Evangelos Vassos

Background and aims: Polygenic risk scores (PRSs) for cannabis use disorder (CUD), major depressive disorder (MDD), insomnia and chronic pain, and early age at cannabis initiation might be linked to problematic cannabis use. We tested whether: (1) higher PRS for CUD, MDD, insomnia and chronic pain are associated with earlier initiation and problematic cannabis use; (2) age at cannabis initiation explains more variance in problematic use than any single PRS; and (3) PRSs moderate the association between early initiation and problematic cannabis use. Design: Observational study involving two cohorts: (1) the Avon Longitudinal Study of Parents and Children (ALSPAC) population‐based birth cohort and (2) for conceptual replication, the EUropean Gene–Environment Interaction (EU‐GEI) first‐episode psychosis case–control study. Setting: (1) United Kingdom; (2b) 16 population‐based catchment areas in Europe and 1 in Brazil, with FEP cases recruited from mental health services and controls from the general population of the same catchment areas. Participants: For ALSPAC, n = 8224 genotyped (51% male); subsamples with Cannabis Abuse Screening Test (CAST) at 17 years ( n = 895), 20y ( n = 1587), 24y ( n = 836) and frequency of use at 24y ( n = 2908). EU‐GEI: n = 776 [controls: mean age 37.2, standard deviation (SD) = 13.5; 47% male] and 491 FEP cases (mean age 31.5, SD = 11.2; 62.5% male). Measurements: Outcomes were self‐reported lifetime and frequency of cannabis use and CAST score (problematic use questionnaire). Predictors were PRSs for CUD, MDD, insomnia and chronic pain. Models were adjusted for sex, age and genetic principal components (ALSPAC), and additionally for site and case–control status (EU‐GEI). Results: In ALSPAC, CUD PRS was associated with CAST score at 20 [beta coefficient (β) = 0.04, False Discovery Rate‐corrected P ‐value (p _FDR ) = 0.03] and CAST at 24 years (β = 0.11, p _FDR = 0.005). MDD PRS was associated with CAST scores at 17y (β = 0.1, p _FDR = 0.002), 20y (β = 0.06, p _FDR = 0.01) and 24y (β = 0.09, p _FDR = 0.01). Pain PRS was associated with the CAST scores at 17y (β = 0.09, p _FDR = 0.002), 20y (β = 0.07, p _FDR < 0.001) and 24y (β = 0.09, p _FDR = 0.005). In EU‐GEI, CUD PRS (RRR = 1.82, p _FDR = 0.001) and pain PRS (RRR = 1.27, p _FDR = 0.007) were associated with weekly‐to‐daily use. The Relative Excess Risk due to Interaction RERI model showed evidence of additive interaction for CUD PRS, RERI = 2.4, P = 0.009, while MDD and schizophrenia PRS showed no evidence of additive interaction. Summary: Genetic liability, measured by polygenic risk scores, to cannabis use disorder, major depressive disorder, and chronic pain appears to be associated with heavier and more problematic cannabis use. Age at cannabis initiation explains more variance in problematic cannabis use than polygenic liability.

Background: The psychosis continuum conceptualises psychotic phenomena as existing on a spectrum from subclinical experiences to psychotic disorders. Despite epidemiological and clinical evidence in support of this model, few studies have examined polygenic liability across the severity continuum: from subclinical experiences to first-episode psychosis (FEP). Aims: To map polygenic liability for schizophrenia (SCZ), bipolar disorder and major depressive disorder (MDD) across the psychosis continuum. We hypothesised that schizophrenia polygenic scores (SCZ-PGS) would track phenotypic severity most closely across this continuum. Method: We harmonised data from 5 south-east London cohorts ( N = 1315; 860 European, 455 African ancestries) across 4 ordered groups indicative of different levels of phenotypic severity: controls ( n = 647), individuals with self-reported psychotic experiences ( n = 89), individuals at clinical high risk (CHR) that did not convert to psychosis ( n = 124) and individuals with FEP ( n = 455). We computed ancestry-specific PGS for SCZ, bipolar disorder and MDD and compared these across groups using ordinal logistic regression. Results: In European ancestry participants, elevated SCZ-PGS (odds ratio 1.72, p = 4.57 × 10 ⁻¹⁵ ), bipolar disorder-PGS (odds ratio 1.56, p = 1.03 × 10 ⁻¹⁰ ) and MDD-PGS (odds ratio 1.44, p = 4.86 × 10 ⁻⁸ ) were individually associated with increasing phenotypic severity. SCZ-PGS explained the largest variance ( R ² = 0.084) across the psychosis continuum and showed pairwise differences between CHR and controls ( β = 0.32, p = 0.027), and between FEP and CHR ( β = 0.38, p = 0.017). Multivariable models, combining three PGS, outperformed univariable models ( R ² = 0.105). Similar trends in SCZ-PGS across the psychosis continuum phenotype were seen in African ancestry participants, but only controls and FEP groups differed significantly ( β = 0.27, p = 0.037). Conclusions: Our findings provide biological evidence supporting the psychosis continuum framework. In European ancestry, SCZ-PGS differences suggest that CHR who do not transition to psychosis have intermediate genetic vulnerability. Multivariable models, utilising all three PGS, explained additional variance compared with univariable ones, supporting transdiagnostic genetic liability. Improving PGS predictive power in African ancestry remains a priority.

Goh et al. recently reported that most recurrent copy number variants (CNVs) are far less penetrant for intellectual disability (ID) than previously estimated, and recommended that fourteen should no longer be reported. We show that the lower estimates arise mainly from assuming the population prevalence of ID alone, while case counts derive from broad diagnostic referral cohorts in which about half of individuals were referred for other indications. The proposed formula estimates a risk difference rather than penetrance, and cases and controls were selected, assayed and phenotyped differently. Restricting the outcome to ID also leaves the analysis unable to inform risk for autism, epilepsy, psychiatric, cardiac and other phenotypes. We recommend that families receive phenotype-specific absolute risks for carriers alongside background risks, and that reporting decisions be made locus by locus by guideline bodies, considering all relevant phenotypes. The recalculated estimates inform this debate but are not yet sufficient grounds for changing reporting practice.

Background: Prolonged childhood and adolescent loneliness (CAL) may mark an early socio-emotional vulnerability relevant to psychosis and interact with genetic liability. We examined whether CAL increases odds of first-episode psychosis (FEP), relates to diagnostic subtype and functioning, and interacts with polygenic risk scores (PGS) for schizophrenia (SCZ), bipolar disorder (BIP), and major depression (MDD). Study Methods: We analyzed 2565 participants (1084 FEP, 1481 controls) from European Gene–Environment Interactions in Schizophrenia (EU-GEI) study. CAL was assessed retrospectively before age 12 (LNL12) and at 12-16 years (LNL12-16). Functioning was rated with a modified Global Assessment of Functioning (GAF). Genome-wide PGS for SCZ, BIP, and MDD were calculated using polygenic risk score–continuous shrinkage. Mixed-effects logistic and linear regression models tested associations of CAL with FEP, subtype, and functioning, including sex-stratified analyses. Additive gene–environment interaction was examined using dichotomized PGS and the Relative Excess Risk due to Interaction. Study Results: Both LNL12 (OR = 2.90, 95% CI, 2.30-3.66) and LNL12-16 (OR = 2.34, 95% CI, 1.94-2.84) were associated with FEP, independent of premorbid social isolation. CAL increased FEP odds in both sexes. LNL12-16, but not LNL12, was associated with affective versus non-affective psychosis (OR = 1.37, 95% CI, 1.02-1.83), more clearly in females (OR = 1.79, 95% CI, 1.16-2.77). CAL was also associated with poorer functioning, with 3-4-point lower GAF Disability scores. Significant additive interactions were observed between LNL12-16 and SCZ-PGS, and between LNL12 and MDD-PGS, although the latter did not replicate in European-ancestry sensitivity analyses. Conclusions: Prolonged CAL is linked to higher risk of FEP, preferentially affective psychosis in females, poorer functioning, and developmental potentiation of polygenic liability.

Background and Hypothesis: Psychosis prevention can be supported by tools that facilitate the early detection of individuals at clinical high risk (CHR-P) and predicting their clinical outcomes. We aimed to use clinical, genetic, and environmental data to: (1) predict case–control status as a proof-of-concept for CHR-P detection, and (2) predict transition to psychosis. Study Design: We used data from the European Network of National Schizophrenia Networks Studying Gene–Environment Interactions: a multicenter cohort study comprising 344 CHR-P individuals and 67 healthy controls. To predict CHR-P status, we used environmental (Psychosis Polyrisk Score [PPS]) and genetic (polygenic risk score for schizophrenia [PRS]) measures with logistic regression (LR) and random forest (RF). To predict transition to psychosis, we used clinical, environmental, and genetic measures with Cox proportional hazards model and random survival forest. Primary outcomes were discrimination (C-index) and calibration (intercept and slope) in repeated nested cross-validation. Clinical utility was assessed with decision curve analysis. Study Results: For detection of CHR-P, both PPS (LR:C = 0.91, 95% CI, 0.87-0.94, intercept = 1.46, slope = 0.73; RF:C = 0.77, 95% CI, 0.61-0.90, intercept = −0.53, slope = 0.42) and PPS + PRS (LR:C = 0.89, 95% CI, 0.85-0.92, intercept = 1.45, slope = 0.73; RF:C = 0.78, 95% CI, 0.65-0.89, intercept = −0.24, slope = 0.44) had excellent discrimination performance, whereas PRS performed substantially worse (LR:C = 0.60, 95% CI, 0.52-0.67, intercept = 1.63, slope = 0.81; RF:C = 0.57, 95% CI, 0.41-0.72, intercept = −0.84, slope = 0.10). All models over-estimated risk in individuals with low observed risk. Prognosis model performance was poor (C ≤ 0.65). Conclusions: CHR-P detection may be improved by using the PPS, though evidence is needed from more representative detection settings. However, we did not find evidence that baseline clinical, environmental and/or genetic data enhanced the prediction of psychosis onset.

In about a quarter of people with schizophrenia-spectrum disorder (SSD), the illness is unresponsive to standard antipsychotic treatment, yet the biological mechanisms underlying this remain poorly understood. Although such treatment-resistant schizophrenia (TRS) shares a substantial genetic liability with treatment-responsive schizophrenia, the limited efficacy of dopamine antagonists in TRS indicates that mechanisms beyond dopamine signalling likely contribute to treatment-resistance, requiring the identification of alternative biological pathways. This is the first cross-ancestry genetic study to investigate the genetic architecture of TRS, by directly comparing patients with treatment-resistant and treatment-responsive schizophrenia in two independent Hong Kong (N=798) and STRATA-G consortium (N=1243) cohorts. Using an integrated multi-level analytic framework, we conducted a genome-wide association study (GWAS) with gene-based and gene set-based analyses, pathway polygenic-risk-scores, and transcriptome-wide association study (TWAS). We further conducted gene-set enrichment analysis focusing on expert-curated synaptic pathways and brain tissues. Genetic signals at the gene, pathway, and genetically predicted expression levels were identified within each ancestry. Whereas limited power constrained individual loci discovery and cross-ancestry concordance, enrichment analyses indicated heterogeneous signals across cohorts, including differences in effect direction, but highlighted cohort-specific, synapse-related biology, particularly pathways involved in presynaptic vesicle dynamics, neurotransmission, and synaptic organization. Collectively, these findings highlight synaptic biology as one potential pathway-level signal from common-variant genetic effects associated with treatment resistance in SSD, despite minimal SNP-level discovery. Our work suggests there is promise in pathway-level and multi-omics approaches to elucidate biologically meaningful heterogeneity within SSD and provides support for synaptic mechanisms as potential targets for understanding and stratifying treatment-resistance.

Psychotic disorders are complex, multifactorial conditions influenced by both genetic liability and early environmental adversity. Polygenic risk scores (PRSs) derived from genome-wide association studies have shown utility in capturing genetic predisposition, but their biological interpretability remains limited. In this study, we evaluated whether biologically informed pathway-specific polygenic scores (pPGSs) for psychosis, restricted to neurotransmitter-related pathways, could help clarify gene-environment interplay. Using data from 1 192 individuals in the EU-GEI multi-site case-control study, we constructed pPGSs for dopamine, glutamate, GABA, and serotonin systems. We investigated associations between pPGSs and childhood trauma (rGE), their interactions on psychosis risk (GxE), and the influence of the genome-wide psychosis PRS on these relationships. Serotonin, dopamine, and glutamate pPGSs were positively associated with a composite trauma exposure (i.e., abuse and neglect), suggesting shared genetic factors contributing to both psychosis liability and early adversity. Significant negative GxE effects were observed for both dopamine and serotonin pPGSs, indicating that higher trauma exposure diminished the relative influence of genetic liability on psychosis risk. Adjustment for the genome-wide psychosis PRS attenuated most effects, but serotonergic and dopaminergic associations remained robust, supporting pathway-specific contributions beyond general polygenic risk. These findings provide proof-of-concept for the utility of pPGSs in psychiatric research, suggesting both genetic contributions to trauma exposure and GxE effects on psychosis risk. Further research incorporating epigenetic data and longitudinal designs may enhance mechanistic insight and translational potential.

Background: Antidepressants are the most frequently prescribed medications in psychiatry. Medical records of thousands of individuals provide a valuable opportunity to explore prescribing patterns and identify factors that influence treatment outcomes. Aims: To investigate antidepressant change patterns in depression and non-depression indications and assess clinical and genetic factors associated with outcomes of antidepressant treatment. Methods: Using primary care records from the UK Biobank, we examined outcomes including number of antidepressant changes and discontinuation due to either side-effects or inadequate response. Genetic analyses including heritability estimation, genetic correlation and polygenic score association were performed. Results: A total of 82 633 individuals were prescribed at least 1 antidepressant. Of these, 28 332 individuals with at least 1 primary care depression diagnosis were classified as the depression group, and 24 543 individuals without evidence of depression were classified as the non-depression group. Citalopram and fluoxetine were the most prescribed antidepressants for depression, whereas amitriptyline dominated prescriptions for non-depression indications. Individuals with depression were more likely to stay on antidepressants longer than those without depression and to follow preferred antidepressants that changed over time. Antidepressant changes and discontinuation were associated with a range of psychiatric and somatic conditions, including recurrent depression (early discontinuation: odds ratio = 1.96; late discontinuation: odds ratio = 2.63) and anxiety (early discontinuation: odds ratio = 1.37; late discontinuation: odds ratio = 1.99) in the depression group, and pain-related conditions in the non-depression group. Genetic analyses identified two novel variants associated with early discontinuation of selective serotonin reuptake inhibitors. Notable genetic overlap was shown between these outcomes and multiple psychiatric and physical traits, including the number of antidepressant changes with anxiety and depression ( r _g = 0.81–0.83), and polygenic scores for depression and attention-deficit hyperactivity disorder showed significant predictive value with respect to treatment outcomes. Conclusions: These findings characterise antidepressant change patterns in primary care records and highlight the potential value of integrating clinical and genetic data to better understand factors associated with treatment outcomes.

Background: Psychotic-like experiences (PLEs) index early risk for psychotic disorders and are consistently associated with childhood trauma, yet underlying biological mechanisms remain poorly understood. DNA methylation (DNAm) may capture the biological embedding of early adversity, while adolescent exposures such as cannabis use may modify these processes. We examined epigenome-wide associations of childhood trauma and PLEs, tested the moderating role of early cannabis use, and evaluated DNAm as a potential mediator. Methods: We analysed data from the Avon Longitudinal Study of Parents and Children (ALSPAC), a UK population-based birth cohort. Childhood trauma was assessed prospectively and retrospectively. Epigenome-wide DNAm was measured in peripheral blood at ∼17 years using the Illumina 450K array, and PLEs were assessed at 18 using a structured interview. Epigenome-wide association studies were conducted for trauma-DNAm and DNAm-PLEs associations in the final sample ( n = 1,457), adjusting for demographic, biological, and technical covariates. Differentially methylated regions (DMRs) were identified using DMRff, followed by functional enrichment analyses. Cannabis use at 15.5 was modelled as a moderator with multiple imputation for missing data. Mediation was tested using the Divide-Aggregate Composite-null Test (DACT). Results: Childhood trauma was associated with widespread DNAm differences, primarily at the regional level, with enrichment in pathways related to cellular stress responses. In contrast, DNAm associated with PLEs was more limited and implicated loci involved in epigenetic regulatory processes. These signatures were largely distinct, and there was no evidence supporting mediation after multiple testing correction. Incorporating cannabis use altered the pattern and extent of DNAm associations, with stronger and more significant signals observed at both CpG and regional levels, although these did not translate into evidence of mediation. Conclusion: Childhood trauma and PLEs show distinct DNAm signatures in adolescence, with trauma-related DNAm reflecting broad stress-related processes and PLE-associated DNAm implicating regulatory mechanisms. We found little evidence that DNAm mediates the trauma-PLE association. Instead, adolescent exposures, particularly cannabis use, may distinctly influence trauma-related epigenetic variation with limited detectable downstream effects on PLEs. These findings support a context-dependent model of epigenetic risk and highlight the need for larger longitudinal studies to clarify causal pathways linking early adversity to psychosis.