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Tiego J, Thompson K, Arnatkeviciute A, Hawi Z, Finlay A, Sabaroedin K, Johnson B, Bellgrove MA, Fornito A. Dissecting Schizotypy and Its Association With Cognition and Polygenic Risk for Schizophrenia in a Nonclinical Sample. Schizophr Bull 2023; 49:1217-1228. [PMID: 36869759 PMCID: PMC10483465 DOI: 10.1093/schbul/sbac016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Schizotypy is a multidimensional construct that captures a continuum of risk for developing schizophrenia-spectrum psychopathology. Existing 3-factor models of schizotypy, consisting of positive, negative, and disorganized dimensions have yielded mixed evidence of genetic continuity with schizophrenia using polygenic risk scores. Here, we propose an approach that involves splitting positive and negative schizotypy into more specific subdimensions that are phenotypically continuous with distinct positive symptoms and negative symptoms recognized in clinical schizophrenia. We used item response theory to derive high-precision estimates of psychometric schizotypy using 251 self-report items obtained from a non-clinical sample of 727 (424 females) adults. These subdimensions were organized hierarchically using structural equation modeling into 3 empirically independent higher-order dimensions enabling associations with polygenic risk for schizophrenia to be examined at different levels of phenotypic generality and specificity. Results revealed that polygenic risk for schizophrenia was associated with variance specific to delusional experiences (γ = 0.093, P = .001) and reduced social interest and engagement (γ = 0.076, P = .020), and these effects were not mediated via the higher-order general, positive, or negative schizotypy factors. We further fractionated general intellectual functioning into fluid and crystallized intelligence in 446 (246 females) participants that underwent onsite cognitive assessment. Polygenic risk scores explained 3.6% of the variance in crystallized intelligence. Our precision phenotyping approach could be used to enhance the etiologic signal in future genetic association studies and improve the detection and prevention of schizophrenia-spectrum psychopathology.
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Affiliation(s)
- Jeggan Tiego
- Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC 3800, Australia
- School of Psychological Sciences, Monash University, Clayton, VIC 3800, Australia
- Monash Biomedical Imaging, Monash University, 770 Blackburn Rd, Clayton, VIC 3800, Australia
| | - Kate Thompson
- Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC 3800, Australia
- School of Psychological Sciences, Monash University, Clayton, VIC 3800, Australia
- Monash Biomedical Imaging, Monash University, 770 Blackburn Rd, Clayton, VIC 3800, Australia
| | - Aurina Arnatkeviciute
- Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC 3800, Australia
- School of Psychological Sciences, Monash University, Clayton, VIC 3800, Australia
| | - Ziarih Hawi
- Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC 3800, Australia
- School of Psychological Sciences, Monash University, Clayton, VIC 3800, Australia
| | - Amy Finlay
- Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC 3800, Australia
- School of Psychological Sciences, Monash University, Clayton, VIC 3800, Australia
| | - Kristina Sabaroedin
- Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC 3800, Australia
- School of Psychological Sciences, Monash University, Clayton, VIC 3800, Australia
- Monash Biomedical Imaging, Monash University, 770 Blackburn Rd, Clayton, VIC 3800, Australia
| | - Beth Johnson
- Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC 3800, Australia
- School of Psychological Sciences, Monash University, Clayton, VIC 3800, Australia
| | - Mark A Bellgrove
- Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC 3800, Australia
- School of Psychological Sciences, Monash University, Clayton, VIC 3800, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC 3800, Australia
- School of Psychological Sciences, Monash University, Clayton, VIC 3800, Australia
- Monash Biomedical Imaging, Monash University, 770 Blackburn Rd, Clayton, VIC 3800, Australia
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2
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Knott R, Johnson BP, Tiego J, Mellahn O, Finlay A, Kallady K, Kouspos M, Mohanakumar Sindhu VP, Hawi Z, Arnatkeviciute A, Chau T, Maron D, Mercieca EC, Furley K, Harris K, Williams K, Ure A, Fornito A, Gray K, Coghill D, Nicholson A, Phung D, Loth E, Mason L, Murphy D, Buitelaar J, Bellgrove MA. The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project design and methodologies: a dimensional approach to understanding neurobiological and genetic aetiology. Mol Autism 2021; 12:55. [PMID: 34353377 PMCID: PMC8340366 DOI: 10.1186/s13229-021-00457-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 07/05/2021] [Indexed: 11/20/2022] Open
Abstract
Background ASD and ADHD are prevalent neurodevelopmental disorders that frequently co-occur and have strong evidence for a degree of shared genetic aetiology. Behavioural and neurocognitive heterogeneity in ASD and ADHD has hampered attempts to map the underlying genetics and neurobiology, predict intervention response, and improve diagnostic accuracy. Moving away from categorical conceptualisations of psychopathology to a dimensional approach is anticipated to facilitate discovery of data-driven clusters and enhance our understanding of the neurobiological and genetic aetiology of these conditions. The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project is one of the first large-scale, family-based studies to take a truly transdiagnostic approach to ASD and ADHD. Using a comprehensive phenotyping protocol capturing dimensional traits central to ASD and ADHD, the MAGNET project aims to identify data-driven clusters across ADHD-ASD spectra using deep phenotyping of symptoms and behaviours; investigate the degree of familiality for different dimensional ASD-ADHD phenotypes and clusters; and map the neurocognitive, brain imaging, and genetic correlates of these data-driven symptom-based clusters. Methods The MAGNET project will recruit 1,200 families with children who are either typically developing, or who display elevated ASD, ADHD, or ASD-ADHD traits, in addition to affected and unaffected biological siblings of probands, and parents. All children will be comprehensively phenotyped for behavioural symptoms, comorbidities, neurocognitive and neuroimaging traits and genetics. Conclusion The MAGNET project will be the first large-scale family study to take a transdiagnostic approach to ASD-ADHD, utilising deep phenotyping across behavioural, neurocognitive, brain imaging and genetic measures. Supplementary Information The online version contains supplementary material available at 10.1186/s13229-021-00457-3.
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Affiliation(s)
- Rachael Knott
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia.
| | - Beth P Johnson
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Jeggan Tiego
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Olivia Mellahn
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Amy Finlay
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Kathryn Kallady
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Maria Kouspos
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Vishnu Priya Mohanakumar Sindhu
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Ziarih Hawi
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Aurina Arnatkeviciute
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Tracey Chau
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Dalia Maron
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Emily-Clare Mercieca
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Kirsten Furley
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Katrina Harris
- Department of Paediatrics, Monash University, Melbourne, VIC, 3800, Australia.,Department of Developmental Paediatrics, Monash Children's Hospital, 246 Clayton Rd, Clayton, VIC, 3168, Australia
| | - Katrina Williams
- Department of Paediatrics, Monash University, Melbourne, VIC, 3800, Australia.,Department of Developmental Paediatrics, Monash Children's Hospital, 246 Clayton Rd, Clayton, VIC, 3168, Australia.,Murdoch Children's Research Institute, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, Australia.,Department of Paediatrics, Faculty of Medicine, Dentistry and Health Sciences, Royal Children's Hospital, 50 Flemington Road, Parkville, VIC, 3052, Australia
| | - Alexandra Ure
- Department of Paediatrics, Monash University, Melbourne, VIC, 3800, Australia.,Murdoch Children's Research Institute, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, Australia.,Department of Mental Health, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, Australia.,Neurodevelopment and Disability Research, Murdoch Children's Research Institute, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
| | - Kylie Gray
- Centre for Educational Development, Appraisal, and Research, University of Warwick, Coventry, CV4 7AL, UK.,Department of Psychiatry, School of Clinical Sciences, Monash University, 246 Clayton Rd, Melbourne, VIC, 3168, Australia
| | - David Coghill
- Department of Paediatrics, Faculty of Medicine, Dentistry and Health Sciences, Royal Children's Hospital, 50 Flemington Road, Parkville, VIC, 3052, Australia.,Department of Mental Health, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, Australia.,Neurodevelopment and Disability Research, Murdoch Children's Research Institute, Royal Children's Hospital, 50 Flemington Rd, Parkville, VIC, 3052, Australia
| | - Ann Nicholson
- Faculty of Information and Technology, Monash University, Melbourne, VIC, 3800, Australia
| | - Dinh Phung
- Faculty of Information and Technology, Monash University, Melbourne, VIC, 3800, Australia
| | - Eva Loth
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK
| | - Luke Mason
- Centre for Brain and Cognitive Development, Birkbeck, University of London, Henry Welcome Building, Malet Street, London, WC1E 7HX, UK
| | - Declan Murphy
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK
| | - Jan Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Kapittelweg 29, 6525 EN, Nijmegen, The Netherlands
| | - Mark A Bellgrove
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, 18 Innovation Walk, Melbourne, VIC, 3800, Australia
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Waszczuk MA, Eaton NR, Krueger RF, Shackman AJ, Waldman ID, Zald DH, Lahey BB, Patrick CJ, Conway CC, Ormel J, Hyman SE, Fried EI, Forbes MK, Docherty AR, Althoff RR, Bach B, Chmielewski M, DeYoung CG, Forbush KT, Hallquist M, Hopwood CJ, Ivanova MY, Jonas KG, Latzman RD, Markon KE, Mullins-Sweatt SN, Pincus AL, Reininghaus U, South SC, Tackett JL, Watson D, Wright AGC, Kotov R. Redefining phenotypes to advance psychiatric genetics: Implications from hierarchical taxonomy of psychopathology. JOURNAL OF ABNORMAL PSYCHOLOGY 2020; 129:143-161. [PMID: 31804095 PMCID: PMC6980897 DOI: 10.1037/abn0000486] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Genetic discovery in psychiatry and clinical psychology is hindered by suboptimal phenotypic definitions. We argue that the hierarchical, dimensional, and data-driven classification system proposed by the Hierarchical Taxonomy of Psychopathology (HiTOP) consortium provides a more effective approach to identifying genes that underlie mental disorders, and to studying psychiatric etiology, than current diagnostic categories. Specifically, genes are expected to operate at different levels of the HiTOP hierarchy, with some highly pleiotropic genes influencing higher order psychopathology (e.g., the general factor), whereas other genes conferring more specific risk for individual spectra (e.g., internalizing), subfactors (e.g., fear disorders), or narrow symptoms (e.g., mood instability). We propose that the HiTOP model aligns well with the current understanding of the higher order genetic structure of psychopathology that has emerged from a large body of family and twin studies. We also discuss the convergence between the HiTOP model and findings from recent molecular studies of psychopathology indicating broad genetic pleiotropy, such as cross-disorder SNP-based shared genetic covariance and polygenic risk scores, and we highlight molecular genetic studies that have successfully redefined phenotypes to enhance precision and statistical power. Finally, we suggest how to integrate a HiTOP approach into future molecular genetic research, including quantitative and hierarchical assessment tools for future data-collection and recommendations concerning phenotypic analyses. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Bo Bach
- Centre of Excellence on Personality Disorder
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Luningham JM, McArtor DB, Hendriks AM, van Beijsterveldt CEM, Lichtenstein P, Lundström S, Larsson H, Bartels M, Boomsma DI, Lubke GH. Data Integration Methods for Phenotype Harmonization in Multi-Cohort Genome-Wide Association Studies With Behavioral Outcomes. Front Genet 2020; 10:1227. [PMID: 31921287 PMCID: PMC6914843 DOI: 10.3389/fgene.2019.01227] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 11/05/2019] [Indexed: 01/03/2023] Open
Abstract
Parallel meta-analysis is a popular approach for increasing the power to detect genetic effects in genome-wide association studies across multiple cohorts. Consortia studying the genetics of behavioral phenotypes are oftentimes faced with systematic differences in phenotype measurement across cohorts, introducing heterogeneity into the meta-analysis and reducing statistical power. This study investigated integrative data analysis (IDA) as an approach for jointly modeling the phenotype across multiple datasets. We put forth a bi-factor integration model (BFIM) that provides a single common phenotype score and accounts for sources of study-specific variability in the phenotype. In order to capitalize on this modeling strategy, a phenotype reference panel was utilized as a supplemental sample with complete data on all behavioral measures. A simulation study showed that a mega-analysis of genetic variant effects in a BFIM were more powerful than meta-analysis of genetic effects on a cohort-specific sum score of items. Saving the factor scores from the BFIM and using those as the outcome in meta-analysis was also more powerful than the sum score in most simulation conditions, but a small degree of bias was introduced by this approach. The reference panel was necessary to realize these power gains. An empirical demonstration used the BFIM to harmonize aggression scores in 9-year old children across the Netherlands Twin Register and the Child and Adolescent Twin Study in Sweden, providing a template for application of the BFIM to a range of different phenotypes. A supplemental data collection in the Netherlands Twin Register served as a reference panel for phenotype modeling across both cohorts. Our results indicate that model-based harmonization for the study of complex traits is a useful step within genetic consortia.
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Affiliation(s)
- Justin M Luningham
- Department of Psychology, University of Notre Dame, Notre Dame, IN, United States
| | - Daniel B McArtor
- Department of Psychology, University of Notre Dame, Notre Dame, IN, United States
| | - Anne M Hendriks
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Faculty of Behavioural and Movement Sciences, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Catharina E M van Beijsterveldt
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Faculty of Behavioural and Movement Sciences, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Paul Lichtenstein
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Sebastian Lundström
- Gillberg Neuropsychiatry Centre, University of Gothenburg, Gothenburg, Sweden
| | - Henrik Larsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Meike Bartels
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Faculty of Behavioural and Movement Sciences, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
| | - Dorret I Boomsma
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Faculty of Behavioural and Movement Sciences, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
| | - Gitta H Lubke
- Department of Psychology, University of Notre Dame, Notre Dame, IN, United States
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Kaparounaki CK, Koraka CA, Kotsi ES, Ntziovara AMP, Kyriakidis GC, Fountoulakis KN. Greek university student's attitudes and beliefs concerning mental illness and its treatment. Int J Soc Psychiatry 2019; 65:515-526. [PMID: 31311387 DOI: 10.1177/0020764019864122] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
INTRODUCTION Stigma concerning mental disorder is a widespread phenomenon concerning the beliefs and attitudes of the public toward mental patients with a significant negative impact on state policy and the outcome of the patients. MATERIAL AND METHODS The study included 1,363 students of the Aristotle University of Thessaloniki. The survey was based on an Internet-based electronic platform. The statistical analysis included analysis of variance (ANOVA) and Yates corrected chi-square test. RESULTS Approximately, 87% of students responded that they know what mental disorder is, 70% were informed from the Internet, 30% consider mental patients responsible for their condition, more than 95% blame the way they were raised and almost 60% consider mental disorder to be heritable. Only a minority feel negatively with a mental patient around and close to 80% would socialize with them. More than 80% accept the need for psychiatric medication treatment but the opinion is split concerning compulsory treatment, and one-third consider medication to be harmful. DISCUSSION The results of this study suggest that most students believe they know much about mental illness; however, overall their responses are contradictory. They reply with confidence although they are informed mainly by the media and the Internet in an unreliable way. A number of factors including gender, specific school or personal experience of mental disorder in the family seem to influence the result. A combined educational plus contact might be necessary to reduce stigma, since education alone seems to exert a weak effect.
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Affiliation(s)
- Chrysi K Kaparounaki
- 1 Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Chrysoula A Koraka
- 1 Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Eleni S Kotsi
- 1 Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Anna-Maria P Ntziovara
- 1 Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Gerasimos C Kyriakidis
- 1 Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Konstantinos N Fountoulakis
- 2 3rd Department of Psychiatry, Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Jones HJ, Heron J, Hammerton G, Stochl J, Jones PB, Cannon M, Smith GD, Holmans P, Lewis G, Linden DEJ, O'Donovan MC, Owen MJ, Walters J, Zammit S. Investigating the genetic architecture of general and specific psychopathology in adolescence. Transl Psychiatry 2018; 8:145. [PMID: 30089819 PMCID: PMC6082910 DOI: 10.1038/s41398-018-0204-9] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Revised: 06/12/2018] [Accepted: 06/15/2018] [Indexed: 01/08/2023] Open
Abstract
Whilst associations between polygenic risk scores (PRSs) for schizophrenia and various phenotypic outcomes have been reported, an understanding of developmental pathways can only be gained by modelling comorbidity across psychopathology. We examine how genetic risk for schizophrenia relates to adolescent psychosis-related and internalizing psychopathology using a latent modelling approach, and compare this to genetic risk for other psychiatric disorders, to gain a more comprehensive understanding of the developmental pathways at this age. PRSs for schizophrenia, major depressive disorder, neuroticism and bipolar disorder were generated for individuals in the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort. Multivariate linear regression was used to examine the relationships of these PRSs with psychopathology factors modelled within (i) a correlated factors structure and (ii) a bifactor structure. The schizophrenia PRS was associated with an increase in factors describing psychotic experiences, negative dimension, depression and anxiety, but, when modelling a general psychopathology factor based on these measures, specific effects above this persisted only for the negative dimension. Similar factor relationships were observed for the neuroticism PRS, with a (weak) specific effect only for anxiety once modelling general psychopathology. Psychopathology during adolescence can be described by a general psychopathology construct that captures common variance as well as by specific constructs capturing remaining non-shared variance. Schizophrenia risk genetic variants identified through genome-wide association studies mainly index negative rather than positive symptom psychopathology during adolescence. This has potentially important implications both for research and risk prediction in high-risk samples.
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Affiliation(s)
- Hannah J Jones
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK.
- NIHR Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust, University of Bristol, Bristol, UK.
| | - Jon Heron
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Gemma Hammerton
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jan Stochl
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Peter B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Mary Cannon
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - George Davey Smith
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
| | - Peter Holmans
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Glyn Lewis
- Division of Psychiatry, University College London, London, UK
| | - David E J Linden
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Michael C O'Donovan
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Michael J Owen
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - James Walters
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Stanley Zammit
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust, University of Bristol, Bristol, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
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7
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Xu MK, Gaysina D, Tsonaka R, Morin AJS, Croudace TJ, Barnett JH, Houwing-Duistermaat J, Richards M, Jones PB. Monoamine Oxidase A ( MAOA) Gene and Personality Traits from Late Adolescence through Early Adulthood: A Latent Variable Investigation. Front Psychol 2017; 8:1736. [PMID: 29075213 PMCID: PMC5641687 DOI: 10.3389/fpsyg.2017.01736] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Accepted: 09/20/2017] [Indexed: 11/13/2022] Open
Abstract
Very few molecular genetic studies of personality traits have used longitudinal phenotypic data, therefore molecular basis for developmental change and stability of personality remains to be explored. We examined the role of the monoamine oxidase A gene (MAOA) on extraversion and neuroticism from adolescence to adulthood, using modern latent variable methods. A sample of 1,160 male and 1,180 female participants with complete genotyping data was drawn from a British national birth cohort, the MRC National Survey of Health and Development (NSHD). The predictor variable was based on a latent variable representing genetic variations of the MAOA gene measured by three SNPs (rs3788862, rs5906957, and rs979606). Latent phenotype variables were constructed using psychometric methods to represent cross-sectional and longitudinal phenotypes of extraversion and neuroticism measured at ages 16 and 26. In males, the MAOA genetic latent variable (AAG) was associated with lower extraversion score at age 16 (β = −0.167; CI: −0.289, −0.045; p = 0.007, FDRp = 0.042), as well as greater increase in extraversion score from 16 to 26 years (β = 0.197; CI: 0.067, 0.328; p = 0.003, FDRp = 0.036). No genetic association was found for neuroticism after adjustment for multiple testing. Although, we did not find statistically significant associations after multiple testing correction in females, this result needs to be interpreted with caution due to issues related to x-inactivation in females. The latent variable method is an effective way of modeling phenotype- and genetic-based variances and may therefore improve the methodology of molecular genetic studies of complex psychological traits.
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Affiliation(s)
- Man K Xu
- Faculty of Psychology and Educational Sciences, Welten Institute, Open University of the Netherlands, Heerlen, Netherlands.,Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, Leiden, Netherlands.,Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.,Department of Psychology, Education, and Child Studies, Erasmus University Rotterdam, Rotterdam, Netherlands
| | - Darya Gaysina
- EDGE Lab, School of Psychology, University of Sussex, Brighton, United Kingdom
| | - Roula Tsonaka
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, Leiden, Netherlands
| | - Alexandre J S Morin
- Substantive-Methodological Synergy Research Laboratory, Department of Psychology, Concordia University, Montreal, QC, Canada
| | - Tim J Croudace
- School of Nursing and Health Sciences, University of Dundee, Dundee, United Kingdom
| | | | | | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, London, United Kingdom
| | - Peter B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
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8
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St Clair MC, Neufeld S, Jones PB, Fonagy P, Bullmore ET, Dolan RJ, Moutoussis M, Toseeb U, Goodyer IM. Characterising the latent structure and organisation of self-reported thoughts, feelings and behaviours in adolescents and young adults. PLoS One 2017; 12:e0175381. [PMID: 28403164 PMCID: PMC5389661 DOI: 10.1371/journal.pone.0175381] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 03/24/2017] [Indexed: 01/01/2023] Open
Abstract
Little is known about the underlying relationships between self-reported mental health items measuring both positive and negative emotional and behavioural symptoms at the population level in young people. Improved measurement of the full range of mental well-being and mental illness may aid in understanding the aetiological substrates underlying the development of both mental wellness as well as specific psychiatric diagnoses. A general population sample aged 14 to 24 years completed self-report questionnaires on anxiety, depression, psychotic-like symptoms, obsessionality and well-being. Exploratory and confirmatory factor models for categorical data and latent profile analyses were used to evaluate the structure of both mental wellness and illness items. First order, second order and bifactor structures were evaluated on 118 self-reported items obtained from 2228 participants. A bifactor solution was the best fitting latent variable model with one general latent factor termed 'distress' and five 'distress independent' specific factors defined as self-confidence, antisocial behaviour, worry, aberrant thinking, and mood. Next, six distinct subgroups were derived from a person-centred latent profile analysis of the factor scores. Finally, concurrent validity was assessed using information on hazardous behaviours (alcohol use, substance misuse, self-harm) and treatment for mental ill health: both discriminated between the latent traits and latent profile subgroups. The findings suggest a complex, multidimensional mental health structure in the youth population rather than the previously assumed first or second order factor structure. Additionally, the analysis revealed a low hazardous behaviour/low mental illness risk subgroup not previously described. Population sub-groups show greater validity over single variable factors in revealing mental illness risks. In conclusion, our findings indicate that the structure of self reported mental health is multidimensional in nature and uniquely finds improved prediction to mental illness risk within person-centred subgroups derived from the multidimensional latent traits.
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Affiliation(s)
| | - Sharon Neufeld
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Peter B. Jones
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Peter Fonagy
- Division of Psychology and Language Sciences, University College London, London, United Kingdom
| | - Edward T. Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Raymond J. Dolan
- Max Planck UCL Centre for Computational Psychiatry and Ageing, University College London, London, United Kingdom
| | - Michael Moutoussis
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
| | - Umar Toseeb
- Department of Psychology, Manchester Metropolitan University, Manchester, United Kingdom
| | - Ian M. Goodyer
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
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9
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Smith LE, Smith DK, Blume JD, Siew ED, Billings FT. Latent variable modeling improves AKI risk factor identification and AKI prediction compared to traditional methods. BMC Nephrol 2017; 18:55. [PMID: 28178929 PMCID: PMC5299779 DOI: 10.1186/s12882-017-0465-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Accepted: 01/30/2017] [Indexed: 01/27/2023] Open
Abstract
Background Acute kidney injury (AKI) is diagnosed based on postoperative serum creatinine change, but AKI models have not consistently performed well, in part due to the omission of clinically important but practically unmeasurable variables that affect creatinine. We hypothesized that a latent variable mixture model of postoperative serum creatinine change would partially account for these unmeasured factors and therefore increase power to identify risk factors of AKI and improve predictive accuracy. Methods We constructed a two-component latent variable mixture model and a linear model using data from a prospective, 653-subject randomized clinical trial of AKI following cardiac surgery (NCT00791648) and included established AKI risk factors and covariates known to affect serum creatinine. We compared model fit, discrimination, power to detect AKI risk factors, and ability to predict AKI between the latent variable mixture model and the linear model. Results The latent variable mixture model demonstrated superior fit (likelihood ratio of 6.68 × 1071) and enhanced discrimination (permutation test of Spearman’s correlation coefficients, p < 0.001) compared to the linear model. The latent variable mixture model was 94% (−13 to 1132%) more powerful (median [range]) at identifying risk factors than the linear model, and demonstrated increased ability to predict change in serum creatinine (relative mean square error reduction of 6.8%). Conclusions A latent variable mixture model better fit a clinical cohort of cardiac surgery patients than a linear model, thus providing better assessment of the associations between risk factors of AKI and serum creatinine change and more accurate prediction of AKI. Incorporation of latent variable mixture modeling into AKI research will allow clinicians and investigators to account for clinically meaningful patient heterogeneity resulting from unmeasured variables, and therefore provide improved ability to examine risk factors, measure mechanisms and mediators of kidney injury, and more accurately predict AKI in clinical cohorts. Electronic supplementary material The online version of this article (doi:10.1186/s12882-017-0465-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Loren E Smith
- Department of Anesthesiology, Vanderbilt University Medical Center, 1211 21st Avenue South, Nashville, TN, 37205, USA
| | - Derek K Smith
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jeffrey D Blume
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Edward D Siew
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.,Division of Nephrology and Hypertension, Vanderbilt Center for Kidney Disease and Integrated Program for AKI Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Frederic T Billings
- Department of Anesthesiology, Vanderbilt University Medical Center, 1211 21st Avenue South, Nashville, TN, 37205, USA. .,Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
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10
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van de Lagemaat LN, Stanford LE, Pettit CM, Strathdee DJ, Strathdee KE, Elsegood KA, Fricker DG, Croning MDR, Komiyama NH, Grant SGN. Standardized experiments in mutant mice reveal behavioural similarity on 129S5 and C57BL/6J backgrounds. GENES BRAIN AND BEHAVIOR 2017; 16:409-418. [DOI: 10.1111/gbb.12364] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Revised: 09/12/2016] [Accepted: 11/21/2016] [Indexed: 01/27/2023]
Affiliation(s)
- L. N. van de Lagemaat
- Centre for Clinical Brain Sciences, Chancellor's Building; University of Edinburgh; Edinburgh UK
- Genes to Cognition Programme; Wellcome Trust Sanger Institute; Cambridge UK
| | - L. E. Stanford
- Genes to Cognition Programme; Wellcome Trust Sanger Institute; Cambridge UK
| | - C. M. Pettit
- Genes to Cognition Programme; Wellcome Trust Sanger Institute; Cambridge UK
| | - D. J. Strathdee
- Genes to Cognition Programme; Wellcome Trust Sanger Institute; Cambridge UK
- Transgenic Technology Division; CRUK Beatson Institute; Glasgow UK
| | - K. E. Strathdee
- Genes to Cognition Programme; Wellcome Trust Sanger Institute; Cambridge UK
| | - K. A. Elsegood
- Centre for Clinical Brain Sciences, Chancellor's Building; University of Edinburgh; Edinburgh UK
- Genes to Cognition Programme; Wellcome Trust Sanger Institute; Cambridge UK
| | - D. G. Fricker
- Centre for Clinical Brain Sciences, Chancellor's Building; University of Edinburgh; Edinburgh UK
- Genes to Cognition Programme; Wellcome Trust Sanger Institute; Cambridge UK
| | - M. D. R. Croning
- Centre for Clinical Brain Sciences, Chancellor's Building; University of Edinburgh; Edinburgh UK
- Genes to Cognition Programme; Wellcome Trust Sanger Institute; Cambridge UK
| | - N. H. Komiyama
- Centre for Clinical Brain Sciences, Chancellor's Building; University of Edinburgh; Edinburgh UK
- Genes to Cognition Programme; Wellcome Trust Sanger Institute; Cambridge UK
| | - S. G. N. Grant
- Centre for Clinical Brain Sciences, Chancellor's Building; University of Edinburgh; Edinburgh UK
- Genes to Cognition Programme; Wellcome Trust Sanger Institute; Cambridge UK
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11
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Reise SP, Rodriguez A. Item response theory and the measurement of psychiatric constructs: some empirical and conceptual issues and challenges. Psychol Med 2016; 46:2025-2039. [PMID: 27056796 DOI: 10.1017/s0033291716000520] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Item response theory (IRT) measurement models are now commonly used in educational, psychological, and health-outcomes measurement, but their impact in the evaluation of measures of psychiatric constructs remains limited. Herein we present two, somewhat contradictory, theses. The first is that, when skillfully applied, IRT has much to offer psychiatric measurement in terms of scale development, psychometric analysis, and scoring. The second argument, however, is that psychiatric measurement presents some unique challenges to the application of IRT - challenges that may not be easily addressed by application of conventional IRT models and methods. These challenges include, but are not limited to, the modeling of conceptually narrow constructs and their associated limited item pools, and unipolar constructs where the expected latent trait distribution is highly skewed.
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Affiliation(s)
- S P Reise
- University of California,Los Angeles,USA
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