90 research outputs found

    Interaction Between Functional Genetic Variation of DRD2 and Cannabis Use on Risk of Psychosis

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    Both cannabis use and the dopamine receptor (DRD2) gene have been associated with schizophrenia, psychosis-like experiences, and cognition. However, there are no published data investigating whether genetically determined variation in DRD2 dopaminergic signaling might play a role in individual susceptibility to cannabis-associated psychosis. We genotyped (1) a case-control study of 272 patients with their first episode of psychosis and 234 controls, and also from (2) a sample of 252 healthy subjects, for functional variation in DRD2, rs1076560. Data on history of cannabis use were collected on all the studied subjects by administering the Cannabis Experience Questionnaire. In the healthy subjects' sample, we also collected data on schizotypy and cognitive performance using the Schizotypal Personality Questionnaire and the N-back working memory task. In the case-control study, we found a significant interaction between the rs1076560 DRD2 genotype and cannabis use in influencing the likelihood of a psychotic disorder. Among cannabis users, carriers of the DRD2, rs1076560, T allele showed a 3-fold increased probability to suffer a psychotic disorder compared with GG carriers (OR = 3.07; 95% confidence interval [CI]: 1.22-7.63). Among daily users, T carrying subjects showed a 5-fold increase in the odds of psychosis compared to GG carriers (OR = 4.82; 95% CI: 1.39-16.71). Among the healthy subjects, T carrying cannabis users had increased schizotypy compared with T carrying cannabis-naïve subjects, GG cannabis users, and GG cannabis-naïve subjects (all P ≤ .025). T carrying cannabis users had reduced working memory accuracy compared with the other groups (all P ≤ .008). Thus, variation of the DRD2, rs1076560, genotype may modulate the psychosis-inducing effect of cannabis us

    Interaction Between Functional Genetic Variation of DRD2 and Cannabis Use on Risk of Psychosis

    Get PDF
    Both cannabis use and the dopamine receptor (DRD2) gene have been associated with schizophrenia, psychosis-like experiences, and cognition. However, there are no published data investigating whether genetically determined variation in DRD2 dopaminergic signaling might play a role in individual susceptibility to cannabis-associated psychosis. We genotyped (1) a case-control study of 272 patients with their first episode of psychosis and 234 controls, and also from (2) a sample of 252 healthy subjects, for functional variation in DRD2, rs1076560. Data on history of cannabis use were collected on all the studied subjects by administering the Cannabis Experience Questionnaire. In the healthy subjects’ sample, we also collected data on schizotypy and cognitive performance using the Schizotypal Personality Questionnaire and the N-back working memory task. In the case-control study, we found a significant interaction between the rs1076560 DRD2 genotype and cannabis use in influencing the likelihood of a psychotic disorder. Among cannabis users, carriers of the DRD2, rs1076560, T allele showed a 3-fold increased probability to suffer a psychotic disorder compared with GG carriers (OR = 3.07; 95% confidence interval [CI]: 1.22–7.63). Among daily users, T carrying subjects showed a 5-fold increase in the odds of psychosis compared to GG carriers (OR = 4.82; 95% CI: 1.39–16.71). Among the healthy subjects, T carrying cannabis users had increased schizotypy compared with T carrying cannabis-naïve subjects, GG cannabis users, and GG cannabis-naïve subjects (all P ≤ .025). T carrying cannabis users had reduced working memory accuracy compared with the other groups (all P ≤ .008). Thus, variation of the DRD2, rs1076560, genotype may modulate the psychosisinducing effect of cannabis use

    Clinical, cognitive and neuroanatomical associations of serum NMDAR autoantibodies in people at clinical high risk for psychosis

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    Serum neuronal autoantibodies, such as those to the NMDA receptor (NMDAR), are detectable in a subgroup of patients with psychotic disorders. It is not known if they are present before the onset of psychosis or whether they are associated with particular clinical features or outcomes. In a case-control study, sera from 254 subjects at clinical high risk (CHR) for psychosis and 116 healthy volunteers were tested for antibodies against multiple neuronal antigens implicated in CNS autoimmune disorders, using fixed and live cell-based assays (CBAs). Within the CHR group, the relationship between NMDAR antibodies and symptoms, cognitive function and clinical outcomes over 24 month follow-up was examined. CHR subjects were not more frequently seropositive for neuronal autoantibodies than controls (8.3% vs. 5.2%; OR = 1.50; 95% CI: 0.58-3.90). The NMDAR was the most common target antigen and NMDAR IgGs were more sensitively detected with live versus fixed CBAs (p < 0.001). Preliminary phenotypic analyses revealed that within the CHR sample, the NMDAR antibody seropositive subjects had higher levels of current depression, performed worse on the Rey Auditory Verbal Learning Task (p < 0.05), and had a markedly lower IQ (p < 0.01). NMDAR IgGs were not more frequent in subjects who later became psychotic than those who did not. NMDAR antibody serostatus and titre was associated with poorer levels of functioning at follow-up (p < 0.05) and the presence of a neuronal autoantibody was associated with larger amygdala volumes (p < 0.05). Altogether, these findings demonstrate that NMDAR autoantibodies are detectable in a subgroup of CHR subjects at equal rates to controls. In the CHR group, they are associated with affective psychopathology, impairments in verbal memory, and overall cognitive function: these findings are qualitatively and individually similar to core features of autoimmune encephalitis and/or animal models of NMDAR antibody-mediated CNS disease. Overall the current work supports further evaluation of NMDAR autoantibodies as a possible prognostic biomarker and aetiological factor in a subset of people already meeting CHR criteria

    Use of schizophrenia and bipolar disorder polygenic risk scores to identify psychotic disorders

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    BACKGROUND: There is increasing evidence for shared genetic susceptibility between schizophrenia and bipolar disorder. Although genetic variants only convey subtle increases in risk individually, their combination into a polygenic risk score constitutes a strong disease predictor.AimsTo investigate whether schizophrenia and bipolar disorder polygenic risk scores can distinguish people with broadly defined psychosis and their unaffected relatives from controls. METHOD: Using the latest Psychiatric Genomics Consortium data, we calculated schizophrenia and bipolar disorder polygenic risk scores for 1168 people with psychosis, 552 unaffected relatives and 1472 controls. RESULTS: Patients with broadly defined psychosis had dramatic increases in schizophrenia and bipolar polygenic risk scores, as did their relatives, albeit to a lesser degree. However, the accuracy of predictive models was modest. CONCLUSIONS: Although polygenic risk scores are not ready for clinical use, it is hoped that as they are refined they could help towards risk reduction advice and early interventions for psychosis.Declaration of interestR.M.M. has received honoraria for lectures from Janssen, Lundbeck, Lilly, Otsuka and Sunovian.Funding: This work was funded by the Medical Research Council (G0901310), the Wellcome Trust (grants 085475/B/08/Z, 085475/Z/08/Z), the European Union’s Seventh Framework Programme for research, technological development and demonstration (grant 602450). This study was also supported by the NIHR Biomedical Research Centre at University College London (mental health theme) and by the NIHR Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust and Institute of Psychiatry – Kings College London. Further support: NHIR Academic Clinical fellowship awarded to M.S.C.. E.B. acknowledges research funding from: BMA Margaret Temple grants 2016 and 2006, MRC – Korean Health Industry Development Institute Partnering Award (MC_PC_16014), MRC New Investigator Award and a MRC Centenary Award (G0901310), National Institute of Health Research UK post-doctoral fellowship, the Psychiatry Research Trust, the Schizophrenia Research Fund, the Brain and Behaviour Research foundation’s NARSAD Young Investigator Awards 2005, 2008, Wellcome Trust Research Training Fellowship and the NIHR Biomedical Research Centre for Mental Health at the South London and Maudsley NHS Foundation Trust and Institute of Psychiatry Kings College London. The Brain and Behaviour Research foundation’s (NARSAD’s) Young Investigator Award (Grant 22604, awarded to C.I.). The BMA Margaret Temple grant 2016 to J. H.T. European Research Council Marie Curie award to A.D.-R. The infrastructure for the GROUP consortium is funded through the Geestkracht programme of the Dutch Health Research Council (ZON-MW, grant number 10-000-1001), and matching funds from participating pharmaceutical companies (Lundbeck, AstraZeneca, Eli Lilly, Janssen Cilag) and universities and mental healthcare organisations. Amsterdam: Academic Psychiatric Centre of the Academic Medical Center and the mental health institutions: GGZ Ingeest, Arkin, Dijk en Duin, GGZ Rivierduinen, Erasmus Medical Centre, GGZ Noord Holland Noord. Maastricht: Maastricht University Medical Centre and the mental health institutions: GGZ Eindhoven en de kempen, GGZ Breburg, GGZ Oost-Brabant, Vincent van Gogh voor Geestelijke Gezondheid, Mondriaan Zorggroep, Prins Clauscentrum Sittard, RIAGG Roermond, Universitair Centrum Sint-Jozef Kortenberg, CAPRI University of Antwerp, PC Ziekeren Sint-Truiden, PZ Sancta Maria Sint-Truiden, GGZ Overpelt, OPZ Rekem. Groningen: University Medical Center Groningen and the mental health institutions: Lentis, GGZ Friesland, GGZ Drenthe, Dimence, Mediant, GGNet Warnsveld, Yulius Dordrecht and Parnassia psychomedical center (The Hague). Utrecht: University Medical Center Utrecht and the mental health institutions Altrecht, GGZ Centraal, Riagg Amersfoort and Delta. The sample from Spain was collected at the Hospital Universitario Marqués de Valdecilla, University of Cantabria, Santander, Spain, under the following grant support: Carlos III Health Institute PI020499, PI050427, PI060507, Plan Nacional de Drugs Research Grant 2005- Orden sco/3246/2004, SENY Fundació Research Grant CI 2005-0308007 and Fundación Marqués de Valdecilla API07/011. The present data were obtained at the Hospital Marqués de Valdecilla, University of Cantabria, Santander, Spain, under the following grant support: MINECO Exp.: SAF2013-46292-R

    An examination of polygenic score risk prediction in individuals with first-episode psychosis

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    Background Polygenic risk scores (PRSs) have successfully summarized genome-wide effects of genetic variants in schizophrenia with significant predictive power. In a clinical sample of first-episode psychosis (FEP) patients, we estimated the ability of PRSs to discriminate case-control status and to predict the development of schizophrenia as opposed to other psychoses. Methods The sample (445 case and 265 control subjects) was genotyped on the Illumina HumanCore Exome BeadChip with an additional 828 control subjects of African ancestry genotyped on the Illumina Multi-Ethnic Genotyping Array. To calculate PRSs, we used the results from the latest Psychiatric Genomics Consortium schizophrenia meta-analysis. We examined the association of PRSs with case-control status and with schizophrenia versus other psychoses in European and African ancestry FEP patients and in a second sample of 248 case subjects with chronic psychosis. Results PRS had good discriminative ability of case-control status in FEP European ancestry individuals (9.4% of the variance explained, p < 10−6), but lower in individuals of African ancestry (R2 = 1.1%, p = .004). Furthermore, PRS distinguished European ancestry case subjects who went on to acquire a schizophrenia diagnosis from those who developed other psychotic disorders (R2 = 9.2%, p = .002). Conclusions PRS was a powerful predictor of case-control status in a European sample of patients with FEP, even though a large proportion did not have an established diagnosis of schizophrenia at the time of assessment. PRS was significantly different between those case subjects who developed schizophrenia from those who did not, although the discriminative accuracy may not yet be sufficient for clinical utility in FEP

    Psychosis Endophenotypes:A Gene-Set-Specific Polygenic Risk Score Analysis

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    BACKGROUND AND HYPOTHESIS: Endophenotypes can help to bridge the gap between psychosis and its genetic predispositions, but their underlying mechanisms remain largely unknown. This study aims to identify biological mechanisms that are relevant to the endophenotypes for psychosis, by partitioning polygenic risk scores into specific gene sets and testing their associations with endophenotypes. STUDY DESIGN: We computed polygenic risk scores for schizophrenia and bipolar disorder restricted to brain-related gene sets retrieved from public databases and previous publications. Three hundred and seventy-eight gene-set-specific polygenic risk scores were generated for 4506 participants. Seven endophenotypes were also measured in the sample. Linear mixed-effects models were fitted to test associations between each endophenotype and each gene-set-specific polygenic risk score. STUDY RESULTS: After correction for multiple testing, we found that a reduced P300 amplitude was associated with a higher schizophrenia polygenic risk score of the forebrain regionalization gene set (mean difference per SD increase in the polygenic risk score: -1.15 µV; 95% CI: -1.70 to -0.59 µV; P = 6 × 10-5). The schizophrenia polygenic risk score of forebrain regionalization also explained more variance of the P300 amplitude (R2 = 0.032) than other polygenic risk scores, including the genome-wide polygenic risk scores. CONCLUSIONS: Our finding on reduced P300 amplitudes suggests that certain genetic variants alter early brain development thereby increasing schizophrenia risk years later. Gene-set-specific polygenic risk scores are a useful tool to elucidate biological mechanisms of psychosis and endophenotypes, offering leads for experimental validation in cellular and animal models

    Psychosis Endophenotypes: A Gene-Set-Specific Polygenic Risk Score Analysis

    Get PDF
    BACKGROUND AND HYPOTHESIS: Endophenotypes can help to bridge the gap between psychosis and its genetic predispositions, but their underlying mechanisms remain largely unknown. This study aims to identify biological mechanisms that are relevant to the endophenotypes for psychosis, by partitioning polygenic risk scores into specific gene sets and testing their associations with endophenotypes. STUDY DESIGN: We computed polygenic risk scores for schizophrenia and bipolar disorder restricted to brain-related gene sets retrieved from public databases and previous publications. Three hundred and seventy-eight gene-set-specific polygenic risk scores were generated for 4506 participants. Seven endophenotypes were also measured in the sample. Linear mixed-effects models were fitted to test associations between each endophenotype and each gene-set-specific polygenic risk score. STUDY RESULTS: After correction for multiple testing, we found that a reduced P300 amplitude was associated with a higher schizophrenia polygenic risk score of the forebrain regionalization gene set (mean difference per SD increase in the polygenic risk score: -1.15 µV; 95% CI: -1.70 to -0.59 µV; P = 6 × 10-5). The schizophrenia polygenic risk score of forebrain regionalization also explained more variance of the P300 amplitude (R2 = 0.032) than other polygenic risk scores, including the genome-wide polygenic risk scores. CONCLUSIONS: Our finding on reduced P300 amplitudes suggests that certain genetic variants alter early brain development thereby increasing schizophrenia risk years later. Gene-set-specific polygenic risk scores are a useful tool to elucidate biological mechanisms of psychosis and endophenotypes, offering leads for experimental validation in cellular and animal models
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