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Tume CE, Chick SL, Holmans PA, Rees E, O’Donovan MC, Cameron D, Bray NJ. Genetic Implication of Specific Glutamatergic Neurons of the Prefrontal Cortex in the Pathophysiology of Schizophrenia. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100345. [PMID: 39099730 PMCID: PMC11295574 DOI: 10.1016/j.bpsgos.2024.100345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 05/03/2024] [Accepted: 05/19/2024] [Indexed: 08/06/2024] Open
Abstract
Background The prefrontal cortex (PFC) has been strongly implicated in the pathophysiology of schizophrenia. Here, we combined high-resolution single-nuclei RNA sequencing data from the human PFC with large-scale genomic data for schizophrenia to identify constituent cell populations likely to mediate genetic liability to the disorder. Methods Gene expression specificity values were calculated from a single-nuclei RNA sequencing dataset comprising 84 cell populations from the human PFC, spanning gestation to adulthood. Enrichment of schizophrenia common variant liability and burden of rare protein-truncating coding variants were tested in genes with high expression specificity for each cell type. We also explored schizophrenia common variant associations in relation to gene expression across the developmental trajectory of implicated neurons. Results Common risk variation for schizophrenia was prominently enriched in genes with high expression specificity for a population of mature layer 4 glutamatergic neurons emerging in infancy. Common variant liability to schizophrenia increased along the developmental trajectory of this neuronal population. Fine-mapped genes at schizophrenia genome-wide association study risk loci had significantly higher expression specificity than other genes in these neurons and in a population of layer 5/6 glutamatergic neurons. People with schizophrenia had a higher rate of rare protein-truncating coding variants in genes expressed by cells of the PFC than control individuals, but no cell population was significantly enriched above this background rate. Conclusions We identified a population of layer 4 glutamatergic PFC neurons likely to be particularly affected by common variant genetic risk for schizophrenia, which may contribute to disturbances in thalamocortical connectivity in the condition.
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Affiliation(s)
- Claire E. Tume
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Sophie L. Chick
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Peter A. Holmans
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Elliott Rees
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Michael C. O’Donovan
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Darren Cameron
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Nicholas J. Bray
- Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, Wales, United Kingdom
- Neuroscience & Mental Health Innovation Institute, Cardiff University, Cardiff, Wales, United Kingdom
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Bouttle K, Ingold N, O’Mara TA. Using Genetics to Investigate Relationships between Phenotypes: Application to Endometrial Cancer. Genes (Basel) 2024; 15:939. [PMID: 39062718 PMCID: PMC11276418 DOI: 10.3390/genes15070939] [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: 06/25/2024] [Revised: 07/14/2024] [Accepted: 07/16/2024] [Indexed: 07/28/2024] Open
Abstract
Genome-wide association studies (GWAS) have accelerated the exploration of genotype-phenotype associations, facilitating the discovery of replicable genetic markers associated with specific traits or complex diseases. This narrative review explores the statistical methodologies developed using GWAS data to investigate relationships between various phenotypes, focusing on endometrial cancer, the most prevalent gynecological malignancy in developed nations. Advancements in analytical techniques such as genetic correlation, colocalization, cross-trait locus identification, and causal inference analyses have enabled deeper exploration of associations between different phenotypes, enhancing statistical power to uncover novel genetic risk regions. These analyses have unveiled shared genetic associations between endometrial cancer and many phenotypes, enabling identification of novel endometrial cancer risk loci and furthering our understanding of risk factors and biological processes underlying this disease. The current status of research in endometrial cancer is robust; however, this review demonstrates that further opportunities exist in statistical genetics that hold promise for advancing the understanding of endometrial cancer and other complex diseases.
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Affiliation(s)
| | | | - Tracy A. O’Mara
- Cancer Research Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia (N.I.)
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3
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Rødevand L, Rahman Z, Hindley GFL, Smeland OB, Frei O, Tekin TF, Kutrolli G, Bahrami S, Hoseth EZ, Shadrin A, Lin A, Djurovic S, Dale AM, Steen NE, Andreassen OA. Characterizing the Shared Genetic Underpinnings of Schizophrenia and Cardiovascular Disease Risk Factors. Am J Psychiatry 2023; 180:815-826. [PMID: 37752828 DOI: 10.1176/appi.ajp.20220660] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
OBJECTIVE Schizophrenia is associated with increased risk of cardiovascular disease (CVD), although there is variation in risk among individuals. There are indications of shared genetic etiology between schizophrenia and CVD, but the nature of the overlap remains unclear. The aim of this study was to fill this gap in knowledge. METHODS Overlapping genetic architectures between schizophrenia and CVD risk factors were assessed by analyzing recent genome-wide association study (GWAS) results. The bivariate causal mixture model (MiXeR) was applied to estimate the number of shared variants and the conjunctional false discovery rate (conjFDR) approach was used to pinpoint specific shared loci. RESULTS Extensive genetic overlap was found between schizophrenia and CVD risk factors, particularly smoking initiation (N=8.6K variants) and body mass index (BMI) (N=8.1K variants). Several specific shared loci were detected between schizophrenia and BMI (N=304), waist-to-hip ratio (N=193), smoking initiation (N=293), systolic (N=294) and diastolic (N=259) blood pressure, type 2 diabetes (N=147), lipids (N=471), and coronary artery disease (N=35). The schizophrenia risk loci shared with smoking initiation had mainly concordant effect directions, and the risk loci shared with BMI had mainly opposite effect directions. The overlapping loci with lipids, blood pressure, waist-to-hip ratio, type 2 diabetes, and coronary artery disease had mixed effect directions. Functional analyses implicated mapped genes that are expressed in brain tissue and immune cells. CONCLUSIONS These findings indicate a genetic propensity to smoking and a reduced genetic risk of obesity among individuals with schizophrenia. The bidirectional effects of the shared loci with the other CVD risk factors may imply differences in genetic liability to CVD across schizophrenia subgroups, possibly underlying the variation in CVD comorbidity.
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Affiliation(s)
- Linn Rødevand
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Zillur Rahman
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Guy F L Hindley
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Olav B Smeland
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Oleksandr Frei
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Tahir Filiz Tekin
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Gleda Kutrolli
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Shahram Bahrami
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Eva Z Hoseth
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Alexey Shadrin
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Aihua Lin
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Srdjan Djurovic
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Anders M Dale
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Nils Eiel Steen
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
| | - Ole A Andreassen
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, Oslo University Hospital, University of Oslo, Oslo (Rødevand, Rahman, Hindley, Smeland, Frei, Tekin, Kutrolli, Bahrami, Hoseth, Shadrin, Lin, Steen, Andreassen); Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Hindley); Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo (Frei); Division of Mental Health, Helse Møre Romsdal HF, Kristiansund, Norway (Hoseth); Department of Medical Genetics, Oslo University Hospital, Oslo, and NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway (Djurovic); Multimodal Imaging Laboratory and Departments of Radiology, Psychiatry, and Neurosciences, University of California San Diego, La Jolla (Dale)
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4
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Muntané G, Vázquez-Bourgon J, Sada E, Martorell L, Papiol S, Bosch E, Navarro A, Crespo-Facorro B, Vilella E. Polygenic risk scores enhance prediction of body mass index increase in individuals with a first episode of psychosis. Eur Psychiatry 2023; 66:e28. [PMID: 36852609 PMCID: PMC10044301 DOI: 10.1192/j.eurpsy.2023.9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/01/2023] Open
Abstract
BACKGROUND Individuals with a first episode of psychosis (FEP) show rapid weight gain during the first months of treatment, which is associated with a reduction in general physical health. Although genetics is assumed to be a significant contributor to weight gain, its exact role is unknown. METHODS We assembled a population-based FEP cohort of 381 individuals that was split into a Training (n = 224) set and a Validation (n = 157) set to calculate the polygenic risk score (PRS) in a two-step process. In parallel, we obtained reference genome-wide association studies for body mass index (BMI) and schizophrenia (SCZ) to examine the pleiotropic landscape between the two traits. BMI PRSs were added to linear models that included sociodemographic and clinical variables to predict BMI increase (∆BMI) in the Validation set. RESULTS The results confirmed considerable shared genetic susceptibility for the two traits involving 449 near-independent genomic loci. The inclusion of BMI PRSs significantly improved the prediction of ∆BMI at 12 months after the onset of antipsychotic treatment by 49.4% compared to a clinical model. In addition, we demonstrated that the PRS containing pleiotropic information between BMI and SCZ predicted ∆BMI better at 3 (12.2%) and 12 months (53.2%). CONCLUSIONS We prove for the first time that genetic factors play a key role in determining ∆BMI during the FEP. This finding has important clinical implications for the early identification of individuals most vulnerable to weight gain and highlights the importance of examining genetic pleiotropy in the context of medically important comorbidities for predicting future outcomes.
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Affiliation(s)
- Gerard Muntané
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Institut de Biologia Evolutiva (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain
| | - Javier Vázquez-Bourgon
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Department of Psychiatry, University Hospital Marqués de Valdecilla, Instituto de Investigación Marqués de Valdecilla (IDIVAL), Santander, Spain.,Departamento de Medicina y Psiquiatría, Facultad de Medicina, Universidad de Cantabria, Santander, Spain
| | - Ester Sada
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - Lourdes Martorell
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - Sergi Papiol
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Department of Psychiatry, Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University, Munich, Germany
| | - Elena Bosch
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Institut de Biologia Evolutiva (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain
| | - Arcadi Navarro
- Institut de Biologia Evolutiva (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain.,Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.,Barcelonaβeta Brain Research Center, Fundació Pasqual Maragall, Barcelona, Spain
| | - Benedicto Crespo-Facorro
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.,Department of Psychiatry, Instituto de Biomedicina de Sevilla (IBiS), University Hospital Virgen del Rocío, Seville, Spain
| | - Elisabet Vilella
- Hospital Universitari Institut Pere Mata, IISPV, Universitat Rovira i Virgili, Reus, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
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5
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Gong W, Guo P, Li Y, Liu L, Yan R, Liu S, Wang S, Xue F, Zhou X, Yuan Z. Role of the Gut-Brain Axis in the Shared Genetic Etiology Between Gastrointestinal Tract Diseases and Psychiatric Disorders: A Genome-Wide Pleiotropic Analysis. JAMA Psychiatry 2023; 80:360-370. [PMID: 36753304 PMCID: PMC9909581 DOI: 10.1001/jamapsychiatry.2022.4974] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
IMPORTANCE Comorbidities and genetic correlations between gastrointestinal tract diseases and psychiatric disorders have been widely reported, with the gut-brain axis (GBA) hypothesized as a potential biological basis. However, the degree to which the shared genetic determinants are involved in these associations underlying the GBA is unclear. OBJECTIVE To investigate the shared genetic etiology between gastrointestinal tract diseases and psychiatric disorders and to identify shared genomic loci, genes, and pathways. DESIGN, SETTING, AND PARTICIPANTS This genome-wide pleiotropic association study using genome-wide association summary statistics from publicly available data sources was performed with various statistical genetic approaches to sequentially investigate the pleiotropic associations from genome-wide single-nucleotide variation (SNV; formerly single-nucleotide polymorphism [SNP]), and gene levels and biological pathways to disentangle the underlying shared genetic etiology between 4 gastrointestinal tract diseases (inflammatory bowel disease, irritable bowel syndrome, peptic ulcer disease, and gastroesophageal reflux disease) and 6 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, attention-deficit/hyperactivity disorder, posttraumatic stress disorder, and anorexia nervosa). Data were collected from March 10, 2021, to August 25, 2021, and analysis was performed from January 8 through May 30, 2022. MAIN OUTCOMES AND MEASURES The primary outcomes consisted of a list of genetic loci, genes, and pathways shared between gastrointestinal tract diseases and psychiatric disorders. RESULTS Extensive genetic correlations and genetic overlaps were found among 22 of 24 trait pairs. Pleiotropic analysis under a composite null hypothesis identified 2910 significant potential pleiotropic SNVs in 19 trait pairs, with 83 pleiotropic loci and 24 colocalized loci detected. Gene-based analysis found 158 unique candidate pleiotropic genes, which were highly enriched in certain GBA-related phenotypes and tissues, whereas pathway enrichment analysis further highlighted biological pathways primarily involving cell adhesion, synaptic structure and function, and immune cell differentiation. Several identified pleiotropic loci also shared causal variants with gut microbiomes. Mendelian randomization analysis further illustrated vertical pleiotropy across 8 pairwise traits. Notably, many pleiotropic loci were identified for multiple pairwise traits, such as 1q32.1 (INAVA), 19q13.33 (FUT2), 11q23.2 (NCAM1), and 1p32.3 (LRP8). CONCLUSIONS AND RELEVANCE These findings suggest that the pleiotropic genetic determinants between gastrointestinal tract diseases and psychiatric disorders are extensively distributed across the genome. These findings not only support the shared genetic basis underlying the GBA but also have important implications for intervention and treatment targets of these diseases simultaneously.
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Affiliation(s)
- Weiming Gong
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Ping Guo
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Yuanming Li
- School of Medicine, Cheeloo College of Medicine, Shandong University Jinan, China
| | - Lu Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Ran Yan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Shuai Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Shukang Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor,Center for Statistical Genetics, University of Michigan, Ann Arbor
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
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6
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What can genetics tell us about the schizophrenia construct? Schizophr Res 2022; 242:126-129. [PMID: 35125284 DOI: 10.1016/j.schres.2021.12.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 12/09/2021] [Indexed: 12/25/2022]
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7
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Qian L, He X, Gao F, Fan Y, Zhao B, Ma Q, Yan B, Wang W, Ma X, Yang J. Estimation of the bidirectional relationship between schizophrenia and inflammatory bowel disease using the mendelian randomization approach. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:31. [PMID: 35347152 PMCID: PMC9261100 DOI: 10.1038/s41537-022-00244-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 03/07/2022] [Indexed: 12/18/2022]
Abstract
It has been reported that schizophrenia (SCZ) and inflammatory bowel disease (IBD) are related. However, whether there is a bidirectional interaction between them remains unclear. The aim of this study was to conduct a bidirectional Mendelian randomization (MR) analysis to elucidate the causal relationship between SCZ and IBD and its subtypes, including Crohn's disease (CD) and ulcerative colitis (UC). Single-nucleotide polymorphisms (SNPs) extracted from the summary data of genome-wide association studies were used as genetic instruments. MR was performed using the inverse-variance-weighted method. The MR-Egger and weighted median methods were used for sensitivity analyses. Analysis using 70 SNPs as genetic instruments showed that SCZ was associated with an increased risk of IBD (OR = 1.14, 95% CI: 1.09-1.20, P = 9.21 × 10-8), CD (OR = 1.16, 95% CI: 1.07-1.25, P = 1.42 × 10-4), and UC (OR = 1.14, 95% CI: 1.07-1.21, P = 2.72 × 10-5). The results of the sensitivity analyses were robust and no evidence of pleiotropy was observed. Bidirectional MR analyses showed no causal effects of IBD, CD, or UC on SCZ. This study suggests that SCZ has causal effects on IBD and its subtypes, whereas IBD has no effect on SCZ. Brain-gut axis interactions may help clarify the causal relationship between SCZ and IBD. However, further studies are needed to elucidate the biological mechanisms behind the brain-gut interactions.
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Affiliation(s)
- Li Qian
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Center for Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xiaoyan He
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Center for Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Fengjie Gao
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yajuan Fan
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Binbin Zhao
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Qingyan Ma
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Bin Yan
- Clinical Research Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Wei Wang
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xiancang Ma
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jian Yang
- Clinical Research Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
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8
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Byun J, Han Y, Walsh KM, Park AS, Bondy ML, Amos CI. Shared genomic architecture between COVID-19 severity and numerous clinical and physiologic parameters revealed by LD score regression analysis. Sci Rep 2022; 12:1891. [PMID: 35115602 PMCID: PMC8814062 DOI: 10.1038/s41598-022-05832-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 01/19/2022] [Indexed: 12/31/2022] Open
Abstract
The COVID-19 pandemic has produced broad clinical manifestations, from asymptomatic infection to hospitalization and death. Despite progress from genomic and clinical epidemiology research, risk factors for developing severe COVID-19 are incompletely understood and identification of modifiable risk factors is desperately needed. We conducted linkage disequilibrium score regression (LDSR) analysis to estimate cross-trait genetic correlation between COVID-19 severity and various polygenic phenotypes. To attenuate the genetic contribution of smoking and BMI, we further conducted sensitivity analyses by pruning genomic regions associated with smoking/BMI and repeating LDSR analyses. We identified robust positive associations between the genetic architecture of severe COVID-19 and both BMI and smoking. We observed strong positive genetic correlation (rg) with diabetes (rg = 0.25) and shortness of breath walking on level ground (rg = 0.28) and novel protective associations with vitamin E (rg = - 0.53), calcium (rg = - 0.33), retinol (rg = - 0.59), Apolipoprotein A (rg = - 0.13), and HDL (rg = - 0.17), but no association with vitamin D (rg = - 0.02). Removing genomic regions associated with smoking and BMI generally attenuated the associations, but the associations with nutrient biomarkers persisted. This study provides a comprehensive assessment of the shared genetic architecture of COVID-19 severity and numerous clinical/physiologic parameters. Associations with blood and plasma-derived traits identified biomarkers for Mendelian randomization studies to explore causality and nominates therapeutic targets for clinical evaluation.
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Affiliation(s)
- Jinyoung Byun
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA. .,Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
| | - Younghun Han
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA.,Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Kyle M Walsh
- Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA
| | - Amy S Park
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Melissa L Bondy
- Department of Epidemiology and Population Health, School of Medicine, Stanford University, Stanford, CA, USA
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA. .,Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA. .,Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA.
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9
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A scoping review and comparison of approaches for measuring genetic heterogeneity in psychiatric disorders. Psychiatr Genet 2022; 32:1-8. [PMID: 34694248 DOI: 10.1097/ypg.0000000000000304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
An improved understanding of genetic etiological heterogeneity in a psychiatric condition may help us (a) isolate a neurophysiological 'final common pathway' by identifying its upstream genetic origins and (b) facilitate characterization of the condition's phenotypic variation. This review aims to identify existing genetic heterogeneity measurements in the psychiatric literature and provides a conceptual review of their mechanisms, limitations, and assumptions. The Scopus database was searched for studies that quantified genetic heterogeneity or correlation of psychiatric phenotypes with human genetic data. Ninety studies were included. Eighty-seven reports quantified genetic correlation, five applied genomic structural equation modelling, three evaluated departure from the Hardy-Weinberg equilibrium at one or more loci, and two applied a novel approach known as MiXeR. We found no study that rigorously measured genetic etiological heterogeneity across a large number of markers. Developing such approaches may help better characterize the biological diversity of psychopathology.
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10
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Guan F, Ni T, Zhu W, Williams LK, Cui LB, Li M, Tubbs J, Sham PC, Gui H. Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction. Mol Psychiatry 2022; 27:113-126. [PMID: 34193973 PMCID: PMC11018294 DOI: 10.1038/s41380-021-01201-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 06/15/2021] [Accepted: 06/17/2021] [Indexed: 02/06/2023]
Abstract
Schizophrenia (SCZ) is a debilitating neuropsychiatric disorder with high heritability and complex inheritance. In the past decade, successful identification of numerous susceptibility loci has provided useful insights into the molecular etiology of SCZ. However, applications of these findings to clinical classification and diagnosis, risk prediction, or intervention for SCZ have been limited, and elucidating the underlying genomic and molecular mechanisms of SCZ is still challenging. More recently, multiple Omics technologies - genomics, transcriptomics, epigenomics, proteomics, metabolomics, connectomics, and gut microbiomics - have all been applied to examine different aspects of SCZ pathogenesis. Integration of multi-Omics data has thus emerged as an approach to provide a more comprehensive view of biological complexity, which is vital to enable translation into assessments and interventions of clinical benefit to individuals with SCZ. In this review, we provide a broad survey of the single-omics studies of SCZ, summarize the advantages and challenges of different Omics technologies, and then focus on studies in which multiple omics data are integrated to unravel the complex pathophysiology of SCZ. We believe that integration of multi-Omics technologies would provide a roadmap to create a more comprehensive picture of interactions involved in the complex pathogenesis of SCZ, constitute a rich resource for elucidating the potential molecular mechanisms of the illness, and eventually improve clinical assessments and interventions of SCZ to address clinical translational questions from bench to bedside.
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Affiliation(s)
- Fanglin Guan
- Department of Forensic Psychiatry, School of Medicine & Forensics, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Tong Ni
- Department of Forensic Psychiatry, School of Medicine & Forensics, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Weili Zhu
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
| | - Long-Biao Cui
- Department of Clinical Psychology, School of Medical Psychology, Air Force Medical University, Xi'an, Shaanxi, China
| | - Ming Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Justin Tubbs
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for PanorOmic Sciences, The University of Hong Kong, Hong Kong SAR, China
| | - Pak-Chung Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- Centre for PanorOmic Sciences, The University of Hong Kong, Hong Kong SAR, China.
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China.
| | - Hongsheng Gui
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA.
- Behavioral Health Services, Henry Ford Health System, Detroit, MI, USA.
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11
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Genetic Overlap Profiles of Cognitive Ability in Psychotic and Affective Illnesses: A Multisite Study of Multiplex Pedigrees. Biol Psychiatry 2021; 90:373-384. [PMID: 33975707 PMCID: PMC8403107 DOI: 10.1016/j.biopsych.2021.03.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 01/08/2021] [Accepted: 03/10/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Cognitive impairment is a key feature of psychiatric illness, making cognition an important tool for exploring of the genetics of illness risk. It remains unclear which measures should be prioritized in pleiotropy-guided research. Here, we generate profiles of genetic overlap between psychotic and affective disorders and cognitive measures in Caucasian and Hispanic groups. METHODS Data were from 4 samples of extended pedigrees (N = 3046). Coefficient of relationship analyses were used to estimate genetic overlap between illness risk and cognitive ability. Results were meta-analyzed. RESULTS Psychosis was characterized by cognitive impairments on all measures with a generalized profile of genetic overlap. General cognitive ability shared greatest genetic overlap with psychosis risk (average endophenotype ranking value [ERV] across samples from a random-effects meta-analysis = 0.32), followed by verbal memory (ERV = 0.24), executive function (ERV = 0.22), and working memory (ERV = 0.21). For bipolar disorder, there was genetic overlap with processing speed (ERV = 0.05) and verbal memory (ERV = 0.11), but these were confined to select samples. Major depressive disorder was characterized by enhanced working and face memory performance, as reflected in significant genetic overlap in 2 samples. CONCLUSIONS There is substantial genetic overlap between risk for psychosis and a range of cognitive abilities (including general intelligence). Most of these effects are largely stable across of ascertainment strategy and ethnicity. Genetic overlap between affective disorders and cognition, on the other hand, tends to be specific to ascertainment strategy, ethnicity, and cognitive test battery.
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12
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Dashti HS, Ordovás JM. Genetics of Sleep and Insights into Its Relationship with Obesity. Annu Rev Nutr 2021; 41:223-252. [PMID: 34102077 DOI: 10.1146/annurev-nutr-082018-124258] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Considerable recent advancements in elucidating the genetic architecture of sleep traits and sleep disorders may provide insight into the relationship between sleep and obesity. Despite the considerable involvement of the circadian clock in sleep and metabolism, few shared genes, including FTO, were implicated in genome-wide association studies (GWASs) of sleep and obesity. Polygenic scores composed of signals from GWASs of sleep traits show largely null associations with obesity, suggesting lead variants are unique to sleep. Modest genome-wide genetic correlations are observed between many sleep traits and obesity and are largest for snoring.Notably, U-shaped positive genetic correlations with body mass index (BMI) exist for both short and long sleep durations. Findings from Mendelian randomization suggest robust causal effects of insomnia on higher BMI and, conversely, of higher BMI on snoring and daytime sleepiness. Bidirectional effects between sleep duration and daytime napping with obesity may also exist. Limited gene-sleep interaction studies suggest that achieving favorable sleep, as part of a healthy lifestyle, may attenuate genetic predisposition to obesity, but whether these improvements produce clinically meaningful reductions in obesity risk remains unclear. Investigations of the genetic link between sleep and obesity for sleep disorders other than insomnia and in populations of non-European ancestry are currently limited. Expected final online publication date for the Annual Review of Nutrition, Volume 41 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Hassan S Dashti
- Center for Genomic Medicine and Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA; .,Broad Institute, Cambridge, Massachusetts 02142, USA
| | - José M Ordovás
- Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, Massachusetts 02111, USA.,Precision Nutrition and Obesity Program, IMDEA Alimentación, 28049 Madrid, Spain
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13
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Mei H, Jiang F, Li L, Griswold M, Liu S, Mosley T. Study of genetic correlation between children's sleep and obesity. J Hum Genet 2020; 65:949-959. [PMID: 32555314 DOI: 10.1038/s10038-020-0791-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 06/03/2020] [Accepted: 06/03/2020] [Indexed: 11/09/2022]
Abstract
Laboratory and epidemiological studies have shown that short sleep time is associated with obesity. In this study, we conducted a post-GWAS analysis to test genetic correlation between children's sleep and obesity due to linkage disequilibrium (LD) SNPs, shared genes and pathways. Our analysis showed that genetic heritability was 0.14 (p-value = 0.0005) and 0.41 (p-value = 1.18E-24) for children's sleep and obesity, respectively, but genetic correlation due to LD SNPs was insignificant. Gene associations at children's GWAS were measured based on SNP associations and ranked by their uniform score (U-score). After adjusting for gene size, measured as the number of independent SNPs, children's sleep and obesity GWAS had significant gene correlation (r = 0.23). Pathway enrichment analysis showed that "Suz12 target genes" was the significant pathway for both children's sleep and obesity; pathways were significantly shared among top enriched pathways with an OR of 8.1-59.4; and significant correlation coefficient of pathway U-score was r = 0.36. Analysis of sleep time and obesity GWAS variants for all ages in the NHGRI-EBI GWAS Catalog also presented significant pathway correlation (r = 0.30). The "PAX3-FOXO1 target genes" was the significant pathway for all-age obesity phenotype and ranked as the second top associated pathway for all-age sleep time. Our study suggested that genetic correlation of children's sleep time and obesity is attributed to genes with pleiotropy effects and common pathway regulations that may contain only weak SNP associations.
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Affiliation(s)
- Hao Mei
- Department of Data Science, School of Population Health, University of Mississippi Medical Center, Jackson, MS, USA.
| | - Fan Jiang
- Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lianna Li
- Department of Biology, Tougaloo College, Jackson, MS, USA
| | - Michael Griswold
- Department of Medicine, The MIND Center, University of Mississippi Medical Center, Jackson, MS, USA
| | - Shijian Liu
- Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Thomas Mosley
- Department of Medicine, The MIND Center, University of Mississippi Medical Center, Jackson, MS, USA.,Department of Neurology, University of Mississippi Medical Center, Jackson, MS, USA
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14
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Bahrami S, Steen NE, Shadrin A, O’Connell K, Frei O, Bettella F, Wirgenes KV, Krull F, Fan CC, Dale AM, Smeland OB, Djurovic S, Andreassen OA. Shared Genetic Loci Between Body Mass Index and Major Psychiatric Disorders: A Genome-wide Association Study. JAMA Psychiatry 2020; 77:503-512. [PMID: 31913414 PMCID: PMC6990967 DOI: 10.1001/jamapsychiatry.2019.4188] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 10/30/2019] [Indexed: 01/02/2023]
Abstract
Importance People with major psychiatric disorders (MPDs) have a 10- to 20-year shorter life span than the rest of the population, and this difference is mainly due to comorbid cardiovascular diseases. Genome-wide association studies have identified common variants involved in schizophrenia (SCZ), bipolar disorder (BIP), and major depression (MD) and body mass index (BMI), a key cardiometabolic risk factor. However, genetic variants jointly influencing MPD and BMI remain largely unknown. Objective To assess the extent of the overlap between the genetic architectures of MPDs and BMI and identify genetic loci shared between them. Design, Setting, and Participants Using a conditional false discovery rate statistical framework, independent genome-wide association study data on individuals with SCZ (n = 82 315), BIP (n = 51 710), MD (n = 480 359), and BMI (n = 795 640) were analyzed. The UK Biobank cohort (n = 29 740) was excluded from the MD data set to avoid sample overlap. Data were collected from August 2017 to May 2018, and analysis began July 2018. Main Outcomes and Measures The primary outcomes were a list of genetic loci shared between BMI and MPDs and their functional pathways. Results Genome-wide association study data from 1 380 284 participants were analyzed, and the genetic correlation between BMI and MPDs varied (SCZ: r for genetic = -0.11, P = 2.1 × 10-10; BIP: r for genetic = -0.06, P = .0103; MD: r for genetic = 0.12, P = 6.7 × 10-10). Overall, 63, 17, and 32 loci shared between BMI and SCZ, BIP, and MD, respectively, were analyzed at conjunctional false discovery rate less than 0.01. Of the shared loci, 34% (73 of 213) in SCZ, 52% (36 of 69) in BIP, and 57% (56 of 99) in MD had risk alleles associated with higher BMI (conjunctional false discovery rate <0.05), while the rest had opposite directions of associations. Functional analyses indicated that the overlapping loci are involved in several pathways including neurodevelopment, neurotransmitter signaling, and intracellular processes, and the loci with concordant and opposite association directions pointed mostly to different pathways. Conclusions and Relevance In this genome-wide association study, extensive polygenic overlap between BMI and SCZ, BIP, and MD were found, and 111 shared genetic loci were identified, implicating novel functional mechanisms. There was mixture of association directions in SCZ and BMI, albeit with a preponderance of discordant ones.
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Affiliation(s)
- Shahram Bahrami
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Nils Eiel Steen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Alexey Shadrin
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Kevin O’Connell
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Francesco Bettella
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | | | - Florian Krull
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Chun C. Fan
- Department of Radiology, University of California, San Diego, La Jolla
- Department of Cognitive Science, University of California, San Diego, La Jolla
| | - Anders M Dale
- Department of Radiology, University of California, San Diego, La Jolla
- Multimodal Imaging Laboratory, University of California, San Diego, La Jolla
- Department of Psychiatry, University of California, San Diego, La Jolla
- Department of Neurosciences, University of California, San Diego, La Jolla
| | - Olav B. Smeland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT Centre, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Ole A. Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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15
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Lin BD, Alkema A, Peters T, Zinkstok J, Libuda L, Hebebrand J, Antel J, Hinney A, Cahn W, Adan R, Luykx JJ. Assessing causal links between metabolic traits, inflammation and schizophrenia: a univariable and multivariable, bidirectional Mendelian-randomization study. Int J Epidemiol 2020; 48:1505-1514. [PMID: 31504541 PMCID: PMC7070229 DOI: 10.1093/ije/dyz176] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/07/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Blood immunoreactive biomarkers, such as C-reactive protein (CRP), and metabolic abnormalities have been associated with schizophrenia. Studies comprehensively and bidirectionally probing possible causal links between such blood constituents and liability to schizophrenia are lacking. METHODS To disentangle putative causal links between CRP blood levels and schizophrenia in both directions, we conducted multiple univariable Mendelian-randomization (MR) analyses, ranging from fixed-effect to inverse variance-weighted (IVW), weighted-median, MR Egger and generalized summary-data-based Mendelian-randomization (GSMR) models. To prioritize metabolic risk factors for schizophrenia, a novel multivariable approach was applied: multivariable Mendelian-randomization-Bayesian model averaging (MR-BMA). RESULTS All forward univariable MR analyses consistently showed that CRP has a protective effect on schizophrenia, whereas reverse MR analyses consistently suggested absent causal effects of schizophrenia liability on CRP blood levels. Using MR-BMA, as the top protective factors for schizophrenia we prioritized leucine and as the prime risk-factor triglycerides in medium very-low-density lipoprotein (VLDL). The five best-performing MR-BMA models provided one additional risk factor: triglycerides in large VLDL; and two additional protective factors: citrate and lactate. CONCLUSIONS Our results add to a growing body of literature hinting at metabolic changes-in particular of triglycerides-independently of medication status in schizophrenia. We also highlight the absent effects of genetic liability to schizophrenia on CRP levels.
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Affiliation(s)
- Bochao D Lin
- Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands.,Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, School of Basic Medical Sciences, Henan University, Kaifeng, China.,Department of Translational Neuroscience, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Anne Alkema
- Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Triinu Peters
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University of Duisburg-Essen, University Hospital Essen, Essen, Germany
| | - Janneke Zinkstok
- Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Lars Libuda
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University of Duisburg-Essen, University Hospital Essen, Essen, Germany
| | - Johannes Hebebrand
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University of Duisburg-Essen, University Hospital Essen, Essen, Germany
| | - Jochen Antel
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University of Duisburg-Essen, University Hospital Essen, Essen, Germany
| | - Anke Hinney
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University of Duisburg-Essen, University Hospital Essen, Essen, Germany
| | - Wiepke Cahn
- Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Roger Adan
- Department of Translational Neuroscience, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Jurjen J Luykx
- Department of Psychiatry, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands.,Department of Translational Neuroscience, UMC Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands.,GGNet Mental Health, Apeldoorn, The Netherlands
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16
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Pouget JG, Han B, Wu Y, Mignot E, Ollila HM, Barker J, Spain S, Dand N, Trembath R, Martin J, Mayes MD, Bossini-Castillo L, López-Isac E, Jin Y, Santorico SA, Spritz RA, Hakonarson H, Polychronakos C, Raychaudhuri S, Knight J. Cross-disorder analysis of schizophrenia and 19 immune-mediated diseases identifies shared genetic risk. Hum Mol Genet 2019; 28:3498-3513. [PMID: 31211845 PMCID: PMC6891073 DOI: 10.1093/hmg/ddz145] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Revised: 05/24/2019] [Accepted: 06/13/2019] [Indexed: 01/01/2023] Open
Abstract
Many immune diseases occur at different rates among people with schizophrenia compared to the general population. Here, we evaluated whether this phenomenon might be explained by shared genetic risk factors. We used data from large genome-wide association studies to compare the genetic architecture of schizophrenia to 19 immune diseases. First, we evaluated the association with schizophrenia of 581 variants previously reported to be associated with immune diseases at genome-wide significance. We identified five variants with potentially pleiotropic effects. While colocalization analyses were inconclusive, functional characterization of these variants provided the strongest evidence for a model in which genetic variation at rs1734907 modulates risk of schizophrenia and Crohn's disease via altered methylation and expression of EPHB4-a gene whose protein product guides the migration of neuronal axons in the brain and the migration of lymphocytes towards infected cells in the immune system. Next, we investigated genome-wide sharing of common variants between schizophrenia and immune diseases using cross-trait LD score regression. Of the 11 immune diseases with available genome-wide summary statistics, we observed genetic correlation between six immune diseases and schizophrenia: inflammatory bowel disease (rg = 0.12 ± 0.03, P = 2.49 × 10-4), Crohn's disease (rg = 0.097 ± 0.06, P = 3.27 × 10-3), ulcerative colitis (rg = 0.11 ± 0.04, P = 4.05 × 10-3), primary biliary cirrhosis (rg = 0.13 ± 0.05, P = 3.98 × 10-3), psoriasis (rg = 0.18 ± 0.07, P = 7.78 × 10-3) and systemic lupus erythematosus (rg = 0.13 ± 0.05, P = 3.76 × 10-3). With the exception of ulcerative colitis, the degree and direction of these genetic correlations were consistent with the expected phenotypic correlation based on epidemiological data. Our findings suggest shared genetic risk factors contribute to the epidemiological association of certain immune diseases and schizophrenia.
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Affiliation(s)
- Jennie G Pouget
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5S 1A8, Canada
| | | | - Buhm Han
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yang Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Emmanuel Mignot
- Center for Sleep Sciences and Medicine, Department of Psychiatry and Behavioral Sciences, Stanford University, School of Medicine, Palo Alto, CA, USA
| | - Hanna M Ollila
- Center for Sleep Sciences and Medicine, Department of Psychiatry and Behavioral Sciences, Stanford University, School of Medicine, Palo Alto, CA, USA
- Finnish Institute for Molecular Medicine, Helsinki, Finland
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA and Broad Institute, Cambridge, MA, USA
| | - Jonathan Barker
- School of Basic and Medical Biosciences, Faculty of Life Sciences and Medicine, King’s College London, London, UK
- St. John’s Institute of Dermatology, Faculty of Life Sciences and Medicine, King’s College London, London, UK
| | - Sarah Spain
- School of Basic and Medical Biosciences, Faculty of Life Sciences and Medicine, King’s College London, London, UK
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Nick Dand
- School of Basic and Medical Biosciences, Faculty of Life Sciences and Medicine, King’s College London, London, UK
| | - Richard Trembath
- School of Basic and Medical Biosciences, Faculty of Life Sciences and Medicine, King’s College London, London, UK
- Queen Mary University of London, Barts and the London School of Medicine and Dentistry, London, UK
| | - Javier Martin
- Institute of Parasitology and Biomedicine López-Neyra, Consejo Superior de Investigaciones Científicas (IPBLN-CSIC), Granada, Spain
| | - Maureen D Mayes
- The University of Texas Health Science Center–Houston, Houston, USA
| | - Lara Bossini-Castillo
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Elena López-Isac
- Institute of Parasitology and Biomedicine López-Neyra, Consejo Superior de Investigaciones Científicas (IPBLN-CSIC), Granada, Spain
| | - Ying Jin
- Human Medical Genetics and Genomics Program, University of Colorado School of Medicine, Aurora, CO, USA
- Department of Pediatrics, University of Colorado School of Medicine, Aurora 80045, CO, USA
| | - Stephanie A Santorico
- Human Medical Genetics and Genomics Program, University of Colorado School of Medicine, Aurora, CO, USA
- Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, USA
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado, Aurora, CO, USA
| | - Richard A Spritz
- Human Medical Genetics and Genomics Program, University of Colorado School of Medicine, Aurora, CO, USA
- Department of Pediatrics, University of Colorado School of Medicine, Aurora 80045, CO, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Constantin Polychronakos
- Endocrine Genetics Laboratory, Department of Pediatrics and the Child Health Program of the Research Institute, McGill University Health Centre, Montreal, QC, Canada
| | - Soumya Raychaudhuri
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Partners HealthCare Center for Personalized Genetic Medicine, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK
| | - Jo Knight
- Lancaster Medical School and Data Science Institute, Lancaster University, Lancaster, UK
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Zheutlin AB, Dennis J, Karlsson Linnér R, Moscati A, Restrepo N, Straub P, Ruderfer D, Castro VM, Chen CY, Ge T, Huckins LM, Charney A, Kirchner HL, Stahl EA, Chabris CF, Davis LK, Smoller JW. Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia in 106,160 Patients Across Four Health Care Systems. Am J Psychiatry 2019; 176:846-855. [PMID: 31416338 PMCID: PMC6961974 DOI: 10.1176/appi.ajp.2019.18091085] [Citation(s) in RCA: 138] [Impact Index Per Article: 27.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE Individuals at high risk for schizophrenia may benefit from early intervention, but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts. The authors sought to test the utility of this approach in clinical settings and to evaluate the broader health consequences of high genetic risk for schizophrenia. METHODS The authors used electronic health records for 106,160 patients from four health care systems to evaluate the penetrance and pleiotropy of genetic risk for schizophrenia. Polygenic risk scores (PRSs) for schizophrenia were calculated from summary statistics and tested for association with 1,359 disease categories, including schizophrenia and psychosis, in phenome-wide association studies. Effects were combined through meta-analysis across sites. RESULTS PRSs were robustly associated with schizophrenia (odds ratio per standard deviation increase in PRS, 1.55; 95% CI=1.4, 1.7), and patients in the highest risk decile of the PRS distribution had up to 4.6-fold higher odds of schizophrenia compared with those in the bottom decile (95% CI=2.9, 7.3). PRSs were also positively associated with other phenotypes, including anxiety, mood, substance use, neurological, and personality disorders, as well as suicidal behavior, memory loss, and urinary syndromes; they were inversely related to obesity. CONCLUSIONS The study demonstrates that an available measure of genetic risk for schizophrenia is robustly associated with schizophrenia in health care settings and has pleiotropic effects on related psychiatric disorders as well as other medical syndromes. The results provide an initial indication of the opportunities and limitations that may arise with the future application of PRS testing in health care systems.
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Affiliation(s)
- Amanda B Zheutlin
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Jessica Dennis
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Richard Karlsson Linnér
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Arden Moscati
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Nicole Restrepo
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Peter Straub
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Douglas Ruderfer
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Victor M Castro
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Chia-Yen Chen
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Laura M Huckins
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Alexander Charney
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - H Lester Kirchner
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Eli A Stahl
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Christopher F Chabris
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Lea K Davis
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit (Zheutlin, Chen, Ge, Smoller) and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston (Chen); Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Mass. (Zheutlin, Chen, Stahl, Smoller); Division of Genetic Medicine, Department of Medicine (Dennis, Straub, Ruderfer, Davis), Vanderbilt Genetics Institute (Dennis, Straub, Ruderfer, Davis), and Department of Biomedical Informatics (Ruderfer), Vanderbilt University Medical Center, Nashville; Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam (Karlsson Linnér); Autism and Developmental Medicine Institute, Geisinger, Lewisburg, Pa. (Karlsson Linnér, Chabris); Charles Bronfman Institute for Personalized Medicine (Moscati), Pamela Sklar Division of Psychiatric Genomics (Huckins, Charney, Stahl), and Department of Genetics and Genomic Sciences (Huckins, Charney, Stahl, ), Icahn School of Medicine at Mount Sinai, New York; Department of Biomedical and Translational Informatics, Geisinger, Rockville, Md. (Restrepo, Kirchner); Research Information Science and Computing, Partners HealthCare, Somerville, Mass. (Castro)
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Duan J, Sanders AR, Gejman PV. From Schizophrenia Genetics to Disease Biology: Harnessing New Concepts and Technologies. JOURNAL OF PSYCHIATRY AND BRAIN SCIENCE 2019; 4:e190014. [PMID: 31555746 PMCID: PMC6760308 DOI: 10.20900/jpbs.20190014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Schizophrenia (SZ) is a severe mental disorder afflicting around 1% of the population. It is highly heritable but with complex genetics. Recent research has unraveled a plethora of risk loci for SZ. Accordingly, our conceptual understanding of SZ genetics has been rapidly evolving, from oligogenic models towards polygenic or even omnigenic models. A pressing challenge to the field, however, is the translation of the many genetic findings of SZ into disease biology insights leading to more effective treatments. Bridging this gap requires the integration of genetic findings and functional genomics using appropriate cellular models. Harnessing new technologies, such as the development of human induced pluripotent stem cells (hiPSC) and the CRISPR/Cas-based genome/epigenome editing approach are expected to change our understanding of SZ disease biology to a fundamentally higher level. Here, we discuss some new developments.
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Affiliation(s)
- Jubao Duan
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Psychiatry and Behavioral Neurosciences, The University of Chicago, Chicago, IL 60637, USA
| | - Alan R. Sanders
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Psychiatry and Behavioral Neurosciences, The University of Chicago, Chicago, IL 60637, USA
| | - Pablo V. Gejman
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Psychiatry and Behavioral Neurosciences, The University of Chicago, Chicago, IL 60637, USA
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19
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Porcu E, Rüeger S, Lepik K, Santoni FA, Reymond A, Kutalik Z. Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits. Nat Commun 2019; 10:3300. [PMID: 31341166 PMCID: PMC6656778 DOI: 10.1038/s41467-019-10936-0] [Citation(s) in RCA: 159] [Impact Index Per Article: 31.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 06/11/2019] [Indexed: 01/21/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified thousands of variants associated with complex traits, but their biological interpretation often remains unclear. Most of these variants overlap with expression QTLs, indicating their potential involvement in regulation of gene expression. Here, we propose a transcriptome-wide summary statistics-based Mendelian Randomization approach (TWMR) that uses multiple SNPs as instruments and multiple gene expression traits as exposures, simultaneously. Applied to 43 human phenotypes, it uncovers 3,913 putatively causal gene-trait associations, 36% of which have no genome-wide significant SNP nearby in previous GWAS. Using independent association summary statistics, we find that the majority of these loci were missed by GWAS due to power issues. Noteworthy among these links is educational attainment-associated BSCL2, known to carry mutations leading to a Mendelian form of encephalopathy. We also find pleiotropic causal effects suggestive of mechanistic connections. TWMR better accounts for pleiotropy and has the potential to identify biological mechanisms underlying complex traits.
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Affiliation(s)
- Eleonora Porcu
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland. .,Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| | - Sina Rüeger
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.,University Center for Primary Care and Public Health, University of Lausanne, Switzerland, Lausanne, Switzerland
| | - Kaido Lepik
- University Center for Primary Care and Public Health, University of Lausanne, Switzerland, Lausanne, Switzerland.,Institute of Computer Science, University of Tartu, Tartu, Estonia
| | | | | | - Federico A Santoni
- Endocrine, Diabetes, and Metabolism Service, CHUV and University of Lausanne, Lausanne, Switzerland
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland. .,University Center for Primary Care and Public Health, University of Lausanne, Switzerland, Lausanne, Switzerland.
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Abstract
PURPOSE OF REVIEW The purpose of this review is to contextualize findings from the first 25 years of PTSD genetics research, focusing on the most robust findings and interpreting results in light of principles that have emerged from modern genetics studies. RECENT FINDINGS Genome-wide association studies (GWAS) encompassing tens of thousands of participants enabled the first molecular genetic heritability and genetic correlation estimates for PTSD in 2017. In 2018, highly promising loci for PTSD were reported, including variants in and near the CAMKV, KANSL1, and TCF4 genes. Twin studies from 25 years ago established that PTSD is genetically influenced and foreshadowed the molecular genetic findings of today. Discoveries that were impossible with smaller studies have been achieved via collaborative/team-science efforts. Most promisingly, individual genomic loci offer entirely novel clues about PTSD etiology, providing the raw material for transformative discoveries, and the future of PTSD research is bright.
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Affiliation(s)
- Laramie E Duncan
- Department of Psychiatry and Behavioral Sciences, Stanford University, 401 Quarry Road, Room 3320, Stanford, CA, 94305, USA.
| | | | - Hanyang Shen
- Department of Psychiatry and Behavioral Sciences, Stanford University, 401 Quarry Road, Room 3320, Stanford, CA, 94305, USA
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