1
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Ling E, Nemesh J, Goldman M, Kamitaki N, Reed N, Handsaker RE, Genovese G, Vogelgsang JS, Gerges S, Kashin S, Ghosh S, Esposito JM, Morris K, Meyer D, Lutservitz A, Mullally CD, Wysoker A, Spina L, Neumann A, Hogan M, Ichihara K, Berretta S, McCarroll SA. A concerted neuron-astrocyte program declines in ageing and schizophrenia. Nature 2024; 627:604-611. [PMID: 38448582 PMCID: PMC10954558 DOI: 10.1038/s41586-024-07109-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 01/23/2024] [Indexed: 03/08/2024]
Abstract
Human brains vary across people and over time; such variation is not yet understood in cellular terms. Here we describe a relationship between people's cortical neurons and cortical astrocytes. We used single-nucleus RNA sequencing to analyse the prefrontal cortex of 191 human donors aged 22-97 years, including healthy individuals and people with schizophrenia. Latent-factor analysis of these data revealed that, in people whose cortical neurons more strongly expressed genes encoding synaptic components, cortical astrocytes more strongly expressed distinct genes with synaptic functions and genes for synthesizing cholesterol, an astrocyte-supplied component of synaptic membranes. We call this relationship the synaptic neuron and astrocyte program (SNAP). In schizophrenia and ageing-two conditions that involve declines in cognitive flexibility and plasticity1,2-cells divested from SNAP: astrocytes, glutamatergic (excitatory) neurons and GABAergic (inhibitory) neurons all showed reduced SNAP expression to corresponding degrees. The distinct astrocytic and neuronal components of SNAP both involved genes in which genetic risk factors for schizophrenia were strongly concentrated. SNAP, which varies quantitatively even among healthy people of similar age, may underlie many aspects of normal human interindividual differences and may be an important point of convergence for multiple kinds of pathophysiology.
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Affiliation(s)
- Emi Ling
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
| | - James Nemesh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Melissa Goldman
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Nolan Kamitaki
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Nora Reed
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Robert E Handsaker
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Giulio Genovese
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Jonathan S Vogelgsang
- McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Sherif Gerges
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Seva Kashin
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Sulagna Ghosh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | | | | | - Daniel Meyer
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Alyssa Lutservitz
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Christopher D Mullally
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Alec Wysoker
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Liv Spina
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Anna Neumann
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Marina Hogan
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Kiku Ichihara
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Sabina Berretta
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
- Program in Neuroscience, Harvard Medical School, Boston, MA, USA.
| | - Steven A McCarroll
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
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Jonas KG, Cannon TD, Docherty AR, Dwyer D, Gur RC, Gur RE, Nelson B, Reininghaus U, Kotov R. Psychosis superspectrum I: Nosology, etiology, and lifespan development. Mol Psychiatry 2024:10.1038/s41380-023-02388-2. [PMID: 38200290 DOI: 10.1038/s41380-023-02388-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 12/05/2023] [Accepted: 12/15/2023] [Indexed: 01/12/2024]
Abstract
This review describes the Hierarchical Taxonomy of Psychopathology (HiTOP) model of psychosis-related psychopathology, the psychosis superspectrum. The HiTOP psychosis superspectrum was developed to address shortcomings of traditional diagnoses for psychotic disorders and related conditions including low reliability, arbitrary boundaries between psychopathology and normality, high symptom co-occurrence, and heterogeneity within diagnostic categories. The psychosis superspectrum is a transdiagnostic dimensional model comprising two spectra-psychoticism and detachment-which are in turn broken down into fourteen narrow components, and two auxiliary domains-cognition and functional impairment. The structure of the spectra and their components are shown to parallel the genetic structure of psychosis and related traits. Psychoticism and detachment have distinct patterns of association with urbanicity, migrant and ethnic minority status, childhood adversity, and cannabis use. The superspectrum also provides a useful model for describing the emergence and course of psychosis, as components of the superspectrum are relatively stable over time. Changes in psychoticism predict the onset of psychosis-related psychopathology, whereas changes in detachment and cognition define later course. Implications of the superspectrum for genetic, socio-environmental, and longitudinal research are discussed. A companion review focuses on neurobiology, treatment response, and clinical utility of the superspectrum, and future research directions.
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Affiliation(s)
- Katherine G Jonas
- Department of Psychiatry & Behavioral Health, Stony Brook University, Stony Brook, NY, USA.
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Anna R Docherty
- Huntsman Mental Health Institute, Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
| | - Ruben C Gur
- Brain Behavior Laboratory, Department of Psychiatry and the Penn-CHOP Lifespan Brain Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Raquel E Gur
- Brain Behavior Laboratory, Department of Psychiatry and the Penn-CHOP Lifespan Brain Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Barnaby Nelson
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
| | - Ulrich Reininghaus
- Department of Public Mental Health, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- ESRC Centre for Society and Mental Health and Centre for Epidemiology and Public Health, Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Roman Kotov
- Department of Psychiatry & Behavioral Health, Stony Brook University, Stony Brook, NY, USA
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3
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Ling E, Nemesh J, Goldman M, Kamitaki N, Reed N, Handsaker RE, Genovese G, Vogelgsang JS, Gerges S, Kashin S, Ghosh S, Esposito JM, French K, Meyer D, Lutservitz A, Mullally CD, Wysoker A, Spina L, Neumann A, Hogan M, Ichihara K, Berretta S, McCarroll SA. Concerted neuron-astrocyte gene expression declines in aging and schizophrenia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.07.574148. [PMID: 38260461 PMCID: PMC10802483 DOI: 10.1101/2024.01.07.574148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Human brains vary across people and over time; such variation is not yet understood in cellular terms. Here we describe a striking relationship between people's cortical neurons and cortical astrocytes. We used single-nucleus RNA-seq to analyze the prefrontal cortex of 191 human donors ages 22-97 years, including healthy individuals and persons with schizophrenia. Latent-factor analysis of these data revealed that in persons whose cortical neurons more strongly expressed genes for synaptic components, cortical astrocytes more strongly expressed distinct genes with synaptic functions and genes for synthesizing cholesterol, an astrocyte-supplied component of synaptic membranes. We call this relationship the Synaptic Neuron-and-Astrocyte Program (SNAP). In schizophrenia and aging - two conditions that involve declines in cognitive flexibility and plasticity 1,2 - cells had divested from SNAP: astrocytes, glutamatergic (excitatory) neurons, and GABAergic (inhibitory) neurons all reduced SNAP expression to corresponding degrees. The distinct astrocytic and neuronal components of SNAP both involved genes in which genetic risk factors for schizophrenia were strongly concentrated. SNAP, which varies quantitatively even among healthy persons of similar age, may underlie many aspects of normal human interindividual differences and be an important point of convergence for multiple kinds of pathophysiology.
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Affiliation(s)
- Emi Ling
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - James Nemesh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Melissa Goldman
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Nolan Kamitaki
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Nora Reed
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Robert E. Handsaker
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Giulio Genovese
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Jonathan S. Vogelgsang
- McLean Hospital, Belmont, MA 02478, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
| | - Sherif Gerges
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Seva Kashin
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Sulagna Ghosh
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | | | | | - Daniel Meyer
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Alyssa Lutservitz
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Christopher D. Mullally
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Alec Wysoker
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Liv Spina
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Anna Neumann
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Marina Hogan
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Kiku Ichihara
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Sabina Berretta
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- McLean Hospital, Belmont, MA 02478, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
- Program in Neuroscience, Harvard Medical School, Boston, MA 02215, USA
| | - Steven A. McCarroll
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
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Sun L, Qiu Q, Ban C, Fan S, Xiao S, Li X. Decrease levels of bone morphogenetic protein 6 and noggin in chronic schizophrenia elderly. Cogn Neurodyn 2023; 17:695-701. [PMID: 37265647 PMCID: PMC10229485 DOI: 10.1007/s11571-022-09855-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 07/04/2022] [Accepted: 07/11/2022] [Indexed: 11/03/2022] Open
Abstract
Objective Bone morphogenetic protein 6 (BMP6) and noggin both have been implicated in the pathophysiology of chronic dementia, and chronic schizophrenia (SCZ) has high risk for progressing to dementia in later life. The current study investigated the relationship between blood BMP6/noggin levels and cognitive function in chronic SCZ elderly. Methods A total of 159 chronic SCZ elderly and 171 community normal controls (NC) were involved in the present study. Blood cytokines including BMP6 and its antagonist-noggin, and cognitive function were measured in all subjects, 157 subjects among them received apolipoprotein E (APOE) genotype test, and 208 subjects received cognitive assessment at 1-year follow-up. Results Chronic SCZ elderly had decreased levels of blood BMP6 and noggin compared to healthy controls, especially in the subgroup of chronic SCZ with dementia. Blood BMP6 combing with noggin could distinguish chronic SCZ from NC elderly. APOE ε4 carriers had lower levels of BMP6 than APOE non-ε4 carriers under chronic SCZ. Conclusions There was a significant relationship of blood BMP6/noggin with cognitive performance in chronic SCZ.
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Affiliation(s)
- Lin Sun
- Department of Geriatric Psychiatry, Alzheimer’s Disease and Related Disorders Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, No. 600 South Wanping Road, Xuhui Distinct, Shanghai, People’s Republic of China
| | - Qi Qiu
- Department of Geriatric Psychiatry, Alzheimer’s Disease and Related Disorders Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, No. 600 South Wanping Road, Xuhui Distinct, Shanghai, People’s Republic of China
| | - Chunxia Ban
- Department of Psychiatry, Jiading Mental Health Center, Shanghai, People’s Republic of China
| | - Sijia Fan
- Department of Psychiatry, Qingpu Mental Health Center, Shanghai, People’s Republic of China
| | - Shifu Xiao
- Department of Geriatric Psychiatry, Alzheimer’s Disease and Related Disorders Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, No. 600 South Wanping Road, Xuhui Distinct, Shanghai, People’s Republic of China
| | - Xia Li
- Department of Geriatric Psychiatry, Alzheimer’s Disease and Related Disorders Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, No. 600 South Wanping Road, Xuhui Distinct, Shanghai, People’s Republic of China
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5
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Pedersen ML, Alnæs D, van der Meer D, Fernandez-Cabello S, Berthet P, Dahl A, Kjelkenes R, Schwarz E, Thompson WK, Barch DM, Andreassen OA, Westlye LT. Computational Modeling of the n-Back Task in the ABCD Study: Associations of Drift Diffusion Model Parameters to Polygenic Scores of Mental Disorders and Cardiometabolic Diseases. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:290-299. [PMID: 35427796 DOI: 10.1016/j.bpsc.2022.03.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 03/04/2022] [Accepted: 03/31/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Cognitive dysfunction is common in mental disorders and represents a potential risk factor in childhood. The nature and extent of associations between childhood cognitive function and polygenic risk for mental disorders is unclear. We applied computational modeling to gain insight into mechanistic processes underlying decision making and working memory in childhood and their associations with polygenic risk scores (PRSs) for mental disorders and comorbid cardiometabolic diseases. METHODS We used the drift diffusion model to infer latent computational processes underlying decision making and working memory during the n-back task in 3707 children ages 9 to 10 years from the Adolescent Brain Cognitive Development (ABCD) Study. Single nucleotide polymorphism-based heritability was estimated for cognitive phenotypes, including computational parameters, aggregated n-back task performance, and neurocognitive assessments. PRSs were calculated for Alzheimer's disease, bipolar disorder, coronary artery disease (CAD), major depressive disorder, obsessive-compulsive disorder, schizophrenia, and type 2 diabetes. RESULTS Heritability estimates of cognitive phenotypes ranged from 12% to 38%. Bayesian mixed models revealed that slower accumulation of evidence was associated with higher PRSs for CAD and schizophrenia. Longer nondecision time was associated with higher PRSs for Alzheimer's disease and lower PRSs for CAD. Narrower decision threshold was associated with higher PRSs for CAD. Load-dependent effects on nondecision time and decision threshold were associated with PRSs for Alzheimer's disease and CAD, respectively. Aggregated neurocognitive test scores were not associated with PRSs for any of the mental or cardiometabolic phenotypes. CONCLUSIONS We identified distinct associations between computational cognitive processes and genetic risk for mental illness and cardiometabolic disease, which could represent childhood cognitive risk factors.
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Affiliation(s)
- Mads L Pedersen
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Bjørknes College, Institute of Psychology, Oslo, Norway
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Sara Fernandez-Cabello
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Pierre Berthet
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Andreas Dahl
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Rikka Kjelkenes
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Wesley K Thompson
- Division of Biostatistics and Department of Radiology, Population Neuroscience and Genetics Laboratory, University of California San Diego, La Jolla, California
| | - Deanna M Barch
- Departments of Psychological & Brain Sciences, Psychiatry, and Radiology, Washington University, St. Louis, Missouri
| | - Ole A Andreassen
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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6
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Ma Y, Kvarta MD, Adhikari BM, Chiappelli J, Du X, van der Vaart A, Goldwaser EL, Bruce H, Hatch KS, Gao S, Summerfelt A, Jahanshad N, Thompson PM, Nichols TE, Hong LE, Kochunov P. Association between brain similarity to severe mental illnesses and comorbid cerebral, physical, and cognitive impairments. Neuroimage 2023; 265:119786. [PMID: 36470375 PMCID: PMC9910181 DOI: 10.1016/j.neuroimage.2022.119786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/10/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
Severe mental illnesses (SMIs) are often associated with compromised brain health, physical comorbidities, and cognitive deficits, but it is incompletely understood whether these comorbidities are intrinsic to SMI pathophysiology or secondary to having SMIs. We tested the hypothesis that cerebral, cardiometabolic, and cognitive impairments commonly observed in SMIs can be observed in non-psychiatric individuals with SMI-like brain patterns of deviation as seen on magnetic resonance imaging. 22,883 participants free of common neuropsychiatric conditions from the UK Biobank (age = 63.4 ± 7.5 years, range = 45-82 years, 50.9% female) were split into discovery and replication samples. The regional vulnerability index (RVI) was used to quantify each participant's respective brain similarity to meta-analytical patterns of schizophrenia spectrum disorder, bipolar disorder, and major depressive disorder in gray matter thickness, subcortical gray matter volume, and white matter integrity. Cluster analysis revealed five clusters with distinct RVI profiles. Compared with a cluster with no RVI elevation, a cluster with RVI elevation across all SMIs and brain structures showed significantly higher volume of white matter hyperintensities (Cohen's d = 0.59, pFDR < 10-16), poorer cardiovascular (Cohen's d = 0.30, pFDR < 10-16) and metabolic (Cohen's d = 0.12, pFDR = 1.3 × 10-4) health, and slower speed of information processing (|Cohen's d| = 0.11-0.17, pFDR = 1.6 × 10-3-4.6 × 10-8). This cluster also had significantly higher level of C-reactive protein and alcohol use (Cohen's d = 0.11 and 0.28, pFDR = 4.1 × 10-3 and 1.1 × 10-11). Three other clusters with respective RVI elevation in gray matter thickness, subcortical gray matter volume, and white matter integrity showed intermediate level of white matter hyperintensities, cardiometabolic health, and alcohol use. Our results suggest that cerebral, physical, and cognitive impairments in SMIs may be partly intrinsic via shared pathophysiological pathways with SMI-related brain anatomical changes.
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Affiliation(s)
- Yizhou Ma
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Mark D Kvarta
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bhim M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Joshua Chiappelli
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Xiaoming Du
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Andrew van der Vaart
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Eric L Goldwaser
- Department of Psychiatry, Weill Cornell Medical College/New York-Presbyterian Hospital, New York, NY, USA
| | - Heather Bruce
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kathryn S Hatch
- School of Medicine, University of California, San Diego, CA, USA
| | - Si Gao
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ann Summerfelt
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | - Thomas E Nichols
- Big Data Science Institute, Department of Statistics, University of Oxford, Oxford, UK
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
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7
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Kochunov P, Ma Y, Hatch KS, Gao S, Jahanshad N, Thompson PM, Adhikari BM, Bruce H, Van der vaart A, Goldwaser EL, Sotiras A, Kvarta MD, Ma T, Chen S, Nichols TE, Hong LE. Brain-wide versus genome-wide vulnerability biomarkers for severe mental illnesses. Hum Brain Mapp 2022; 43:4970-4983. [PMID: 36040723 PMCID: PMC9582367 DOI: 10.1002/hbm.26056] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 07/21/2022] [Accepted: 08/02/2022] [Indexed: 01/06/2023] Open
Abstract
Severe mental illnesses (SMI), including major depressive (MDD), bipolar (BD), and schizophrenia spectrum (SSD) disorders have multifactorial risk factors and capturing their complex etiopathophysiology in an individual remains challenging. Regional vulnerability index (RVI) was used to measure individual's brain-wide similarity to the expected SMI patterns derived from meta-analytical studies. It is analogous to polygenic risk scores (PRS) that measure individual's similarity to genome-wide patterns in SMI. We hypothesized that RVI is an intermediary phenotype between genome and symptoms and is sensitive to both genetic and environmental risks for SMI. UK Biobank sample of N = 17,053/19,265 M/F (age = 64.8 ± 7.4 years) and an independent sample of SSD patients and controls (N = 115/111 M/F, age = 35.2 ± 13.4) were used to test this hypothesis. UKBB participants with MDD had significantly higher RVI-MDD (Cohen's d = 0.20, p = 1 × 10-23 ) and PRS-MDD (d = 0.17, p = 1 × 10-15 ) than nonpsychiatric controls. UKBB participants with BD and SSD showed significant elevation in the respective RVIs (d = 0.65 and 0.60; p = 3 × 10-5 and .009, respectively) and PRS (d = 0.57 and 1.34; p = .002 and .002, respectively). Elevated RVI-SSD were replicated in an independent sample (d = 0.53, p = 5 × 10-5 ). RVI-MDD and RVI-SSD but not RVI-BD were associated with childhood adversity (p < .01). In nonpsychiatric controls, elevation in RVI and PRS were associated with lower cognitive performance (p < 10-5 ) in six out of seven domains and showed specificity with disorder-associated deficits. In summary, the RVI is a novel brain index for SMI and shows similar or better specificity for SMI than PRS, and together they may complement each other in the efforts to characterize the genomic to brain level risks for SMI.
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Affiliation(s)
- Peter Kochunov
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Yizhou Ma
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Kathryn S. Hatch
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Si Gao
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging & Informatics InstituteKeck School of Medicine of USCLos AngelesCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics InstituteKeck School of Medicine of USCLos AngelesCaliforniaUSA
| | - Bhim M. Adhikari
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Heather Bruce
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Andrew Van der vaart
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Eric L. Goldwaser
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Aris Sotiras
- Institute of Informatics, University of WashingtonSchool of MedicineSt. LouisMissouriUSA
| | - Mark D. Kvarta
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Tianzhou Ma
- Department of Epidemiology and BiostatisticsUniversity of MarylandCollege ParkMarylandUSA
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Thomas E. Nichols
- Nuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - L. Elliot Hong
- Maryland Psychiatric Research Center, Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
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8
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Brown MT, Mutambudzi M. Psychiatric history and later-life cognitive change: effect modification by sex, race and ethnicity. Aging Ment Health 2022:1-8. [PMID: 36016466 DOI: 10.1080/13607863.2022.2116398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
Objective: We explored associations between psychiatric history and cognitive functioning, and differences by sex and race/ethnicity (SRE) in 20,155 Health and Retirement Study (1995-2014) participants aged 65 or older.Methods: Multi-level growth curve models examined cognition scores and their trajectories over time by SRE.Results: A history of psychiatric, emotional, or nervous problems was significantly related to cognition scores and rates of decline. Hispanic and Black participants had significantly lower cognition scores at age 75 and steeper rates of decline than White females, and Black race and the Hispanic race/ethnicity-sex interaction erased the protective effects of being female.Conclusions: Future research should include specific psychiatric diagnoses. Population level findings as reported here, along with aggregate findings from similar studies, can inform interventions and policies regarding support for populations that are vulnerable to mental illness and to subsequent cognitive decline.
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Affiliation(s)
- Maria T Brown
- School of Social Work and Aging Studies Institute, Syracuse University, Syracuse, NY, USA
| | - Miriam Mutambudzi
- Department of Public Health, Falk College, Syracuse University, Syracuse, NY, USA
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9
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Kuo SS, Musket CW, Rupert PE, Almasy L, Gur RC, Prasad KM, Roalf DR, Gur RE, Nimgaonkar VL, Pogue-Geile MF. Age-dependent patterns of schizophrenia genetic risk affect cognition. Schizophr Res 2022; 246:39-48. [PMID: 35709646 DOI: 10.1016/j.schres.2022.05.012] [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: 10/28/2021] [Revised: 03/15/2022] [Accepted: 05/15/2022] [Indexed: 11/15/2022]
Abstract
Cognition shares substantial genetic overlap with schizophrenia, yet it remains unclear whether such genetic effects become significant during developmental periods of elevated risk for schizophrenia, such as the peak age of onset. We introduce an investigative framework integrating epidemiological, developmental, and genetic approaches to determine whether genetic effects shared between schizophrenia and cognition are significant across periods of differing risk for schizophrenia onset, and whether these effects are shared with depression. 771 European-American participants, including 636 (ages 15-84 years) from families with at least two first-degree relatives with schizophrenia and 135 unrelated controls, were divided into three age-risk groups based on ages relative to epidemiological age of onset patterns for schizophrenia: Pre-Peak (before peak age-of-onset: 15 to 22 years), Post-Peak (after peak age-of-onset: 23-42 years), and Plateau (during plateau of age-of-onset: over 42 years). For general cognition and 11 specific cognitive traits, we estimated genetic correlations with schizophrenia and with depression within each age-risk group. Genetic effects shared between deficits in general cognition and schizophrenia were nonsignificant before peak age of onset, yet were high and significant after peak age of onset and during the plateau of onset. These age-dependent genetic effects were largely consistent across specific cognitive traits and not transdiagnostically shared with depression. Schizophrenia genetic effects appear to influence cognitive traits in an age-dependent manner, supporting late developmental and perhaps neurodegenerative models that hypothesize increased expression of schizophrenia risk genes during and after the peak age of risk. Our findings underscore the utility of cognitive traits for tracking schizophrenia genetic effects across the lifespan.
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Affiliation(s)
- Susan S Kuo
- Department of Psychology, University of Pittsburgh, United States of America; Stanley Center for Psychiatric Genetics, Broad Institute of MIT and Harvard, United States of America; Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, United States of America
| | - Christie W Musket
- Department of Psychology, University of Pittsburgh, United States of America
| | - Petra E Rupert
- Department of Psychology, University of Pittsburgh, United States of America
| | - Laura Almasy
- Department of Genetics, University of Pennsylvania, United States of America
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, United States of America
| | - Konasale M Prasad
- Department of Psychiatry, University of Pittsburgh, United States of America; Department of Bioengineering, University of Pittsburgh, United States of America; Veteran Affairs Pittsburgh Healthcare System, United States of America
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania, United States of America
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, United States of America
| | - Vishwajit L Nimgaonkar
- Department of Psychiatry, University of Pittsburgh, United States of America; Department of Human Genetics, University of Pittsburgh, United States of America
| | - Michael F Pogue-Geile
- Department of Psychology, University of Pittsburgh, United States of America; Department of Psychiatry, University of Pittsburgh, United States of America.
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10
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Liu H, Wang Z, Zou L, Gu S, Zhang M, Hukportie DN, Zheng J, Zhou R, Yuan Z, Wu K, Huang Z, Zhong Q, Huang Y, Wu X. Favourable Lifestyle Protects Cognitive Function in Older Adults With High Genetic Risk of Obesity: A Prospective Cohort Study. Front Mol Neurosci 2022; 15:808209. [PMID: 35677584 PMCID: PMC9169719 DOI: 10.3389/fnmol.2022.808209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
The relationship between body mass index (BMI) and cognitive impairment remains controversial, especially in older people. This study aims to confirm the association of phenotypic and genetic obesity with cognitive impairment and the benefits of adhering to a healthy lifestyle. This prospective study included 10,798 participants (aged ≥ 50 years) with normal cognitive function from the Health and Retirement Study in the United States. Participants were divided into low (lowest quintile), intermediate (quintiles 2–4), and high (highest quintile) groups according to their polygenic risk score (PRS) for BMI. The risk of cognitive impairment was estimated using Cox proportional hazard models. Higher PRS for BMI was associated with an increased risk, whereas phenotypic obesity was related to a decreased risk of cognitive impairment. Never smoking, moderate drinking, and active physical activity were considered favourable and associated with a lower risk of cognitive impairment compared with current smoking, never drinking, and inactive, respectively. A favourable lifestyle was associated with a low risk of cognitive impairment, even in subjects with low BMI and high PRS for BMI. This study suggest that regardless of obesity status, including phenotypic and genetic, adhering to a favourable lifestyle is beneficial to cognitive function.
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Affiliation(s)
- Huamin Liu
- Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Zhenghe Wang
- Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Lianwu Zou
- Baiyun Jingkang Hospital, Guangzhou, China
| | | | - Minyi Zhang
- Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Daniel Nyarko Hukportie
- Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Jiazhen Zheng
- Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Rui Zhou
- Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Zelin Yuan
- Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Keyi Wu
- Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Zhiwei Huang
- Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Qi Zhong
- Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Yining Huang
- Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Xianbo Wu
- Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China
- *Correspondence: Xianbo Wu,
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11
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Mapping normative trajectories of cognitive function and its relation to psychopathology symptoms and genetic risk in youth. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2022; 3:255-263. [PMID: 37124356 PMCID: PMC10140446 DOI: 10.1016/j.bpsgos.2022.01.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 12/08/2021] [Accepted: 01/16/2022] [Indexed: 12/19/2022] Open
Abstract
Background Adolescence hosts a sharp increase in the incidence of mental disorders. The prodromal phases are often characterized by cognitive deficits that predate disease onset by several years. Characterization of cognitive performance in relation to normative trajectories may have value for early risk assessment and monitoring. Methods Youth aged 8 to 21 years (N = 6481) from the Philadelphia Neurodevelopmental Cohort were included. Performance scores from a computerized neurocognitive battery were decomposed using principal component analysis, yielding a general cognitive score. Items reflecting various aspects of psychopathology from self-report questionnaires and collateral caregiver information were decomposed using independent component analysis, providing individual domain scores. Using normative modeling and Bayesian statistics, we estimated normative trajectories of cognitive function and tested for associations between cognitive deviance and psychopathological domain scores. In addition, we tested for associations with polygenic scores for mental and behavioral disorders often involving cognition, including schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, and Alzheimer's disease. Results More negative normative cognitive deviations were associated with higher general psychopathology burden and domains reflecting positive and prodromal psychosis, attention problems, norm-violating behavior, and anxiety. In addition, better performance was associated with higher joint burden of depression, suicidal ideation, and negative psychosis symptoms. The analyses revealed no evidence for associations with polygenic scores. Conclusions Our results show that cognitive performance is associated with general and specific domains of psychopathology in youth. These findings support the close links between cognition and psychopathology in youth and highlight the potential of normative modeling for early risk assessment.
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12
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Sex-specific effects of polygenic risk for schizophrenia on lifespan cognitive functioning in healthy individuals. Transl Psychiatry 2021; 11:520. [PMID: 34635642 PMCID: PMC8505489 DOI: 10.1038/s41398-021-01649-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/16/2021] [Accepted: 09/27/2021] [Indexed: 12/13/2022] Open
Abstract
Polygenic risk for schizophrenia has been associated with lower cognitive ability and age-related cognitive change in healthy individuals. Despite well-established neuropsychological sex differences in schizophrenia patients, genetic studies on sex differences in schizophrenia in relation to cognitive phenotypes are scarce. Here, we investigated whether the effect of a polygenic risk score (PRS) for schizophrenia on childhood, midlife, and late-life cognitive function in healthy individuals is modified by sex, and if PRS is linked to accelerated cognitive decline. Using a longitudinal data set from healthy individuals aged 25-100 years (N = 1459) spanning a 25-year period, we found that PRS was associated with lower cognitive ability (episodic memory, semantic memory, visuospatial ability), but not with accelerated cognitive decline. A significant interaction effect between sex and PRS was seen on cognitive task performance, and sex-stratified analyses showed that the effect of PRS was male-specific. In a sub-sample, we observed a male-specific effect of the PRS on school performance at age 12 (N = 496). Our findings of sex-specific effects of schizophrenia genetics on cognitive functioning across the lifespan indicate that the effects of underlying disease genetics on cognitive functioning is dependent on biological processes that differ between the sexes.
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13
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Türközer HB, Ivleva EI, Palka J, Clementz BA, Shafee R, Pearlson GD, Sweeney JA, Keshavan MS, Gershon ES, Tamminga CA. Biomarker Profiles in Psychosis Risk Groups Within Unaffected Relatives Based on Familiality and Age. Schizophr Bull 2021; 47:1058-1067. [PMID: 33693883 PMCID: PMC8266584 DOI: 10.1093/schbul/sbab013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Investigating biomarkers in unaffected relatives (UR) of individuals with psychotic disorders has already proven productive in research on psychosis neurobiology. However, there is considerable heterogeneity among UR based on features linked to psychosis vulnerability. Here, using the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) dataset, we examined cognitive and neurophysiologic biomarkers in first-degree UR of psychosis probands, stratified by 2 widely used risk factors: familiality status of the respective proband (the presence or absence of a first- or second-degree relative with a history of psychotic disorder) and age (within or older than the common age range for developing psychosis). We investigated biomarkers that best differentiate the above specific risk subgroups. Additionally, we examined the relationship of biomarkers with Polygenic Risk Scores for Schizophrenia (PRSSCZ) in a subsample of Caucasian probands and healthy controls (HC). Our results demonstrate that the Brief Assessment of Cognition in Schizophrenia (BACS) score, antisaccade error (ASE) factor, and stop-signal task (SST) factor best differentiate UR (n = 169) from HC (n = 137) (P = .013). Biomarker profiles of UR of familial (n = 82) and non-familial (n = 83) probands were not significantly different. Furthermore, ASE and SST factors best differentiated younger UR (age ≤ 30) (n = 59) from older UR (n = 110) and HC from both age groups (age ≤ 30 years, n=49; age > 30 years, n = 88) (P < .001). In addition, BACS (r = -0.175, P = .006) and ASE factor (r = 0.188, P = .006) showed associations with PRSSCZ. Taken together, our findings indicate that cognitive biomarkers-"top-down inhibition" impairments in particular-may be of critical importance as indicators of psychosis vulnerability.
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Affiliation(s)
- Halide Bilge Türközer
- Department of Psychiatry, the University of Texas Southwestern Medical Center, Dallas, TX
| | - Elena I Ivleva
- Department of Psychiatry, the University of Texas Southwestern Medical Center, Dallas, TX
| | - Jayme Palka
- Department of Psychiatry, the University of Texas Southwestern Medical Center, Dallas, TX
| | - Brett A Clementz
- Department of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA
| | - Rebecca Shafee
- Department of Genetics, Harvard Medical School, Boston, MA
| | - Godfrey D Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, CT
- Departments of Psychiatry and Neuroscience, Yale University, New Haven, CT
| | - John A Sweeney
- Department of Psychiatry, the University of Texas Southwestern Medical Center, Dallas, TX
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH
| | - Matcheri S Keshavan
- Department of Psychiatry and Cognitive Neurology, Beth Israel Deaconess Medical Center, Boston, MA
- Harvard Medical School, Boston, MA
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL
| | - Carol A Tamminga
- Department of Psychiatry, the University of Texas Southwestern Medical Center, Dallas, TX
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14
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Schizophrenia polygenic risk predicts general cognitive deficit but not cognitive decline in healthy older adults. Transl Psychiatry 2020; 10:422. [PMID: 33293510 PMCID: PMC7722936 DOI: 10.1038/s41398-020-01114-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 11/02/2020] [Accepted: 11/17/2020] [Indexed: 12/22/2022] Open
Abstract
There has been a long argument over whether schizophrenia is a neurodegenerative disorder associated with progressive cognitive impairment. Given high heritability of schizophrenia, ascertaning if genetic susceptibility to schizophrenia is also associated with cognitive decline in healthy people would support the view that schizophrenia leads to an accelerated cognitive decline. Using the population representative sample of 6817 adults aged >50 years from the English Longitudinal Study of Ageing, we investigated associations between the biennial rate of decline in cognitive ability and the schizophrenia polygenic score (SZ-PGS) during the 10-year follow-up period. SZ-PGS was calculated based on summary statistics from the Schizophrenia Working Group of the Psychiatric Genomics Consortium. Cognition was measured sequentially across four time points using verbal memory and semantic fluency tests. The average baseline verbal memory was 10.4 (SD = 3.4) and semantic fluency was 20.7 (SD = 6.3). One standard deviation (1-SD) increase in SZ-PGS was associated with lower baseline semantic fluency (β = -0.25, 95%CI = -0.40 to -0.10, p = 0.002); this association was significant in men (β = -0.36, 95%CI = -0.59 to -0.12, p = 0.003) and in those who were aged 60-69 years old (β = -0.32, 95%CI = -0.58 to -0.05, p = 0.019). Similarly, 1-SD increase in SZ-PGS was associated with lower verbal memory score at baseline in men only (β = -0.12, 95%CI = -0.23 to -0.01, p = 0.040). However, SZ-PGS was not associated with a greater rate of decline in these cognitive domains during the 10-year follow-up. Our findings highlight that while genetic susceptibility to schizophrenia conveys developmental cognitive deficit, it is not associated with an ongoing cognitive decline, at least in later life. These results do not support the neo-Kraepelinian notion of schizophrenia as a genetically determined progressively deteriorating brain disease.
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15
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Soler J, Lera-Miguel S, Lázaro L, Calvo R, Ferentinos P, Fañanás L, Fatjó-Vilas M. Familial aggregation analysis of cognitive performance in early-onset bipolar disorder. Eur Child Adolesc Psychiatry 2020; 29:1705-1716. [PMID: 32052174 DOI: 10.1007/s00787-020-01486-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 01/27/2020] [Indexed: 01/03/2023]
Abstract
We analysed the familial aggregation (familiality) of cognitive dimensions and explored their role as liability markers for early-onset bipolar disorder (EOBD). The sample comprised 99 subjects from 26 families, each with an offspring diagnosed with EOBD. Four cognitive dimensions were assessed: reasoning skills; attention and working memory; memory; and executive functions. Their familiality was investigated in the total sample and in a subset of healthy relatives. The intra-family resemblance score (IRS), a family-based index of the similarity of cognitive performance among family members, was calculated. Familiality was detected for the attention and working memory (AW) dimension in the total sample (ICC = 0.37, p = 0.0004) and in the subsample of healthy relatives (ICC = 0.37, p = 0.016). The IRS reflected that there are families with similar AW mean scores (either high or low) and families with heterogeneous scores. Families with the most common background for the AW dimension (IRS > 0) were selected and dichotomized in two groups according to the mean family AW score. This allowed differentiating families whose members had similar high scores than those with similar low scores: both patients (t = - 4.82, p = 0.0005) and relatives (t = - 5.04, p < 0.0001) of the two groups differed in their AW scores. AW dimension showed familial aggregation, suggesting its putative role as a familial vulnerability marker for EOBD. The IRS estimation allowed the identification of families with homogeneous scores for this dimension. This represents a first step towards the investigation of the underlying mechanisms of AW dimension and the identification of etiological subgroups.
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Affiliation(s)
- Jordi Soler
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
- Institut de Biomedicina de la Universitat de Barcelona (IBUB), Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Sara Lera-Miguel
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neurosciences, Hospital Clínic, Barcelona, Spain
- Department of Medicine, Faculty of Medicine, Universitat de Barcelona, Barcelona, Spain
| | - Luisa Lázaro
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neurosciences, Hospital Clínic, Barcelona, Spain
- Institut d'Investigació Biomèdica August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department of Medicine, Faculty of Medicine, Universitat de Barcelona, Barcelona, Spain
| | - Rosa Calvo
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neurosciences, Hospital Clínic, Barcelona, Spain
- Department of Medicine, Faculty of Medicine, Universitat de Barcelona, Barcelona, Spain
| | - Panagiotis Ferentinos
- 2nd Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, Athens, Greece
| | - Lourdes Fañanás
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
- Institut de Biomedicina de la Universitat de Barcelona (IBUB), Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Mar Fatjó-Vilas
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain.
- Institut de Biomedicina de la Universitat de Barcelona (IBUB), Universitat de Barcelona, Barcelona, Spain.
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain.
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain.
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16
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Andrews SJ, McFall GP, Booth A, Dixon RA, Anstey KJ. Association of Alzheimer's Disease Genetic Risk Loci with Cognitive Performance and Decline: A Systematic Review. J Alzheimers Dis 2020; 69:1109-1136. [PMID: 31156182 DOI: 10.3233/jad-190342] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The association of Apolipoprotein E (APOE) with late-onset Alzheimer's disease (LOAD) and cognitive endophenotypes of aging has been widely investigated. There is increasing interest in evaluating the association of other LOAD risk loci with cognitive performance and decline. The results of these studies have been inconsistent and inconclusive. We conducted a systematic review of studies investigating the association of non-APOE LOAD risk loci with cognitive performance in older adults. Studies published from January 2009 to April 2018 were identified through a PubMed database search using keywords and by scanning reference lists. Studies were included if they were either cross-sectional or longitudinal in design, included at least one genome-wide significant LOAD risk loci or a genetic risk score, and had one objective measure of cognition. Quality assessment of the studies was conducted using the quality of genetic studies (Q-Genie) tool. Of 2,466 studies reviewed, 49 met inclusion criteria. Fifteen percent of the associations between non-APOE LOAD risk loci and cognition were significant. However, these associations were not replicated across studies, and the majority were rendered non-significant when adjusting for multiple testing. One-third of the studies included genetic risk scores, and these were typically significant only when APOE was included. The findings of this systematic review do not support a consistent association between individual non-APOE LOAD risk and cognitive performance or decline. However, evidence suggests that aggregate LOAD genetic risk exerts deleterious effects on decline in episodic memory and global cognition.
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Affiliation(s)
- Shea J Andrews
- Ronald M. Loeb Center for Alzheimer's disease, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - G Peggy McFall
- Department of Psychology, University of Alberta, Edmonton, Canada.,Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
| | - Andrew Booth
- School of Health and Related Research, University of Sheffield, Sheffield, UK
| | - Roger A Dixon
- Department of Psychology, University of Alberta, Edmonton, Canada.,Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada
| | - Kaarin J Anstey
- UNSW Ageing Futures Institute, University of New South Wales, Australia.,School of Psychology, University of New South Wales, Australia.,Neuroscience Research Australia, Australia
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Polygenic analysis suggests the involvement of calcium signaling in executive function in schizophrenia patients. Eur Arch Psychiatry Clin Neurosci 2020; 270:425-431. [PMID: 30523404 DOI: 10.1007/s00406-018-0961-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 11/30/2018] [Indexed: 12/14/2022]
Abstract
Cognitive deficits are increasingly recognized as a core dimension rather than a consequence of schizophrenia (SCZ). The previous evidence supports the hypothesis of shared genetic factors between SCZ and cognitive ability. The objective of this study was to test whether and to what extent the variation of disease-relevant neurocognitive function in a sample of SCZ patients from the previous clinical interventional studies can be explained by SCZ polygenic risk scores (PRSs) or by hypothesis-driven and biomedical PRSs. The previous studies have described associations of the SNAP25 gene with cognition in SCZ. Likewise, the enrichment of several calcium signaling-related gene sets has been reported by genome-wide association studies (GWAS) in SCZ. Hypothesis-driven PRSs were calculated on the basis of the SNAP-25 interactome and also for genes regulated by phorbol myristate acetate (PMA), an activator of the signal transduction of protein kinase C (PKC) enzymes. In a cohort of 127 SCZ patients who had completed a comprehensive neurocognitive test battery as part of the previous antipsychotic intervention studies, we investigated the association between neurocognitive dimensions and PRSs. The PRS for SCZ and SNAP-25-associated genes could not explain the variance of neurocognition in this cohort. At a p value threshold of 0.05, the PRS for PMA was able to explain 2% of the variance in executive function (p = 0.05, uncorrected). The correlation between the PRS for PMA-regulated genes and cognition can give hints for further patient-derived cellular assays. In conclusion, incorporating biological information into PRSs and other en masse genetic analyses may help to close the gap between genetic vulnerability and the biological processes underlying neuropsychiatric diseases such as SCZ.
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18
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Mallet J, Le Strat Y, Dubertret C, Gorwood P. Polygenic Risk Scores Shed Light on the Relationship between Schizophrenia and Cognitive Functioning: Review and Meta-Analysis. J Clin Med 2020; 9:E341. [PMID: 31991840 PMCID: PMC7074036 DOI: 10.3390/jcm9020341] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 01/14/2020] [Accepted: 01/23/2020] [Indexed: 12/26/2022] Open
Abstract
Schizophrenia is a multifactorial disease associated with widespread cognitive impairment. Although cognitive deficits are one of the factors most strongly associated with functional impairment in schizophrenia (SZ), current treatment strategies hardly tackle these impairments. To develop more efficient treatment strategies in patients, a better understanding of their pathogenesis is needed. Recent progress in genetics, driven by large genome-wide association studies (GWAS) and the use of polygenic risk scores (PRS), has provided new insights about the genetic architecture of complex human traits, including cognition and SZ. Here, we review the recent findings examining the genetic links between SZ and cognitive functions in population-based samples as well as in participants with SZ. The performed meta-analysis showed a negative correlation between the polygenetic risk score of schizophrenia and global cognition (p < 0.001) when the samples rely on general and healthy participants, while no significant correlation was detected when the three studies devoted to schizophrenia patients were meta-analysed (p > 0.05). Our review and meta-analysis therefore argues against universal pleiotropy for schizophrenia alleles and cognition, since cognition in SZ patients would be underpinned by the same genetic factors than in the general population, and substantially independent of common variant liability to the disorder.
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Affiliation(s)
- Jasmina Mallet
- APHP; Department of Psychiatry, Universitary Hospital Louis Mourier, 92700 Colombes, France; (Y.L.S.); (C.D.)
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
| | - Yann Le Strat
- APHP; Department of Psychiatry, Universitary Hospital Louis Mourier, 92700 Colombes, France; (Y.L.S.); (C.D.)
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
| | - Caroline Dubertret
- APHP; Department of Psychiatry, Universitary Hospital Louis Mourier, 92700 Colombes, France; (Y.L.S.); (C.D.)
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
| | - Philip Gorwood
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
- GHU Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, F-75014 Paris, France
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Turovsky YA, Gureev AP, Vitkalova IY, Popov VN. Connection between polymorphisms in HTR2A, TPH2, BDNF, TOMM40 genes and the successful mastering of human-computer interfaces. J Genet 2019; 98:93. [PMID: 31767813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The development of human-computer interfaces in different individuals occur with different efficiencies, this is due to the individual characteristics of the genotype determined by the single-nucleotide polymorphism (SNP) of a person. Here, we checked the connection between the success of the acquisition of the brain-computer, eye-tracking, electromyographic, and respiratory interfaces and SNP of the TOMM40, BDNF, HTR2A and TPH2 genes. Here, we show that the T-allele in rs6313 of the HTR2A gene is associated with an increase in the number of correctly submitted commands of the electromyographic and eye-tracking interfaces. This is probably due to the fact that, the T-allele carriers decrease expression of this serotonin receptor. The decreased expression of HTR2A may be a reason for an increase in the number of accurately submitted commands. It was shown that the TT genotype of rs4290270 polymorphism was associated with an increase in the accuracy of work with the myographic interface. In addition, the association of subjective interfaces work time with polymorphisms rs429358 and rs2030324 was noted. Thus, the genotypic characteristics of individuals can be a predictive sign for the degree of success of mastering human-computer interfaces, which can allow to expand the understanding of training the neural mechanisms when working with this class of devices.
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Affiliation(s)
- Yaroslav A Turovsky
- Laboratory of Medical Cybernetics, Voronezh State University, Voronezh 394018, Russia.
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20
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Smeland OB, Frei O, Fan CC, Shadrin A, Dale AM, Andreassen OA. The emerging pattern of shared polygenic architecture of psychiatric disorders, conceptual and methodological challenges. Psychiatr Genet 2019; 29:152-159. [PMID: 31464996 PMCID: PMC10752571 DOI: 10.1097/ypg.0000000000000234] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Genome-wide association studies have transformed psychiatric genetics and provided novel insights into the genetic etiology of psychiatric disorders. Two major discoveries have emerged; the disorders are polygenic, with a large number of common variants each with a small effect and many genetic variants influence more than one phenotype, suggesting shared genetic etiology. These concepts have the potential to revolutionize the current classification system with diagnostic categories and facilitate development of better treatments. However, to reach clinical impact, we need larger samples and better analytical tools, as most polygenic factors remain undetected. We here present statistical approaches designed to improve the yield of existing genome-wide association studies for polygenic phenotypes. We review how these tools have informed the current knowledge on the genetic architecture of psychiatric disorders, focusing on schizophrenia, bipolar disorder and major depression, and overlap with psychological and cognitive traits. We discuss application of statistical tools for stratification and prediction.
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Affiliation(s)
- Olav B. Smeland
- NORMENT Centre, Institute of Clinical Medicine, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Chun-Chieh Fan
- Center for Human Development, University of California, San Diego, USA
| | - Alexey Shadrin
- NORMENT Centre, Institute of Clinical Medicine, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Anders M. Dale
- Department of Radiology, University of California, USA
- Department of Neuroscience, University of California, San Diego, USA
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, California, USA
| | - Ole A. Andreassen
- NORMENT Centre, Institute of Clinical Medicine, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
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21
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Turovsky YA, Gureev AP, Vitkalova IY, Popov VN. Connection between polymorphisms in HTR2A, TPH2, BDNF, TOMM40 genes and the successful mastering of human–computer interfaces. J Genet 2019. [DOI: 10.1007/s12041-019-1138-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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22
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Toulopoulou T, Zhang X, Cherny S, Dickinson D, Berman KF, Straub RE, Sham P, Weinberger DR. Polygenic risk score increases schizophrenia liability through cognition-relevant pathways. Brain 2019; 142:471-485. [PMID: 30535067 DOI: 10.1093/brain/awy279] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 09/19/2018] [Indexed: 02/02/2023] Open
Abstract
Cognitive deficit is thought to represent, at least in part, genetic mechanisms of risk for schizophrenia, with recent evidence from statistical modelling of twin data suggesting direct causality from the former to the latter. However, earlier evidence was based on inferences from twin not molecular genetic data and it is unclear how much genetic influence 'passes through' cognition on the way to diagnosis. Thus, we included direct measurements of genetic risk (e.g. schizophrenia polygenic risk scores) in causation models to assess the extent to which cognitive deficit mediates some of the effect of polygenic risk scores on the disorder. Causal models of family data tested relationships among key variables and allowed parsing of genetic variance components. Polygenic risk scores were calculated from summary statistics from the current largest genome-wide association study of schizophrenia and were represented as a latent trait. Cognition was also modelled as a latent trait. Participants were 1313 members of 1078 families: 416 patients with schizophrenia, 290 unaffected siblings, and 607 controls. Modelling supported earlier findings that cognitive deficit has a putatively causal role in schizophrenia. In total, polygenic risk score explained 8.07% [confidence interval (CI) 5.45-10.74%] of schizophrenia risk in our sample. Of this, more than a third (2.71%, CI 2.41-3.85%) of the polygenic risk score influence was mediated through cognition paths, exceeding the direct influence of polygenic risk score on schizophrenia risk (1.43%, CI 0.46-3.08%). The remainder of the polygenic risk score influence (3.93%, CI 2.37-4.48%) reflected reciprocal causation between schizophrenia liability and cognition (e.g. mutual influences in a cyclical manner). Analysis of genetic variance components of schizophrenia liability indicated that 26.87% (CI 21.45-32.57%) was associated with cognition-related pathways not captured by polygenic risk score. The remaining variance in schizophrenia was through pathways other than cognition-related and polygenic risk score. Although our results are based on inference through statistical modelling and do not provide an absolute proof of causality, we find that cognition pathways mediate a significant part of the influence of cumulative genetic risk on schizophrenia. We estimate from our model that 33.51% (CI 27.34-43.82%) of overall genetic risk is mediated through influences on cognition, but this requires further studies and analyses as the genetics of schizophrenia becomes better characterized.
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Affiliation(s)
- Timothea Toulopoulou
- Department of Psychology, Bilkent University, Bilkent, Ankara, Turkey.,The State Key Laboratory of Brain and Cognitive Sciences, the University of Hong Kong, Hong Kong SAR, China.,Department of Psychology, the University of Hong Kong, Hong Kong SAR, China.,Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience at King's College London, London, UK
| | - Xiaowei Zhang
- Department of Psychiatry, The University of Hong Kong, Hong Kong SAR, China
| | - Stacey Cherny
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience at King's College London, London, UK.,Department of Psychiatry, The University of Hong Kong, Hong Kong SAR, China
| | - Dwight Dickinson
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - Karen F Berman
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - Richard E Straub
- Lieber Institute for Brain Development, Johns Hopkins University, USA
| | - Pak Sham
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience at King's College London, London, UK.,Department of Psychiatry, The University of Hong Kong, Hong Kong SAR, China
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins University, USA.,Departments of Psychiatry, Neurology, Neuroscience, The McKusick Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Johns Hopkins University, USA
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23
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Chen J, Liu J, Calhoun VD. The Translational Potential of Neuroimaging Genomic Analyses To Diagnosis And Treatment In The Mental Disorders. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2019; 107:912-927. [PMID: 32051642 PMCID: PMC7015534 DOI: 10.1109/jproc.2019.2913145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Imaging genomics focuses on characterizing genomic influence on the variation of neurobiological traits, holding promise for illuminating the pathogenesis, reforming the diagnostic system, and precision medicine of mental disorders. This paper aims to provide an overall picture of the current status of neuroimaging-genomic analyses in mental disorders, and how we can increase their translational potential into clinical practice. The review is organized around three perspectives. (a) Towards reliability, generalizability and interpretability, where we summarize the multivariate models and discuss the considerations and trade-offs of using these methods and how reliable findings may be reached, to serve as ground for further delineation. (b) Towards improved diagnosis, where we outline the advantages and challenges of constructing a dimensional transdiagnostic model and how imaging genomic analyses map into this framework to aid in deconstructing heterogeneity and achieving an optimal stratification of patients that better inform treatment planning. (c) Towards improved treatment. Here we highlight recent efforts and progress in elucidating the functional annotations that bridge between genomic risk and neurobiological abnormalities, in detecting genomic predisposition and prodromal neurodevelopmental changes, as well as in identifying imaging genomic biomarkers for predicting treatment response. Providing an overview of the challenges and promises, this review hopefully motivates imaging genomic studies with multivariate, dimensional and transdiagnostic designs for generalizable and interpretable findings that facilitate development of personalized treatment.
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Affiliation(s)
- Jiayu Chen
- The Mind Research Network, Albuquerque, NM 87106 USA
| | - Jingyu Liu
- The Mind Research Network, Albuquerque, NM 87106 USA, and also with the Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131 USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106 USA, and also with the Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131 USA
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24
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Genome-wide analysis reveals extensive genetic overlap between schizophrenia, bipolar disorder, and intelligence. Mol Psychiatry 2019; 25:844-853. [PMID: 30610197 PMCID: PMC6609490 DOI: 10.1038/s41380-018-0332-x] [Citation(s) in RCA: 117] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 10/18/2018] [Accepted: 11/29/2018] [Indexed: 02/08/2023]
Abstract
Schizophrenia (SCZ) and bipolar disorder (BD) are severe mental disorders associated with cognitive impairment, which is considered a major determinant of functional outcome. Despite this, the etiology of the cognitive impairment is poorly understood, and no satisfactory cognitive treatments exist. Increasing evidence indicates that genetic risk for SCZ may contribute to cognitive impairment, whereas the genetic relationship between BD and cognitive function remains unclear. Here, we combined large genome-wide association study data on SCZ (n = 82,315), BD (n = 51,710), and general intelligence (n = 269,867) to investigate overlap in common genetic variants using conditional false discovery rate (condFDR) analysis. We observed substantial genetic enrichment in both SCZ and BD conditional on associations with intelligence indicating polygenic overlap. Using condFDR analysis, we leveraged this enrichment to increase statistical power and identified 75 distinct genomic loci associated with both SCZ and intelligence, and 12 loci associated with both BD and intelligence at conjunctional FDR < 0.01. Among these loci, 20 are novel for SCZ, and four are novel for BD. Most SCZ risk alleles (61 of 75, 81%) were associated with poorer cognitive performance, whereas most BD risk alleles (9 of 12, 75%) were associated with better cognitive performance. A gene set analysis of the loci shared between SCZ and intelligence implicated biological processes related to neurodevelopment, synaptic integrity, and neurotransmission; the same analysis for BD was underpowered. Altogether, the study demonstrates that both SCZ and BD share genetic influences with intelligence, albeit in a different manner, providing new insights into their genetic architectures.
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25
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Alfimova MV, Kondratiev NV, Golimbet VE. [Results and promises of genetics of cognitive impairment in schizophrenia: molecular-genetic approaches]. Zh Nevrol Psikhiatr Im S S Korsakova 2018. [PMID: 28635752 DOI: 10.17116/jnevro2016116111137-144] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
This review highlights the basic paradigms and directions of molecular genetic studies of cognitive deficits in schizophrenia. Along with the traditional approach based on functional candidate genes, it covers genome-wide association studies (GWAS) for cognition in general population and schizophrenic patients, attempts to integrate GWAS results in polygenic profiles that can be used in personalized care of schizophrenic patients, and a search for biological pathways implicated in the development of cognitive impairments with bioinformatics methods. However, despite significant advances in understanding the genetic basis of the disease and a rapidly growing amount of data on genes associated with cognitive functions, most of the variability of cognitive impairments in patients remains unexplained. The data on the functional complexity of the genome accumulated in the fields of molecular biology and genetics underscore the importance of studying epigenetic mechanisms of cognitive deficits in schizophrenia.
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26
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Ohi K, Sumiyoshi C, Fujino H, Yasuda Y, Yamamori H, Fujimoto M, Shiino T, Sumiyoshi T, Hashimoto R. Genetic Overlap between General Cognitive Function and Schizophrenia: A Review of Cognitive GWASs. Int J Mol Sci 2018; 19:E3822. [PMID: 30513630 PMCID: PMC6320986 DOI: 10.3390/ijms19123822] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 11/25/2018] [Accepted: 11/26/2018] [Indexed: 11/17/2022] Open
Abstract
General cognitive (intelligence) function is substantially heritable, and is a major determinant of economic and health-related life outcomes. Cognitive impairments and intelligence decline are core features of schizophrenia which are evident before the onset of the illness. Genetic overlaps between cognitive impairments and the vulnerability for the illness have been suggested. Here, we review the literature on recent large-scale genome-wide association studies (GWASs) of general cognitive function and correlations between cognitive function and genetic susceptibility to schizophrenia. In the last decade, large-scale GWASs (n > 30,000) of general cognitive function and schizophrenia have demonstrated that substantial proportions of the heritability of the cognitive function and schizophrenia are explained by a polygenic component consisting of many common genetic variants with small effects. To date, GWASs have identified more than 100 loci linked to general cognitive function and 108 loci linked to schizophrenia. These genetic variants are mostly intronic or intergenic. Genes identified around these genetic variants are densely expressed in brain tissues. Schizophrenia-related genetic risks are consistently correlated with lower general cognitive function (rg = -0.20) and higher educational attainment (rg = 0.08). Cognitive functions are associated with many of the socioeconomic and health-related outcomes. Current treatment strategies largely fail to improve cognitive impairments of schizophrenia. Therefore, further study is needed to understand the molecular mechanisms underlying both cognition and schizophrenia.
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Affiliation(s)
- Kazutaka Ohi
- Department of Neuropsychiatry, Kanazawa Medical University, Uchinada, Ishikawa 920-0293, Japan.
- Medical Research Institute, Kanazawa Medical University, Ishikawa 920-0293, Japan.
| | - Chika Sumiyoshi
- Faculty of Human Development and Culture, Fukushima University, Fukushima 960-1296, Japan.
| | - Haruo Fujino
- Graduate School of Education, Oita University, Oita 870-1192, Japan.
| | - Yuka Yasuda
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo 187-8553, Japan.
| | - Hidenaga Yamamori
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo 187-8553, Japan.
| | - Michiko Fujimoto
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan.
| | - Tomoko Shiino
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo 187-8553, Japan.
| | - Tomiki Sumiyoshi
- Department of Preventive Interventions for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Tokyo 187-8553, Japan.
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo 187-8553, Japan.
- Osaka University, Suita, Osaka 565-0871, Japan.
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27
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Smeland OB, Andreassen OA. How can genetics help understand the relationship between cognitive dysfunction and schizophrenia? Scand J Psychol 2018; 59:26-31. [PMID: 29356008 DOI: 10.1111/sjop.12407] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 08/14/2017] [Indexed: 01/05/2023]
Abstract
Despite the consistent finding that cognitive dysfunction is a core characteristic of schizophrenia (SCZ), little is known about the underlying pathophysiology. Recent progress in human genetics, driven by large genome-wide association studies (GWAS), has provided new data about the genetic architecture of complex human traits, including cognition and SCZ. Novel analytical tools have provided unprecedented opportunities to leverage the large amount of information from GWAS. Here we review the latest findings related to genetic architecture and risk genes of SCZ and cognitive functions, and recent findings of overlapping genetic factors. The recent GWAS of SCZ implicate over 100 risk gene loci, each with a small effect. A similar genetic architecture seems to be present in cognitive domains, suggesting that these phenotypes are highly polygenic. Further, GWAS have revealed more than 20 gene loci associated with cognitive traits, including intelligence, general cognition (g-factor), reaction time and verbal-numerical reasoning. Several gene loci have been implicated in educational attainment, a proxy measure of cognitive function. Recently, overlapping gene loci were found between education and SCZ, and between SCZ and cognitive traits, suggesting common genetic risk between SCZ and cognitive dysfunction. Mathematical modeling of GWAS of cognition and SCZ indicate that only a fraction of the heritability is identified. The evidence suggests a polygenic architecture for SCZ and cognitive functions, and a large degree of shared genetic risk. This indicates novel molecular genetic mechanisms and strengthens the notion that SCZ is more likely a part of the normal distribution and not a separate entity.
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Affiliation(s)
- Olav B Smeland
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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28
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Krug A, Dietsche B, Zöllner R, Yüksel D, Nöthen MM, Forstner AJ, Rietschel M, Dannlowski U, Baune BT, Maier R, Witt SH, Kircher T. Polygenic risk for schizophrenia affects working memory and its neural correlates in healthy subjects. Schizophr Res 2018; 197:315-320. [PMID: 29409757 DOI: 10.1016/j.schres.2018.01.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Revised: 11/14/2017] [Accepted: 01/17/2018] [Indexed: 12/27/2022]
Abstract
Schizophrenia is a disorder with a high heritability. Patients as well as their first degree relatives display lower levels of performance in a number of cognitive domains compared to subjects without genetic risk. Several studies could link these aberrations to single genetic variants, however, only recently, polygenic risk scores as proxies for genetic risk have been associated with cognitive domains and their neural correlates. In the present study, a sample of healthy subjects (n=137) performed a letter version of the n-back task while scanned with 3-T fMRI. All subjects were genotyped with the PsychChip and polygenic risk scores were calculated based on the PGC2 schizophrenia GWAS results. Polygenic risk for schizophrenia was associated with a lower degree of brain activation in prefrontal areas during the 3-back compared to the 0-back baseline condition. Furthermore, polygenic risk was associated with lower levels of brain activation in the right inferior frontal gyrus during the 3-back compared to a 2-back condition. Polygenic risk leads to a shift in the underlying activation pattern to the left side, thus resembling results reported in patients with schizophrenia. The data may point to polygenic risk for schizophrenia being associated with brain function in a cognitive task known to be impaired in patients and their relatives.
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Affiliation(s)
- Axel Krug
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany.
| | - Bruno Dietsche
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Rebecca Zöllner
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Dilara Yüksel
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, Bonn, Germany; Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Andreas J Forstner
- Institute of Human Genetics, University of Bonn, Bonn, Germany; Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany; Department of Psychiatry (UPK), University of Basel, Switzerland; Division of Medical Genetics and Department of Biomedicine, University of Basel, Switzerland
| | - Marcella Rietschel
- Department of Genetic Epidemiology, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Udo Dannlowski
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany; Department of Psychiatry, University of Münster, Münster, Germany
| | - Bernhard T Baune
- Discipline of Psychiatry, The University of Adelaide, Adelaide, SA, Australia
| | - Robert Maier
- Queensland Brain Institute, The University of Queensland, Australia
| | - Stephanie H Witt
- Department of Genetic Epidemiology, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
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29
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Mistry S, Harrison JR, Smith DJ, Escott-Price V, Zammit S. The use of polygenic risk scores to identify phenotypes associated with genetic risk of schizophrenia: Systematic review. Schizophr Res 2018; 197:2-8. [PMID: 29129507 DOI: 10.1016/j.schres.2017.10.037] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Revised: 10/27/2017] [Accepted: 10/28/2017] [Indexed: 12/12/2022]
Abstract
Studying the phenotypic manifestations of increased genetic liability for schizophrenia can increase our understanding of this disorder. Specifically, information from alleles identified in genome-wide association studies can be collapsed into a polygenic risk score (PRS) to explore how genetic risk is manifest within different samples. In this systematic review, we provide a comprehensive assessment of studies examining associations between schizophrenia PRS (SZ-PRS) and several phenotypic measures. We searched EMBASE, Medline and PsycINFO (from August 2009-14th March 2016) plus references of included studies, following PRISMA guidelines. Study inclusion was based on predetermined criteria and data were extracted independently and in duplicate. Overall, SZ-PRS was associated with increased risk for psychiatric disorders such as depression and bipolar disorder, lower performance IQ and negative symptoms. SZ-PRS explained up to 6% of genetic variation in psychiatric phenotypes, compared to <0.7% in measures of cognition. Future gains from using the PRS approach may be greater if used for examining phenotypes that are more closely related to biological substrates, for scores based on gene-pathways, and where PRSs are used to stratify individuals for study of treatment response. As it was difficult to interpret findings across studies due to insufficient information provided by many studies, we propose a framework to guide robust reporting of PRS associations in the future.
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Affiliation(s)
- Sumit Mistry
- Institute of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, UK.
| | - Judith R Harrison
- Institute of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, UK
| | - Daniel J Smith
- Institute of Health and Wellbeing, 1 Lilybank Gardens, University of Glasgow, UK
| | - Valentina Escott-Price
- Institute of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, UK
| | - Stanley Zammit
- Institute of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, UK; Centre for Academic Mental Health, School of Social and Community Medicine, University of Bristol, UK
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30
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McCarthy NS, Badcock JC, Clark ML, Knowles EEM, Cadby G, Melton PE, Morgan VA, Blangero J, Moses EK, Glahn DC, Jablensky A. Assessment of Cognition and Personality as Potential Endophenotypes in the Western Australian Family Study of Schizophrenia. Schizophr Bull 2018; 44:908-921. [PMID: 29040798 PMCID: PMC6007328 DOI: 10.1093/schbul/sbx141] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [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
Phenotypic heterogeneity is a major barrier to understanding the genetic architecture underlying schizophrenia. Incorporating endophenotypes is one way to reduce heterogeneity and facilitate more powerful genetic analysis. Candidate endophenotypes require systematic assessment against endophenotype criteria, and a ranking of their potential utility for genetic analysis. In this study we assess 20 cognitive and personality measures in a sample of 127 families with at least 2 cases of schizophrenia per family (n = 535) plus a set of 30 control families (n = 121) against 4 endophenotype criteria: (a) be associated with the illness but not be a part of its diagnosis, (b) be heritable, (c) co-segregate with the illness in families, and (d) be found in unaffected relatives at a higher rate than in the general population. The endophenotype ranking score (endophenotype ranking variable [ERV]) was used to rank candidate endophenotypes based on their heritability and genetic correlation with schizophrenia. Finally, we used factor analysis to explore latent factors underlying the cognitive and personality measures. Evidence for personality measures as endophenotypes was at least equivalent to that of the cognitive measures. Factor analysis indicated that personality and cognitive traits contribute to independent latent dimensions. The results suggest for this first time that a number of cognitive and personality measures are independent and informative endophenotypes. Use of these endophenotypes in genetic studies will likely improve power and facilitate novel aetiological insights.
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Affiliation(s)
- Nina S McCarthy
- Centre for Genetic Origins of Health and Disease, Faculty of Medicine, Dentistry & Health Sciences, The University of Western Australia and Faculty of Health Sciences, Curtin University, Perth, Australia
- Centre for Clinical Research in Neuropsychiatry, Division of Psychiatry, Medical School, University of Western Australia, Perth, Australia
- Cooperative Research Centre for Mental Health, Carlton South, Australia
| | - Johanna C Badcock
- Centre for Clinical Research in Neuropsychiatry, Division of Psychiatry, Medical School, University of Western Australia, Perth, Australia
- Cooperative Research Centre for Mental Health, Carlton South, Australia
| | - Melanie L Clark
- Centre for Clinical Research in Neuropsychiatry, Division of Psychiatry, Medical School, University of Western Australia, Perth, Australia
| | - Emma E M Knowles
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
| | - Gemma Cadby
- Centre for Genetic Origins of Health and Disease, Faculty of Medicine, Dentistry & Health Sciences, The University of Western Australia and Faculty of Health Sciences, Curtin University, Perth, Australia
| | - Phillip E Melton
- Centre for Genetic Origins of Health and Disease, Faculty of Medicine, Dentistry & Health Sciences, The University of Western Australia and Faculty of Health Sciences, Curtin University, Perth, Australia
| | - Vera A Morgan
- Centre for Clinical Research in Neuropsychiatry, Division of Psychiatry, Medical School, University of Western Australia, Perth, Australia
- Neuropsychiatric Epidemiology Research Unit, Division of Psychiatry, Medical School, University of Western Australia, Perth, Australia
| | - John Blangero
- South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX
| | - Eric K Moses
- Centre for Genetic Origins of Health and Disease, Faculty of Medicine, Dentistry & Health Sciences, The University of Western Australia and Faculty of Health Sciences, Curtin University, Perth, Australia
| | - David C Glahn
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, CT
| | - Assen Jablensky
- Centre for Clinical Research in Neuropsychiatry, Division of Psychiatry, Medical School, University of Western Australia, Perth, Australia
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31
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Schork AJ, Brown TT, Hagler DJ, Thompson WK, Chen CH, Dale AM, Jernigan TL, Akshoomoff N. Polygenic risk for psychiatric disorders correlates with executive function in typical development. GENES BRAIN AND BEHAVIOR 2018; 18:e12480. [PMID: 29660215 DOI: 10.1111/gbb.12480] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 03/25/2018] [Accepted: 04/09/2018] [Indexed: 01/02/2023]
Abstract
Executive functions are a diverse and critical suite of cognitive abilities that are often disrupted in individuals with psychiatric disorders. Despite their moderate to high heritability, little is known about the molecular genetic factors that contribute to variability in executive functions and how these factors may be related to those that predispose to psychiatric disorders. We examined the relationship between polygenic risk scores built from large genome-wide association studies of psychiatric disorders and executive functioning in typically developing children. In our discovery sample (N = 417), consistent with previous reports on general cognitive abilities, polygenic risk for autism spectrum disorder was associated with better performance on the Dimensional Change Card Sort test from the NIH Cognition Toolbox, with the largest effect in the youngest children. Polygenic risk for major depressive disorder was associated with poorer performance on the Flanker test in the same sample. This second association replicated for performance on the Penn Conditional Exclusion Test in an independent cohort (N = 3681). Our results suggest that the molecular genetic factors contributing to variability in executive function during typical development are at least partially overlapping with those associated with psychiatric disorders, although larger studies and further replication are needed.
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Affiliation(s)
- A J Schork
- Department of Cognitive Sciences, UC San Diego, San Diego, California.,Center for Human Development, UC San Diego, San Diego, California.,Center for Multimodal Imaging and Genetics, UC San Diego School of Medicine, San Diego, California
| | - T T Brown
- Center for Human Development, UC San Diego, San Diego, California.,Center for Multimodal Imaging and Genetics, UC San Diego School of Medicine, San Diego, California.,Department of Neurosciences, UC San Diego, San Diego, California
| | - D J Hagler
- Center for Multimodal Imaging and Genetics, UC San Diego School of Medicine, San Diego, California.,Department of Radiology, UC San Diego, San Diego, California
| | - W K Thompson
- Institute of Biological Psychiatry, Mental Health Centre Sct. Hans, Roskilde, Denmark.,Department of Psychiatry, UC San Diego, San Diego, California
| | - C-H Chen
- Center for Multimodal Imaging and Genetics, UC San Diego School of Medicine, San Diego, California.,Department of Radiology, UC San Diego, San Diego, California
| | - A M Dale
- Center for Multimodal Imaging and Genetics, UC San Diego School of Medicine, San Diego, California.,Department of Neurosciences, UC San Diego, San Diego, California.,Department of Radiology, UC San Diego, San Diego, California.,Department of Psychiatry, UC San Diego, San Diego, California
| | - T L Jernigan
- Department of Cognitive Sciences, UC San Diego, San Diego, California.,Center for Human Development, UC San Diego, San Diego, California.,Department of Radiology, UC San Diego, San Diego, California.,Department of Psychiatry, UC San Diego, San Diego, California
| | - N Akshoomoff
- Center for Human Development, UC San Diego, San Diego, California.,Department of Psychiatry, UC San Diego, San Diego, California
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32
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Bogdan R, Baranger DAA, Agrawal A. Polygenic Risk Scores in Clinical Psychology: Bridging Genomic Risk to Individual Differences. Annu Rev Clin Psychol 2018; 14:119-157. [PMID: 29579395 PMCID: PMC7772939 DOI: 10.1146/annurev-clinpsy-050817-084847] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Genomewide association studies (GWASs) across psychiatric phenotypes have shown that common genetic variants generally confer risk with small effect sizes (odds ratio < 1.1) that additively contribute to polygenic risk. Summary statistics derived from large discovery GWASs can be used to generate polygenic risk scores (PRS) in independent, target data sets to examine correlates of polygenic disorder liability (e.g., does genetic liability to schizophrenia predict cognition?). The intuitive appeal and generalizability of PRS have led to their widespread use and new insights into mechanisms of polygenic liability. However, when currently applied across traits they account for small amounts of variance (<3%), are relatively uninformative for clinical treatment, and, in isolation, provide no insight into molecular mechanisms. Larger GWASs are needed to increase the precision of PRS, and novel approaches integrating various data sources (e.g., multitrait analysis of GWASs) may improve the utility of current PRS.
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Affiliation(s)
- Ryan Bogdan
- BRAINLab, Department of Psychological and Brain Sciences, and Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, Missouri 63110, USA;
| | - David A A Baranger
- BRAINLab, Department of Psychological and Brain Sciences, and Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, Missouri 63110, USA;
| | - Arpana Agrawal
- Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, USA
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33
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Blokland GAM, del Re EC, Mesholam-Gately RI, Jovicich J, Trampush JW, Keshavan MS, DeLisi LE, Walters JTR, Turner JA, Malhotra AK, Lencz T, Shenton ME, Voineskos AN, Rujescu D, Giegling I, Kahn RS, Roffman JL, Holt DJ, Ehrlich S, Kikinis Z, Dazzan P, Murray RM, Di Forti M, Lee J, Sim K, Lam M, Wolthusen RPF, de Zwarte SMC, Walton E, Cosgrove D, Kelly S, Maleki N, Osiecki L, Picchioni MM, Bramon E, Russo M, David AS, Mondelli V, Reinders AATS, Falcone MA, Hartmann AM, Konte B, Morris DW, Gill M, Corvin AP, Cahn W, Ho NF, Liu JJ, Keefe RSE, Gollub RL, Manoach DS, Calhoun VD, Schulz SC, Sponheim SR, Goff DC, Buka SL, Cherkerzian S, Thermenos HW, Kubicki M, Nestor PG, Dickie EW, Vassos E, Ciufolini S, Marques TR, Crossley NA, Purcell SM, Smoller JW, van Haren NEM, Toulopoulou T, Donohoe G, Goldstein JM, Seidman LJ, McCarley RW, Petryshen TL. The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) consortium: A collaborative cognitive and neuroimaging genetics project. Schizophr Res 2018; 195:306-317. [PMID: 28982554 PMCID: PMC5882601 DOI: 10.1016/j.schres.2017.09.024] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 09/15/2017] [Accepted: 09/20/2017] [Indexed: 01/02/2023]
Abstract
BACKGROUND Schizophrenia has a large genetic component, and the pathways from genes to illness manifestation are beginning to be identified. The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) Consortium aims to clarify the role of genetic variation in brain abnormalities underlying schizophrenia. This article describes the GENUS Consortium sample collection. METHODS We identified existing samples collected for schizophrenia studies consisting of patients, controls, and/or individuals at familial high-risk (FHR) for schizophrenia. Samples had single nucleotide polymorphism (SNP) array data or genomic DNA, clinical and demographic data, and neuropsychological and/or brain magnetic resonance imaging (MRI) data. Data were subjected to quality control procedures at a central site. RESULTS Sixteen research groups contributed data from 5199 psychosis patients, 4877 controls, and 725 FHR individuals. All participants have relevant demographic data and all patients have relevant clinical data. The sex ratio is 56.5% male and 43.5% female. Significant differences exist between diagnostic groups for premorbid and current IQ (both p<1×10-10). Data from a diversity of neuropsychological tests are available for 92% of participants, and 30% have structural MRI scans (half also have diffusion-weighted MRI scans). SNP data are available for 76% of participants. The ancestry composition is 70% European, 20% East Asian, 7% African, and 3% other. CONCLUSIONS The Consortium is investigating the genetic contribution to brain phenotypes in a schizophrenia sample collection of >10,000 participants. The breadth of data across clinical, genetic, neuropsychological, and MRI modalities provides an important opportunity for elucidating the genetic basis of neural processes underlying schizophrenia.
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Affiliation(s)
- Gabriëlla A. M. Blokland
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic
Medicine, Massachusetts General Hospital, Boston, MA, United States,Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Stanley Center for Psychiatric Research, Broad Institute of MIT and
Harvard, Cambridge, MA, United States
| | - Elisabetta C. del Re
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Department of Psychiatry, Veterans Affairs Boston Healthcare System,
Brockton, MA, United States,Psychiatry Neuroimaging Laboratory, Department of Psychiatry,
Brigham and Women’s Hospital, Boston, MA, United States
| | - Raquelle I. Mesholam-Gately
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Massachusetts Mental Health Center Public Psychiatry Division, Beth
Israel Deaconess Medical Center, Boston, MA, United States
| | - Jorge Jovicich
- Center for Mind/Brain Sciences (CiMEC), University of Trento,
Trento, Italy
| | - Joey W. Trampush
- Center for Psychiatric Neuroscience, The Feinstein Institute for
Medical Research, Division of Northwell Health, Manhasset, NY, United States;
Division of Psychiatry Research, The Zucker Hillside Hospital, Division of Northwell
Health, Glen Oaks, NY, United States; Hofstra Northwell School of Medicine,
Departments of Psychiatry and Molecular Medicine, Hempstead, NY, United States,BrainWorkup, LLC, Los Angeles, CA, United States
| | - Matcheri S. Keshavan
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Massachusetts Mental Health Center Public Psychiatry Division, Beth
Israel Deaconess Medical Center, Boston, MA, United States,University of Pittsburgh Medical Center, Pittsburgh, PA, United
States
| | - Lynn E. DeLisi
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Department of Psychiatry, Veterans Affairs Boston Healthcare System,
Brockton, MA, United States
| | - James T. R. Walters
- Department of Psychological Medicine, Cardiff University, Cardiff,
United Kingdom
| | - Jessica A. Turner
- The Mind Research Network, Albuquerque, NM, United States,Department of Psychology and Neuroscience Institute, Georgia State
University, GA, United States
| | - Anil K. Malhotra
- Center for Psychiatric Neuroscience, The Feinstein Institute for
Medical Research, Division of Northwell Health, Manhasset, NY, United States;
Division of Psychiatry Research, The Zucker Hillside Hospital, Division of Northwell
Health, Glen Oaks, NY, United States; Hofstra Northwell School of Medicine,
Departments of Psychiatry and Molecular Medicine, Hempstead, NY, United States
| | - Todd Lencz
- Center for Psychiatric Neuroscience, The Feinstein Institute for
Medical Research, Division of Northwell Health, Manhasset, NY, United States;
Division of Psychiatry Research, The Zucker Hillside Hospital, Division of Northwell
Health, Glen Oaks, NY, United States; Hofstra Northwell School of Medicine,
Departments of Psychiatry and Molecular Medicine, Hempstead, NY, United States
| | - Martha E. Shenton
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Department of Psychiatry, Veterans Affairs Boston Healthcare System,
Brockton, MA, United States,Psychiatry Neuroimaging Laboratory, Department of Psychiatry,
Brigham and Women’s Hospital, Boston, MA, United States,Department of Radiology, Brigham and Women’s Hospital,
Harvard Medical School, Boston, MA, United States
| | - Aristotle N. Voineskos
- Kimel Family Translational Imaging Genetics Laboratory, Research
Imaging Centre, Campbell Family Mental Health Institute, Centre for Addiction and
Mental Health, Department of Psychiatry, Faculty of Medicine, University of Toronto,
Toronto, ON, Canada,Department of Psychiatry and Institute of Medical Science,
University of Toronto, Toronto, ON, Canada
| | - Dan Rujescu
- Department of Psychiatry, Psychotherapy and Psychosomatics,
University of Halle-Wittenberg, Halle an der Saale, Germany,Department of Psychiatry, Ludwig Maximilians University, Munich,
Germany
| | - Ina Giegling
- Department of Psychiatry, Psychotherapy and Psychosomatics,
University of Halle-Wittenberg, Halle an der Saale, Germany
| | - René S. Kahn
- Brain Centre Rudolf Magnus, Department of Psychiatry, University
Medical Centre Utrecht, Utrecht, The Netherlands
| | - Joshua L. Roffman
- Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,MGH/HST Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA, United States
| | - Daphne J. Holt
- Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,MGH/HST Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA, United States
| | - Stefan Ehrlich
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,MGH/HST Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA, United States,Division of Psychological & Social Medicine and Developmental
Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden,
Germany
| | - Zora Kikinis
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Psychiatry Neuroimaging Laboratory, Department of Psychiatry,
Brigham and Women’s Hospital, Boston, MA, United States
| | - Paola Dazzan
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Robin M. Murray
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Marta Di Forti
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Jimmy Lee
- Institute of Mental Health, Woodbridge Hospital, Singapore
| | - Kang Sim
- Institute of Mental Health, Woodbridge Hospital, Singapore
| | - Max Lam
- Institute of Mental Health, Woodbridge Hospital, Singapore
| | - Rick P. F. Wolthusen
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,MGH/HST Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA, United States,Division of Psychological & Social Medicine and Developmental
Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden,
Germany
| | - Sonja M. C. de Zwarte
- Brain Centre Rudolf Magnus, Department of Psychiatry, University
Medical Centre Utrecht, Utrecht, The Netherlands
| | - Esther Walton
- Division of Psychological & Social Medicine and Developmental
Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden,
Germany
| | - Donna Cosgrove
- The Cognitive Genetics and Cognitive Therapy Group, Department of
Psychology, National University of Ireland, Galway, Ireland
| | - Sinead Kelly
- Neuropsychiatric Genetics Research Group, Department of Psychiatry,
Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland; Trinity
College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland,Laboratory of NeuroImaging, Keck School of Medicine, University of
Southern California, Los Angeles, CA, United States
| | - Nasim Maleki
- Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,MGH/HST Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA, United States
| | - Lisa Osiecki
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic
Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Marco M. Picchioni
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Elvira Bramon
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom,Mental Health Neuroscience Research Department, UCL Division of
Psychiatry, University College London, United Kingdom
| | - Manuela Russo
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Anthony S. David
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Valeria Mondelli
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Antje A. T. S. Reinders
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - M. Aurora Falcone
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Annette M. Hartmann
- Department of Psychiatry, Psychotherapy and Psychosomatics,
University of Halle-Wittenberg, Halle an der Saale, Germany
| | - Bettina Konte
- Department of Psychiatry, Psychotherapy and Psychosomatics,
University of Halle-Wittenberg, Halle an der Saale, Germany
| | - Derek W. Morris
- Cognitive Genetics and Cognitive Therapy Group, Neuroimaging and
Cognitive Genomics (NICOG) Centre and NCBES Galway Neuroscience Centre, School of
Psychology and Discipline of Biochemistry, National University of Ireland, Galway,
Ireland
| | - Michael Gill
- Neuropsychiatric Genetics Research Group, Department of Psychiatry,
Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland; Trinity
College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Aiden P. Corvin
- Neuropsychiatric Genetics Research Group, Department of Psychiatry,
Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland; Trinity
College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Wiepke Cahn
- Brain Centre Rudolf Magnus, Department of Psychiatry, University
Medical Centre Utrecht, Utrecht, The Netherlands
| | - New Fei Ho
- Institute of Mental Health, Woodbridge Hospital, Singapore
| | | | - Richard S. E. Keefe
- Department of Psychiatry and Behavioral Sciences, Duke University
Medical Center, Durham, NC, United States
| | - Randy L. Gollub
- Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,MGH/HST Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA, United States
| | - Dara S. Manoach
- Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,MGH/HST Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA, United States
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, United States,Department of Electrical and Computer Engineering, University of
New Mexico, Albuquerque, NM, United States
| | - S. Charles Schulz
- Department of Psychiatry, University of Minnesota, Minneapolis, MN,
United States
| | - Scott R. Sponheim
- Department of Psychiatry, University of Minnesota, Minneapolis, MN,
United States
| | - Donald C. Goff
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Nathan S. Kline Institute for Psychiatric Research, Department of
Psychiatry, New York University Langone Medical Center, New York, NY, United
States
| | - Stephen L. Buka
- Department of Epidemiology, Brown University, Providence, RI,
United States
| | - Sara Cherkerzian
- Department of Medicine, Division of Women’s Health, Brigham
and Women’s Hospital, Harvard Medical School, Boston, MA, United
States
| | - Heidi W. Thermenos
- Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Massachusetts Mental Health Center Public Psychiatry Division, Beth
Israel Deaconess Medical Center, Boston, MA, United States
| | - Marek Kubicki
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Psychiatry Neuroimaging Laboratory, Department of Psychiatry,
Brigham and Women’s Hospital, Boston, MA, United States,Department of Radiology, Brigham and Women’s Hospital,
Harvard Medical School, Boston, MA, United States,MGH/HST Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, MA, United States
| | - Paul G. Nestor
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Department of Psychiatry, Veterans Affairs Boston Healthcare System,
Brockton, MA, United States,Laboratory of Applied Neuropsychology, University of Massachusetts,
Boston, MA, United States
| | - Erin W. Dickie
- Kimel Family Translational Imaging Genetics Laboratory, Research
Imaging Centre, Campbell Family Mental Health Institute, Centre for Addiction and
Mental Health, Department of Psychiatry, Faculty of Medicine, University of Toronto,
Toronto, ON, Canada
| | - Evangelos Vassos
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Simone Ciufolini
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Tiago Reis Marques
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Nicolas A. Crossley
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,National Institute for Health Research (NIHR) Mental Health
Biomedical Research Centre at South London and Maudsley NHS Foundation Trust,
London, United Kingdom
| | - Shaun M. Purcell
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Stanley Center for Psychiatric Research, Broad Institute of MIT and
Harvard, Cambridge, MA, United States,Department of Psychiatry, Brigham and Women’s Hospital,
Boston, MA, United States,Division of Psychiatric Genomics, Departments of Psychiatry and
Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York,
NY, United States
| | - Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic
Medicine, Massachusetts General Hospital, Boston, MA, United States,Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Stanley Center for Psychiatric Research, Broad Institute of MIT and
Harvard, Cambridge, MA, United States
| | - Neeltje E. M. van Haren
- Brain Centre Rudolf Magnus, Department of Psychiatry, University
Medical Centre Utrecht, Utrecht, The Netherlands
| | - Timothea Toulopoulou
- Institute of Psychiatry, Psychology, and Neuroscience,
King’s College London, London, United Kingdom,Department of Psychology, Bilkent University, Bilkent, Ankara,
Turkey,Department of Psychology, The University of Hong Kong, Pokfulam,
Hong Kong, SAR, China
| | - Gary Donohoe
- Neuropsychiatric Genetics Research Group, Department of Psychiatry,
Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland; Trinity
College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland,Cognitive Genetics and Cognitive Therapy Group, Neuroimaging and
Cognitive Genomics (NICOG) Centre and NCBES Galway Neuroscience Centre, School of
Psychology and Discipline of Biochemistry, National University of Ireland, Galway,
Ireland
| | - Jill M. Goldstein
- Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Department of Medicine, Division of Women’s Health, Brigham
and Women’s Hospital, Harvard Medical School, Boston, MA, United
States,Department of Psychiatry, Brigham and Women’s Hospital,
Boston, MA, United States
| | - Larry J. Seidman
- Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Massachusetts Mental Health Center Public Psychiatry Division, Beth
Israel Deaconess Medical Center, Boston, MA, United States
| | - Robert W. McCarley
- Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Department of Psychiatry, Veterans Affairs Boston Healthcare System,
Brockton, MA, United States
| | - Tracey L. Petryshen
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic
Medicine, Massachusetts General Hospital, Boston, MA, United States,Department of Psychiatry, Massachusetts General Hospital, Boston,
MA, United States,Department of Psychiatry, Harvard Medical School, Boston, MA, United
States,Stanley Center for Psychiatric Research, Broad Institute of MIT and
Harvard, Cambridge, MA, United States
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34
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Viswanath B, Rao NP, Narayanaswamy JC, Sivakumar PT, Kandasamy A, Kesavan M, Mehta UM, Venkatasubramanian G, John JP, Mukherjee O, Purushottam M, Kannan R, Mehta B, Kandavel T, Binukumar B, Saini J, Jayarajan D, Shyamsundar A, Moirangthem S, Vijay Kumar KG, Thirthalli J, Chandra PS, Gangadhar BN, Murthy P, Panicker MM, Bhalla US, Chattarji S, Benegal V, Varghese M, Reddy JYC, Raghu P, Rao M, Jain S. Discovery biology of neuropsychiatric syndromes (DBNS): a center for integrating clinical medicine and basic science. BMC Psychiatry 2018; 18:106. [PMID: 29669557 PMCID: PMC5907468 DOI: 10.1186/s12888-018-1674-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Accepted: 03/21/2018] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND There is emerging evidence that there are shared genetic, environmental and developmental risk factors in psychiatry, that cut across traditional diagnostic boundaries. With this background, the Discovery biology of neuropsychiatric syndromes (DBNS) proposes to recruit patients from five different syndromes (schizophrenia, bipolar disorder, obsessive compulsive disorder, Alzheimer's dementia and substance use disorders), identify those with multiple affected relatives, and invite these families to participate in this study. The families will be assessed: 1) To compare neuro-endophenotype measures between patients, first degree relatives (FDR) and healthy controls., 2) To identify cellular phenotypes which differentiate the groups., 3) To examine the longitudinal course of neuro-endophenotype measures., 4) To identify measures which correlate with outcome, and 5) To create a unified digital database and biorepository. METHODS The identification of the index participants will occur at well-established specialty clinics. The selected individuals will have a strong family history (with at least another affected FDR) of mental illness. We will also recruit healthy controls without family history of such illness. All recruited individuals (N = 4500) will undergo brief clinical assessments and a blood sample will be drawn for isolation of DNA and peripheral blood mononuclear cells (PBMCs). From among this set, a subset of 1500 individuals (300 families and 300 controls) will be assessed on several additional assessments [detailed clinical assessments, endophenotype measures (neuroimaging- structural and functional, neuropsychology, psychophysics-electroencephalography, functional near infrared spectroscopy, eye movement tracking)], with the intention of conducting repeated measurements every alternate year. PBMCs from this set will be used to generate lymphoblastoid cell lines, and a subset of these would be converted to induced pluripotent stem cell lines and also undergo whole exome sequencing. DISCUSSION We hope to identify unique and overlapping brain endophenotypes for major psychiatric syndromes. In a proportion of subjects, we expect these neuro-endophenotypes to progress over time and to predict treatment outcome. Similarly, cellular assays could differentiate cell lines derived from such groups. The repository of biomaterials as well as digital datasets of clinical parameters, will serve as a valuable resource for the broader scientific community who wish to address research questions in the area.
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Affiliation(s)
- Biju Viswanath
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | - Naren P. Rao
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | | | | | - Arun Kandasamy
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | - Muralidharan Kesavan
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | | | | | - John P. John
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | - Odity Mukherjee
- Institute for Stem Cell Biology and Regenerative Medicine (InStem), Bangalore, India
| | - Meera Purushottam
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | - Ramakrishnan Kannan
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | - Bhupesh Mehta
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | - Thennarasu Kandavel
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | - B. Binukumar
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | - Jitender Saini
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | - Deepak Jayarajan
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | - A. Shyamsundar
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | - Sydney Moirangthem
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | - K. G. Vijay Kumar
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | - Jagadisha Thirthalli
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | - Prabha S. Chandra
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | | | - Pratima Murthy
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | - Mitradas M. Panicker
- National Centre for Biological Sciences, Tata Institute of Fundamental Research (NCBS-TIFR), Bangalore, India
| | - Upinder S. Bhalla
- National Centre for Biological Sciences, Tata Institute of Fundamental Research (NCBS-TIFR), Bangalore, India
| | - Sumantra Chattarji
- Institute for Stem Cell Biology and Regenerative Medicine (InStem), Bangalore, India
- National Centre for Biological Sciences, Tata Institute of Fundamental Research (NCBS-TIFR), Bangalore, India
| | - Vivek Benegal
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | - Mathew Varghese
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
| | | | - Padinjat Raghu
- National Centre for Biological Sciences, Tata Institute of Fundamental Research (NCBS-TIFR), Bangalore, India
| | - Mahendra Rao
- Institute for Stem Cell Biology and Regenerative Medicine (InStem), Bangalore, India
| | - Sanjeev Jain
- National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India
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35
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Smeland OB, Frei O, Kauppi K, Hill WD, Li W, Wang Y, Krull F, Bettella F, Eriksen JA, Witoelar A, Davies G, Fan CC, Thompson WK, Lam M, Lencz T, Chen CH, Ueland T, Jönsson EG, Djurovic S, Deary IJ, Dale AM, Andreassen OA. Identification of Genetic Loci Jointly Influencing Schizophrenia Risk and the Cognitive Traits of Verbal-Numerical Reasoning, Reaction Time, and General Cognitive Function. JAMA Psychiatry 2017; 74:1065-1075. [PMID: 28746715 PMCID: PMC5710474 DOI: 10.1001/jamapsychiatry.2017.1986] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
IMPORTANCE Schizophrenia is associated with widespread cognitive impairments. Although cognitive deficits are one of the factors most strongly associated with functional outcome in schizophrenia, current treatment strategies largely fail to ameliorate these impairments. To develop more efficient treatment strategies in patients with schizophrenia, a better understanding of the pathogenesis of these cognitive deficits is needed. Accumulating evidence indicates that genetic risk of schizophrenia may contribute to cognitive dysfunction. OBJECTIVE To identify genomic regions jointly influencing schizophrenia and the cognitive domains of reaction time and verbal-numerical reasoning, as well as general cognitive function, a phenotype that captures the shared variation in performance across cognitive domains. DESIGN, SETTING, AND PARTICIPANTS Combining data from genome-wide association studies from multiple phenotypes using conditional false discovery rate analysis provides increased power to discover genetic variants and could elucidate shared molecular genetic mechanisms. Data from the following genome-wide association studies, published from July 24, 2014, to January 17, 2017, were combined: schizophrenia in the Psychiatric Genomics Consortium cohort (n = 79 757 [cases, 34 486; controls, 45 271]); verbal-numerical reasoning (n = 36 035) and reaction time (n = 111 483) in the UK Biobank cohort; and general cognitive function in CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) (n = 53 949) and COGENT (Cognitive Genomics Consortium) (n = 27 888). MAIN OUTCOMES AND MEASURES Genetic loci identified by conditional false discovery rate analysis. Brain messenger RNA expression and brain expression quantitative trait locus functionality were determined. RESULTS Among the participants in the genome-wide association studies, 21 loci jointly influencing schizophrenia and cognitive traits were identified: 2 loci shared between schizophrenia and verbal-numerical reasoning, 6 loci shared between schizophrenia and reaction time, and 14 loci shared between schizophrenia and general cognitive function. One locus was shared between schizophrenia and 2 cognitive traits and represented the strongest shared signal detected (nearest gene TCF20; chromosome 22q13.2), and was shared between schizophrenia (z score, 5.01; P = 5.53 × 10-7), general cognitive function (z score, -4.43; P = 9.42 × 10-6), and verbal-numerical reasoning (z score, -5.43; P = 5.64 × 10-8). For 18 loci, schizophrenia risk alleles were associated with poorer cognitive performance. The implicated genes are expressed in the developmental and adult human brain. Replicable expression quantitative trait locus functionality was identified for 4 loci in the adult human brain. CONCLUSIONS AND RELEVANCE The discovered loci improve the understanding of the common genetic basis underlying schizophrenia and cognitive function, suggesting novel molecular genetic mechanisms.
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Affiliation(s)
- Olav B. Smeland
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway,Department of Neuroscience, University of California San Diego, La Jolla
| | - Oleksandr Frei
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Karolina Kauppi
- Department of Radiology, University of California San Diego, La Jolla,Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla,Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - W. David Hill
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom,Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Wen Li
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Yunpeng Wang
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Department of Radiology, University of California San Diego, La Jolla,Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla
| | - Florian Krull
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Francesco Bettella
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Jon A. Eriksen
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Aree Witoelar
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Gail Davies
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom,Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Chun C. Fan
- Department of Radiology, University of California San Diego, La Jolla,Department of Cognitive Science, University of California, San Diego, La Jolla
| | - Wesley K. Thompson
- Department of Family Medicine and Public Health, University of California, San Diego, La Jolla,Institute of Biological Psychiatry, Roskilde, Denmark
| | - Max Lam
- Institute of Mental Health, Singapore
| | - Todd Lencz
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, New York,Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York,Department of Psychiatry, Hofstra Northwell School of Medicine, Hempstead, New York
| | - Chi-Hua Chen
- Department of Radiology, University of California San Diego, La Jolla,Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla
| | - Torill Ueland
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Department of Psychology, University of Oslo, Olso, Norway
| | - Erik G. Jönsson
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway,Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet, Stockholm, Sweden
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway,NORMENT, KG Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Ian J. Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom,Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Anders M. Dale
- Department of Neuroscience, University of California San Diego, La Jolla,Department of Radiology, University of California San Diego, La Jolla,Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla,Department of Psychiatry, University of California, San Diego, La Jolla
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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36
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Blokland GAM, Mesholam-Gately RI, Toulopoulou T, del Re EC, Lam M, DeLisi LE, Donohoe G, Walters JTR, Seidman LJ, Petryshen TL. Heritability of Neuropsychological Measures in Schizophrenia and Nonpsychiatric Populations: A Systematic Review and Meta-analysis. Schizophr Bull 2017; 43:788-800. [PMID: 27872257 PMCID: PMC5472145 DOI: 10.1093/schbul/sbw146] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Schizophrenia is characterized by neuropsychological deficits across many cognitive domains. Cognitive phenotypes with high heritability and genetic overlap with schizophrenia liability can help elucidate the mechanisms leading from genes to psychopathology. We performed a meta-analysis of 170 published twin and family heritability studies of >800 000 nonpsychiatric and schizophrenia subjects to accurately estimate heritability across many neuropsychological tests and cognitive domains. The proportion of total variance of each phenotype due to additive genetic effects (A), shared environment (C), and unshared environment and error (E), was calculated by averaging A, C, and E estimates across studies and weighting by sample size. Heritability ranged across phenotypes, likely due to differences in genetic and environmental effects, with the highest heritability for General Cognitive Ability (32%-67%), Verbal Ability (43%-72%), Visuospatial Ability (20%-80%), and Attention/Processing Speed (28%-74%), while the lowest heritability was observed for Executive Function (20%-40%). These results confirm that many cognitive phenotypes are under strong genetic influences. Heritability estimates were comparable in nonpsychiatric and schizophrenia samples, suggesting that environmental factors and illness-related moderators (eg, medication) do not substantially decrease heritability in schizophrenia samples, and that genetic studies in schizophrenia samples are informative for elucidating the genetic basis of cognitive deficits. Substantial genetic overlap between cognitive phenotypes and schizophrenia liability (average rg = -.58) in twin studies supports partially shared genetic etiology. It will be important to conduct comparative studies in well-powered samples to determine whether the same or different genes and genetic variants influence cognition in schizophrenia patients and the general population.
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Affiliation(s)
- Gabriëlla A. M. Blokland
- Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry and Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA;,Department of Psychiatry, Harvard Medical School, Boston, MA;,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Raquelle I. Mesholam-Gately
- Department of Psychiatry, Harvard Medical School, Boston, MA;,Commonwealth Research Center, Harvard Medical School, Boston, MA;,Massachusetts Mental Health Center Public Psychiatry Division of the Beth Israel Deaconess Medical Center, Boston, MA
| | - Timothea Toulopoulou
- Psychology Department, Bilkent University, Ankara, Turkey;,Department of Psychology, University of Hong Kong, Pokfulam, Hong Kong;,Department of Psychosis Studies, Institute of Psychiatry, King’s College London, London, UK
| | - Elisabetta C. del Re
- Department of Psychiatry, Harvard Medical School, Boston, MA;,Clinical Neuroscience Division, Laboratory of Neuroscience, Department of Psychiatry, Veterans Affairs Boston Healthcare System, Brockton, MA
| | - Max Lam
- Institute of Mental Health, Woodbridge Hospital, Singapore
| | - Lynn E. DeLisi
- Department of Psychiatry, Harvard Medical School, Boston, MA;,Clinical Neuroscience Division, Laboratory of Neuroscience, Department of Psychiatry, Veterans Affairs Boston Healthcare System, Brockton, MA
| | - Gary Donohoe
- School of Psychology, National University of Ireland, Galway, Ireland;,Neuropsychiatric Genetics Group, Department of Psychiatry and Trinity College Institute of Neuroscience, Trinity College, Dublin, Ireland
| | - James T. R. Walters
- Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, UK
| | | | - Larry J. Seidman
- Department of Psychiatry, Harvard Medical School, Boston, MA;,Commonwealth Research Center, Harvard Medical School, Boston, MA;,Massachusetts Mental Health Center Public Psychiatry Division of the Beth Israel Deaconess Medical Center, Boston, MA
| | - Tracey L. Petryshen
- Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry and Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA;,Department of Psychiatry, Harvard Medical School, Boston, MA;,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
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37
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Papiol S, Popovic D, Keeser D, Hasan A, Schneider-Axmann T, Degenhardt F, Rossner MJ, Bickeböller H, Schmitt A, Falkai P, Malchow B. Polygenic risk has an impact on the structural plasticity of hippocampal subfields during aerobic exercise combined with cognitive remediation in multi-episode schizophrenia. Transl Psychiatry 2017; 7:e1159. [PMID: 28654095 PMCID: PMC5537649 DOI: 10.1038/tp.2017.131] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 05/12/2017] [Accepted: 05/17/2017] [Indexed: 02/06/2023] Open
Abstract
Preliminary studies suggest that, besides improving cognition, aerobic exercise might increase hippocampal volume in schizophrenia patients; however, results are not consistent. Individual mechanisms of volume changes are unknown but might be connected to the load of risk genes. Genome-wide association studies have uncovered the polygenic architecture of schizophrenia. The secondary analysis presented here aimed to determine the modulatory role of schizophrenia polygenic risk scores (PRSs) on volume changes in the total hippocampus and cornu ammonis (CA) 1, CA2/3, CA4/dentate gyrus (DG) and subiculum over time. We studied 20 multi-episode schizophrenia patients and 23 healthy controls who performed aerobic exercise (endurance training) combined with cognitive remediation for 3 months and 21 multi-episode schizophrenia patients allocated to a control intervention (table soccer) combined with cognitive remediation. Magnetic resonance imaging-based assessments were performed at baseline and after 3 months with FreeSurfer. No effects of PRSs were found on total hippocampal volume change. Subfield analyses showed that the volume changes between baseline and 3 months in the left CA4/DG were significantly influenced by PRSs in schizophrenia patients performing aerobic exercise. A larger genetic risk burden was associated with a less pronounced volume increase or a decrease in volume over the course of the exercise intervention. Results of exploratory enrichment analyses reinforced the notion of genetic risk factors modulating biological processes tightly related to synaptic ion channel activity, calcium signaling, glutamate signaling and regulation of cell morphogenesis. We hypothesize that a high polygenic risk may negatively influence neuroplasticity in CA4/DG during aerobic exercise in schizophrenia.
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Affiliation(s)
- S Papiol
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany
- Institute of Psychiatric Phenomics and Genomics (IPPG), Medical Center of the University of Munich, Munich, Germany
| | - D Popovic
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany
| | - D Keeser
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany
- Institute of Clinical Radiology, Ludwig Maximilian University Munich, Munich, Germany
| | - A Hasan
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany
| | - T Schneider-Axmann
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany
| | - F Degenhardt
- Institute of Human Genetics, University of Bonn, Bonn, Germany
| | - M J Rossner
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany
| | - H Bickeböller
- Department of Genetic Epidemiology, University Medical Center, Georg-August-Universität Göttingen, Göttingen, Germany
| | - A Schmitt
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany
- Laboratory of Neuroscience, Institute of Psychiatry, University of Sao Paulo, Sao Paulo, Brazil
| | - P Falkai
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany
| | - B Malchow
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany
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38
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Murante T, Cohen CI. Cognitive Functioning in Older Adults With Schizophrenia. FOCUS: JOURNAL OF LIFE LONG LEARNING IN PSYCHIATRY 2017; 15:26-34. [PMID: 31975837 DOI: 10.1176/appi.focus.20160032] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Cognitive deficits are thought to be a core feature in schizophrenia and have been found to be strongly associated with impairments in functioning. It is estimated that more than 70% of patients with schizophrenia have cognitive impairment. The aim of this article is to critically review the emerging literature on cognition in older adults with schizophrenia. Specifically, we address the following questions: Are there differences in cognitive functioning between older adults with schizophrenia and their healthy age peers as well as with younger people with schizophrenia? What are the factors associated with cognitive deficits and their interaction over time? What are the life course trajectories of cognitive deficits, especially in later life? Are older adults with schizophrenia more likely to develop dementia, and, if so, does it differ from other dementias? Are there pharmacological and psychosocial interventions that can successfully treat cognitive deficits in older adults with schizophrenia?
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Affiliation(s)
- Tessa Murante
- Dr. Murante is with the Psychiatric Residency Training Program and Dr. Cohen is with the Division of Geriatric Psychiatry, SUNY Downstate Medical College, Brooklyn, New York. Send correspondence to Dr. Cohen (e-mail: )
| | - Carl I Cohen
- Dr. Murante is with the Psychiatric Residency Training Program and Dr. Cohen is with the Division of Geriatric Psychiatry, SUNY Downstate Medical College, Brooklyn, New York. Send correspondence to Dr. Cohen (e-mail: )
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39
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Whalley HC, Adams MJ, Hall LS, Clarke TK, Fernandez-Pujals AM, Gibson J, Wigmore E, Hafferty J, Hagenaars SP, Davies G, Campbell A, Hayward C, Lawrie SM, Porteous DJ, Deary IJ, McIntosh AM. Dissection of major depressive disorder using polygenic risk scores for schizophrenia in two independent cohorts. Transl Psychiatry 2016; 6:e938. [PMID: 27801894 PMCID: PMC5314119 DOI: 10.1038/tp.2016.207] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 08/30/2016] [Indexed: 12/20/2022] Open
Abstract
Major depressive disorder (MDD) is known for its substantial clinical and suspected causal heterogeneity. It is characterized by low mood, psychomotor slowing and increased levels of the personality trait neuroticism; factors also associated with schizophrenia (SCZ). It is possible that some cases of MDD may have a substantial genetic loading for SCZ. The presence of SCZ-like MDD subgroups would be indicated by an interaction between MDD status and polygenic risk of SCZ on cognitive, personality and mood measures. Here, we hypothesized that higher SCZ polygenic risk would define larger MDD case-control differences in cognitive ability, and smaller differences in distress and neuroticism. Polygenic risk scores (PRSs) for SCZ and their association with cognitive variables, neuroticism, mood and psychological distress were estimated in a large population-based cohort (Generation Scotland: Scottish Family Health Study, GS:SFHS). The individuals were divided into those with, and without, depression (n=2587 and n=16 764, respectively) to test for the interactions between MDD status and schizophrenia risk. Replication was sought in UK Biobank (UKB; n=6049 and n=27 476 cases and controls, respectively). In both the cohorts, we found significant interactions between SCZ-PRS and MDD status for measures of psychological distress (βGS=-0.04, PGS=0.014 and βUKB=-0.09, PUKB⩽0.001 for GS:SFHS and UKB, respectively) and neuroticism (βGS=-0.04, PGS=0.002 and βUKB=-0.06, PUKB=0.023). In both the cohorts, there was a reduction of case-control differences on a background of higher genetic risk of SCZ. These findings suggest that depression on a background of high genetic risk for SCZ may show attenuated associations with distress and neuroticism. This may represent a causally distinct form of MDD more closely related to SCZ.
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Affiliation(s)
- H C Whalley
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - M J Adams
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - L S Hall
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - T-K Clarke
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - A M Fernandez-Pujals
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - J Gibson
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - E Wigmore
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - J Hafferty
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - S P Hagenaars
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - G Davies
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - A Campbell
- Centre for Genetics and Molecular Medicine, Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh, UK
| | - C Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - S M Lawrie
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - D J Porteous
- Centre for Genetics and Molecular Medicine, Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh, UK
| | - I J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - A M McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
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