1
|
González-Peñas J, Alloza C, Brouwer R, Díaz-Caneja CM, Costas J, González-Lois N, Gallego AG, de Hoyos L, Gurriarán X, Andreu-Bernabeu Á, Romero-García R, Fañanás L, Bobes J, González-Pinto A, Crespo-Facorro B, Martorell L, Arrojo M, Vilella E, Gutiérrez-Zotes A, Perez-Rando M, Moltó MD, Buimer E, van Haren N, Cahn W, O'Donovan M, Kahn RS, Arango C, Pol HH, Janssen J, Schnack H. Accelerated Cortical Thinning in Schizophrenia Is Associated With Rare and Common Predisposing Variation to Schizophrenia and Neurodevelopmental Disorders. Biol Psychiatry 2024; 96:376-389. [PMID: 38521159 DOI: 10.1016/j.biopsych.2024.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 02/22/2024] [Accepted: 03/05/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Schizophrenia is a highly heritable disorder characterized by increased cortical thinning throughout the life span. Studies have reported a shared genetic basis between schizophrenia and cortical thickness. However, no genes whose expression is related to abnormal cortical thinning in schizophrenia have been identified. METHODS We conducted linear mixed models to estimate the rates of accelerated cortical thinning across 68 regions from the Desikan-Killiany atlas in individuals with schizophrenia compared with healthy control participants from a large longitudinal sample (ncases = 169 and ncontrols = 298, ages 16-70 years). We studied the correlation between gene expression data from the Allen Human Brain Atlas and accelerated thinning estimates across cortical regions. Finally, we explored the functional and genetic underpinnings of the genes that contribute most to accelerated thinning. RESULTS We found a global pattern of accelerated cortical thinning in individuals with schizophrenia compared with healthy control participants. Genes underexpressed in cortical regions that exhibit this accelerated thinning were downregulated in several psychiatric disorders and were enriched for both common and rare disrupting variation for schizophrenia and neurodevelopmental disorders. In contrast, none of these enrichments were observed for baseline cross-sectional cortical thickness differences. CONCLUSIONS Our findings suggest that accelerated cortical thinning, rather than cortical thickness alone, serves as an informative phenotype for neurodevelopmental disruptions in schizophrenia. We highlight the genetic and transcriptomic correlates of this accelerated cortical thinning, emphasizing the need for future longitudinal studies to elucidate the role of genetic variation and the temporal-spatial dynamics of gene expression in brain development and aging in schizophrenia.
Collapse
Affiliation(s)
- Javier González-Peñas
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain.
| | - Clara Alloza
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain
| | - Rachel Brouwer
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Covadonga M Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain; School of Medicine, Universidad Complutense, Madrid, Spain
| | - Javier Costas
- Instituto de Investigación Sanitària de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela, Servizo Galego de Saúde, Santiago de Compostela, Galicia, Spain
| | - Noemí González-Lois
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain
| | - Ana Guil Gallego
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain
| | - Lucía de Hoyos
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain
| | - Xaquín Gurriarán
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain
| | - Álvaro Andreu-Bernabeu
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain
| | - Rafael Romero-García
- Department of Medical Physiology and Biophysics, Instituto de Biomedicina de Sevilla, HUVR/CSIC/Universidad de Sevilla/CIBERSAM, Instituto de Salud Carlos III, Sevilla, Spain; Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Lourdes Fañanás
- CIBERSAM, Madrid, Spain; Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | - Julio Bobes
- CIBERSAM, Madrid, Spain; Faculty of Medicine and Health Sciences-Psychiatry, Universidad de Oviedo, Instituto de Investigación Sanitaria del Principado de Asturias, Instituto de Neurociencias del Principado de Asturias, Oviedo, Spain
| | - Ana González-Pinto
- CIBERSAM, Madrid, Spain; BIOARABA Health Research Institute, Organización Sanitaria Integrada Araba, University Hospital, University of the Basque Country, Vitoria, Spain
| | - Benedicto Crespo-Facorro
- CIBERSAM, Madrid, Spain; Hospital Universitario Virgen del Rocío, Department of Psychiatry, Universidad de Sevilla, Sevilla, Spain
| | - Lourdes Martorell
- CIBERSAM, Madrid, Spain; Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili-Centres de Recerca de Catalunya, Universitat Rovira i Virgili, Reus, Spain
| | - Manuel Arrojo
- Instituto de Investigación Sanitària de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela, Servizo Galego de Saúde, Santiago de Compostela, Galicia, Spain
| | - Elisabet Vilella
- CIBERSAM, Madrid, Spain; Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili-Centres de Recerca de Catalunya, Universitat Rovira i Virgili, Reus, Spain
| | - Alfonso Gutiérrez-Zotes
- CIBERSAM, Madrid, Spain; Hospital Universitari Institut Pere Mata, Institut d'Investigació Sanitària Pere Virgili-Centres de Recerca de Catalunya, Universitat Rovira i Virgili, Reus, Spain
| | - Marta Perez-Rando
- Fundación Investigación Hospital Clínico de València, Fundación Investigación Hospital Clínico de Valencia, València, Spain; Unidad de Neurobiología, Instituto de Biotecnología y Biomedicina, Universitat de València, València, Spain
| | - María Dolores Moltó
- CIBERSAM, Madrid, Spain; Unidad de Neurobiología, Instituto de Biotecnología y Biomedicina, Universitat de València, València, Spain; Department of Genetics, Universitat de València, Campus of Burjassot, València, Spain
| | - Elizabeth Buimer
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Neeltje van Haren
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Rotterdam, the Netherlands
| | - Wiepke Cahn
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; Altrecht Mental Health Institute, Altrecht Science, Utrecht, the Netherlands
| | - Michael O'Donovan
- Medical Research Council for Neuropsychiatric Genetics and Genomics and Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - René S Kahn
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain; School of Medicine, Universidad Complutense, Madrid, Spain
| | - Hilleke Hulshoff Pol
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Joost Janssen
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain; Instituto de Investigación Sanitària Gregorio Marañón, Madrid, Spain; CIBERSAM, Madrid, Spain; Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Hugo Schnack
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| |
Collapse
|
2
|
Chung J, Bridgeford EW, Powell M, Pisner D, Xu T, Vogelstein JT. Are human connectomes heritable? BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.02.532875. [PMID: 37066291 PMCID: PMC10103997 DOI: 10.1101/2023.04.02.532875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
A complete understanding of human behavior and disease depends upon our ability to parse genetic and environmental influences in the human brain. The heritability of a trait quantifies the degree of its variability due to genetic influences. Classical approach for quantifying heritability operate on simple traits, and sometimes do not properly model and control for other potential sources of variation, such as age or sex. We therefore develop Causal Heritability of Networks (CHaiN) to rigorously quantify heritability of human brain networks (i.e., connectomes). We applied CHaiN to 1024 anatomical connectomes derived from the Human Connectome Project. Connectomes appeared to be heritable, but heritability was insignificant once we addressed variability within networks. These results suggest that previous conclusions on connectome heritability may be driven by the shared network structures, and highlights the importance of modeling networks and other sources of variability when studying heritability of connectomes.
Collapse
|
3
|
Hu X, Fang Z, Wang F, Mei Z, Huang X, Lin Y, Lin Z. A causal relationship between gut microbiota and subcortical brain structures contributes to the microbiota-gut-brain axis: a Mendelian randomization study. Cereb Cortex 2024; 34:bhae056. [PMID: 38415993 DOI: 10.1093/cercor/bhae056] [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: 12/06/2023] [Revised: 01/27/2024] [Accepted: 01/28/2024] [Indexed: 02/29/2024] Open
Abstract
A correlation between gut microbiota and brain structure, referring to as a component of the gut-brain axis, has been observed in observational studies. However, the causality of this relationship and its specific bacterial taxa remains uncertain. To reveal the causal effects of gut microbiota on subcortical brain volume, we applied Mendelian randomization (MR) studies in this study. Genome-wide association study data were obtained from the MiBioGen Consortium (n = 18,340) and the Enhancing Neuro Imaging Genetics through Meta-Analysis Consortium (n = 13,170). The primary estimate was obtained utilizing the inverse-variance weighted, while heterogeneity and pleiotropy were assessed using the Cochrane Q statistic, MR Pleiotropy RESidual Sum and Outlier, and MR-Egger intercept. Our findings provide strong evidence that a higher abundance of the genus Parasutterella is causally correlated with a decrease in intracranial volume (β = -30,921.33, 95% CI -46,671.78 to -15,170.88, P = 1.19 × 10-4), and the genus FamilyXIIIUCG001 is associated with a decrease in thalamus volume (β = -141.96, 95% CI: -214.81 to -69.12, P = 1.0× 10-4). This MR study offers novel perspectives on the intricate interplay between the gut microbiota and subcortical brain volume, thereby lending some support to the existence of the microbiota-gut-brain axis.
Collapse
Affiliation(s)
- Xuequn Hu
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Changle District, Fuzhou 350209, Fujian Province, China
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, 20 Chazhong Road, Taijiang District, Fuzhou 350005, Fujian Province, China
| | - Zhiyong Fang
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Changle District, Fuzhou 350209, Fujian Province, China
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, 20 Chazhong Road, Taijiang District, Fuzhou 350005, Fujian Province, China
| | - Feng Wang
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Changle District, Fuzhou 350209, Fujian Province, China
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, 20 Chazhong Road, Taijiang District, Fuzhou 350005, Fujian Province, China
| | - Zhen Mei
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Changle District, Fuzhou 350209, Fujian Province, China
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, 20 Chazhong Road, Taijiang District, Fuzhou 350005, Fujian Province, China
| | - Xiaofen Huang
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Changle District, Fuzhou 350209, Fujian Province, China
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, 20 Chazhong Road, Taijiang District, Fuzhou 350005, Fujian Province, China
| | - Yuanxiang Lin
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Changle District, Fuzhou 350209, Fujian Province, China
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, 20 Chazhong Road, Taijiang District, Fuzhou 350005, Fujian Province, China
| | - Zhangya Lin
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Changle District, Fuzhou 350209, Fujian Province, China
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, 20 Chazhong Road, Taijiang District, Fuzhou 350005, Fujian Province, China
| |
Collapse
|
4
|
Stauffer EM, Bethlehem RAI, Dorfschmidt L, Won H, Warrier V, Bullmore ET. The genetic relationships between brain structure and schizophrenia. Nat Commun 2023; 14:7820. [PMID: 38016951 PMCID: PMC10684873 DOI: 10.1038/s41467-023-43567-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 11/14/2023] [Indexed: 11/30/2023] Open
Abstract
Genetic risks for schizophrenia are theoretically mediated by genetic effects on brain structure but it has been unclear which genes are associated with both schizophrenia and cortical phenotypes. We accessed genome-wide association studies (GWAS) of schizophrenia (N = 69,369 cases; 236,642 controls), and of three magnetic resonance imaging (MRI) metrics (surface area, cortical thickness, neurite density index) measured at 180 cortical areas (N = 36,843, UK Biobank). Using Hi-C-coupled MAGMA, 61 genes were significantly associated with both schizophrenia and one or more MRI metrics. Whole genome analysis with partial least squares demonstrated significant genetic covariation between schizophrenia and area or thickness of most cortical regions. Genetic similarity between cortical areas was strongly coupled to their phenotypic covariance, and genetic covariation between schizophrenia and brain phenotypes was strongest in the hubs of structural covariance networks. Pleiotropically associated genes were enriched for neurodevelopmental processes and positionally concentrated in chromosomes 3p21, 17q21 and 11p11. Mendelian randomization analysis indicated that genetically determined variation in a posterior cingulate cortical area could be causal for schizophrenia. Parallel analyses of GWAS on bipolar disorder, Alzheimer's disease and height showed that pleiotropic association with MRI metrics was stronger for schizophrenia compared to other disorders.
Collapse
Affiliation(s)
| | - Richard A I Bethlehem
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Lena Dorfschmidt
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Hyejung Won
- Department of Genetics and the Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire & Peterborough NHS Foundation Trust, Cambridge, UK
| |
Collapse
|
5
|
González-Peñas J, de Hoyos L, Díaz-Caneja CM, Andreu-Bernabeu Á, Stella C, Gurriarán X, Fañanás L, Bobes J, González-Pinto A, Crespo-Facorro B, Martorell L, Vilella E, Muntané G, Molto MD, Gonzalez-Piqueras JC, Parellada M, Arango C, Costas J. Recent natural selection conferred protection against schizophrenia by non-antagonistic pleiotropy. Sci Rep 2023; 13:15500. [PMID: 37726359 PMCID: PMC10509162 DOI: 10.1038/s41598-023-42578-0] [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: 11/16/2022] [Accepted: 09/12/2023] [Indexed: 09/21/2023] Open
Abstract
Schizophrenia is a debilitating psychiatric disorder associated with a reduced fertility and decreased life expectancy, yet common predisposing variation substantially contributes to the onset of the disorder, which poses an evolutionary paradox. Previous research has suggested balanced selection, a mechanism by which schizophrenia risk alleles could also provide advantages under certain environments, as a reliable explanation. However, recent studies have shown strong evidence against a positive selection of predisposing loci. Furthermore, evolutionary pressures on schizophrenia risk alleles could have changed throughout human history as new environments emerged. Here in this study, we used 1000 Genomes Project data to explore the relationship between schizophrenia predisposing loci and recent natural selection (RNS) signatures after the human diaspora out of Africa around 100,000 years ago on a genome-wide scale. We found evidence for significant enrichment of RNS markers in derived alleles arisen during human evolution conferring protection to schizophrenia. Moreover, both partitioned heritability and gene set enrichment analyses of mapped genes from schizophrenia predisposing loci subject to RNS revealed a lower involvement in brain and neuronal related functions compared to those not subject to RNS. Taken together, our results suggest non-antagonistic pleiotropy as a likely mechanism behind RNS that could explain the persistence of schizophrenia common predisposing variation in human populations due to its association to other non-psychiatric phenotypes.
Collapse
Affiliation(s)
- Javier González-Peñas
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain.
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain.
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain.
| | - Lucía de Hoyos
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Covadonga M Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Álvaro Andreu-Bernabeu
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Carol Stella
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Xaquín Gurriarán
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Lourdes Fañanás
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | - Julio Bobes
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Faculty of Medicine and Health Sciences - Psychiatry, Universidad de Oviedo, ISPA, INEUROPA, Oviedo, Spain
| | - Ana González-Pinto
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- BIOARABA Health Research Institute, OSI Araba, University Hospital, University of the Basque Country, Vitoria, Spain
| | - Benedicto Crespo-Facorro
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Department of Psychiatry, Hospital Universitario Virgen del Rocío, Universidad de Sevilla, Seville, Spain
| | - Lourdes Martorell
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira I Virgili, Reus, Spain
| | - Elisabet Vilella
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira I Virgili, Reus, Spain
| | - Gerard Muntané
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira I Virgili, Reus, Spain
| | - María Dolores Molto
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Department of Genetics, University of Valencia, Campus of Burjassot, Valencia, Spain
- Department of Medicine, Universitat de València, Valencia, Spain
| | - Jose Carlos Gonzalez-Piqueras
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- Department of Medicine, Universitat de València, Valencia, Spain
- Fundación Investigación Hospital Clínico de Valencia, INCLIVA, 46010, Valencia, Spain
| | - Mara Parellada
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Calle Ibiza, 43, 28009, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- CIBERSAM, Centro Investigación Biomédica en Red Salud Mental, Madrid, Spain
- School of Medicine, Universidad Complutense, Madrid, Spain
| | - Javier Costas
- Instituto de Investigación Sanitaria (IDIS) de Santiago de Compostela, Complexo Hospitalario Universitario de Santiago de Compostela (CHUS), Servizo Galego de Saúde (SERGAS), Santiago de Compostela, Galicia, Spain
| |
Collapse
|
6
|
Koller D, Benítez-Burraco A, Polimanti R. Enrichment of self-domestication and neural crest function loci in the heritability of neurodevelopmental disorders. Hum Genet 2023; 142:1271-1279. [PMID: 36930228 PMCID: PMC10472204 DOI: 10.1007/s00439-023-02541-5] [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: 12/27/2022] [Accepted: 03/01/2023] [Indexed: 03/18/2023]
Abstract
Self-domestication could contribute to shaping the biology of human brain and consequently the predisposition to neurodevelopmental disorders. Leveraging genome-wide data from the Psychiatric Genomics Consortium, we tested the enrichment of self-domestication and neural crest function loci with respect to the heritability of autism spectrum disorder, schizophrenia (SCZ in East Asian and European ancestries, EAS and EUR, respectively), attention-deficit/hyperactivity disorder, obsessive-compulsive disorder, and Tourette's syndrome (TS). Considering only self-domestication and neural-crest-function annotations in the linkage disequilibrium score regression (LDSC) model, our partitioned heritability analysis revealed statistically significant enrichments across all disorders investigated. The estimates of the heritability enrichments for self-domestication loci were similar across neurodevelopmental disorders, ranging from 0.902 (EAS SCZ, p = 4.55 × 10-20) to 1.577 (TS, p = 5.85 × 10-5). Conversely, a wider spectrum of heritability enrichment estimates was present for neural crest function with the highest enrichment observed for TS (enrichment = 3.453, p = 2.88 × 10-3) and the lowest for EAS SCZ (enrichment = 1.971, p = 3.81 × 10-3). Although these estimates appear to be strong, the enrichments for self-domestication and neural crest function were null once we included additional annotations related to different genomic features. This indicates that the effect of self-domestication on the polygenic architecture of neurodevelopmental disorders is not independent of other functions of human genome.
Collapse
Affiliation(s)
- Dora Koller
- Department of Psychiatry, Yale School of Medicine, VA CT 116A2, 950 Campbell Avenue, West Haven, CT, 06516, USA
- VA CT Healthcare Center, West Haven, CT, 06516, USA
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, University of Barcelona, 08028, Barcelona, Catalonia, Spain
| | - Antonio Benítez-Burraco
- Department of Spanish, Linguistics, and Theory of Literature (Linguistics), Faculty of Philology, University of Seville, 41004, Seville, Spain
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine, VA CT 116A2, 950 Campbell Avenue, West Haven, CT, 06516, USA.
- VA CT Healthcare Center, West Haven, CT, 06516, USA.
| |
Collapse
|
7
|
Gupta N, Gupta M, Esang M. Lost in Translation: Challenges in the Diagnosis and Treatment of Early-Onset Schizophrenia. Cureus 2023; 15:e39488. [PMID: 37362509 PMCID: PMC10290525 DOI: 10.7759/cureus.39488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/25/2023] [Indexed: 06/28/2023] Open
Abstract
Early-onset schizophrenia (EOS) is a heterogeneous condition that has a serious, insidious clinical course and poor long-term mental health outcomes. The clinical presentations are highly complex due to the overlapping symptomatology with other illnesses, which contributes to a delay in the diagnosis. The objective of the review is to study if an earlier age of onset (AAO) of EOS has poor clinical outcomes, the diagnostic challenges of EOS, and effective treatment strategies. The review provides a comprehensive literature search of 5966 articles and summarizes 126 selected for empirical evidence to methodically consider challenges in diagnosing and treating EOS for practicing clinicians. The risk factors of EOS are unique but have been shared with many other neuropsychiatric illnesses. Most of the risk factors, including genetics and obstetric complications, are nonmodifiable. The role of early diagnosis in reducing the duration of untreated psychosis (DUP) remains critical to reducing overall morbidity. Many specific issues contribute to the risk and clinical outcomes. Therefore, issues around diagnostic ambiguity, treatment resistance, nonadherence, and rehospitalizations further extend the DUP. There is hesitancy to initiate clozapine early, even though the empirical evidence strongly supports its use. There is a growing body of research that suggests the use of long-acting injectables to address nonadherence, and these measures are largely underutilized in acute settings. The clinical presentations of EOS are complex. In addition to the presence of specific risk factors, patients with an early onset of illness are also at a higher risk for treatment resistance. While there is a need to develop tools for early diagnosis, established evidence-based measures to address nonadherence, psychoeducation, and resistance must be incorporated into the treatment planning.
Collapse
Affiliation(s)
- Nihit Gupta
- Psychiatry, Dayton Children's Hospital, Dayton, USA
| | - Mayank Gupta
- Psychiatry and Behavioral Sciences, Southwood Psychiatric Hospital, Pittsburgh, USA
| | - Michael Esang
- Psychiatry and Behavioral Sciences, Clarion Psychiatric Center, Clarion, USA
| |
Collapse
|
8
|
Sun BB, Loomis SJ, Pizzagalli F, Shatokhina N, Painter JN, Foley CN, Jensen ME, McLaren DG, Chintapalli SS, Zhu AH, Dixon D, Islam T, Ba Gari I, Runz H, Medland SE, Thompson PM, Jahanshad N, Whelan CD. Genetic map of regional sulcal morphology in the human brain from UK biobank data. Nat Commun 2022; 13:6071. [PMID: 36241887 PMCID: PMC9568560 DOI: 10.1038/s41467-022-33829-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 10/05/2022] [Indexed: 12/24/2022] Open
Abstract
Genetic associations with macroscopic brain structure can provide insights into brain function and disease. However, specific associations with measures of local brain folding are largely under-explored. Here, we conducted large-scale genome- and exome-wide associations of regional cortical sulcal measures derived from magnetic resonance imaging scans of 40,169 individuals in UK Biobank. We discovered 388 regional brain folding associations across 77 genetic loci, with genes in associated loci enriched for expression in the cerebral cortex, neuronal development processes, and differential regulation during early brain development. We integrated brain eQTLs to refine genes for various loci, implicated several genes involved in neurodevelopmental disorders, and highlighted global genetic correlations with neuropsychiatric phenotypes. We provide an interactive 3D visualisation of our summary associations, emphasising added resolution of regional analyses. Our results offer new insights into the genetic architecture of brain folding and provide a resource for future studies of sulcal morphology in health and disease.
Collapse
Affiliation(s)
- Benjamin B Sun
- Translational Biology, Research & Development, Biogen Inc., Cambridge, MA, US.
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
| | - Stephanie J Loomis
- Translational Biology, Research & Development, Biogen Inc., Cambridge, MA, US
| | - Fabrizio Pizzagalli
- Department of Neuroscience "Rita Levi Montalcini", University of Turin, Turin, Italy
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US
| | - Natalia Shatokhina
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US
| | - Jodie N Painter
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Christopher N Foley
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Optima Partners, Edinburgh, UK
| | - Megan E Jensen
- Clinical Sciences, Research & Development, Biogen Inc., Cambridge, MA, US
| | - Donald G McLaren
- Clinical Sciences, Research & Development, Biogen Inc., Cambridge, MA, US
| | | | - Alyssa H Zhu
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US
| | - Daniel Dixon
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US
| | - Tasfiya Islam
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US
| | - Iyad Ba Gari
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US
| | - Heiko Runz
- Translational Biology, Research & Development, Biogen Inc., Cambridge, MA, US
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US.
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, US.
| | | |
Collapse
|
9
|
A cognitive neurogenetic approach to uncovering the structure of executive functions. Nat Commun 2022; 13:4588. [PMID: 35933428 PMCID: PMC9357028 DOI: 10.1038/s41467-022-32383-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 07/27/2022] [Indexed: 11/08/2022] Open
Abstract
One central mission of cognitive neuroscience is to understand the ontology of complex cognitive functions. We addressed this question with a cognitive neurogenetic approach using a large-scale dataset of executive functions (EFs), whole-brain resting-state functional connectivity, and genetic polymorphisms. We found that the bifactor model with common and shifting-specific components not only was parsimonious but also showed maximal dissociations among the EF components at behavioral, neural, and genetic levels. In particular, the genes with enhanced expression in the middle frontal gyrus (MFG) and the subcallosal cingulate gyrus (SCG) showed enrichment for the common and shifting-specific component, respectively. Finally, High-dimensional mediation models further revealed that the functional connectivity patterns significantly mediated the genetic effect on the common EF component. Our study not only reveals insights into the ontology of EFs and their neurogenetic basis, but also provides useful tools to uncover the structure of complex constructs of human cognition.
Collapse
|
10
|
Kuo SS, Roalf DR, Prasad KM, Musket CW, Rupert PE, Wood J, Gur RC, Almasy L, Gur RE, Nimgaonkar VL, Pogue-Geile MF. Age-dependent effects of schizophrenia genetic risk on cortical thickness and cortical surface area: Evaluating evidence for neurodevelopmental and neurodegenerative models of schizophrenia. JOURNAL OF PSYCHOPATHOLOGY AND CLINICAL SCIENCE 2022; 131:674-688. [PMID: 35737559 PMCID: PMC9339500 DOI: 10.1037/abn0000765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Risk for schizophrenia peaks during early adulthood, a critical period for brain development. Although several influential theoretical models have been proposed for the developmental relationship between brain pathology and clinical onset, to our knowledge, no study has directly evaluated the predictions of these models for schizophrenia developmental genetic effects on brain structure. To address this question, we introduce a framework to estimate the effects of schizophrenia genetic variation on brain structure phenotypes across the life span. Five-hundred and six participants, including 30 schizophrenia probands, 200 of their relatives (aged 12-85 years) from 32 families with at least two first-degree schizophrenia relatives, and 276 unrelated controls, underwent MRI to assess regional cortical thickness (CT) and cortical surface area (CSA). Genetic variance decomposition analyses were conducted to distinguish among schizophrenia neurogenetic effects that are most salient before schizophrenia peak age-of-risk (i.e., early neurodevelopmental effects), after peak age-of-risk (late neurodevelopmental effects), and during the later plateau of age-of-risk (neurodegenerative effects). Genetic correlations between schizophrenia and cortical traits suggested early neurodevelopmental effects for frontal and insula CSA, late neurodevelopmental effects for overall CSA and frontal, parietal, and occipital CSA, and possible neurodegenerative effects for temporal CT and parietal CSA. Importantly, these developmental neurogenetic effects were specific to schizophrenia and not found with nonpsychotic depression. Our findings highlight the potentially dynamic nature of schizophrenia genetic effects across the lifespan and emphasize the utility of integrating neuroimaging methods with developmental behavior genetic approaches to elucidate the nature and timing of risk-conferring processes in psychopathology. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Collapse
|
11
|
de Zwarte SMC, Brouwer RM, Kahn RS, van Haren NEM. Schizophrenia and Bipolar Polygenic Risk Scores in Relation to Intracranial Volume. Genes (Basel) 2022; 13:genes13040695. [PMID: 35456501 PMCID: PMC9026378 DOI: 10.3390/genes13040695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/24/2022] [Accepted: 04/08/2022] [Indexed: 01/27/2023] Open
Abstract
Schizophrenia and bipolar disorder are neurodevelopmental disorders with overlapping symptoms and a shared genetic background. Deviations in intracranial volume (ICV)—a marker for neurodevelopment—differ between schizophrenia and bipolar disorder. Here, we investigated whether genetic risk for schizophrenia and bipolar disorder is related to ICV in the general population by using the UK Biobank data (n = 20,196). Polygenic risk scores for schizophrenia (SZ-PRS) and bipolar disorder (BD-PRS) were computed for 12 genome wide association study P-value thresholds (PT) for each individual and correlations with ICV were investigated. Partial correlations were performed at each PT to investigate whether disease specific genetic risk variants for schizophrenia and bipolar disorder show different relationships with ICV. ICV showed a negative correlation with SZ-PRS at PT ≥ 0.005 (r < −0.02, p < 0.005). ICV was not associated with BD-PRS; however, a positive correlation between BD-PRS and ICV at PT = 0.2 and PT = 0.4 (r = +0.02, p < 0.005) appeared when the genetic overlap between schizophrenia and bipolar disorder was accounted for. Despite small effect sizes, a higher load of schizophrenia risk genes is associated with a smaller ICV in the general population, while risk genes specific for bipolar disorder are correlated with a larger ICV. These findings suggest that schizophrenia and bipolar disorder risk genes, when accounting for the genetic overlap between both disorders, have opposite effects on early brain development.
Collapse
Affiliation(s)
- Sonja M. C. de Zwarte
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CG Utrecht, The Netherlands; (R.M.B.); (R.S.K.)
- Correspondence: (S.M.C.d.Z.); (N.E.M.v.H.)
| | - Rachel M. Brouwer
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CG Utrecht, The Netherlands; (R.M.B.); (R.S.K.)
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - René S. Kahn
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CG Utrecht, The Netherlands; (R.M.B.); (R.S.K.)
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- VISN 2 Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Neeltje E. M. van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre Sophia, 3000 CB Rotterdam, The Netherlands
- Correspondence: (S.M.C.d.Z.); (N.E.M.v.H.)
| |
Collapse
|
12
|
Bustamante D, Amstadter AB, Pritikin JN, Brick TR, Neale MC. Associations Between Traumatic Stress, Brain Volumes and Post-traumatic Stress Disorder Symptoms in Children: Data from the ABCD Study. Behav Genet 2022; 52:75-91. [PMID: 34860306 PMCID: PMC8860798 DOI: 10.1007/s10519-021-10092-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 11/07/2021] [Indexed: 11/26/2022]
Abstract
Reduced volumes in brain regions of interest (ROIs), primarily from adult samples, are associated with posttraumatic stress disorder (PTSD). We extended this work to children using data from the Adolescent Brain Cognitive Development (ABCD) Study® (N = 11,848; Mage = 9.92). Structural equation modeling and an elastic-net (EN) machine-learning approach were used to identify potential effects of traumatic events (TEs) on PTSD symptoms (PTSDsx) directly, and indirectly via the volumes 300 subcortical and cortical ROIs. We then estimated the genetic and environmental variation in the phenotypes. TEs were directly associated with PTSDsx (r = 0.92) in children, but their indirect effects (r < 0.0004)-via the volumes of EN-identified subcortical and cortical ROIs-were negligible at this age. Additive genetic factors explained a modest proportion of the variance in TEs (23.4%) and PTSDsx (21.3%), and accounted for most of the variance of EN-identified volumes of four of the five subcortical (52.4-61.8%) three of the nine cortical ROIs (46.4-53.3%) and cerebral white matter in the left hemisphere (57.4%). Environmental factors explained most of the variance in TEs (C = 61.6%, E = 15.1%), PTSDsx (residual-C = 18.4%, residual-E = 21.8%), right lateral ventricle (C = 15.2%, E = 43.1%) and six of the nine EN-identified cortical ROIs (C = 4.0-13.6%, E = 56.7-74.8%). There is negligible evidence that the volumes of brain ROIs are associated with the indirect effects of TEs on PTSDsx at this age. Overall, environmental factors accounted for more of the variation in TEs and PTSDsx. Whereas additive genetic factors accounted for most of the variability in the volumes of a minority of cortical and in most of subcortical ROIs.
Collapse
Affiliation(s)
- Daniel Bustamante
- Virginia Institute for Psychiatric and Behavioral Genetics, 800 E Leigh Street, Biotech One, Box 980126, Richmond, VA, 23298, USA.
- Integrative Life Sciences Doctoral Program, Virginia Commonwealth University, Richmond, VA, USA.
| | - Ananda B Amstadter
- Virginia Institute for Psychiatric and Behavioral Genetics, 800 E Leigh Street, Biotech One, Box 980126, Richmond, VA, 23298, USA
- Department of Psychiatry, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Joshua N Pritikin
- Virginia Institute for Psychiatric and Behavioral Genetics, 800 E Leigh Street, Biotech One, Box 980126, Richmond, VA, 23298, USA
- Department of Psychiatry, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Timothy R Brick
- Department of Human Development and Family Studies, and Institute for Computational and Data Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavioral Genetics, 800 E Leigh Street, Biotech One, Box 980126, Richmond, VA, 23298, USA
- Department of Psychiatry, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| |
Collapse
|
13
|
Kang J, Jiao Z, Qin Y, Wang Y, Wang J, Jin L, Feng J, Wang F, Tang Y, Gong X. Associations between polygenic risk scores and amplitude of low-frequency fluctuation of inferior frontal gyrus in schizophrenia. J Psychiatr Res 2022; 147:4-12. [PMID: 34999338 DOI: 10.1016/j.jpsychires.2021.12.043] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 12/14/2021] [Accepted: 12/20/2021] [Indexed: 10/19/2022]
Abstract
Schizophrenia (SCZ) is a serious and complex mental disorder with high heritability. Polygenic risk score (PRS) is a useful tool calculating the accumulating effects of multiple common genetic variants of schizophrenia. The amplitude of low-frequency fluctuation (ALFF) is an efficient index to reflect spontaneous, intrinsic neuronal activity. Aberrant ALFF of brain regions were reported in schizophrenia frequently, but the relationship between PRS and ALFF has not been studied. In the present study, we compared PRS and ALFF in 101 schizophrenia patients and 106 age-matched healthy controls to test their associations with schizophrenia. Then, the correlation of PRS with ALFF was measured to reveal the effect of polygenic risk on brain activity in schizophrenia. We found that schizophrenia patients showed significant differences in PRS and ALFF compared with controls. Twenty-six brain regions showed significant difference of ALFF between schizophrenia cases and controls, of which left inferior frontal gyrus, triangular part (IFGtriang.L) showed increased activity in schizophrenia. PRS-SCZ was positively correlated with ALFF in IFGtriang.L in 57 non-chronic patients. Genes involved in synaptic organization and transmission, especially in glutamatergic synapse, were highly enriched in PRS-SCZ genes, suggesting the dysfunction of synapses in schizophrenia. These results help to understand the molecular mechanism underlying schizophrenia and related brain dysfunction.
Collapse
Affiliation(s)
- Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Shanghai Center for Mathematical Science, Fudan University, Shanghai, China
| | - Zeyu Jiao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Shanghai Center for Mathematical Science, Fudan University, Shanghai, China
| | - Yue Qin
- School of Life Sciences, Fudan University, Shanghai, China
| | - Yi Wang
- School of Life Sciences, Fudan University, Shanghai, China
| | - Jiucun Wang
- School of Life Sciences, Fudan University, Shanghai, China; Human Phoneme Institute, Fudan University, Shanghai, China
| | - Li Jin
- School of Life Sciences, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Shanghai Center for Mathematical Science, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK
| | - Fei Wang
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, China.
| | - Xiaohong Gong
- School of Life Sciences, Fudan University, Shanghai, China.
| |
Collapse
|
14
|
Kraljević N, Schaare HL, Eickhoff SB, Kochunov P, Yeo BTT, Kharabian Masouleh S, Valk SL. Behavioral, Anatomical and Heritable Convergence of Affect and Cognition in Superior Frontal Cortex. Neuroimage 2021; 243:118561. [PMID: 34506912 PMCID: PMC8526801 DOI: 10.1016/j.neuroimage.2021.118561] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 08/31/2021] [Accepted: 09/06/2021] [Indexed: 11/26/2022] Open
Abstract
Cognitive abilities and affective experience are key human traits that are interrelated in behavior and brain. Individual variation of cognitive and affective traits, as well as brain structure, has been shown to partly underlie genetic effects. However, to what extent affect and cognition have a shared genetic relationship with local brain structure is incompletely understood. Here we studied phenotypic and genetic correlations of cognitive and affective traits in behavior and brain structure (cortical thickness, surface area and subcortical volumes) in the pedigree-based Human Connectome Project sample (N = 1091). Both cognitive and affective trait scores were highly heritable and showed significant phenotypic correlation on the behavioral level. Cortical thickness in the left superior frontal cortex showed a phenotypic association with both affect and cognition. Decomposing the phenotypic correlations into genetic and environmental components showed that the associations were accounted for by shared genetic effects between the traits. Quantitative functional decoding of the left superior frontal cortex further indicated that this region is associated with cognitive and emotional functioning. This study provides a multi-level approach to study the association between affect and cognition and suggests a convergence of both in superior frontal cortical thickness.
Collapse
Affiliation(s)
- Nevena Kraljević
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - H Lina Schaare
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany; Otto Hahn group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstr. 1A, Leipzig 04103, Germany.
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Peter Kochunov
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Centre for Sleep and Cognition, Centre for Translational MR Research, N.1 Institute for Health and Institute for Digital Medicine, National University of Singapore, Singapore; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
| | - Shahrzad Kharabian Masouleh
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sofie L Valk
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Otto Hahn group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstr. 1A, Leipzig 04103, Germany.
| |
Collapse
|
15
|
Reineberg AE, Hatoum AS, Hewitt JK, Banich MT, Friedman NP. Genetic and Environmental Influence on the Human Functional Connectome. Cereb Cortex 2021; 30:2099-2113. [PMID: 31711120 DOI: 10.1093/cercor/bhz225] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 08/09/2019] [Accepted: 08/14/2019] [Indexed: 12/19/2022] Open
Abstract
Detailed mapping of genetic and environmental influences on the functional connectome is a crucial step toward developing intermediate phenotypes between genes and clinical diagnoses or cognitive abilities. We analyzed resting-state functional magnetic resonance imaging data from two adult twin samples (Nos = 446 and 371) to quantify genetic and environmental influence on all pairwise functional connections between 264 brain regions (~35 000 functional connections). Nonshared environmental influence was high across the whole connectome. Approximately 14-22% of connections had nominally significant genetic influence in each sample, 4.6% were significant in both samples, and 1-2% had heritability estimates greater than 30%. Evidence of shared environmental influence was weak. Genetic influences on connections were distinct from genetic influences on a global summary measure of the connectome, network-based estimates of connectivity, and movement during the resting-state scan, as revealed by a novel connectome-wide bivariate genetic modeling procedure. The brain's genetic organization is diverse and not as one would expect based solely on structure evident in nongenetically informative data or lower resolution data. As follow-up, we make novel classifications of functional connections and examine highly localized connections with particularly strong genetic influence. This high-resolution genetic taxonomy of brain connectivity will be useful in understanding genetic influences on brain disorders.
Collapse
Affiliation(s)
- Andrew E Reineberg
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, 80309, USA
| | - Alexander S Hatoum
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, 80309, USA
| | - John K Hewitt
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, 80309, USA.,Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, 80309, USA
| | - Marie T Banich
- Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, 80309, USA
| | - Naomi P Friedman
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, 80309, USA.,Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, 80309, USA
| |
Collapse
|
16
|
O'Connell KS, Shadrin A, Bahrami S, Smeland OB, Bettella F, Frei O, Krull F, Askeland RB, Walters GB, Davíðsdóttir K, Haraldsdóttir GS, Guðmundsson ÓÓ, Stefánsson H, Fan CC, Steen NE, Reichborn-Kjennerud T, Dale AM, Stefánsson K, Djurovic S, Andreassen OA. Identification of genetic overlap and novel risk loci for attention-deficit/hyperactivity disorder and bipolar disorder. Mol Psychiatry 2021; 26:4055-4065. [PMID: 31792363 DOI: 10.1038/s41380-019-0613-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 11/07/2019] [Accepted: 11/15/2019] [Indexed: 12/13/2022]
Abstract
Differential diagnosis between childhood onset attention-deficit/hyperactivity disorder (ADHD) and bipolar disorder (BD) remains a challenge, mainly due to overlapping symptoms and high rates of comorbidity. Despite this, genetic correlation reported for these disorders is low and non-significant. Here we aimed to better characterize the genetic architecture of these disorders utilizing recent large genome-wide association studies (GWAS). We analyzed independent GWAS summary statistics for ADHD (19,099 cases and 34,194 controls) and BD (20,352 cases and 31,358 controls) applying the conditional/conjunctional false discovery rate (condFDR/conjFDR) statistical framework that increases the power to detect novel phenotype-specific and shared loci by leveraging the combined power of two GWAS. We observed cross-trait polygenic enrichment for ADHD conditioned on associations with BD, and vice versa. Leveraging this enrichment, we identified 19 novel ADHD risk loci and 40 novel BD risk loci at condFDR <0.05. Further, we identified five loci jointly associated with ADHD and BD (conjFDR < 0.05). Interestingly, these five loci show concordant directions of effect for ADHD and BD. These results highlight a shared underlying genetic risk for ADHD and BD which may help to explain the high comorbidity rates and difficulties in differentiating between ADHD and BD in the clinic. Improving our understanding of the underlying genetic architecture of these disorders may aid in the development of novel stratification tools to help reduce these diagnostic difficulties.
Collapse
Affiliation(s)
- Kevin S O'Connell
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway.
| | - Alexey Shadrin
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Shahram Bahrami
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Olav B Smeland
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Francesco Bettella
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Florian Krull
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Ragna B Askeland
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - G Bragi Walters
- deCODE genetics/Amgen, Reykjavík, Iceland.,Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Katrín Davíðsdóttir
- The Centre for Child Development and Behaviour, Capital Area Primary Health Care, Reykjavik, Iceland
| | - Gyða S Haraldsdóttir
- The Centre for Child Development and Behaviour, Capital Area Primary Health Care, Reykjavik, Iceland
| | - Ólafur Ó Guðmundsson
- deCODE genetics/Amgen, Reykjavík, Iceland.,Faculty of Medicine, University of Iceland, Reykjavík, Iceland.,Department of Child and Adolescent Psychiatry, National University Hospital, Reykjavik, Iceland
| | | | - Chun C Fan
- Department of Radiology, University of California, San Diego, La Jolla, CA, 92093, USA.,Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | - Nils Eiel Steen
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway
| | - Ted Reichborn-Kjennerud
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Anders M Dale
- Department of Radiology, University of California, San Diego, La Jolla, CA, 92093, USA.,Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, 92093, USA.,Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA.,Department of Neurosciences, University of California San Diego, La Jolla, CA, 92093, USA
| | - Kári Stefánsson
- deCODE genetics/Amgen, Reykjavík, Iceland.,Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway.,NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, 0407, Oslo, Norway. .,Departments of Neurology and Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
| |
Collapse
|
17
|
O'Connell KS, Sønderby IE, Frei O, van der Meer D, Athanasiu L, Smeland OB, Alnæs D, Kaufmann T, Westlye LT, Steen VM, Andreassen OA, Hughes T, Djurovic S. Association between complement component 4A expression, cognitive performance and brain imaging measures in UK Biobank. Psychol Med 2021; 52:1-11. [PMID: 33653435 PMCID: PMC9772918 DOI: 10.1017/s0033291721000179] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 01/06/2021] [Accepted: 01/19/2021] [Indexed: 12/30/2022]
Abstract
Abstract. BACKGROUND Altered expression of the complement component C4A gene is a known risk factor for schizophrenia. Further, predicted brain C4A expression has also been associated with memory function highlighting that altered C4A expression in the brain may be relevant for cognitive and behavioral traits. METHODS We obtained genetic information and performance measures on seven cognitive tasks for up to 329 773 individuals from the UK Biobank, as well as brain imaging data for a subset of 33 003 participants. Direct genotypes for variants (n = 3213) within the major histocompatibility complex region were used to impute C4 structural variation, from which predicted expression of the C4A and C4B genes in human brain tissue were predicted. We investigated if predicted brain C4A or C4B expression were associated with cognitive performance and brain imaging measures using linear regression analyses. RESULTS We identified significant negative associations between predicted C4A expression and performance on select cognitive tests, and significant associations with MRI-based cortical thickness and surface area in select regions. Finally, we observed significant inconsistent partial mediation of the effects of predicted C4A expression on cognitive performance, by specific brain structure measures. CONCLUSIONS These results demonstrate that the C4 risk locus is associated with the central endophenotypes of cognitive performance and brain morphology, even when considered independently of other genetic risk factors and in individuals without mental or neurological disorders.
Collapse
Affiliation(s)
- Kevin S. O'Connell
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ida E. Sønderby
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Dennis van der Meer
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Lavinia Athanasiu
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Olav B. Smeland
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Dag Alnæs
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Tobias Kaufmann
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Lars T. Westlye
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Vidar M. Steen
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Dr Einar Martens' Research Group for Biological Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Ole A. Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Timothy Hughes
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| |
Collapse
|
18
|
Genome-wide Association Analysis of Parkinson's Disease and Schizophrenia Reveals Shared Genetic Architecture and Identifies Novel Risk Loci. Biol Psychiatry 2021; 89:227-235. [PMID: 32201043 PMCID: PMC7416467 DOI: 10.1016/j.biopsych.2020.01.026] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 01/14/2020] [Accepted: 01/30/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Parkinson's disease (PD) and schizophrenia (SCZ) are heritable brain disorders that involve dysregulation of the dopaminergic system. Epidemiological studies have reported potential comorbidity between the disorders, and movement disturbances are common in patients with SCZ before treatment with antipsychotic drugs. Despite this, little is known about shared genetic etiology between the disorders. METHODS We analyzed recent large genome-wide association studies on patients with SCZ (N = 77,096) and PD (N = 417,508) using a conditional/conjunctional false discovery rate (FDR) approach to evaluate overlap in common genetic variants and improve statistical power for genetic discovery. Using a variety of biological resources, we functionally characterized the identified genomic loci. RESULTS We observed genetic enrichment in PD conditional on associations with SCZ and vice versa, indicating polygenic overlap. We then leveraged this cross-trait enrichment using conditional FDR analysis and identified 9 novel PD risk loci and 1 novel SCZ locus at conditional FDR < .01. Furthermore, we identified 9 genomic loci jointly associated with PD and SCZ at conjunctional FDR < .05. There was an even distribution of antagonistic and agonistic effect directions among the shared loci, in line with the insignificant genetic correlation between the disorders. Of 67 genes mapped to the shared loci, 65 are expressed in the human brain and show cell type-specific expression profiles. CONCLUSIONS Altogether, the study increases understanding of the genetic architectures underlying SCZ and PD, indicating that common molecular genetic mechanisms may contribute to overlapping pathophysiological and clinical features between the disorders.
Collapse
|
19
|
Liu W, Zhang X, Qiao Y, Cai Y, Yin H, Zheng M, Zhu Y, Wang H. Functional Connectivity Combined With a Machine Learning Algorithm Can Classify High-Risk First-Degree Relatives of Patients With Schizophrenia and Identify Correlates of Cognitive Impairments. Front Neurosci 2020; 14:577568. [PMID: 33324147 PMCID: PMC7725002 DOI: 10.3389/fnins.2020.577568] [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: 06/29/2020] [Accepted: 10/21/2020] [Indexed: 12/21/2022] Open
Abstract
Schizophrenia (SCZ) is an inherited disease, with the familial risk being among the most important factors when evaluating an individual's risk for SCZ. However, robust imaging biomarkers for the disease that can be used for diagnosis and determination of the prognosis are lacking. Here, we explore the potential of functional connectivity (FC) for use as a biomarker for the early detection of high-risk first-degree relatives (FDRs). Thirty-eight first-episode SCZ patients, 38 healthy controls (HCs), and 33 FDRs were scanned using resting-state functional magnetic resonance imaging. The subjects' brains were parcellated into 200 regions using the Craddock atlas, and the FC between each pair of regions was used as a classification feature. Multivariate pattern analysis using leave-one-out cross-validation achieved a correct classification rate of 88.15% [sensitivity 84.06%, specificity 92.18%, and area under the receiver operating characteristic curve (AUC) 0.93] for differentiating SCZ patients from HCs. FC located within the default mode, frontal-parietal, auditory, and sensorimotor networks contributed mostly to the accurate classification. The FC patterns of each FDR were input into each classification model as test data to obtain a corresponding prediction label (a total of 76 individual classification scores), and the averaged individual classification score was then used as a robust measure to characterize whether each FDR showed an SCZ-type or HC-type FC pattern. A significant negative correlation was found between the average classification scores of the FDRs and their semantic fluency scores. These findings suggest that FC combined with a machine learning algorithm could help to predict whether FDRs are likely to show an SCZ-specific or HC-specific FC pattern.
Collapse
Affiliation(s)
- Wenming Liu
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Xiao Zhang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Yuting Qiao
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Yanhui Cai
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Minwen Zheng
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Yuanqiang Zhu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| |
Collapse
|
20
|
Tomyshev AS, Lebedeva IS, Kananovich PS, Pomytkin AN, Bazhenova DA, Kaleda VG. Multimodal MRI of Conduction Tracts and Anatomy of the Cerebral Gray Matter in Familial Risk of Affective Disorders and Schizophrenia. Bull Exp Biol Med 2020; 169:614-618. [PMID: 32986216 DOI: 10.1007/s10517-020-04939-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Indexed: 11/27/2022]
Abstract
The present study analyzed diffusion characteristics of white matter tracts and grey matter anatomy in 48 mentally healthy participants, including first-degree relatives of patients with schizophrenia (N=13) and affective spectrum disorders (N=13). The subgroup with familial risk of schizophrenia displayed abnormalities in the structural connectivity and increased cortical thickness in the superior frontal gyrus. No differences in the analyzed characteristics were revealed in the subgroup with familial risk for affective disorders. The results are discussed within the framework of the concepts of endophenotypes and processes reflecting compensatory and protective mechanisms.
Collapse
Affiliation(s)
- A S Tomyshev
- Research Center of Mental Health, Moscow, Russia.
| | - I S Lebedeva
- Research Center of Mental Health, Moscow, Russia
| | | | - A N Pomytkin
- Research Center of Mental Health, Moscow, Russia
| | - D A Bazhenova
- Faculty of Fundamental Medicine, M. V. Lomonosov Moscow State University, Moscow, Russia
| | - V G Kaleda
- Research Center of Mental Health, Moscow, Russia
| |
Collapse
|
21
|
Brain imaging genomics: influences of genomic variability on the structure and function of the human brain. MED GENET-BERLIN 2020. [DOI: 10.1515/medgen-2020-2007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Brain imaging genomics is an emerging discipline in which genomic and brain imaging data are integrated in order to elucidate the molecular mechanisms that underly brain phenotypes and diseases, including neuropsychiatric disorders. As with all genetic analyses of complex traits and diseases, brain imaging genomics has evolved from small, individual candidate gene investigations towards large, collaborative genome-wide association studies. Recent investigations, mostly population-based, have studied well-powered cohorts comprising tens of thousands of individuals and identified multiple robust associations of single-nucleotide polymorphisms and copy number variants with structural and functional brain phenotypes. Such systematic genomic screens of millions of genetic variants have generated initial insights into the genetic architecture of brain phenotypes and demonstrated that their etiology is polygenic in nature, involving multiple common variants with small effect sizes and rare variants with larger effect sizes. Ongoing international collaborative initiatives are now working to obtain a more complete picture of the underlying biology. As in other complex phenotypes, novel approaches – such as gene–gene interaction, gene–environment interaction, and epigenetic analyses – are being implemented in order to investigate their contribution to the observed phenotypic variability. An important consideration for future research will be the translation of brain imaging genomics findings into clinical practice.
Collapse
|
22
|
Smeland OB, Frei O, Dale AM, Andreassen OA. The polygenic architecture of schizophrenia — rethinking pathogenesis and nosology. Nat Rev Neurol 2020; 16:366-379. [DOI: 10.1038/s41582-020-0364-0] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2020] [Indexed: 02/07/2023]
|
23
|
Levchenko A, Nurgaliev T, Kanapin A, Samsonova A, Gainetdinov RR. Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders. Heliyon 2020; 6:e03990. [PMID: 32462093 PMCID: PMC7240336 DOI: 10.1016/j.heliyon.2020.e03990] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Revised: 10/31/2019] [Accepted: 05/12/2020] [Indexed: 12/13/2022] Open
Abstract
A personalized medicine approach seems to be particularly applicable to psychiatry. Indeed, considering mental illness as deregulation, unique to each patient, of molecular pathways, governing the development and functioning of the brain, seems to be the most justified way to understand and treat disorders of this medical category. In order to extract correct information about the implicated molecular pathways, data can be drawn from sampling phenotypic and genetic biomarkers and then analyzed by a machine learning algorithm. This review describes current difficulties in the field of personalized psychiatry and gives several examples of possibly actionable biomarkers of psychotic and other psychiatric disorders, including several examples of genetic studies relevant to personalized psychiatry. Most of these biomarkers are not yet ready to be introduced in clinical practice. In a next step, a perspective on the path personalized psychiatry may take in the future is given, paying particular attention to machine learning algorithms that can be used with the goal of handling multidimensional datasets.
Collapse
Affiliation(s)
- Anastasia Levchenko
- Theodosius Dobzhansky Center for Genome Bioinformatics, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
| | - Timur Nurgaliev
- Institute of Translational Biomedicine, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
| | - Alexander Kanapin
- Theodosius Dobzhansky Center for Genome Bioinformatics, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
| | - Anastasia Samsonova
- Theodosius Dobzhansky Center for Genome Bioinformatics, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
| | - Raul R. Gainetdinov
- Institute of Translational Biomedicine, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
| |
Collapse
|
24
|
Ohi K, Shimada T, Kataoka Y, Yasuyama T, Kawasaki Y, Shioiri T, Thompson PM. Genetic correlations between subcortical brain volumes and psychiatric disorders. Br J Psychiatry 2020; 216:280-283. [PMID: 32000869 DOI: 10.1192/bjp.2019.277] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Psychiatric disorders as well as subcortical brain volumes are highly heritable. Large-scale genome-wide association studies (GWASs) for these traits have been performed. We investigated the genetic correlations between five psychiatric disorders and the seven subcortical brain volumes and the intracranial volume from large-scale GWASs by linkage disequilibrium score regression. We revealed weak overlaps between the genetic variants associated with psychiatric disorders and subcortical brain and intracranial volumes, such as in schizophrenia and the hippocampus and bipolar disorder and the accumbens. We confirmed shared aetiology and polygenic architecture across the psychiatric disorders and the specific subcortical brain and intracranial volume.
Collapse
Affiliation(s)
- Kazutaka Ohi
- Associate Professor, Medical Research Institute, Kanazawa Medical University; Department of Neuropsychiatry, Kanazawa Medical University; Department of General Internal Medicine, Kanazawa Medical University; and Department of Psychiatry and Psychotherapy, Gifu University Graduate School of Medicine, Japan
| | - Takamitsu Shimada
- Assistant Professor, Department of Neuropsychiatry, Kanazawa Medical University, Japan
| | - Yuzuru Kataoka
- Graduate Student, Department of Neuropsychiatry, Kanazawa Medical University, Japan
| | - Toshiki Yasuyama
- Assistant Professor, Department of Neuropsychiatry, Kanazawa Medical University, Japan
| | - Yasuhiro Kawasaki
- Professor, Department of Neuropsychiatry, Kanazawa Medical University, Japan
| | - Toshiki Shioiri
- Professor, Department of Psychiatry and Psychotherapy, Gifu University Graduate School of Medicine, Japan
| | - Paul M Thompson
- Professor, Imaging Genetics Center, Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, USA
| |
Collapse
|
25
|
Feng J, Chen C, Cai Y, Ye Z, Feng K, Liu J, Zhang L, Yang Q, Li A, Sheng J, Zhu B, Yu Z, Chen C, Dong Q, Xue G. Partitioning heritability analyses unveil the genetic architecture of human brain multidimensional functional connectivity patterns. Hum Brain Mapp 2020; 41:3305-3317. [PMID: 32329556 PMCID: PMC7375050 DOI: 10.1002/hbm.25018] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 03/27/2020] [Accepted: 04/12/2020] [Indexed: 01/22/2023] Open
Abstract
Resting-state functional connectivity profiles have been increasingly shown to be important endophenotypes that are tightly linked to human cognitive functions and psychiatric diseases, yet the genetic architecture of this multidimensional trait is barely understood. Using a unique sample of 1,704 unrelated, young and healthy Chinese Han individuals, we revealed a significant heritability of functional connectivity patterns in the whole brain and several subnetworks. We further proposed a partitioned heritability analysis for multidimensional functional connectivity patterns, which revealed the common and unique enrichment patterns of the genetic contributions to brain connectivity patterns for several gene sets linked to brain functions, including the genes expressed preferentially in the central nervous system and those associated with intelligence, educational attainment, attention-deficit/hyperactivity disorder, and schizophrenia. These results for the first time reveal the genetic architecture of multidimensional brain connectivity patterns across different networks and advance our understanding of the complex relationship between gene sets, neural networks, and behaviors.
Collapse
Affiliation(s)
- Junjiao Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chunhui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ying Cai
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China
| | - Zhifang Ye
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Kanyin Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Liang Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qinghao Yang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Anqi Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jintao Sheng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Bi Zhu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zhaoxia Yu
- Department of Statistics, University of California, Irvine, California, USA
| | - Chuansheng Chen
- Department of Psychological Science, University of California, Irvine, California, USA
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Gui Xue
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| |
Collapse
|
26
|
Rodrigue AL, Alexander-Bloch AF, Knowles EEM, Mathias SR, Mollon J, Koenis MMG, Perrone-Bizzozero NI, Almasy L, Turner JA, Calhoun VD, Glahn DC. Genetic Contributions to Multivariate Data-Driven Brain Networks Constructed via Source-Based Morphometry. Cereb Cortex 2020; 30:4899-4913. [PMID: 32318716 DOI: 10.1093/cercor/bhaa082] [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] [Received: 10/09/2019] [Revised: 02/12/2020] [Accepted: 03/17/2020] [Indexed: 11/14/2022] Open
Abstract
Identifying genetic factors underlying neuroanatomical variation has been difficult. Traditional methods have used brain regions from predetermined parcellation schemes as phenotypes for genetic analyses, although these parcellations often do not reflect brain function and/or do not account for covariance between regions. We proposed that network-based phenotypes derived via source-based morphometry (SBM) may provide additional insight into the genetic architecture of neuroanatomy given its data-driven approach and consideration of covariance between voxels. We found that anatomical SBM networks constructed on ~ 20 000 individuals from the UK Biobank were heritable and shared functionally meaningful genetic overlap with each other. We additionally identified 27 unique genetic loci that contributed to one or more SBM networks. Both GWA and genetic correlation results indicated complex patterns of pleiotropy and polygenicity similar to other complex traits. Lastly, we found genetic overlap between a network related to the default mode and schizophrenia, a disorder commonly associated with neuroanatomic alterations.
Collapse
Affiliation(s)
- Amanda L Rodrigue
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | | | - Emma E M Knowles
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Samuel R Mathias
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Josephine Mollon
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Marinka M G Koenis
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA.,Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT 06106, USA
| | - Nora I Perrone-Bizzozero
- Department of Neurosciences, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA.,Department of Psychiatry and Behavioral Sciences, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Laura Almasy
- Department of Genetics, Perelman School of Medicine, and the Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jessica A Turner
- Psychology Department, Neurosciences Institute, Georgia State University, Atlanta, GA 30303, USA.,The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Vince D Calhoun
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA.,Psychology Department, Neurosciences Institute, Georgia State University, Atlanta, GA 30303, USA.,The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA.,Mind Research Network, Department of Psychiatry and Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico 87131, USA
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT 06106, USA
| |
Collapse
|
27
|
Thompson PM, Jahanshad N, Ching CRK, Salminen LE, Thomopoulos SI, Bright J, Baune BT, Bertolín S, Bralten J, Bruin WB, Bülow R, Chen J, Chye Y, Dannlowski U, de Kovel CGF, Donohoe G, Eyler LT, Faraone SV, Favre P, Filippi CA, Frodl T, Garijo D, Gil Y, Grabe HJ, Grasby KL, Hajek T, Han LKM, Hatton SN, Hilbert K, Ho TC, Holleran L, Homuth G, Hosten N, Houenou J, Ivanov I, Jia T, Kelly S, Klein M, Kwon JS, Laansma MA, Leerssen J, Lueken U, Nunes A, Neill JO, Opel N, Piras F, Piras F, Postema MC, Pozzi E, Shatokhina N, Soriano-Mas C, Spalletta G, Sun D, Teumer A, Tilot AK, Tozzi L, van der Merwe C, Van Someren EJW, van Wingen GA, Völzke H, Walton E, Wang L, Winkler AM, Wittfeld K, Wright MJ, Yun JY, Zhang G, Zhang-James Y, Adhikari BM, Agartz I, Aghajani M, Aleman A, Althoff RR, Altmann A, Andreassen OA, Baron DA, Bartnik-Olson BL, Marie Bas-Hoogendam J, Baskin-Sommers AR, Bearden CE, Berner LA, Boedhoe PSW, Brouwer RM, Buitelaar JK, Caeyenberghs K, Cecil CAM, Cohen RA, Cole JH, Conrod PJ, De Brito SA, de Zwarte SMC, Dennis EL, Desrivieres S, Dima D, Ehrlich S, Esopenko C, Fairchild G, Fisher SE, Fouche JP, Francks C, Frangou S, Franke B, Garavan HP, Glahn DC, Groenewold NA, Gurholt TP, Gutman BA, Hahn T, Harding IH, Hernaus D, Hibar DP, Hillary FG, Hoogman M, Hulshoff Pol HE, Jalbrzikowski M, Karkashadze GA, Klapwijk ET, Knickmeyer RC, Kochunov P, Koerte IK, Kong XZ, Liew SL, Lin AP, Logue MW, Luders E, Macciardi F, Mackey S, Mayer AR, McDonald CR, McMahon AB, Medland SE, Modinos G, Morey RA, Mueller SC, Mukherjee P, Namazova-Baranova L, Nir TM, Olsen A, Paschou P, Pine DS, Pizzagalli F, Rentería ME, Rohrer JD, Sämann PG, Schmaal L, Schumann G, Shiroishi MS, Sisodiya SM, Smit DJA, Sønderby IE, Stein DJ, Stein JL, Tahmasian M, Tate DF, Turner JA, van den Heuvel OA, van der Wee NJA, van der Werf YD, van Erp TGM, van Haren NEM, van Rooij D, van Velzen LS, Veer IM, Veltman DJ, Villalon-Reina JE, Walter H, Whelan CD, Wilde EA, Zarei M, Zelman V. ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries. Transl Psychiatry 2020; 10:100. [PMID: 32198361 PMCID: PMC7083923 DOI: 10.1038/s41398-020-0705-1] [Citation(s) in RCA: 305] [Impact Index Per Article: 76.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 12/11/2019] [Accepted: 12/20/2019] [Indexed: 02/07/2023] Open
Abstract
This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.
Collapse
Affiliation(s)
- Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Lauren E Salminen
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Joanna Bright
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Bernhard T Baune
- Department of Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Sara Bertolín
- Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, Barcelona, Spain
| | - Janita Bralten
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Willem B Bruin
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Robin Bülow
- Institute for Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Jian Chen
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA
| | - Yann Chye
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, VIC, Australia
| | - Udo Dannlowski
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Carolien G F de Kovel
- Biometris Wageningen University and Research, Wageningen, The Netherlands
- Language & Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Gary Donohoe
- The Center for Neuroimaging and Cognitive Genomics, School of Psychology, National University of Ireland, Galway, Ireland
| | - Lisa T Eyler
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Desert-Pacific Mental Illness Research, Education, and Clinical Center, VA San Diego Healthcare System, San Diego, CA, USA
| | - Stephen V Faraone
- Departments of Psychiatry and of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Pauline Favre
- INSERM Unit 955 Team 15 'Translational Psychiatry', Créteil, France
- NeuroSpin, UNIACT Lab, Psychiatry Team, CEA Saclay, Gif-Sur-Yvette, France
| | - Courtney A Filippi
- National Institute of Mental Health, National of Health, Bethesda, MD, USA
| | - Thomas Frodl
- Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Magdeburg, Germany
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Daniel Garijo
- Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA
| | - Yolanda Gil
- Information Sciences Institute, University of Southern California, Marina del Rey, CA, USA
- Department of Computer Science, University of Southern California, Los Angeles, CA, USA
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany
| | - Katrina L Grasby
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Tomas Hajek
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
- National Institute of Mental Health, Klecany, Czech Republic
| | - Laura K M Han
- Department of Psychiatry, Amsterdam University Medical Centers, VU University Medical Center, GGZ inGeest, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Sean N Hatton
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA
- Brain and Mind Centre, University of Sydney, Sydney, Australia
| | - Kevin Hilbert
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Tiffany C Ho
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA
- Department of Psychiatry & Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Laurena Holleran
- The Center for Neuroimaging and Cognitive Genomics, School of Psychology, National University of Ireland, Galway, Ireland
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Norbert Hosten
- Institute for Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Josselin Houenou
- INSERM Unit 955 Team 15 'Translational Psychiatry', Créteil, France
- NeuroSpin, UNIACT Lab, Psychiatry Team, CEA Saclay, Gif-Sur-Yvette, France
- APHP, Mondor University Hospitals, School of Medicine, DMU Impact, Psychiatry Department, Créteil, France
| | - Iliyan Ivanov
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tianye Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Centre for Population Neuroscience and Precision Medicine (PONS), MRC SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Sinead Kelly
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA
| | - Marieke Klein
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Jun Soo Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Max A Laansma
- Department of Anatomy & Neurosciences, Amsterdam UMC, Location VUmc, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Jeanne Leerssen
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Ulrike Lueken
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Abraham Nunes
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
- Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada
| | - Joseph O' Neill
- Child & Adolescent Psychiatry, University of California, Los Angeles, Los Angeles, CA, USA
| | - Nils Opel
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Federica Piras
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Merel C Postema
- Language & Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Elena Pozzi
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia
- Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, VIC, Australia
| | - Natalia Shatokhina
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Carles Soriano-Mas
- Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, Barcelona, Spain
- CIBERSAM-G17, Madrid, Spain
- Department of Psychobiology and Methodology in Health Sciences, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Daqiang Sun
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Mental Health, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Amanda K Tilot
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Leonardo Tozzi
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Celia van der Merwe
- Stanley Center for Psychiatric Research, The Broad Institute, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Eus J W Van Someren
- Department of Sleep and Cognition, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
- Psychiatry and Integrative Neurophysiology, VU University, Amsterdam UMC, Amsterdam, The Netherlands
| | - Guido A van Wingen
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- German Centre for Cardiovascular Research, Partner Site Greifswald, Greifswald, Germany
| | - Esther Walton
- Department of Psychology, University of Bath, Bath, UK
| | - Lei Wang
- Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Anderson M Winkler
- National Institute of Mental Health, National of Health, Bethesda, MD, USA
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | - Je-Yeon Yun
- Seoul National University Hospital, Seoul, Republic of Korea
- Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Guohao Zhang
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, MD, USA
| | - Yanli Zhang-James
- Departments of Psychiatry and of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
- Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Bhim M Adhikari
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health & Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Moji Aghajani
- Department of Psychiatry, Amsterdam UMC, Location VUmc, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Research & Innovation, GGZ InGeest, Amsterdam, The Netherlands
| | - André Aleman
- University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Robert R Althoff
- Psychiatry, Pediatrics, and Psychological Sciences, University of Vermont, Burlington, VT, USA
| | - Andre Altmann
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health & Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - David A Baron
- Provost and Senior Vice President, Western University of Health Sciences, Pomona, CA, USA
| | | | - Janna Marie Bas-Hoogendam
- Institute of Psychology, Leiden University, Leiden, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | | | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - Laura A Berner
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Premika S W Boedhoe
- Department of Psychiatry, Amsterdam UMC, Location VUmc, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Rachel M Brouwer
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Jan K Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Burwood, VIC, Australia
| | - Charlotte A M Cecil
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Centre, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Ronald A Cohen
- Center for Cognitive Aging and Memory, University of Florida, Gainesville, FL, USA
- Clinical and Health Psychology, Gainesville, FL, USA
| | - James H Cole
- Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, London, UK
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Patricia J Conrod
- Universite de Montreal, Centre de Recherche CHU Ste-Justine, Montreal, QC, Canada
| | - Stephane A De Brito
- School of Psychology and Centre for Human Brain Health, University of Birmingham, Birmingham, UK
| | - Sonja M C de Zwarte
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Emily L Dennis
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
- Psychiatry Neuroimaging Laboratory, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sylvane Desrivieres
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Danai Dima
- Department of Psychology, School of Arts and Social Sciences, City, University of London, London, UK
- Department of Neuroimaging, Institute of Psychology, Psychiatry and Neurosciences, King's College London, London, UK
| | - Stefan Ehrlich
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Carrie Esopenko
- Department of Rehabilitation and Movement Sciences, School of Health Professions, Rutgers Biomedical Health Sciences, Newark, NJ, USA
| | | | - Simon E Fisher
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Language & Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Jean-Paul Fouche
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
- SU/UCT MRC Unit on Risk & Resilience in Mental Disorders, University of Stellenbosch, Stellenbosch, South Africa
| | - Clyde Francks
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Language & Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- University of British Columbia, Vancouver, Canada
| | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Hugh P Garavan
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford, CT, USA
| | - Nynke A Groenewold
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Tiril P Gurholt
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health & Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Boris A Gutman
- Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
- Institute for Information Transmission Problems, Kharkevich Institute, Moscow, Russian Federation
| | - Tim Hahn
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Ian H Harding
- Turner Institute for Brain and Mental Health & School of Psychological Sciences, Monash University, Melbourne, VIC, Australia
| | - Dennis Hernaus
- Department of Psychiatry & Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | | | - Frank G Hillary
- Department of Psychology, Penn State University, University Park, PA, USA
- Social Life and Engineering Sciences Imaging Center, University Park, PA, USA
| | - Martine Hoogman
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Hilleke E Hulshoff Pol
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | | | - George A Karkashadze
- Research and Scientific Institute of Pediatrics and Child Health, CCH RAS, Ministry of Science and Higher Education, Moscow, Russian Federation
| | - Eduard T Klapwijk
- Institute of Psychology, Leiden University, Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Rebecca C Knickmeyer
- Department of Pediatrics, Michigan State University, East Lansing, MI, USA
- Institute for Quantitative Health Science and Engineering, East Lansing, MI, USA
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Peter Kochunov
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Inga K Koerte
- Psychiatry Neuroimaging Laboratory, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- CBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Xiang-Zhen Kong
- Language & Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Sook-Lei Liew
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Chan Division of Occupational Science and Occupational Therapy, Los Angeles, CA, USA
| | - Alexander P Lin
- Center for Clinical Spectroscopy, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Mark W Logue
- National Center for PTSD at Boston VA Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
- Biomedical Genetics, Boston University School of Medicine, Boston, MA, USA
| | - Eileen Luders
- School of Psychology, University of Auckland, Auckland, New Zealand
- Laboratory of Neuro Imaging, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Fabio Macciardi
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, USA
| | - Scott Mackey
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | | | - Carrie R McDonald
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, USA
- Psychiatry, San Diego, CA, USA
| | - Agnes B McMahon
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
- The Kavli Foundation, Los Angeles, CA, USA
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Gemma Modinos
- Department of Neuroimaging, Institute of Psychology, Psychiatry and Neurosciences, King's College London, London, UK
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Rajendra A Morey
- Department of Psychiatry, Duke University School of Medicine, Durham, NC, USA
- Mental Illness Research Education and Clinical Center, Durham VA Medical Center, Durham, NC, USA
| | - Sven C Mueller
- Experimental Clinical & Health Psychology, Ghent University, Ghent, Belgium
- Department of Personality, Psychological Assessment and Treatment, University of Deusto, Bilbao, Spain
| | | | - Leyla Namazova-Baranova
- Research and Scientific Institute of Pediatrics and Child Health, CCH RAS, Ministry of Science and Higher Education, Moscow, Russian Federation
- Department of Pediatrics, Russian National Research Medical University MoH RF, Moscow, Russian Federation
| | - Talia M Nir
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Alexander Olsen
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Physical Medicine and Rehabilitation, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | | | - Daniel S Pine
- National Institute of Mental Health Intramural Research Program, Bethesda, MD, USA
| | - Fabrizio Pizzagalli
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Miguel E Rentería
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jonathan D Rohrer
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | | | - Lianne Schmaal
- Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, VIC, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Gunter Schumann
- Centre for Population Neuroscience and Precision Medicine (PONS), MRC SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Psychiatry and Psychotherapy, Charite, Humboldt University, Berlin, Germany
| | - Mark S Shiroishi
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
- Department of Radiology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Sanjay M Sisodiya
- Department of Clinical and Experimental Epilepsy, University College London, London, UK
- Chalfont Centre for Epilepsy, Chalfont St Peter, UK
| | - Dirk J A Smit
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Ida E Sønderby
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health & Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Dan J Stein
- Department of Psychiatry & Neuroscience Institute, SA MRC Unit on Risk & Resilience in Mental Disorders, Cape Town, South Africa
| | - Jason L Stein
- Department of Genetics & UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Masoud Tahmasian
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, I. R., Iran
| | - David F Tate
- Department of Neurology, TBI and Concussion Center, Salt Lake City, UT, USA
- Missouri Institute of Mental Health, Berkeley, MO, USA
| | - Jessica A Turner
- Psychology Department & Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | - Odile A van den Heuvel
- Department of Anatomy & Neurosciences, Amsterdam UMC, Location VUmc, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Psychiatry, Amsterdam UMC, Location VUmc, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Nic J A van der Wee
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Ysbrand D van der Werf
- Department of Anatomy & Neurosciences, Amsterdam UMC, Location VUmc, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, USA
| | - Neeltje E M van Haren
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Daan van Rooij
- Donders Centre for Cognitive Neuroimaging, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Laura S van Velzen
- Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, VIC, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Ilya M Veer
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Dick J Veltman
- Department of Psychiatry, Amsterdam UMC, Location VUmc, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Julio E Villalon-Reina
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Henrik Walter
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Christopher D Whelan
- Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin, Ireland
- Research and Early Development, Biogen Inc, Cambridge, MA, USA
| | - Elisabeth A Wilde
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
- VA Salt Lake City Healthcare System, Salt Lake City, UT, USA
- Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
| | - Mojtaba Zarei
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, I. R., Iran
| | - Vladimir Zelman
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Skolkovo Institute of Science and Technology, Moscow, Russian Federation
| |
Collapse
|
28
|
Xu Q, Guo L, Cheng J, Wang M, Geng Z, Zhu W, Zhang B, Liao W, Qiu S, Zhang H, Xu X, Yu Y, Gao B, Han T, Yao Z, Cui G, Liu F, Qin W, Zhang Q, Li MJ, Liang M, Chen F, Xian J, Li J, Zhang J, Zuo XN, Wang D, Shen W, Miao Y, Yuan F, Lui S, Zhang X, Xu K, Zhang LJ, Ye Z, Yu C. CHIMGEN: a Chinese imaging genetics cohort to enhance cross-ethnic and cross-geographic brain research. Mol Psychiatry 2020; 25:517-529. [PMID: 31827248 PMCID: PMC7042768 DOI: 10.1038/s41380-019-0627-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 11/21/2019] [Accepted: 11/27/2019] [Indexed: 02/05/2023]
Abstract
The Chinese Imaging Genetics (CHIMGEN) study establishes the largest Chinese neuroimaging genetics cohort and aims to identify genetic and environmental factors and their interactions that are associated with neuroimaging and behavioral phenotypes. This study prospectively collected genomic, neuroimaging, environmental, and behavioral data from more than 7000 healthy Chinese Han participants aged 18-30 years. As a pioneer of large-sample neuroimaging genetics cohorts of non-Caucasian populations, this cohort can provide new insights into ethnic differences in genetic-neuroimaging associations by being compared with Caucasian cohorts. In addition to micro-environmental measurements, this study also collects hundreds of quantitative macro-environmental measurements from remote sensing and national survey databases based on the locations of each participant from birth to present, which will facilitate discoveries of new environmental factors associated with neuroimaging phenotypes. With lifespan environmental measurements, this study can also provide insights on the macro-environmental exposures that affect the human brain as well as their timing and mechanisms of action.
Collapse
Affiliation(s)
- Qiang Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052, Tianjin, China
| | - Lining Guo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052, Tianjin, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, China
| | - Meiyun Wang
- Department of Radiology, Zhengzhou University People's Hospital and Henan Provincial People's Hospital, 450003, Zhengzhou, China
- Henan Key Laboratory for Medical Imaging of Neurological Diseases, 450003, Zhengzhou, China
| | - Zuojun Geng
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, 050000, Shijiazhuang, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, China
| | - Bing Zhang
- Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, 210008, Nanjing, China
| | - Weihua Liao
- Department of Radiology, Xiangya Hospital, Central South University, 410008, Changsha, China
- National Clinical Research Center for Geriatric Disorder, 410008, Changsha, China
| | - Shijun Qiu
- Department of Medical Imaging, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, 510405, Guangzhou, China
| | - Hui Zhang
- Department of Radiology, The First Hospital of Shanxi Medical University, 030001, Taiyuan, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, 310009, Hangzhou, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022, Hefei, China
| | - Bo Gao
- Department of Radiology, Yantai Yuhuangding Hospital, 264000, Yantai, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, 300350, Tianjin, China
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, 300350, Tianjin, China
| | - Zhenwei Yao
- Department of Radiology, Huashan Hosptial, Fudan University, 200040, Shanghai, China
| | - Guangbin Cui
- Functional and Molecular Imaging Key Lab of Shaanxi Province & Department of Radiology, Tangdu Hospital, The Military Medical University of PLA Airforce (Fourth Military Medical University), 710038, Xi'an, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052, Tianjin, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052, Tianjin, China
| | - Quan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052, Tianjin, China
| | - Mulin Jun Li
- Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, 300070, Tianjin, China
| | - Meng Liang
- School of Medical Imaging, Tianjin Medical University, 300203, Tianjin, China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital, 570311, Haikou, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, 100730, Beijing, China
| | - Jiance Li
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, 325000, Wenzhou, China
| | - Jing Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, 730050, Lanzhou, China
| | - Xi-Nian Zuo
- Department of Psychology, University of Chinese Academy of Sciences (CAS), 100049, Beijing, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, 100101, Beijing, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, 250012, Jinan, China
| | - Wen Shen
- Department of Radiology, Tianjin First Center Hospital, 300192, Tianjin, China
| | - Yanwei Miao
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, 116011, Dalian, China
| | - Fei Yuan
- Department of Radiology, Pingjin Hospital, Logistics University of Chinese People's Armed Police Forces, 300162, Tianjin, China
| | - Su Lui
- Department of Radiology, The Center for Medical Imaging, West China Hospital of Sichuan University, 610041, Chengdu, China
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, 325000, Wenzhou, China
| | - Xiaochu Zhang
- CAS Key Laboratory of Brain Function and Disease, University of Science and Technology of China, 230026, Hefei, China
- School of Life Sciences, University of Science & Technology of China, 230026, Hefei, China
| | - Kai Xu
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, 221006, Xuzhou, China
- School of Medical Imaging, Xuzhou Medical University, 221004, Xuzhou, China
| | - Long Jiang Zhang
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, 210002, Nanjing, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, 300060, Tianjin, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052, Tianjin, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China.
| |
Collapse
|
29
|
Shen L, Thompson PM. Brain Imaging Genomics: Integrated Analysis and Machine Learning. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2020; 108:125-162. [PMID: 31902950 PMCID: PMC6941751 DOI: 10.1109/jproc.2019.2947272] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.
Collapse
Affiliation(s)
- Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA 90232, USA
| |
Collapse
|
30
|
Nesic MJ, Stojkovic B, Maric NP. On the origin of schizophrenia: Testing evolutionary theories in the post-genomic era. Psychiatry Clin Neurosci 2019; 73:723-730. [PMID: 31525268 DOI: 10.1111/pcn.12933] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 09/10/2019] [Accepted: 09/12/2019] [Indexed: 12/13/2022]
Abstract
Considering the relatively high heritability of schizophrenia and the fact that it significantly reduces the reproductive fitness of affected individuals, it is not clear how the disorder is still maintained in human populations at a disproportionally high prevalence. Many theories propose that the disorder is a result of a trade-off between costs and benefits of the evolution of exclusively human adaptations. There have also been suggestions that schizophrenia risk alleles are accompanied with increase in fitness of affected persons or their relatives in both past and current social contexts. The discoveries of novel schizophrenia-related genes and the advancements in comparative genomics (especially comparisons of the human genome and the genomes of related species, such as chimpanzees and extinct hominids) have finally made certain evolutionary theories testable. In this paper, we review the current understanding of the genetics of schizophrenia, the basic principles of evolution that complement our understanding of the subject, and the latest genetic studies that examine long-standing evolutionary theories of schizophrenia using novel methodologies and data. We find that the origin of schizophrenia is complex and likely governed by different evolutionary mechanisms that are not mutually exclusive. Furthermore, the most recent evidence implies that schizophrenia cannot be comprehended as a trait that has elevated fitness in human evolutionary lineage, but has been a mildly deleterious by-product of specific patterns of the evolution of the human brain. In other words, novel findings do not support previous hypotheses stating that schizophrenia risk genes have an evolutionary advantage.
Collapse
Affiliation(s)
- Milica J Nesic
- Clinic for Psychiatry, Clinical Center of Serbia, Belgrade, Serbia
| | - Biljana Stojkovic
- Institute of Zoology, Faculty of Biology, University of Belgrade, Belgrade, Serbia.,Department of Evolutionary Biology, Institute for Biological Research 'Siniša Stanković', University of Belgrade, Belgrade, Serbia
| | - Nadja P Maric
- Clinic for Psychiatry, Clinical Center of Serbia, Belgrade, Serbia.,Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| |
Collapse
|
31
|
Neilson E, Shen X, Cox SR, Clarke TK, Wigmore EM, Gibson J, Howard DM, Adams MJ, Harris MA, Davies G, Deary IJ, Whalley HC, McIntosh AM, Lawrie SM. Impact of Polygenic Risk for Schizophrenia on Cortical Structure in UK Biobank. Biol Psychiatry 2019; 86:536-544. [PMID: 31171358 DOI: 10.1016/j.biopsych.2019.04.013] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Revised: 04/05/2019] [Accepted: 04/05/2019] [Indexed: 11/28/2022]
Abstract
BACKGROUND Schizophrenia is a neurodevelopmental disorder with many genetic variants of individually small effect contributing to phenotypic variation. Lower cortical thickness (CT), surface area, and cortical volume have been demonstrated in people with schizophrenia. Furthermore, a range of obstetric complications (e.g., lower birth weight) are consistently associated with an increased risk for schizophrenia. We investigated whether a high polygenic risk score for schizophrenia (PGRS-SCZ) is associated with CT, surface area, and cortical volume in UK Biobank, a population-based sample, and tested for interactions with birth weight. METHODS Data were available for 2864 participants (nmale/nfemale = 1382/1482; mean age = 62.35 years, SD = 7.40). Linear mixed models were used to test for associations among PGRS-SCZ and cortical volume, surface area, and CT and between PGRS-SCZ and birth weight. Interaction effects of these variables on cortical structure were also tested. RESULTS We found a significant negative association between PGRS-SCZ and global CT; a higher PGRS-SCZ was associated with lower CT across the whole brain. We also report a significant negative association between PGRS-SCZ and insular lobe CT. PGRS-SCZ was not associated with birth weight and no PGRS-SCZ × birth weight interactions were found. CONCLUSIONS These results suggest that individual differences in CT are partly influenced by genetic variants and are most likely not due to factors downstream of disease onset. This approach may help to elucidate the genetic pathophysiology of schizophrenia. Further investigation in case-control and high-risk samples could help identify any localized effects of PGRS-SCZ, and other potential schizophrenia risk factors, on CT as symptoms develop.
Collapse
Affiliation(s)
- Emma Neilson
- Division of Psychiatry, Royal Edinburgh Hospital, Edinburgh, UK.
| | - Xueyi Shen
- Division of Psychiatry, Royal Edinburgh Hospital, Edinburgh, UK
| | - Simon R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Toni-Kim Clarke
- Division of Psychiatry, Royal Edinburgh Hospital, Edinburgh, UK
| | | | - Jude Gibson
- Division of Psychiatry, Royal Edinburgh Hospital, Edinburgh, UK
| | - David M Howard
- Division of Psychiatry, Royal Edinburgh Hospital, Edinburgh, UK
| | - Mark J Adams
- Division of Psychiatry, Royal Edinburgh Hospital, Edinburgh, UK
| | - Mat A Harris
- Division of Psychiatry, Royal Edinburgh Hospital, Edinburgh, UK
| | - Gail Davies
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | | | - Andrew M McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Stephen M Lawrie
- Division of Psychiatry, Royal Edinburgh Hospital, Edinburgh, UK; The Patrick Wild Centre, Royal Edinburgh Hospital, Edinburgh, UK
| |
Collapse
|
32
|
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.
Collapse
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
| |
Collapse
|
33
|
Zeng P, Hao X, Zhou X. Pleiotropic mapping and annotation selection in genome-wide association studies with penalized Gaussian mixture models. Bioinformatics 2019; 34:2797-2807. [PMID: 29635306 DOI: 10.1093/bioinformatics/bty204] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 04/02/2018] [Indexed: 12/11/2022] Open
Abstract
Motivation Genome-wide association studies (GWASs) have identified many genetic loci associated with complex traits. A substantial fraction of these identified loci is associated with multiple traits-a phenomena known as pleiotropy. Identification of pleiotropic associations can help characterize the genetic relationship among complex traits and can facilitate our understanding of disease etiology. Effective pleiotropic association mapping requires the development of statistical methods that can jointly model multiple traits with genome-wide single nucleic polymorphisms (SNPs) together. Results We develop a joint modeling method, which we refer to as the integrative MApping of Pleiotropic association (iMAP). iMAP models summary statistics from GWASs, uses a multivariate Gaussian distribution to account for phenotypic correlation, simultaneously infers genome-wide SNP association pattern using mixture modeling and has the potential to reveal causal relationship between traits. Importantly, iMAP integrates a large number of SNP functional annotations to substantially improve association mapping power, and, with a sparsity-inducing penalty, is capable of selecting informative annotations from a large, potentially non-informative set. To enable scalable inference of iMAP to association studies with hundreds of thousands of individuals and millions of SNPs, we develop an efficient expectation maximization algorithm based on an approximate penalized regression algorithm. With simulations and comparisons to existing methods, we illustrate the benefits of iMAP in terms of both high association mapping power and accurate estimation of genome-wide SNP association patterns. Finally, we apply iMAP to perform a joint analysis of 48 traits from 31 GWAS consortia together with 40 tissue-specific SNP annotations generated from the Roadmap Project. Availability and implementation iMAP is freely available at http://www.xzlab.org/software.html. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Ping Zeng
- Department of Epidemiology and Biostatistics, Xuzhou Medical University, Xuzhou, Jiangsu, China.,Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.,Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Xingjie Hao
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.,Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.,Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| |
Collapse
|
34
|
Shared vulnerability for connectome alterations across psychiatric and neurological brain disorders. Nat Hum Behav 2019; 3:988-998. [DOI: 10.1038/s41562-019-0659-6] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 06/17/2019] [Indexed: 12/13/2022]
|
35
|
Banerjee N, Polushina T, Bettella F, Steen VM, Andreassen OA, Le Hellard S. Analysis of differentially methylated regions in great apes and extinct hominids provides support for the evolutionary hypothesis of schizophrenia. Schizophr Res 2019; 206:209-216. [PMID: 30545758 DOI: 10.1016/j.schres.2018.11.025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 10/12/2018] [Accepted: 11/22/2018] [Indexed: 12/12/2022]
Abstract
INTRODUCTION The persistence of schizophrenia in human populations separated by geography and time led to the evolutionary hypothesis that proposes schizophrenia as a by-product of the higher cognitive abilities of modern humans. To explore this hypothesis, we used here an evolutionary epigenetics approach building on differentially methylated regions (DMRs) of the genome. METHODS We implemented a polygenic enrichment testing pipeline using the summary statistics of genome-wide association studies (GWAS) of schizophrenia and 12 other phenotypes. We investigated the enrichment of association of these traits across genomic regions with variable methylation between modern humans and great apes (orangutans, chimpanzees and gorillas; great ape DMRs) and between modern humans and recently extinct hominids (Neanderthals and Denisovans; hominid DMRs). RESULTS Regions that are hypo-methylated in humans compared to great apes show enrichment of association with schizophrenia only if the major histocompatibility complex (MHC) region is included. With the MHC region removed from the analysis, only a modest enrichment for SNPs of low effect persists. The INRICH pipeline confirms this finding after rigorous permutation and bootstrapping procedures. CONCLUSION The analyses of regions with differential methylation changes in humans and great apes do not provide compelling evidence of enrichment of association with schizophrenia, in contrast to our previous findings on more recent methylation differences between modern humans, Neanderthals and Denisovans. Our results further support the evolutionary hypothesis of schizophrenia and indicate that the origin of some of the genetic susceptibility factors of schizophrenia may lie in recent human evolution.
Collapse
Affiliation(s)
- Niladri Banerjee
- NORMENT - K.G. Jebsen Center for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, Norway; Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway.
| | - Tatiana Polushina
- NORMENT - K.G. Jebsen Center for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, Norway; Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway.
| | - Francesco Bettella
- NORMENT - K.G. Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; NORMENT - K.G. Jebsen Centre, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
| | - Vidar M Steen
- NORMENT - K.G. Jebsen Center for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, Norway; Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway.
| | - Ole A Andreassen
- NORMENT - K.G. Jebsen Center for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; NORMENT - K.G. Jebsen Centre, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
| | - Stephanie Le Hellard
- NORMENT - K.G. Jebsen Center for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, Norway; Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway.
| |
Collapse
|
36
|
Mufford M, Cheung J, Jahanshad N, van der Merwe C, Ding L, Groenewold N, Koen N, Chimusa ER, Dalvie S, Ramesar R, Knowles JA, Lochner C, Hibar DP, Paschou P, van den Heuvel OA, Medland SE, Scharf JM, Mathews CA, Thompson PM, Stein DJ. Concordance of genetic variation that increases risk for tourette syndrome and that influences its underlying neurocircuitry. Transl Psychiatry 2019; 9:120. [PMID: 30902966 PMCID: PMC6430767 DOI: 10.1038/s41398-019-0452-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 02/13/2019] [Accepted: 02/16/2019] [Indexed: 01/18/2023] Open
Abstract
There have been considerable recent advances in understanding the genetic architecture of Tourette syndrome (TS) as well as its underlying neurocircuitry. However, the mechanisms by which genetic variation that increases risk for TS-and its main symptom dimensions-influence relevant brain regions are poorly understood. Here we undertook a genome-wide investigation of the overlap between TS genetic risk and genetic influences on the volume of specific subcortical brain structures that have been implicated in TS. We obtained summary statistics for the most recent TS genome-wide association study (GWAS) from the TS Psychiatric Genomics Consortium Working Group (4644 cases and 8695 controls) and GWAS of subcortical volumes from the ENIGMA consortium (30,717 individuals). We also undertook analyses using GWAS summary statistics of key symptom factors in TS, namely social disinhibition and symmetry behaviour. SNP effect concordance analysis (SECA) was used to examine genetic pleiotropy-the same SNP affecting two traits-and concordance-the agreement in single nucelotide polymorphism (SNP) effect directions across these two traits. In addition, a conditional false discovery rate (FDR) analysis was performed, conditioning the TS risk variants on each of the seven subcortical and the intracranial brain volume GWAS. Linkage disequilibrium score regression (LDSR) was used as validation of the SECA method. SECA revealed significant pleiotropy between TS and putamen (p = 2 × 10-4) and caudate (p = 4 × 10-4) volumes, independent of direction of effect, and significant concordance between TS and lower thalamic volume (p = 1 × 10-3). LDSR lent additional support for the association between TS and thalamus volume (p = 5.85 × 10-2). Furthermore, SECA revealed significant evidence of concordance between the social disinhibition symptom dimension and lower thalamus volume (p = 1 × 10-3), as well as concordance between symmetry behaviour and greater putamen volume (p = 7 × 10-4). Conditional FDR analysis further revealed novel variants significantly associated with TS (p < 8 × 10-7) when conditioning on intracranial (rs2708146, q = 0.046; and rs72853320, q = 0.035) and hippocampal (rs1922786, q = 0.001) volumes, respectively. These data indicate concordance for genetic variation involved in disorder risk and subcortical brain volumes in TS. Further work with larger samples is needed to fully delineate the genetic architecture of these disorders and their underlying neurocircuitry.
Collapse
Affiliation(s)
- Mary Mufford
- 0000 0004 1937 1151grid.7836.aHuman Genetics Research Unit, Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Josh Cheung
- 0000 0001 2156 6853grid.42505.36Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Los Angeles, CA USA
| | - Neda Jahanshad
- 0000 0001 2156 6853grid.42505.36Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Los Angeles, CA USA
| | - Celia van der Merwe
- 0000 0004 1937 1151grid.7836.aHuman Genetics Research Unit, Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Linda Ding
- 0000 0001 2156 6853grid.42505.36Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Los Angeles, CA USA
| | - Nynke Groenewold
- 0000 0004 1937 1151grid.7836.aDepartment of Psychiatry and MRC Unit on Risk & Resilience, University of Cape Town, Cape Town, South Africa
| | - Nastassja Koen
- 0000 0004 1937 1151grid.7836.aDepartment of Psychiatry and MRC Unit on Risk & Resilience, University of Cape Town, Cape Town, South Africa ,0000 0004 0635 1506grid.413335.3Groote Schuur Hospital and Neuroscience Institute, Cape Town, South Africa
| | - Emile R. Chimusa
- 0000 0004 1937 1151grid.7836.aHuman Genetics Research Unit, Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Shareefa Dalvie
- 0000 0004 1937 1151grid.7836.aDepartment of Psychiatry and MRC Unit on Risk & Resilience, University of Cape Town, Cape Town, South Africa ,0000 0004 0635 1506grid.413335.3Groote Schuur Hospital and Neuroscience Institute, Cape Town, South Africa
| | - Raj Ramesar
- 0000 0004 1937 1151grid.7836.aHuman Genetics Research Unit, Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - James A. Knowles
- 0000 0001 2156 6853grid.42505.36Department of Psychiatry and the Behavioural Sciences, Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Christine Lochner
- 0000 0001 2214 904Xgrid.11956.3aDepartment of Psychiatry, University of Stellenbosch, Stellenbosch, South Africa
| | - Derrek P. Hibar
- 0000 0001 2156 6853grid.42505.36Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Los Angeles, CA USA
| | - Peristera Paschou
- 0000 0004 1937 2197grid.169077.eDepartment of Biological Sciences, Purdue University, West Lafayette, IN USA
| | - Odile A. van den Heuvel
- grid.484519.5Department of Psychiatry, Department of Anatomy & Neurosciences, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Sarah E. Medland
- 0000 0001 2294 1395grid.1049.cQIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Jeremiah M. Scharf
- 000000041936754Xgrid.38142.3cPsychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Harvard Medical School, Boston, MA USA ,0000 0004 0386 9924grid.32224.35Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA ,0000 0004 0386 9924grid.32224.35Department of Neurology, Massachusetts General Hospital, Boston, MA USA
| | - Carol A. Mathews
- 0000 0004 1936 8091grid.15276.37Department of Psychiatry, Genetics Institute, University of Florida, Gainesville, FL USA
| | - Paul M. Thompson
- 0000 0001 2156 6853grid.42505.36Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Los Angeles, CA USA
| | - Dan J. Stein
- 0000 0004 1937 1151grid.7836.aDepartment of Psychiatry and MRC Unit on Risk & Resilience, University of Cape Town, Cape Town, South Africa ,0000 0004 0635 1506grid.413335.3Groote Schuur Hospital and Neuroscience Institute, Cape Town, South Africa
| | | |
Collapse
|
37
|
Smoller JW, Andreassen OA, Edenberg HJ, Faraone SV, Glatt SJ, Kendler KS. Psychiatric genetics and the structure of psychopathology. Mol Psychiatry 2019; 24:409-420. [PMID: 29317742 PMCID: PMC6684352 DOI: 10.1038/s41380-017-0010-4] [Citation(s) in RCA: 206] [Impact Index Per Article: 41.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 10/23/2017] [Accepted: 11/01/2017] [Indexed: 12/20/2022]
Abstract
For over a century, psychiatric disorders have been defined by expert opinion and clinical observation. The modern DSM has relied on a consensus of experts to define categorical syndromes based on clusters of symptoms and signs, and, to some extent, external validators, such as longitudinal course and response to treatment. In the absence of an established etiology, psychiatry has struggled to validate these descriptive syndromes, and to define the boundaries between disorders and between normal and pathologic variation. Recent advances in genomic research, coupled with large-scale collaborative efforts like the Psychiatric Genomics Consortium, have identified hundreds of common and rare genetic variations that contribute to a range of neuropsychiatric disorders. At the same time, they have begun to address deeper questions about the structure and classification of mental disorders: To what extent do genetic findings support or challenge our clinical nosology? Are there genetic boundaries between psychiatric and neurologic illness? Do the data support a boundary between disorder and normal variation? Is it possible to envision a nosology based on genetically informed disease mechanisms? This review provides an overview of conceptual issues and genetic findings that bear on the relationships among and boundaries between psychiatric disorders and other conditions. We highlight implications for the evolving classification of psychopathology and the challenges for clinical translation.
Collapse
Affiliation(s)
- Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Ole A Andreassen
- NORMENT-KG Jebsen Centre, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Stephen V Faraone
- Departments of Psychiatry and of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Stephen J Glatt
- Departments of Psychiatry and of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Kenneth S Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| |
Collapse
|
38
|
Chen J, Calhoun VD, Lin D, Perrone-Bizzozero NI, Bustillo JR, Pearlson GD, Potkin SG, van Erp TGM, Macciardi F, Ehrlich S, Ho BC, Sponheim SR, Wang L, Stephen JM, Mayer AR, Hanlon FM, Jung RE, Clementz BA, Keshavan MS, Gershon ES, Sweeney JA, Tamminga CA, Andreassen OA, Agartz I, Westlye LT, Sui J, Du Y, Turner JA, Liu J. Shared Genetic Risk of Schizophrenia and Gray Matter Reduction in 6p22.1. Schizophr Bull 2019; 45:222-232. [PMID: 29474680 PMCID: PMC6293216 DOI: 10.1093/schbul/sby010] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Genetic factors are known to influence both risk for schizophrenia (SZ) and variation in brain structure. A pressing question is whether the genetic underpinnings of brain phenotype and the disorder overlap. Using multivariate analytic methods and focusing on 1,402 common single-nucleotide polymorphisms (SNPs) mapped from the Psychiatric Genomics Consortium (PGC) 108 regions, in 777 discovery samples, we identified 39 SNPs to be significantly associated with SZ-discriminating gray matter volume (GMV) reduction in inferior parietal and superior temporal regions. The findings were replicated in 609 independent samples. These 39 SNPs in chr6:28308034-28684183 (6p22.1), the most significant SZ-risk region reported by PGC, showed regulatory effects on both DNA methylation and gene expression of postmortem brain tissue and saliva. Furthermore, the regulated methylation site and gene showed significantly different levels of methylation and expression in the prefrontal cortex between cases and controls. In addition, for one regulated methylation site we observed a significant in vivo methylation-GMV association in saliva, suggesting a potential SNP-methylation-GMV pathway. Notably, the risk alleles inferred for GMV reduction from in vivo imaging are all consistent with the risk alleles for SZ inferred from postmortem data. Collectively, we provide evidence for shared genetic risk of SZ and regional GMV reduction in 6p22.1 and demonstrate potential molecular mechanisms that may drive the observed in vivo associations. This study motivates dissecting SZ-risk variants to better understand their associations with focal brain phenotypes and the complex pathophysiology of the illness.
Collapse
Affiliation(s)
- Jiayu Chen
- The Mind Research Network, Albuquerque, NM
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM
- Department of Electrical Engineering, University of New Mexico, Albuquerque, NM
| | | | - Nora I Perrone-Bizzozero
- Department of Neurosciences and Psychiatry, University of New Mexico School of Medicine, Albuquerque, NM
- Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque, NM
| | - Juan R Bustillo
- Department of Neurosciences and Psychiatry, University of New Mexico School of Medicine, Albuquerque, NM
- Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque, NM
| | - Godfrey D Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT
- Department of Psychiatry, Yale University, New Haven, CT
- Department of Neuroscience, Yale University, New Haven, CT
| | - Steven G Potkin
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA
| | - Fabio Macciardi
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA
| | - Stefan Ehrlich
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Germany
| | - Beng-Choon Ho
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA
| | - Scott R Sponheim
- Department of Psychiatry, University of Minnesota, Minneapolis, MN
- Minneapolis Veterans Administration Health Care System, Minneapolis, MN
| | - Lei Wang
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL
- Department of Radiology, Northwestern University, Chicago, IL
| | | | | | | | - Rex E Jung
- Department of Psychology, University of New Mexico, Albuquerque, NM
| | | | - Matcheri S Keshavan
- Department of Psychiatry, Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, MA
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL
| | - John A Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH
| | - Carol A Tamminga
- Department of Psychiatry, UT Southwestern Medical School, Dallas, TX
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Jing Sui
- The Mind Research Network, Albuquerque, NM
- National Laboratory of Pattern Recognition, CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yuhui Du
- The Mind Research Network, Albuquerque, NM
| | - Jessica A Turner
- The Mind Research Network, Albuquerque, NM
- Psychology Department and Neuroscience Institute, Georgia State University, Atlanta, GA
| | - Jingyu Liu
- The Mind Research Network, Albuquerque, NM
- Department of Electrical Engineering, University of New Mexico, Albuquerque, NM
| |
Collapse
|
39
|
Polygenic risk for schizophrenia and associated brain structural changes: A systematic review. Compr Psychiatry 2019; 88:77-82. [PMID: 30529765 DOI: 10.1016/j.comppsych.2018.11.014] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 10/22/2018] [Accepted: 11/27/2018] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Genome wide association studies (GWAS) of schizophrenia allow the generation of Polygenic Risk Scores (PRS). PRS can be used to determine the contribution to altered brain structures in this disorder, which have been well described. However, findings from studies using PRS to predict brain structural changes in schizophrenia have been inconsistent. We therefore performed a systematic review to determine the association between schizophrenia PRS and brain structure. METHODS Following PRISMA systematic review guidelines, databases were searched for literature using key search terms. Inclusion criteria for the discovery sample required case-control schizophrenia GWAS summary statistics from European populations. The target sample was required to be of European ancestry, and have brain structure and genotype information. Quality assessment of the publications was conducted using the Mixed Methods Appraisal Tool for quantitative non-randomised studies. MAIN FINDINGS A total of seven studies were found to be eligible for review. Five studies found no significant association and two studies found a significant association of schizophrenia PRS with total brain, reduced white matter volume, and globus pallidus volume. However, the latter studies were conducted using smaller discovery (ncases = 9394 ncontrols = 12,462) and target samples compared to the studies with substantially larger discovery (ncases = 33,636 ncontrols = 43,008) and target samples where no association was observed. Taken together, the results suggest that schizophrenia PRS are not significantly associated with brain structural changes in this disorder. CONCLUSIONS The lack of significant association between schizophrenia PRS and brain structural changes may indicate that intermediate phenotypes other than brain structure should be the focus of future work. Alternatively, however, the lack of association found here may point to limitations of the current evidence-base, and so point to the need for future better powered studies.
Collapse
|
40
|
Exploration of Shared Genetic Architecture Between Subcortical Brain Volumes and Anorexia Nervosa. Mol Neurobiol 2018; 56:5146-5156. [PMID: 30519816 PMCID: PMC6647452 DOI: 10.1007/s12035-018-1439-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 11/05/2018] [Indexed: 12/22/2022]
Abstract
In MRI scans of patients with anorexia nervosa (AN), reductions in brain volume are often apparent. However, it is unknown whether such brain abnormalities are influenced by genetic determinants that partially overlap with those underlying AN. Here, we used a battery of methods (LD score regression, genetic risk scores, sign test, SNP effect concordance analysis, and Mendelian randomization) to investigate the genetic covariation between subcortical brain volumes and risk for AN based on summary measures retrieved from genome-wide association studies of regional brain volumes (ENIGMA consortium, n = 13,170) and genetic risk for AN (PGC-ED consortium, n = 14,477). Genetic correlations ranged from − 0.10 to 0.23 (all p > 0.05). There were some signs of an inverse concordance between greater thalamus volume and risk for AN (permuted p = 0.009, 95% CI: [0.005, 0.017]). A genetic variant in the vicinity of ZW10, a gene involved in cell division, and neurotransmitter and immune system relevant genes, in particular DRD2, was significantly associated with AN only after conditioning on its association with caudate volume (pFDR = 0.025). Another genetic variant linked to LRRC4C, important in axonal and synaptic development, reached significance after conditioning on hippocampal volume (pFDR = 0.021). In this comprehensive set of analyses and based on the largest available sample sizes to date, there was weak evidence for associations between risk for AN and risk for abnormal subcortical brain volumes at a global level (that is, common variant genetic architecture), but suggestive evidence for effects of single genetic markers. Highly powered multimodal brain- and disorder-related genome-wide studies are needed to further dissect the shared genetic influences on brain structure and risk for AN.
Collapse
|
41
|
Ranlund S, Rosa MJ, de Jong S, Cole JH, Kyriakopoulos M, Fu CHY, Mehta MA, Dima D. Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition. Neuroimage Clin 2018; 20:1026-1036. [PMID: 30340201 PMCID: PMC6197704 DOI: 10.1016/j.nicl.2018.10.008] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 10/04/2018] [Accepted: 10/08/2018] [Indexed: 12/24/2022]
Abstract
Psychiatric illnesses are complex and polygenic. They are associated with widespread alterations in the brain, which are partly influenced by genetic factors. There have been some attempts to relate polygenic risk scores (PRS) - a measure of the overall genetic risk an individual carries for a disorder - to brain structure using univariate methods. However, PRS are likely associated with distributed and covarying effects across the brain. We therefore used multivariate machine learning in this proof-of-principle study to investigate associations between brain structure and PRS for four psychiatric disorders; attention deficit-hyperactivity disorder (ADHD), autism, bipolar disorder and schizophrenia. The sample included 213 individuals comprising patients with depression (69), bipolar disorder (33), and healthy controls (111). The five psychiatric PRSs were calculated based on summary data from the Psychiatric Genomics Consortium. T1-weighted magnetic resonance images were obtained and voxel-based morphometry was implemented in SPM12. Multivariate relevance vector regression was implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo). Across the whole sample, a multivariate pattern of grey matter significantly predicted the PRS for autism (r = 0.20, pFDR = 0.03; MSE = 4.20 × 10-5, pFDR = 0.02). For the schizophrenia PRS, the MSE was significant (MSE = 1.30 × 10-5, pFDR = 0.02) although the correlation was not (r = 0.15, pFDR = 0.06). These results lend support to the hypothesis that polygenic liability for autism and schizophrenia is associated with widespread changes in grey matter concentrations. These associations were seen in individuals not affected by these disorders, indicating that this is not driven by the expression of the disease, but by the genetic risk captured by the PRSs.
Collapse
Affiliation(s)
- Siri Ranlund
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Maria Joao Rosa
- Department of Computer Science, University College London, London, UK
| | - Simone de Jong
- NIHR BRC for Mental Health, Institute of Psychiatry, Psychology and Neuroscience, King's College London and SLaM NHS Trust, London, UK; MRC Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - James H Cole
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Computational, Cognitive & Clinical Neuroimaging Laboratory, Division of Brain Sciences, Department of Medicine, Imperial College London, London, UK
| | - Marinos Kyriakopoulos
- National and Specialist Acorn Lodge Inpatient Children Unit, South London and Maudsley NHS Foundation Trust, London, UK; Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Cynthia H Y Fu
- School of Psychology, University of East London, London, UK; Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Mitul A Mehta
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Danai Dima
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Psychology, School of Arts and Social Sciences, City, University of London, London, UK.
| |
Collapse
|
42
|
Steullet P, Cabungcal JH, Bukhari SA, Ardelt MI, Pantazopoulos H, Hamati F, Salt TE, Cuenod M, Do KQ, Berretta S. The thalamic reticular nucleus in schizophrenia and bipolar disorder: role of parvalbumin-expressing neuron networks and oxidative stress. Mol Psychiatry 2018; 23:2057-2065. [PMID: 29180672 PMCID: PMC5972042 DOI: 10.1038/mp.2017.230] [Citation(s) in RCA: 99] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2017] [Revised: 08/29/2017] [Accepted: 09/08/2017] [Indexed: 02/06/2023]
Abstract
Growing evidence points to a disruption of cortico-thalamo-cortical circuits in schizophrenia (SZ) and bipolar disorder (BD). Clues for a specific involvement of the thalamic reticular nucleus (TRN) come from its unique neuronal characteristics and neural connectivity, allowing it to shape the thalamo-cortical information flow. A direct involvement of the TRN in SZ and BD has not been tested thus far. We used a combination of human postmortem and rodent studies to test the hypothesis that neurons expressing parvalbumin (PV neurons), a main TRN neuronal population, and associated Wisteria floribunda agglutinin-labeled perineuronal nets (WFA/PNNs) are altered in SZ and BD, and that these changes may occur early in the course of the disease as a consequence of oxidative stress. In both disease groups, marked decreases of PV neurons (immunoreactive for PV) and WFA/PNNs were observed in the TRN, with no effects of duration of illness or age at onset. Similarly, in transgenic mice with redox dysregulation, numbers of PV neurons and WFA/PNN+PV neurons were decreased in transgenic compared with wild-type mice; these changes were present at postnatal day (P) 20 for PV neurons and P40 for WFA/PNN+PV neurons, accompanied by alterations of their firing properties. These results show profound abnormalities of PV neurons in the TRN of subjects with SZ and BD, and offer support for the hypothesis that oxidative stress may play a key role in impacting TRN PV neurons at early stages of these disorders. We put forth that these TRN abnormalities may contribute to disruptions of sleep spindles, focused attention and emotion processing in these disorders.
Collapse
Affiliation(s)
- Pascal Steullet
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital-CHUV, Prilly-Lausanne, Switzerland
| | - Jan-Harry Cabungcal
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital-CHUV, Prilly-Lausanne, Switzerland
| | - Syed A. Bukhari
- Translational Neuroscience Laboratory, Mclean Hospital, Belmont, MA, USA
| | | | - Harry Pantazopoulos
- Translational Neuroscience Laboratory, Mclean Hospital, Belmont, MA, USA,Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Fadi Hamati
- Translational Neuroscience Laboratory, Mclean Hospital, Belmont, MA, USA
| | - Thomas E. Salt
- The Institute of Ophthalmology, University College London, London, UK
| | - Michel Cuenod
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital-CHUV, Prilly-Lausanne, Switzerland
| | - Kim Q. Do
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital-CHUV, Prilly-Lausanne, Switzerland
| | - Sabina Berretta
- Translational Neuroscience Laboratory, Mclean Hospital, Belmont, MA, USA,Department of Psychiatry, Harvard Medical School, Boston, MA, USA,Program in Neuroscience, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
43
|
Córdova-Palomera A, Kaufmann T, Bettella F, Wang Y, Doan NT, van der Meer D, Alnæs D, Rokicki J, Moberget T, Sønderby IE, Andreassen OA, Westlye LT. Effects of autozygosity and schizophrenia polygenic risk on cognitive and brain developmental trajectories. Eur J Hum Genet 2018; 26:1049-1059. [PMID: 29700391 PMCID: PMC6018758 DOI: 10.1038/s41431-018-0134-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Revised: 02/28/2018] [Accepted: 03/02/2018] [Indexed: 11/08/2022] Open
Abstract
Cognitive and brain development are determined by dynamic interactions between genes and environment across the lifespan. Aside from marker-by-marker analyses of polymorphisms, biologically meaningful features of the whole genome (derived from the combined effect of individual markers) have been postulated to inform on human phenotypes including cognitive traits and their underlying biological substrate. Here, estimates of inbreeding and genetic susceptibility for schizophrenia calculated from genome-wide data-runs of homozygosity (ROH) and schizophrenia polygenic risk score (PGRS)-are analyzed in relation to cognitive abilities (n = 4183) and brain structure (n = 516) in a general-population sample of European-ancestry participants aged 8-22, from the Philadelphia Neurodevelopmental Cohort. The findings suggest that a higher ROH burden and higher schizophrenia PGRS are associated with higher intelligence. Cognition-ROH and cognition-PGRS associations obtained in this cohort may, respectively, evidence that assortative mating influences intelligence, and that individuals with high schizophrenia genetic risk who do not transition to disease status are cognitively resilient. Neuroanatomical data showed that the effects of schizophrenia PGRS on cognition could be modulated by brain structure, although larger imaging datasets are needed to accurately disentangle the underlying neural mechanisms linking IQ with both inbreeding and the genetic burden for schizophrenia.
Collapse
Affiliation(s)
- Aldo Córdova-Palomera
- 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.
| | - Tobias Kaufmann
- 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
| | - Francesco Bettella
- 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
| | - Yunpeng Wang
- 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
| | - Nhat Trung Doan
- 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
| | - Dennis van der Meer
- 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
| | - Dag Alnæs
- 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
| | - Jaroslav Rokicki
- 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
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Torgeir Moberget
- 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
| | - Ida Elken Sønderby
- 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
| | - Lars T Westlye
- 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.
- Department of Psychology, University of Oslo, Oslo, Norway.
| |
Collapse
|
44
|
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.
Collapse
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
| |
Collapse
|
45
|
Genetic correlation between smoking behaviors and schizophrenia. Schizophr Res 2018; 194:86-90. [PMID: 28285025 PMCID: PMC5811408 DOI: 10.1016/j.schres.2017.02.022] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Revised: 02/14/2017] [Accepted: 02/18/2017] [Indexed: 12/19/2022]
Abstract
Nicotine dependence is highly comorbid with schizophrenia, and the etiology of the comorbidity is unknown. To determine whether there is a genetic correlation of smoking behavior with schizophrenia, genome-wide association study (GWAS) meta-analysis results from five smoking phenotypes (ever/never smoker (N=74,035), age of onset of smoking (N=28,647), cigarettes smoked per day (CPD, N=38,860), nicotine dependence (N=10,666), and current/former smoker (N=40,562)) were compared to GWAS meta-analysis results from schizophrenia (N=79,845) using linkage disequilibrium (LD) score regression. First, the SNP heritability (h2g) of each of the smoking phenotypes was computed using LD score regression (ever/never smoker h2g=0.08, age of onset of smoking h2g=0.06, CPD h2g=0.06, nicotine dependence h2g=0.15, current/former smoker h2g=0.07, p<0.001 for all phenotypes). The SNP heritability for nicotine dependence was statistically higher than the SNP heritability for the other smoking phenotypes (p<0.0005 for all two-way comparisons). Next, a statistically significant (p<0.05) genetic correlation was observed between schizophrenia and three of the five smoking phenotypes (nicotine dependence rg=0.14, CPD rg=0.12, and ever/never smoking rg=0.10). These results suggest that there is a component of common genetic variation that is shared between smoking behaviors and schizophrenia.
Collapse
|
46
|
Abstract
The high prevalence of nicotine dependence contributes to excess mortality in schizophrenia. Cue reactivity, or the encounter of drug-related cues or contexts, triggers craving, drug-seeking, and relapse. Prior functional magnetic resonance imaging (fMRI) research indicates that individuals with schizophrenia have blunted neural responses to rewarding stimuli in association with more severe negative symptoms. The objectives of this study are to determine if smokers with schizophrenia have altered neural reactivity to smoking cues compared with non-psychiatrically ill smokers and to evaluate the influence of negative symptoms on cue reactivity. Twenty smokers with schizophrenia and 19 control smokers underwent fMRI while viewing smoking-related and neutral cues. The primary analysis was group comparison of Smoking-Neutral contrast using whole-brain analysis (Pcorrected < .05). Smokers with schizophrenia had significantly greater baseline carbon monoxide levels and longer duration of smoking, suggesting more nicotine use. While both groups had greater brain reactivity to smoking vs neutral cues, smokers with schizophrenia had significantly decreased cue reactivity (Smoking-Neutral) compared to controls in bilateral frontal midline regions. There were significant negative correlations between negative symptoms and frontal midline reactivity. Despite greater nicotine use, smokers with schizophrenia exhibited decreased smoking cue-induced neural reactivity in frontal midline regions, suggesting that increased smoking and low cessation rates in schizophrenia are not primarily driven by responses to smoking-related cues. The finding of negative correlations between cue reactivity and negative symptoms is consistent with previous research demonstrating decreased neural responses to rewarding cues, particularly in patients with negative symptoms.
Collapse
Affiliation(s)
- Lauren V Moran
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Belmont, MA,To whom correspondence should be addressed; McLean Hospital, 115 Mill Street, AB3S, Belmont, MA 02478, US; tel: 617-855-3395; fax: 617-855-2895; e-mail:
| | - Jennifer M Betts
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Belmont, MA
| | - Dost Ongur
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Belmont, MA
| | - Amy C Janes
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Belmont, MA
| |
Collapse
|
47
|
Structural Neuroimaging of Anorexia Nervosa: Future Directions in the Quest for Mechanisms Underlying Dynamic Alterations. Biol Psychiatry 2018; 83:224-234. [PMID: 28967386 PMCID: PMC6053269 DOI: 10.1016/j.biopsych.2017.08.011] [Citation(s) in RCA: 103] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 07/21/2017] [Accepted: 08/14/2017] [Indexed: 02/07/2023]
Abstract
Anorexia nervosa (AN) is a serious eating disorder characterized by self-starvation and extreme weight loss. Pseudoatrophic brain changes are often readily visible in individual brain scans, and AN may be a valuable model disorder to study structural neuroplasticity. Structural magnetic resonance imaging studies have found reduced gray matter volume and cortical thinning in acutely underweight patients to normalize following successful treatment. However, some well-controlled studies have found regionally greater gray matter and persistence of structural alterations following long-term recovery. Findings from diffusion tensor imaging studies of white matter integrity and connectivity are also inconsistent. Furthermore, despite the severity of AN, the number of existing structural neuroimaging studies is still relatively low, and our knowledge of the underlying cellular and molecular mechanisms for macrostructural brain changes is rudimentary. We critically review the current state of structural neuroimaging in AN and discuss the potential neurobiological basis of structural brain alterations in the disorder, highlighting impediments to progress, recent developments, and promising future directions. In particular, we argue for the utility of more standardized data collection, adopting a connectomics approach to understanding brain network architecture, employing advanced magnetic resonance imaging methods that quantify biomarkers of brain tissue microstructure, integrating data from multiple imaging modalities, strategic longitudinal observation during weight restoration, and large-scale data pooling. Our overarching objective is to motivate carefully controlled research of brain structure in eating disorders, which will ultimately help predict therapeutic response and improve treatment.
Collapse
|
48
|
Abstract
Imaging genetics is a research methodology studying the effect of genetic variation on brain structure, function, behavior, and risk for psychopathology. Since the early 2000s, imaging genetics has been increasingly used in the research of schizophrenia (SZ). SZ is a severe mental disorder with no precise knowledge of its underlying neurobiology, however, new genetic and neurobiological data generate a climate for new avenues. The accumulating data of genome wide association studies (GWAS) continuously decode SZ risk genes. Global neuroimaging consortia produce collections of brain phenotypes from tens of thousands of people. In this context, imaging genetics will be strategically important both for the validation and discovery of SZ related findings. Thus, the study of GWAS supported risk variants as candidate genes to validate by neuroimaging is one trend. The study of epigenetic differences in relation to variations of brain phenotypes and the study of large scale multivariate analysis of genome wide and brain wide associations are other trends. While these studies hold a big potential for understanding the neurobiology of SZ, the problem of reproducibility appears as a major challenge, which requires standardizations in study designs and compensations of methodological limitations such as sensitivity and specificity. On the other hand, advancements of neuroimaging, optical and electron microscopy along with the use of genetically encoded fluorescent probes and robust statistical approaches will not only catalyze integrative methodologies but also will help better design the imaging genetics studies. In this invited paper, I will discuss the current perspective of imaging genetics and emerging opportunities of SZ research.
Collapse
Affiliation(s)
- Ayla Arslan
- Faculty of Engineering and Natural Sciences, Department of Genetics and Bioengineering, International University of Sarajevo, Sarajevo, Bosnia and Herzegovina; Faculty of Engineering and Natural Sciences, Department of Molecular Biology and Genetics, Uskudar University, Istanbul, Turkey.
| |
Collapse
|
49
|
Mufford MS, Stein DJ, Dalvie S, Groenewold NA, Thompson PM, Jahanshad N. Neuroimaging genomics in psychiatry-a translational approach. Genome Med 2017; 9:102. [PMID: 29179742 PMCID: PMC5704437 DOI: 10.1186/s13073-017-0496-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Neuroimaging genomics is a relatively new field focused on integrating genomic and imaging data in order to investigate the mechanisms underlying brain phenotypes and neuropsychiatric disorders. While early work in neuroimaging genomics focused on mapping the associations of candidate gene variants with neuroimaging measures in small cohorts, the lack of reproducible results inspired better-powered and unbiased large-scale approaches. Notably, genome-wide association studies (GWAS) of brain imaging in thousands of individuals around the world have led to a range of promising findings. Extensions of such approaches are now addressing epigenetics, gene–gene epistasis, and gene–environment interactions, not only in brain structure, but also in brain function. Complementary developments in systems biology might facilitate the translation of findings from basic neuroscience and neuroimaging genomics to clinical practice. Here, we review recent approaches in neuroimaging genomics—we highlight the latest discoveries, discuss advantages and limitations of current approaches, and consider directions by which the field can move forward to shed light on brain disorders.
Collapse
Affiliation(s)
- Mary S Mufford
- UCT/MRC Human Genetics Research Unit, Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa, 7925
| | - Dan J Stein
- MRC Unit on Risk and Resilience, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa, 7925.,Department of Psychiatry and Mental Health, Groote Schuur Hospital, Cape Town, South Africa, 7925
| | - Shareefa Dalvie
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa, 7925
| | - Nynke A Groenewold
- Department of Psychiatry and Mental Health, Groote Schuur Hospital, Cape Town, South Africa, 7925
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90292, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90292, USA.
| |
Collapse
|
50
|
Doan NT, Kaufmann T, Bettella F, Jørgensen KN, Brandt CL, Moberget T, Alnæs D, Douaud G, Duff E, Djurovic S, Melle I, Ueland T, Agartz I, Andreassen OA, Westlye LT. Distinct multivariate brain morphological patterns and their added predictive value with cognitive and polygenic risk scores in mental disorders. NEUROIMAGE-CLINICAL 2017; 15:719-731. [PMID: 28702349 PMCID: PMC5491456 DOI: 10.1016/j.nicl.2017.06.014] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Revised: 04/28/2017] [Accepted: 06/09/2017] [Indexed: 12/31/2022]
Abstract
The brain underpinnings of schizophrenia and bipolar disorders are multidimensional, reflecting complex pathological processes and causal pathways, requiring multivariate techniques to disentangle. Furthermore, little is known about the complementary clinical value of brain structural phenotypes when combined with data on cognitive performance and genetic risk. Using data-driven fusion of cortical thickness, surface area, and gray matter density maps (GMD), we found six biologically meaningful patterns showing strong group effects, including four statistically independent multimodal patterns reflecting co-occurring alterations in thickness and GMD in patients, over and above two other independent patterns of widespread thickness and area reduction. Case-control classification using cognitive scores alone revealed high accuracy, and adding imaging features or polygenic risk scores increased performance, suggesting their complementary predictive value with cognitive scores being the most sensitive features. Multivariate pattern analyses reveal distinct patterns of brain morphology in mental disorders, provide insights on the relative importance between brain structure, cognitive and polygenetic risk score in classification of patients, and demonstrate the importance of multivariate approaches in studying the pathophysiological substrate of these complex disorders. Linked ICA showed six independent multivariate morphology patterns sensitive to SZ. Machine learning used to compare brain structure, cognitive and genetic scores. Cognition showed highest prediction of SZ, boosted by brain structure or genetics.
Collapse
Affiliation(s)
- Nhat Trung Doan
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway.
| | - Tobias Kaufmann
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Francesco Bettella
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Kjetil Nordbø Jørgensen
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Christine Lycke Brandt
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Torgeir Moberget
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Dag Alnæs
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Gwenaëlle Douaud
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Eugene Duff
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - 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
| | - Ingrid Melle
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Torill Ueland
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Ingrid Agartz
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, 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, Norway
| | - Lars T Westlye
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| |
Collapse
|