Biology subjects

Di Forti, M.

Publications and source records attributed to Di Forti, M..

8 recordsLinked to original sources

bioRxiv2026

Co-expression-based models improve eQTL predictions and highlightnovel transcriptome-wide genes associated with schizophrenia

Non-coding genetic variants statistically associated with complex heritability phenotypes are thought to act primarily through transcriptome regulatory mechanisms. Predictions of gene expression in tissue like the human brain traditionally rely primarily on cis-eQTLs. Here, we introduce INGENE and MODULE, trans-eQTLs models designed to enhance the prediction of gene expression by capturing the collective impact of candidate trans-eQTLs acting within co-expression networks. Exploiting RNA-seq data in six post-mortem brain regions (amygdala, caudate nucleus, dorsal/subgenual anterior cingulate cortex, dorsolateral prefrontal cortex, and hippocampus), we validate our models on two testing datasets, demonstrating increased gene predictability compared to both an original cis-based model and to EpiXcan, the leading benchmark in cis-model performance. Integration of cis- and trans-predictions significantly improves gene-level expression imputation (MLE = 0.05) for 18,744 genes across the six brain regions considered. Applying cis and trans models to PGC wave 3 genotypes identifies 766 SCZ-associated genes across brain regions (pFDR < .01), emphasizing the complementary nature of cis and trans predictions in trait association discovery. Of these genes, 641 represent novel transcriptome-wide associations with schizophrenia, highlighting the role of trans-heritability and genetic interactions underlying risk for this disorder, in addition to further supporting 125 previous candidates.

genomics↗

bioRxiv2023

Systematic review and meta-analysis on the effects of chronic peri-adolescent cannabinoid exposure on schizophrenia-like behaviour in rodents

BackgroundThe link between cannabis use and schizophrenia is well-established in epidemiological studies, especially among adolescents with early-onset use. However, this association in rodent models is less clear. This meta-analysis examined the effects of adolescent cannabinoid exposure on distinct schizophrenia-like behaviours in rodents and how experimental variations influence outcomes. MethodsFollowing a pre-registered protocol (CRD42022338761), we searched PubMed, Ovid Medline, Embse and APA PsychInfo for English-language original studies until 2022. We synthesised data from experiments on schizophrenia-like behaviour in rats and mice after repeated peri-pubertal (onset between P23-P45) cannabinoid exposure. Risk of bias was assessed using the SYRCLEs tool. ResultsWe included 291 experiments from 91 articles across 9 behavioural tests. We found meta-analytic evidence supporting that CB1R agonists, both natural and synthetic, elicited broad schizophrenia-like behavioural alterations, including impaired working memory (g =-0.58 [CI: -1.00,-0.16]), novel object recognition (g=-0.63 [CI: -0.97,-0.30]), novel object location recognition (g=-0.70 [CI: -1.22,-0.28]), social motivation (g=-0.40 [CI: -0.63, -0.16]), pre-pulse inhibition (g=-0.48 [CI: -0.89, -0.08]), and sucrose preference (g=-0.92 [CI: -1.87,0.04]). By contrast, effects on novelty-induced locomotion were negligible. Subgroup analyses revealed similar effects across sexes and species. Substantial variance in the protocols and moderate-to-high heterogeneity in behavioural outcomes were observed. We found CBD may attenuate novelty-induced locomotion in an open field and enhance fear memory recall, but data was limited. DiscussionThis is the first meta-analysis to comprehensively assess the link between cannabinoids and schizophrenia-like behaviours in rodents. Our results support epidemiological links between early cannabis use and schizophrenia-like phenotypes, confirming the utility of animal models. Standardising protocols will optimise models to strengthen reproducibility and comparisons, our work provides a framework for refining rodent models to elucidate biological pathways linking cannabis and schizophrenia.

animal behavior and cognition↗

bioRxiv2019

The independent and combined influence of schizophrenia polygenic risk score and heavy cannabis use on risk for psychotic disorder: A case-control analysis from the EUGEI study.

Background: Some recent studies have challenged the direction of causality for the association between cannabis use and psychotic disorder, suggesting that cannabis use initiation is explained by common genetic variants associated with risk of schizophrenia. We used data from the European Union Gene-Environment Interaction consortium (EUGEI) case-control study to test for the independent and combined effect of heavy cannabis use, and of Schizophrenia Polygenic risk score (SZ PRS), on risk for psychotic disorder. Methods: Genome-wide data were obtained from 492 first episode psychosis patients (FEPp) and from 787 controls of European Ancestry, and used to generate SZ PRS from the summary results of an independent meta-analysis. Information on pattern of cannabis use was used to build a 7-level frequency-type composite cannabis use measure that we previously found was a strong predictor of psychotic disorder. Results: SZ PRS did not predict cannabis initiation (b=0.027; p=0.51) or how frequently controls (b=0.027; p=0.06) or FEPp (b=0.006; p=0.91) used it, or the type of cannabis they used (Controls: b = 0.032; p=0.31); FEPp: b= 0.005; p=0.89). The frequency-type composite cannabis use measure (OR=1.32; 95% CI 1.22-1.44) and SZ PRS (OR=2.29; 95%CI 1.71-3.05) showed independent effects from each other on the OR for psychotic disorder. Conclusion: SZ PRS does not predict an individual s propensity to try cannabis, frequency of use, or the potency of the cannabis used. Our findings provide the first evidence that SZ PRS and heavy cannabis use exert effects independent from each other on the risk for psychotic disorder.

genetics↗

bioRxiv2019

Association of extent of cannabis use and psychotic like intoxication experiences in a multi-national sample of First Episode Psychosis patients and controls

AimsFirst Episode Psychosis (FEP) patients who use cannabis experience more frequent psychotic and euphoric intoxication experiences compared to controls. It is not clear whether this is consequent to patients being more vulnerable to the effects of cannabis use or to their heavier pattern of use. We aimed to determine whether extent of use predicted psychotic-like and euphoric intoxication experiences in FEP patients and controls and whether this differs between groups. MethodsWe analysed data on lifetime cannabis using patients (n=655) and controls (n=654) across 15 sites from six countries in the EU-GEI study (2010-2015). We used multiple regression to model predictors of cannabis-induced experiences and to determine if there was an interaction between caseness and extent of use. ResultsCaseness, frequency of cannabis use and money spent on cannabis predicted psychotic-like and euphoric experiences, independent of other experiences (p[≤]0.001). For psychotic-like experiences there was a significant interaction for caseness x frequency of use (p<0.001) and caseness x money spent on cannabis (p=0.001) such that FEP patients had increased experiences at increased levels of use compared to controls. There was no similar significant interaction for euphoric experiences (p>0.5). Conclusions and RelevanceFEP patients are particularly sensitive to increased psychotic-like, but not euphoric experiences, at higher frequency and amount of cannabis use compared to controls. This suggests a specific psychotomimetic response in patients related particularly to heavy cannabis use.

neuroscience↗

bioRxiv2019

Jumping To Conclusions, General Intelligence, And Psychosis Liability: Findings From The Multicentric EU-GEI Case-Control Study

BackgroundThe \"jumping to conclusions\" (JTC) bias is associated with both psychosis and general cognition but their relationship is unclear. In this study, we set out to clarify the relationship between the JTC bias, IQ, psychosis and polygenic liability to schizophrenia and IQ.\n\nMethods817 FEP patients and 1294 population-based controls completed assessments of general intelligence (IQ), and JTC (assessed by the number of beads drawn on the probabilistic reasoning \"beads\" task) and provided blood or saliva samples from which we extracted DNA and computed polygenic risk scores for IQ and schizophrenia.\n\nResultsThe estimated proportion of the total effect of case/control differences on JTC mediated by IQ was 79%. Schizophrenia Polygenic Risk Score (SZ PRS) was non-significantly associated with a higher number of beads drawn (B= 0.47, 95% CI -0.21 to 1.16, p=0.17); whereas IQ PRS (B=0.51, 95% CI 0.25 to 0.76, p<0.001) significantly predicted the number of beads drawn, and was thus associated with reduced JTC bias. The JTC was more strongly associated with higher level of psychotic-like experiences (PLE) in controls, including after controlling for IQ (B= -1.7, 95% CI -2.8 to -0.5, p=0.006), but did not relate to delusions in patients.\n\nConclusionsthe JTC reasoning bias in psychosis is not a specific cognitive deficit but is rather a manifestation or consequence, of general cognitive impairment. Whereas, in the general population, the JTC bias is related to psychotic-like experiences, independent of IQ. The work has potential to inform interventions targeting cognitive biases in early psychosis.

neuroscience↗

bioRxiv2019

Investigating the role of behaviour in the genetic risk for schizophrenia

The notion that behaviour may be on a causal path from genetics to psychiatric disorders, such as schizophrenia, highlights a potential for practical interventions. Motivated by this, we test the association between schizophrenia (SCZ) polygenic risk scores (PRS) and 420 behavioural traits (personality, psychological, lifestyle, nutritional) in a psychiatrically healthy sub-cohort of the UK Biobank. Higher schizophrenia PRS was associated with a range of traits, including lower verbal-numerical reasoning (P = 6x10-61), higher nervous feelings (P = 2x10-51) and higher self-reported risk-taking (P = 2x10-41). We follow-up the risk-taking association, hypothesising that the association may be due to a genetic propensity for risk-taking leading to greater migration, urbanicity or drug-taking - reported environmental risk factors for schizophrenia, and all positively associated with risk-taking in these data. However, schizophrenia PRS was also associated with traits, such as tea drinking (P = 2x10-34), that are highly unlikely to be on a causal path to schizophrenia. We depict four causal relationships that may in theory underlie such PRS-trait associations and illustrate ways of testing for each. For example, we contrast PRS-trait trends in the healthy sub-cohort to the corresponding trait values of medicated and non-medicated individuals diagnosed with schizophrenia, allowing some differentiation of mediation-by-behaviour, disease-onset effects and treatment effects. However, dedicated follow-up studies and new methods are required to fully disentangle these relationships. Thus, while we urge caution in interpretation of simple PRS cross-trait associations, we propose that well-designed PRS analyses can contribute to identifying behaviours on the causal path from genetics to disease.

genetics↗

bioRxiv2019

Cannabis-associated symptom profiles in patients with first episode psychosis and population controls

ObjectiveThe evidence is mixed on whether cannabis use is associated with a particular symptomatology in first episode psychosis (FEP) patients.\n\nThe authors set out to investigate a) patterns of association between cannabis use and transdiagnostic symptom dimensions; b) whether the extent of use of cannabis contributes to the variation in clinical and subclinical symptom profiles.\n\nMethodThe authors analysed data from 901 patients and 1235 controls recruited across six countries, as part of the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) study. Item response modelling was used to estimate two bifactor models, which included general and specific dimensions of psychotic symptoms in patients and psychotic experiences in controls. The associations between these dimensions and cannabis use was evaluated using linear mixed effects models analyses.\n\nResultsIn patients, there was a linear relationship between the positive symptom dimension and the extent of lifetime exposure to cannabis, with daily users of high potency cannabis having the highest score (B=0.35; 95%CI 0.14 to 0.56). Moreover, negative symptoms were more common among patients who never used cannabis compared with those with any pattern of use (B=-0.27; 95%CI -0.42 to -0.12).\n\nIn controls, psychotic experiences were associated with current use of cannabis but not with the extent of lifetime use.\n\nNeither patients nor controls presented differences in the depressive dimension related to cannabis use.\n\nConclusionsThe extent of use of cannabis explains part of the heterogeneous distribution of positive and negative symptoms of FEP patients.

neuroscience↗

bioRxiv2018

The Maudsley Environmental Risk Score for Psychosis

Risk prediction algorithms have long been used in health research and practice (e.g., in prediction of cardiovascular disease, diabetes, etc.) However, similar tools have not been developed for mental health problems, despite extensive research on risk factors. For example, for psychotic disorders, attempts to sum environmental risk are rare, usually unsystematic and dictated by available data. In light of this, we sought to develop a valid, easy to use measure of the total environmental risk for psychotic disorders, which can be used in research and clinical practice.\n\nWe first reviewed the literature to identify well-replicated and validated environmental risk factors for psychosis and, then, used the largest available meta-analyses to derive current best estimates of risk. We devised a method of scoring individuals based on the level of exposure to each risk factor, using odds ratios from the meta-analyses, to produce an Environmental Risk Score (ERS).\n\nSix risk factors (ethnic minority status, urbanicity, high paternal age, obstetric complications, cannabis use, and childhood adversity) were used to generate the ERS. A distribution for different levels of risk based on permuted data showed that most of population would be at low/moderate risk with a small minority at increased environmental risk for psychosis.\n\nThis is the first systematic approach to develop an aggregate measure of environmental risk for psychoses. This can be used as a continuous measure of liability to disease or transformed to a relative risk. Its predictive ability will improve with the collection of additional, population specific data.

epidemiology↗