affiliation(s)
- Ya Cui
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA, 92697, USA
| | - Frederick J Arnold
- Departments of Pathology & Laboratory Medicine, Neurology, and Biological Chemistry, School of Medicine, and the UCI Institute for Neurotherapeutics, University of California Irvine, Irvine, CA, 92697, USA
| | - Fanglue Peng
- Department of Molecular and Cellular Biology, University Baylor College of Medicine, Houston, TX, 77030, USA
| | - Dan Wang
- Department of Medicine, Division of Cardiology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Jason Sheng Li
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA, 92697, USA
| | - Sebastian Michels
- Departments of Pathology & Laboratory Medicine, Neurology, and Biological Chemistry, School of Medicine, and the UCI Institute for Neurotherapeutics, University of California Irvine, Irvine, CA, 92697, USA
| | - Eric J Wagner
- School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, NY, 14642, USA
| | - Albert R La Spada
- Departments of Pathology & Laboratory Medicine, Neurology, and Biological Chemistry, School of Medicine, and the UCI Institute for Neurotherapeutics, University of California Irvine, Irvine, CA, 92697, USA.
| | - Wei Li
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA, 92697, USA.
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185
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Demontis D, Walters GB, Athanasiadis G, Walters R, Therrien K, Nielsen TT, Farajzadeh L, Voloudakis G, Bendl J, Zeng B, Zhang W, Grove J, Als TD, Duan J, Satterstrom FK, Bybjerg-Grauholm J, Bækved-Hansen M, Gudmundsson OO, Magnusson SH, Baldursson G, Davidsdottir K, Haraldsdottir GS, Agerbo E, Hoffman GE, Dalsgaard S, Martin J, Ribasés M, Boomsma DI, Soler Artigas M, Roth Mota N, Howrigan D, Medland SE, Zayats T, Rajagopal VM, Nordentoft M, Mors O, Hougaard DM, Mortensen PB, Daly MJ, Faraone SV, Stefansson H, Roussos P, Franke B, Werge T, Neale BM, Stefansson K, Børglum AD. Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains. Nat Genet 2023; 55:198-208. [PMID: 36702997 PMCID: PMC10914347 DOI: 10.1038/s41588-022-01285-8] [Citation(s) in RCA: 100] [Impact Index Per Article: 100.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 12/09/2022] [Indexed: 01/27/2023]
Abstract
Attention-deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder with a major genetic component. Here, we present a genome-wide association study meta-analysis of ADHD comprising 38,691 individuals with ADHD and 186,843 controls. We identified 27 genome-wide significant loci, highlighting 76 potential risk genes enriched among genes expressed particularly in early brain development. Overall, ADHD genetic risk was associated with several brain-specific neuronal subtypes and midbrain dopaminergic neurons. In exome-sequencing data from 17,896 individuals, we identified an increased load of rare protein-truncating variants in ADHD for a set of risk genes enriched with probable causal common variants, potentially implicating SORCS3 in ADHD by both common and rare variants. Bivariate Gaussian mixture modeling estimated that 84-98% of ADHD-influencing variants are shared with other psychiatric disorders. In addition, common-variant ADHD risk was associated with impaired complex cognition such as verbal reasoning and a range of executive functions, including attention.
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Affiliation(s)
- Ditte Demontis
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark.
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark.
- Center for Genomics and Personalized Medicine, Aarhus, Denmark.
| | - G Bragi Walters
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Georgios Athanasiadis
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Mental Health Centre Sct. Hans, Capital Region of Denmark, Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Evolutionary Biology, Ecology and Environmental Sciences, University of Barcelona, Barcelona, Spain
| | - Raymond Walters
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Karen Therrien
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Trine Tollerup Nielsen
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Leila Farajzadeh
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Georgios Voloudakis
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Biau Zeng
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Wen Zhang
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jakob Grove
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Thomas D Als
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Jinjie Duan
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - F Kyle Satterstrom
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jonas Bybjerg-Grauholm
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Marie Bækved-Hansen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Olafur O Gudmundsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | | | - Gisli Baldursson
- Department of Child and Adolescent Psychiatry, National University Hospital, Reykjavik, Iceland
| | - Katrin Davidsdottir
- The Centre for Child Development and Behaviour, Capital Area Primary Health Care, Reykjavik, Iceland
| | - Gyda S Haraldsdottir
- The Centre for Child Development and Behaviour, Capital Area Primary Health Care, Reykjavik, Iceland
| | - Esben Agerbo
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- National Centre for Register-Based Research, Business and Social Sciences, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, CIRRAU, Aarhus University, Aarhus, Denmark
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Søren Dalsgaard
- National Centre for Register-Based Research, Business and Social Sciences, Aarhus University, Aarhus, Denmark
- Mental Health Services of the Capital Region, Child and Adolescent Psychiatric Center, Glostrup, Denmark
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Joanna Martin
- Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Marta Ribasés
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Maria Soler Artigas
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
| | - Nina Roth Mota
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Daniel Howrigan
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Tetyana Zayats
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- PROMENTA, Department of Psychology, University of Oslo, Oslo, Norway
| | - Veera M Rajagopal
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Mental Health Centre Copenhagen, Capital Region of Denmark, Copenhagen University Hospital, Copenhagen, Denmark
| | - Ole Mors
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Psychosis Research Unit, Aarhus University Hospital-Psychiatry, Aarhus, Denmark
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Preben Bo Mortensen
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- National Centre for Register-Based Research, Business and Social Sciences, Aarhus University, Aarhus, Denmark
- Centre for Integrated Register-based Research, CIRRAU, Aarhus University, Aarhus, Denmark
| | - Mark J Daly
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Stephen V Faraone
- Departments of Psychiatry and of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | | | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research Education and Clinical Center, JJ Peters VA Medical Center, Bronx, NY, USA
| | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Mental Health Centre Sct. Hans, Capital Region of Denmark, Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
| | - Benjamin M Neale
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kari Stefansson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Anders D Børglum
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark.
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark.
- Center for Genomics and Personalized Medicine, Aarhus, Denmark.
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186
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Facal F, Costas J. Polygenic risk scores for schizophrenia and treatment resistance: New data, systematic review and meta-analysis. Schizophr Res 2023; 252:189-197. [PMID: 36657363 DOI: 10.1016/j.schres.2023.01.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 09/14/2022] [Accepted: 01/05/2023] [Indexed: 01/18/2023]
Affiliation(s)
- Fernando Facal
- Instituto de Investigación Sanitaria (IDIS) de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Galicia, Spain; Servizo de Psiquiatría, Complexo Hospitalario Universitario de Santiago de Compostela, Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Galicia, Spain; Universidade de Santiago de Compostela (USC), Galicia, Spain
| | - Javier Costas
- Instituto de Investigación Sanitaria (IDIS) de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Galicia, Spain.
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187
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Gas C, Ayesa-Arriola R, Vázquez-Bourgon J, Crespo-Facorro B, García-Gavilán J, Labad J, Martorell L, Muntané G, Sanchez-Gistau V, Vilella E. Cross-sectional and longitudinal assessment of the association between DDR1 variants and processing speed in patients with early psychosis and healthy controls. J Psychiatr Res 2023; 158:49-55. [PMID: 36571911 DOI: 10.1016/j.jpsychires.2022.12.020] [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: 04/13/2022] [Revised: 12/07/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Recent evidence indicates that DDR1 participates in myelination and that variants of DDR1 are associated with decreased cognitive processing speed (PS) in schizophrenia (SZ). Here, we explored whether DDR1 variants were associated with PS in subjects diagnosed with an early psychosis (EP), a condition often preceding SZ. Data from two Spanish independent samples (from Reus and Santander) including patients with EP (n = 75 and n = 312, respectively) and healthy controls (HCs; n = 57 and n = 160) were analyzed. The Trail Making Test part A was used to evaluate PS. Participants underwent genotyping to identify DDR1 variants rs1264323 and rs2267641. Cross-sectional data were analyzed with general linear models and longitudinal data were analyzed using mixed models. We examined the combined rs1264323AA-rs2267641AC/CC genotypes (an SZ-risk combination) on PS. The SZ-risk combined genotypes were associated with increased PS in EP patients but not in HCs in the cross-sectional analysis. In the longitudinal analysis, the SZ-risk combined genotypes were significantly associated with increased PS in both HCs and EP patients throughout the 10-year follow-up but no genotype × time interaction was observed. These results provide further evidence that DDR1 is involved in cognition and should be replicated with other samples.
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Affiliation(s)
- Cinta Gas
- Fundació Pere Mata Terres de l'Ebre, Tortosa, Spain; Universitat Rovira i Virgili, Tarragona, Spain.
| | - Rosa Ayesa-Arriola
- Department of Psychiatry, Marqués de Valdecilla University Hospital. IDIVAL. Universidad de Cantabria, Santander, Spain; Institut d'Investigació Sanitària Pere Virgili, Reus, Spain.
| | - Javier Vázquez-Bourgon
- Department of Psychiatry, Marqués de Valdecilla University Hospital. IDIVAL. Universidad de Cantabria, Santander, Spain; Centro Investigación Biomédica en Red en Salud Mental (CIBERSAM), Madrid, Spain.
| | - Benedicto Crespo-Facorro
- Department of Psychiatry, Marqués de Valdecilla University Hospital. IDIVAL. Universidad de Cantabria, Santander, Spain; Hospital Universitario Virgen del Rocío, Department of Psychiatry, Universidad de Sevilla, Sevilla, Spain; Instituto de Investigacion Sanitaria de Sevilla, IBiS, Spain; Centro Investigación Biomédica en Red en Salud Mental (CIBERSAM), Madrid, Spain.
| | - Jesús García-Gavilán
- Universitat Rovira i Virgili, Tarragona, Spain; Institut d'Investigació Sanitària Pere Virgili, Reus, Spain; Hospital Universitari San Joan de Reus, Reus, Spain; Centro Investigación Biomédica en Red en Fisiopatología de la Obesidad y Nutrición (CIBERObn), Madrid, Spain.
| | - Javier Labad
- Consorci Sanitari del Maresme, Hospital de Mataró. Barcelona, Spain; Institut d'Investigació i Innovació Parc Taulí (I3PT). Barcelona, Spain; Centro Investigación Biomédica en Red en Salud Mental (CIBERSAM), Madrid, Spain.
| | - Lourdes Martorell
- Universitat Rovira i Virgili, Tarragona, Spain; Hospital Universitari Institut Pere Mata, Reus, Spain; Institut d'Investigació Sanitària Pere Virgili, Reus, Spain; Centro Investigación Biomédica en Red en Salud Mental (CIBERSAM), Madrid, Spain.
| | - Gerard Muntané
- Universitat Rovira i Virgili, Tarragona, Spain; Hospital Universitari Institut Pere Mata, Reus, Spain; Institut d'Investigació Sanitària Pere Virgili, Reus, Spain; Centro Investigación Biomédica en Red en Salud Mental (CIBERSAM), Madrid, Spain.
| | - Vanessa Sanchez-Gistau
- Universitat Rovira i Virgili, Tarragona, Spain; Hospital Universitari Institut Pere Mata, Reus, Spain; Institut d'Investigació Sanitària Pere Virgili, Reus, Spain; Centro Investigación Biomédica en Red en Salud Mental (CIBERSAM), Madrid, Spain.
| | - Elisabet Vilella
- Universitat Rovira i Virgili, Tarragona, Spain; Hospital Universitari Institut Pere Mata, Reus, Spain; Institut d'Investigació Sanitària Pere Virgili, Reus, Spain; Centro Investigación Biomédica en Red en Salud Mental (CIBERSAM), Madrid, Spain.
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188
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Owen MJ. Genomic insights into schizophrenia. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230125. [PMID: 36844807 PMCID: PMC9943879 DOI: 10.1098/rsos.230125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 06/18/2023]
Abstract
Schizophrenia is a common, complex, heterogeneous psychiatric syndrome which can have profound impacts on affected individuals and imposes significant burdens on society. Despite intensive research, it has been challenging to understand basic mechanisms and to identify novel therapeutic targets. Given its high heritability and the complexity and inaccessibility of the human brain, much hope has been invested in the application of genomics as a route to better understanding. This work has identified many common and rare risk alleles and laid the foundations for a new generation of mechanistic studies. Genomics has also thrown new light on the relationship between schizophrenia and other psychiatric disorders and revealed its previously unappreciated aetiological relationship with childhood neurodevelopmental disorders, providing further evidence that it has its origins in disturbances of brain development. In addition, genomic findings suggest that the condition reflects fundamental disturbances in neuronal, and particularly synaptic, function that impact broadly on brain function, rather than being a disorder of specific brain regions and circuits. Finally, genomics has provided a plausible solution to the evolutionary paradox of how the condition persists in the face of high heritability and reduced fecundity.
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Affiliation(s)
- Michael J. Owen
- Centre for Neuropsychiatric Genetics and Genomics, Neuroscience and Mental Health Innovation Institute and Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff CF10 3AT, UK
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189
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Haghighatfard A, Ghaderi AH, Mostajabi P, Kashfi SS, Mohabati Somehsarayee H, Shahrani M, Mehrasa M, Haghighat S, Farhadi M, Momayez Sefat M, Shiryazdi AA, Ezzati N, Qazvini MG, Alizadenik A, Moghadam ER. The first genome-wide association study of internet addiction; Revealed substantial shared risk factors with neurodevelopmental psychiatric disorders. RESEARCH IN DEVELOPMENTAL DISABILITIES 2023; 133:104393. [PMID: 36566681 DOI: 10.1016/j.ridd.2022.104393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 12/02/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Internet addiction disorder (IAD) is listed as a disorder requiring further studies in the diagnostic and statistical manual of mental disorders (DSM-V). Psychological studies showed significant co-morbidity of IAD with depression, alcohol abuse, and anxiety disorder. Etiology and genetic bases of IAD are unclear. AIMS Present study aimed to investigate the genetic, psychological, and cognitive bases of a tendency to internet addiction. METHODS AND PROCEDURES DNA was extracted from blood samples of IADs (N = 16,520) and 18,000 matched non-psychiatric subjects. Genotyping for the subjects was performed using SNP Array. Psychological, neuropsychological, and neurological characteristics were conducted. OUTCOMES AND RESULTS Seventy-two SNPs in 24 genes have been detected significantly associated with IAD. Most of these SNPs were risk factors for psychiatric disorders. Most similarity detected with autism spectrum disorder, bipolar disorder and schizophrenia. Higher anxiety, stress, and neuroticism and deficits in working memory, attention, planning, and processing speed were detected in IADs. CONCLUSIONS This study is the first genome-wide association study of IAD that showed strong shared genetic bases with neurodevelopmental disabilities and psychiatric disorders. IMPLICATIONS Genetic risk factors in IADs may cause several cognitive and neurodevelopmental brain function abnormalities, which lead to excessive Internet usage. It may suggest that IAD could be a marker for vulnerability to developmental psychiatric disorders.
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Affiliation(s)
- Arvin Haghighatfard
- Neuroimaging Genetic Laboratory, Arvin Gene Company, Tehran, Islamic Republic of Iran; Sleep Education and Research Laboratory, UCL Institute of Education, London, UK; Department of Genetics, Tehran Medical Sciences Branch, Islamic Azad University, Tehran, Islamic Republic of Iran; Department of Biology, North Tehran Branch, Islamic Azad University, Tehran, Islamic Republic of Iran.
| | - Amir Hossein Ghaderi
- Neuroimaging Genetic Laboratory, Arvin Gene Company, Tehran, Islamic Republic of Iran; Vision: ScienCe First to Applications (VISTA), York University, Toronto, Canada; Department of Psychology and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Parmida Mostajabi
- Neuroimaging Genetic Laboratory, Arvin Gene Company, Tehran, Islamic Republic of Iran; Department of Genetics, Tehran Medical Sciences Branch, Islamic Azad University, Tehran, Islamic Republic of Iran
| | - Seyedeh Sara Kashfi
- Neuroimaging Genetic Laboratory, Arvin Gene Company, Tehran, Islamic Republic of Iran; Department of Genetics, Tehran Medical Sciences Branch, Islamic Azad University, Tehran, Islamic Republic of Iran
| | - Hediyeh Mohabati Somehsarayee
- Neuroimaging Genetic Laboratory, Arvin Gene Company, Tehran, Islamic Republic of Iran; Department of Genetics, Tehran Medical Sciences Branch, Islamic Azad University, Tehran, Islamic Republic of Iran
| | - Maryam Shahrani
- Neuroimaging Genetic Laboratory, Arvin Gene Company, Tehran, Islamic Republic of Iran; Department of Genetics, Tehran Medical Sciences Branch, Islamic Azad University, Tehran, Islamic Republic of Iran
| | - Mahla Mehrasa
- Neuroimaging Genetic Laboratory, Arvin Gene Company, Tehran, Islamic Republic of Iran; Department of Genetics, Tehran Medical Sciences Branch, Islamic Azad University, Tehran, Islamic Republic of Iran
| | - Saba Haghighat
- Neuroimaging Genetic Laboratory, Arvin Gene Company, Tehran, Islamic Republic of Iran; Department of Genetics, Tehran Medical Sciences Branch, Islamic Azad University, Tehran, Islamic Republic of Iran; Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Islamic Republic of Iran
| | - Mahdi Farhadi
- Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Islamic Republic of Iran
| | - Maryam Momayez Sefat
- Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Islamic Republic of Iran
| | - Atena Alsadat Shiryazdi
- Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Islamic Republic of Iran
| | - Naghmeh Ezzati
- Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Islamic Republic of Iran
| | | | - Atie Alizadenik
- Department of Biology, Damghan Branch, Islamic Azad University, Damghan, Islamic Republic of Iran
| | - Elham Rastegari Moghadam
- Department of Biology, Damghan Branch, Islamic Azad University, Damghan, Islamic Republic of Iran
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190
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Impact on the Risk and Severity of Childhood Onset Schizophrenia of Schizophrenia Risk Genetic Variants at the DRD2 and ZNF804A Loci. Child Psychiatry Hum Dev 2023; 54:241-247. [PMID: 34524581 DOI: 10.1007/s10578-021-01245-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/07/2021] [Indexed: 01/25/2023]
Abstract
The study explored whether schizophrenia risk alleles of the DRD2 rs2514218 and ZNF804A rs1344706 polymorphisms also influenced the risk and severity of childhood-onset schizophrenia (COS) and differentiated it from autism spectrum disorders (ASD). We compared 75 children with COS to 75 children with ASD, 150 patients with adult-onset schizophrenia and 150 healthy individuals. Frequency of the DRD2 T-allele, assumed to be protective against schizophrenia overall, was higher in COS compared to adult-onset schizophrenia and healthy controls. The risk allele A of ZNF804A was associated with greater severity of negative symptoms in COS. The latter result is consistent with the involvement of ZNF804A in the development of severe forms of schizophrenia. The findings regarding DRD2 suggest that the same genetic variants may play different roles in schizophrenia with childhood and adult onset. This warrants further research, since D2 receptor blockade is a general pharmacodynamic property of antipsychotics.
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191
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The relationship between technology addictions and schizotypal traits: mediating roles of depression, anxiety, and stress. BMC Psychiatry 2023; 23:67. [PMID: 36698079 PMCID: PMC9875437 DOI: 10.1186/s12888-023-04563-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 01/23/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND The way how technology addiction relates to psychosis remains inconclusive and uncertain. The present study aimed to test the hypothesis of a mediating role of depression, anxiety and stress in the association between three technology (behavioral) addictions (i.e., Addiction to the Internet, smartphones and Facebook) and psychosis proneness as estimated through schizotypal traits in emerging adults. METHODS A cross-sectional study was performed among non-clinical Tunisian university students (67.6% females, mean age of 21.5 ± 2.5 years) using a paper-and-pencil self-administered questionnaire. RESULTS Results for the Pearson correlation revealed that higher smartphone, Internet, and Facebook addictions' scores were significantly and positively correlated with each of the depression, anxiety and stress subscores; whereas depression (r = 0.474), anxiety (r = 0.499) and stress (r = 0.461) scores were positively correlated with higher schizotypal traits. The results of the mediation analysis found a significant mediating effect for depressive, anxiety and stress symptoms on the cross-sectional relationship between each facet of the TA and schizotypal traits. CONCLUSION Our findings preliminarily suggest that an addictive use of smartphones, Internet and Facebook may act as a stressor that exacerbates psychosis proneness directly or indirectly through distress. Although future longitudinal research is needed to determine causality, we draw attention to the possibility that treating psychological distress may constitute an effective target of interventions to prevent psychosis in adolescents with technology addictions.
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192
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Duncan L, Shen H, Schulmann A, Li T, Kolachana B, Mandal A, Feng N, Auluck P, Marenco S. Polygenic scores for psychiatric disorders in a diverse postmortem brain tissue cohort. Neuropsychopharmacology 2023; 48:764-772. [PMID: 36694041 PMCID: PMC10066241 DOI: 10.1038/s41386-022-01524-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 12/20/2022] [Accepted: 12/21/2022] [Indexed: 01/25/2023]
Abstract
A new era of human postmortem tissue research has emerged thanks to the development of 'omics technologies that measure genes, proteins, and spatial parameters in unprecedented detail. Also newly possible is the ability to construct polygenic scores, individual-level metrics of genetic risk (also known as polygenic risk scores/PRS), based on genome-wide association studies, GWAS. Here, we report on clinical, educational, and brain gene expression correlates of polygenic scores in ancestrally diverse samples from the Human Brain Collection Core (HBCC). Genotypes from 1418 donors were subjected to quality control filters, imputed, and used to construct polygenic scores. Polygenic scores for schizophrenia predicted schizophrenia status in donors of European ancestry (p = 4.7 × 10-8, 17.2%) and in donors with African ancestry (p = 1.6 × 10-5, 10.4% of phenotypic variance explained). This pattern of higher variance explained among European ancestry samples was also observed for other psychiatric disorders (depression, bipolar disorder, substance use disorders, anxiety disorders) and for height, body mass index, and years of education. For a subset of 223 samples, gene expression from dorsolateral prefrontal cortex (DLPFC) was available through the CommonMind Consortium. In this subgroup, schizophrenia polygenic scores also predicted an aggregate gene expression score for schizophrenia (European ancestry: p = 0.0032, African ancestry: p = 0.15). Overall, polygenic scores performed as expected in ancestrally diverse samples, given historical biases toward use of European ancestry samples and variable predictive power of polygenic scores across phenotypes. The transcriptomic results reported here suggest that inherited schizophrenia genetic risk influences gene expression, even in adulthood. For future research, these and additional polygenic scores are being made available for analyses, and for selecting samples, using postmortem tissue from the Human Brain Collection Core.
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Affiliation(s)
- Laramie Duncan
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305, USA. .,Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA, USA.
| | - Hanyang Shen
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305, USA.,Epidemiology and Clinical Research Graduate Program, Stanford University, Stanford, CA, USA
| | | | - Tayden Li
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305, USA
| | - Bhaskar Kolachana
- Human Brain Collection Core (HBCC), NIMH-IRP, Bethesda, MD, 20892, USA
| | - Ajeet Mandal
- Human Brain Collection Core (HBCC), NIMH-IRP, Bethesda, MD, 20892, USA
| | - Ningping Feng
- Human Brain Collection Core (HBCC), NIMH-IRP, Bethesda, MD, 20892, USA
| | - Pavan Auluck
- Human Brain Collection Core (HBCC), NIMH-IRP, Bethesda, MD, 20892, USA
| | - Stefano Marenco
- Human Brain Collection Core (HBCC), NIMH-IRP, Bethesda, MD, 20892, USA
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193
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Osimo EF, Perry BI, Mallikarjun P, Pritchard M, Lewis J, Katunda A, Murray GK, Perez J, Jones PB, Cardinal RN, Howes OD, Upthegrove R, Khandaker GM. Predicting treatment resistance from first-episode psychosis using routinely collected clinical information. NATURE MENTAL HEALTH 2023; 1:25-35. [PMID: 37034013 PMCID: PMC7614410 DOI: 10.1038/s44220-022-00001-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 11/03/2022] [Indexed: 01/21/2023]
Abstract
Around a quarter of people who experience a first episode of psychosis (FEP) will develop treatment-resistant schizophrenia (TRS), but there are currently no established clinically useful methods to predict this from baseline. We aimed to explore the predictive potential for clozapine use as a proxy for TRS of routinely collected, objective biomedical predictors at FEP onset, and to externally validate the model in a separate clinical sample of people with FEP. We developed and externally validated a forced-entry logistic regression risk prediction Model fOr cloZApine tReaTment, or MOZART, to predict up to 8-year risk of clozapine use from FEP using routinely recorded information including age, sex, ethnicity, triglycerides, alkaline phosphatase levels, and lymphocyte counts. We also produced a least-absolute shrinkage and selection operator (LASSO) based model, additionally including neutrophil count, smoking status, body mass index, and random glucose levels. The models were developed using data from two UK psychosis early intervention services (EIS) and externally validated in another UK EIS. Model performance was assessed via discrimination and calibration. We developed the models in 785 patients, and validated externally in 1,110 patients. Both models predicted clozapine use well at internal validation (MOZART: C 0.70; 95%CI 0.63,0.76; LASSO: 0.69; 95%CI 0.63,0.77). At external validation, discrimination performance reduced (MOZART: 0.63; 0.58,0.69; LASSO: 0.64; 0.58,0.69) but recovered after re-estimation of the lymphocyte predictor (C: 0.67; 0.62,0.73). Calibration plots showed good agreement between observed and predicted risk in the forced-entry model. We also present a decision-curve analysis and an online data visualisation tool. The use of routinely collected clinical information including blood-based biomarkers taken at FEP onset can help to predict the individual risk of clozapine use, and should be considered equally alongside other potentially useful information such as symptom scores in large-scale efforts to predict psychiatric outcomes.
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Affiliation(s)
- Emanuele F. Osimo
- Imperial College London Institute of Clinical Sciences and UKRI MRC London Institute of Medical Sciences, Hammersmith Hospital Campus, London, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Benjamin I. Perry
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Pavan Mallikarjun
- Institute for Mental Health and Centre for Human Brain Health, University of Birmingham, Birmingham, England
- Birmingham Early Intervention Service, Birmingham Women’s and Children’s NHS Foundation trust
| | | | - Jonathan Lewis
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Asia Katunda
- Birmingham Early Intervention Service, Birmingham Women’s and Children’s NHS Foundation trust
| | - Graham K. Murray
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Jesus Perez
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
- Norwich Medical School, University of East Anglia. Norwich, UK
- Applied Research Collaboration East of England, National Institute for Health Research (NIHR), UK
- Institute of Biomedical Research of Salamanca (IBSAL); Psychiatry Unit, Department of Medicine, University of Salamanca, Salamanca, Spain
| | - Peter B. Jones
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
- Applied Research Collaboration East of England, National Institute for Health Research (NIHR), UK
| | - Rudolf N. Cardinal
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Oliver D. Howes
- Imperial College London Institute of Clinical Sciences and UKRI MRC London Institute of Medical Sciences, Hammersmith Hospital Campus, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, De Crespigny Park, London, SE5 8AF, UK
| | - Rachel Upthegrove
- Institute for Mental Health and Centre for Human Brain Health, University of Birmingham, Birmingham, England
- Birmingham Early Intervention Service, Birmingham Women’s and Children’s NHS Foundation trust
| | - Golam M. Khandaker
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, England
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194
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Gene-environment correlations and genetic confounding underlying the association between media use and mental health. Sci Rep 2023; 13:1030. [PMID: 36658215 PMCID: PMC9852440 DOI: 10.1038/s41598-022-25374-0] [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: 10/08/2021] [Accepted: 11/29/2022] [Indexed: 01/20/2023] Open
Abstract
The increase in online media use and mental health problems have prompted investigations into their association, although most literature is focussed on deleterious effects. We assessed the aetiology of media use and mental health associations (M age = 22.14, SD = 0.85) using twin (n = 4000 pairs) and polygenic score methods (n = 6000 unrelated individuals) in the Twins Early Development Study. Beyond the traditionally explored negative uses of online media (online victimisation and problematic internet use), we investigate general media uses such as posting online and watching videos and distinguish both positive (pro-social behaviour) and negative (anxiety, depression, peer and behaviour problems) mental health measures. Negative media use correlated with poor mental health (r = 0.11-0.32), but general media use correlated with prosocial behaviour (r = 0.20) and fewer behavioural problems (r = - 0.24). Twin analyses showed that both general and negative media use were moderately heritable (ranging from 20 to 49%) and their associations with mental health were primarily due to genetic influences (44-88%). Genetic sensitivity analysis combining polygenic scores with heritability estimates also suggest genetic confounding. Results indicate research on the mental health impact of media use should adopt genetically informed designs to strengthen causal inference.
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195
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Burstein D, Hoffman G, Mathur D, Venkatesh S, Therrien K, Fanous AH, Bigdeli TB, Harvey PD, Roussos P, Voloudakis G. Detecting and Adjusting for Hidden Biases due to Phenotype Misclassification in Genome-Wide Association Studies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.17.23284670. [PMID: 36711948 PMCID: PMC9882426 DOI: 10.1101/2023.01.17.23284670] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
With the advent of healthcare-based genotyped biobanks, genome-wide association studies (GWAS) leverage larger sample sizes, incorporate patients with diverse ancestries and introduce noisier phenotypic definitions. Yet the extent and impact of phenotypic misclassification on large-scale datasets is not currently well understood due to a lack of statistical methods to estimate relevant parameters from empirical data. Here, we develop a statistical method and scalable software, PheMED, Phenotypic Measurement of Effective Dilution, to quantify phenotypic misclassification across GWAS using only summary statistics. We illustrate how the parameters estimated by PheMED relate to the negative and positive predictive value of the labeled phenotype, compared to ground truth, and how misclassification of the phenotype yields diluted effect-sizes of variant-phenotype associations. Furthermore, we apply our methodology to detect multiple instances of statistically significant dilution in real-world data. We demonstrate how effective dilution biases downstream GWAS replication and heritability analyses despite utilizing current best practices, and provide a dilution-aware meta-analysis approach that outperforms existing methods. Consequently, we anticipate that PheMED will be a valuable tool for researchers to address phenotypic data quality issues both within and across cohorts.
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Affiliation(s)
- David Burstein
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Gabriel Hoffman
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Deepika Mathur
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sanan Venkatesh
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Karen Therrien
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Ayman H Fanous
- Department of Psychiatry, University of Arizona College of Medicine-Phoenix, Phoenix
- Carl T. Hayden Veterans Affairs Medical Center, Phoenix, Arizona
| | - Tim B Bigdeli
- VA New York Harbor Healthcare System, Brooklyn
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, New York
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, New York
- Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, Brooklyn, New York
| | - Philip D Harvey
- Bruce W. Carter Miami Veterans Affairs (VA) Medical Center, Miami, Florida
- University of Miami Miller School of Medicine, Miami, Florida
| | - Panos Roussos
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Georgios Voloudakis
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
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Kappel DB, Legge SE, Hubbard L, Willcocks IR, O'Connell KS, Smith RL, Molden E, Andreassen OA, King A, Jansen J, Helthuis M, Owen MJ, O'Donovan MC, Walters JTR, Pardiñas AF. Genomic Stratification of Clozapine Prescription Patterns Using Schizophrenia Polygenic Scores. Biol Psychiatry 2023; 93:149-156. [PMID: 36244804 PMCID: PMC10804961 DOI: 10.1016/j.biopsych.2022.07.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 07/14/2022] [Accepted: 07/19/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Treatment-resistant schizophrenia affects approximately 30% of individuals with the disorder. Clozapine is the medication of choice in treatment-resistant schizophrenia, but optimizing administration and dose titration is complex. The identification of factors influencing clozapine prescription and response, including genetics, is of interest in a precision psychiatry framework. METHODS We used linear regression models accounting for demographic, pharmacological, and clinical covariates to determine whether a polygenic risk score (PRS) for schizophrenia would be associated with the highest dose recorded during clozapine treatment. Analyses were performed across 2 independent multiancestry samples of individuals from a UK patient monitoring system, CLOZUK2 (n = 3133) and CLOZUK3 (n = 909), and a European sample from a Norwegian therapeutic drug monitoring service (n = 417). In a secondary analysis merging both UK cohorts, logistic regression models were used to estimate the relationship between schizophrenia PRSs and clozapine doses classified as low, standard, or high. RESULTS After controlling for relevant covariates, the schizophrenia PRS was correlated with the highest clozapine dose on record for each individual across all samples: CLOZUK2 (β = 12.22, SE = 3.78, p = .001), CLOZUK3 (β = 12.73, SE = 5.99, p = .034), and the Norwegian cohort (β = 46.45, SE = 18.83, p = .014). In a secondary analysis, the schizophrenia PRS was associated with taking clozapine doses >600 mg/day (odds ratio = 1.279, p = .006). CONCLUSIONS The schizophrenia PRS was associated with the highest clozapine dose prescribed for an individual in records from 3 independent samples, suggesting that the genetic liability for schizophrenia might index factors associated with therapeutic decisions in cohorts of patients with treatment-resistant schizophrenia.
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Affiliation(s)
- Djenifer B Kappel
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Sophie E Legge
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Leon Hubbard
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Isabella R Willcocks
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Kevin S O'Connell
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Robert L Smith
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway; Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway
| | - Espen Molden
- Section for Pharmacology and Pharmaceutical Biosciences, Department of Pharmacy, University of Oslo, Oslo, Norway; Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Adrian King
- Magna Laboratories Ltd., Ross-on-Wye, United Kingdom
| | | | | | - Michael J Owen
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Michael C O'Donovan
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - James T R Walters
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Antonio F Pardiñas
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom.
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197
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da Silva BS, Grevet EH, Silva LCF, Ramos JKN, Rovaris DL, Bau CHD. An overview on neurobiology and therapeutics of attention-deficit/hyperactivity disorder. DISCOVER MENTAL HEALTH 2023; 3:2. [PMID: 37861876 PMCID: PMC10501041 DOI: 10.1007/s44192-022-00030-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 12/29/2022] [Indexed: 10/21/2023]
Abstract
Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent psychiatric condition characterized by developmentally inappropriate symptoms of inattention and/or hyperactivity/impulsivity, which leads to impairments in the social, academic, and professional contexts. ADHD diagnosis relies solely on clinical assessment based on symptom evaluation and is sometimes challenging due to the substantial heterogeneity of the disorder in terms of clinical and pathophysiological aspects. Despite the difficulties imposed by the high complexity of ADHD etiology, the growing body of research and technological advances provide good perspectives for understanding the neurobiology of the disorder. Such knowledge is essential to refining diagnosis and identifying new therapeutic options to optimize treatment outcomes and associated impairments, leading to improvements in all domains of patient care. This review is intended to be an updated outline that addresses the etiological and neurobiological aspects of ADHD and its treatment, considering the impact of the "omics" era on disentangling the multifactorial architecture of ADHD.
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Affiliation(s)
- Bruna Santos da Silva
- ADHD and Developmental Psychiatry Programs, Hospital de Clínicas de Porto Alegre, Universidade Federal Do Rio Grande Do Sul, Porto Alegre, Brazil
- Department of Genetics and Graduate Program in Genetics and Molecular Biology, Instituto de Biociências, Universidade Federal Do Rio Grande Do Sul, Porto Alegre, Brazil
- Department of Physiology and Biophysics, Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
- Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Institute of Biomedical Sciences, University of Sao Paulo, São Paulo, Brazil
| | - Eugenio Horacio Grevet
- ADHD and Developmental Psychiatry Programs, Hospital de Clínicas de Porto Alegre, Universidade Federal Do Rio Grande Do Sul, Porto Alegre, Brazil
- Department of Psychiatry and Graduate Program in Psychiatry and Behavioral Sciences, Faculdade de Medicina, Universidade Federal Do Rio Grande Do Sul, Porto Alegre, Brazil
| | - Luiza Carolina Fagundes Silva
- ADHD and Developmental Psychiatry Programs, Hospital de Clínicas de Porto Alegre, Universidade Federal Do Rio Grande Do Sul, Porto Alegre, Brazil
- Department of Psychiatry and Graduate Program in Psychiatry and Behavioral Sciences, Faculdade de Medicina, Universidade Federal Do Rio Grande Do Sul, Porto Alegre, Brazil
| | - João Kleber Neves Ramos
- Department of Physiology and Biophysics, Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
- Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Institute of Biomedical Sciences, University of Sao Paulo, São Paulo, Brazil
| | - Diego Luiz Rovaris
- Department of Physiology and Biophysics, Instituto de Ciencias Biomedicas Universidade de Sao Paulo, São Paulo, Brazil
- Laboratory of Physiological Genomics of Mental Health (PhysioGen Lab), Institute of Biomedical Sciences, University of Sao Paulo, São Paulo, Brazil
| | - Claiton Henrique Dotto Bau
- ADHD and Developmental Psychiatry Programs, Hospital de Clínicas de Porto Alegre, Universidade Federal Do Rio Grande Do Sul, Porto Alegre, Brazil.
- Department of Genetics and Graduate Program in Genetics and Molecular Biology, Instituto de Biociências, Universidade Federal Do Rio Grande Do Sul, Porto Alegre, Brazil.
- Department of Psychiatry and Graduate Program in Psychiatry and Behavioral Sciences, Faculdade de Medicina, Universidade Federal Do Rio Grande Do Sul, Porto Alegre, Brazil.
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Sokolov AV, Manu DM, Nordberg DOT, Boström ADE, Jokinen J, Schiöth HB. Methylation in MAD1L1 is associated with the severity of suicide attempt and phenotypes of depression. Clin Epigenetics 2023; 15:1. [PMID: 36600305 PMCID: PMC9811786 DOI: 10.1186/s13148-022-01394-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 11/30/2022] [Indexed: 01/05/2023] Open
Abstract
Depression is a multifactorial disorder representing a significant public health burden. Previous studies have linked multiple single nucleotide polymorphisms with depressive phenotypes and suicidal behavior. MAD1L1 is a mitosis metaphase checkpoint protein that has been linked to depression in GWAS. Using a longitudinal EWAS approach in an adolescent cohort at two time points (n = 216 and n = 154), we identified differentially methylated sites that were associated with depression-related genetic variants in MAD1L1. Three methylation loci (cg02825527, cg18302629, and cg19624444) were consistently hypomethylated in the minor allele carriers, being cross-dependent on several SNPs. We further investigated whether DNA methylation at these CpGs is associated with depressive psychiatric phenotypes in independent cohorts. The first site (cg02825527) was hypomethylated in blood (exp(β) = 84.521, p value ~ 0.003) in participants with severe suicide attempts (n = 88). The same locus showed increased methylation in glial cells (exp(β) = 0.041, p value ~ 0.004) in the validation cohort, involving 29 depressed patients and 29 controls, and showed a trend for association with suicide (n = 40, p value ~ 0.089) and trend for association with depression treatment (n = 377, p value ~ 0.075). The second CpG (cg18302629) was significantly hypomethylated in depressed participants (exp(β) = 56.374, p value ~ 0.023) in glial cells, but did not show associations in the discovery cohorts. The last methylation site (cg19624444) was hypomethylated in the whole blood of severe suicide attempters; however, this association was at the borderline for statistical significance (p value ~ 0.061). This locus, however, showed a strong association with depression treatment in the validation cohort (exp(β) = 2.237, p value ~ 0.003) with 377 participants. The direction of associations between psychiatric phenotypes appeared to be different in the whole blood in comparison with brain samples for cg02825527 and cg19624444. The association analysis between methylation at cg18302629 and cg19624444 and MAD1L1 transcript levels in CD14+ cells shows a potential link between methylation at these CpGs and MAD1L1 expression. This study suggests evidence that methylation at MAD1L1 is important for psychiatric health as supported by several independent cohorts.
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Affiliation(s)
- Aleksandr V. Sokolov
- grid.8993.b0000 0004 1936 9457Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - Diana-Maria Manu
- grid.8993.b0000 0004 1936 9457Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - Didi O. T. Nordberg
- grid.8993.b0000 0004 1936 9457Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - Adrian D. E. Boström
- grid.12650.300000 0001 1034 3451Department of Clinical Sciences/Psychiatry, Umeå University, Umeå, Sweden ,grid.4714.60000 0004 1937 0626Department of Women’s and Children’s Health/Neuropediatrics, Karolinska Institutet, Stockholm, Sweden
| | - Jussi Jokinen
- grid.12650.300000 0001 1034 3451Department of Clinical Sciences/Psychiatry, Umeå University, Umeå, Sweden ,grid.4714.60000 0004 1937 0626Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Helgi B. Schiöth
- grid.8993.b0000 0004 1936 9457Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
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Oliva V, Fanelli G, Kasper S, Zohar J, Souery D, Montgomery S, Albani D, Forloni G, Ferentinos P, Rujescu D, Mendlewicz J, De Ronchi D, Fabbri C, Serretti A. Melancholic features and typical neurovegetative symptoms of major depressive disorder show specific polygenic patterns. J Affect Disord 2023; 320:534-543. [PMID: 36216191 DOI: 10.1016/j.jad.2022.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 09/27/2022] [Accepted: 10/02/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is a highly prevalent psychiatric condition characterised by a heterogeneous clinical presentation and an estimated twin-based heritability of ~40-50 %. Different clinical MDD subtypes might partly reflect distinctive underlying genetics. This study aims to investigate if polygenic risk scores (PRSs) for different psychiatric disorders, personality traits, and substance use-related traits may be associated with different clinical subtypes of MDD (i.e., MDD with melancholic or psychotic features), higher symptom severity, or different clusters of depressive symptoms (i.e., sadness symptoms, typical neurovegetative symptoms, detachment symptoms, and negative thoughts). METHODS The target sample included 1149 patients with MDD, recruited by the European Group for the Study of Resistant Depression. PRSs for 25 psychiatric disorders and traits were computed based on the most recent publicly available summary statistics of the largest genome-wide association studies. PRSs were then used as predictors in regression models, adjusting for age, sex, population stratification, and recruitment sites. RESULTS Patients with MDD having higher PRS for MDD and loneliness were more likely to exhibit melancholic features of MDD (p = 0.0009 and p = 0.005, respectively). Moreover, patients with higher PRS for alcohol intake and post-traumatic stress disorder were more likely to experience greater typical neurovegetative symptoms (p = 0.0012 and p = 0.0045, respectively). LIMITATIONS The proportion of phenotypic variance explained by the PRSs was limited. CONCLUSIONS This study suggests that melancholic features and typical neurovegetative symptoms of MDD may show distinctive underlying genetics. Our findings provide a new contribution to the understanding of the genetic heterogeneity of MDD.
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Affiliation(s)
- Vincenzo Oliva
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Giuseppe Fanelli
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy; Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Joseph Zohar
- Psychiatric Division, Chaim Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel
| | - Daniel Souery
- School of Medicine, Free University of Brussels, Brussels, Belgium; Psy Pluriel-European Centre of Psychological Medicine, Brussels, Belgium
| | - Stuart Montgomery
- Imperial College School of Medicine, University of London, London, UK
| | - Diego Albani
- Laboratory of Biology of Neurodegenerative Disorders, Department of Neuroscience, IRCCS Mario Negri Institute for Pharmacological Research, Milan, Italy
| | - Gianluigi Forloni
- Laboratory of Biology of Neurodegenerative Disorders, Department of Neuroscience, IRCCS Mario Negri Institute for Pharmacological Research, Milan, Italy
| | | | - Dan Rujescu
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | | | - Diana De Ronchi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Chiara Fabbri
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy; Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Alessandro Serretti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
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200
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Jin Y, Cheng Y, Mi J, Xu J. A rare case of schizophrenia coexistence with antiphospholipid syndrome, β-thalassemia, and monoclonal gammopathy of undetermined significance. Front Psychiatry 2023; 14:1178247. [PMID: 37091711 PMCID: PMC10117972 DOI: 10.3389/fpsyt.2023.1178247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 03/21/2023] [Indexed: 04/25/2023] Open
Abstract
A patient with schizophrenia who was treated with chlorpromazine developed lupus anticoagulant (LA) and antiphospholipid syndrome (APS). On protein electrophoresis, a monoclonal immunoglobulin A peak was seen in this patient, defining a condition of monoclonal gammopathy of undetermined significance. Additionally, β-thalassemia was diagnosed with the CD41-42 genotype. This condition is extremely rare, particularly in patients with schizophrenia and APS. We present a case of a patient with schizophrenia and secondary APS who had a positive LA, a significantly prolonged activated partial thromboplastin time, endogenous coagulation factor deficiency and inhibitor, no bleeding, and an unexpected finding of β-thalassemia and monoclonal IgA. Following that, a literature review on the disorders was presented.
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Affiliation(s)
- Yingming Jin
- Department of Hematology and Oncology, Ningbo No.2 Hospital, Ningbo, China
| | - Yiquan Cheng
- Department of Hematology and Oncology, Ningbo No.2 Hospital, Ningbo, China
| | - Jifeng Mi
- Department of Laboratory Medicine, Ningbo No.2 Hospital, Ningbo, China
| | - Jianfen Xu
- Department of Hematology and Oncology, Ningbo No.2 Hospital, Ningbo, China
- *Correspondence: Jianfen Xu,
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