The association between cannabis and psychosis is established, but the role of underlying genetics is unclear. We used data from the EU-GEI case-control study and UK Biobank to examine the independent and combined effect of heavy cannabis use and schizophrenia polygenic risk score (PRS) on risk for psychosis.

Genome-wide association study summary statistics from the Psychiatric Genomics Consortium and the Genomic Psychiatry Cohort were used to calculate schizophrenia and cannabis use disorder (CUD) PRS for 1098 participants from the EU-GEI study and 143600 from the UK Biobank. Both datasets had information on cannabis use.

In both samples, schizophrenia PRS and cannabis use independently increased risk of psychosis. Schizophrenia PRS was not associated with patterns of cannabis use in the EU-GEI cases or controls or UK Biobank cases. It was associated with lifetime and daily cannabis use among UK Biobank participants without psychosis, but the effect was substantially reduced when CUD PRS was included in the model. In the EU-GEI sample, regular users of high-potency cannabis had the highest odds of being a case independently of schizophrenia PRS (OR daily use high-potency cannabis adjusted for PRS = 5.09, 95% CI 3.08–8.43, p = 3.21 × 10−10). We found no evidence of interaction between schizophrenia PRS and patterns of cannabis use.

Regular use of high-potency cannabis remains a strong predictor of psychotic disorder independently of schizophrenia PRS, which does not seem to be associated with heavy cannabis use. These are important findings at a time of increasing use and potency of cannabis worldwide.

Over the past several decades, more research focuses have been made on the inflammation/immune hypothesis of schizophrenia. Building upon synaptic plasticity hypothesis, inflammation may contribute the underlying pathophysiology of schizophrenia. Yet, pinpointing the specific inflammatory agents responsible for schizophrenia remains a complex challenge, mainly due to medication and metabolic status. Multiple lines of evidence point to a wide-spread genetic association across genome underlying the phenotypic variations of schizophrenia.

We collected the latest genome-wide association analysis (GWAS) summary data of schizophrenia, cytokines, and longitudinal change of brain. We utilized the omnigenic model which takes into account all genomic SNPs included in the GWAS of trait, instead of traditional Mendelian randomization (MR) methods. We conducted two round MR to investigate the inflammatory triggers of schizophrenia and the resulting longitudinal changes in the brain.

We identified seven inflammation markers linked to schizophrenia onset, which all passed the Bonferroni correction for multiple comparisons (bNGF, GROA(CXCL1), IL-8, M-CSF, MCP-3 (CCL7), TNF-β, CRP). Moreover, CRP were found to significantly influence the linear rate of brain morphology changes, predominantly in the white matter of the cerebrum and cerebellum.

With an omnigenic approach, our study sheds light on the immune pathology of schizophrenia. Although these findings need confirmation from future studies employing different methodologies, our work provides substantial evidence that pervasive, low-level neuroinflammation may play a pivotal role in schizophrenia, potentially leading to notable longitudinal changes in brain morphology.

Convergent evidence has suggested atypical relationships between brain structure and function in major psychiatric disorders, yet how the abnormal patterns coincide and/or differ across different disorders remains largely unknown. Here, we aim to investigate the common and/or unique dynamic structure–function coupling patterns across major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ).

We quantified the dynamic structure–function coupling in 452 patients with psychiatric disorders (MDD/BD/SZ = 166/168/118) and 205 unaffected controls at three distinct brain network levels, such as global, meso-, and local levels. We also correlated dynamic structure–function coupling with the topological features of functional networks to examine how the structure–function relationship facilitates brain information communication over time.

The dynamic structure–function coupling is preserved for the three disorders at the global network level. Similar abnormalities in the rich-club organization are found in two distinct functional configuration states at the meso-level and are associated with the disease severity of MDD, BD, and SZ. At the local level, shared and unique alterations are observed in the brain regions involving the visual, cognitive control, and default mode networks. In addition, the relationships between structure–function coupling and the topological features of functional networks are altered in a manner indicative of state specificity.

These findings suggest both transdiagnostic and illness-specific alterations in the dynamic structure–function relationship of large-scale brain networks across MDD, BD, and SZ, providing new insights and potential biomarkers into the neurodevelopmental basis underlying the behavioral and cognitive deficits observed in these disorders.

Childhood adversity and cannabis use are considered independent risk factors for psychosis, but whether different patterns of cannabis use may be acting as mediator between adversity and psychotic disorders has not yet been explored. The aim of this study is to examine whether cannabis use mediates the relationship between childhood adversity and psychosis.

Data were utilised on 881 first-episode psychosis patients and 1231 controls from the European network of national schizophrenia networks studying Gene–Environment Interactions (EU-GEI) study. Detailed history of cannabis use was collected with the Cannabis Experience Questionnaire. The Childhood Experience of Care and Abuse Questionnaire was used to assess exposure to household discord, sexual, physical or emotional abuse and bullying in two periods: early (0–11 years), and late (12–17 years). A path decomposition method was used to analyse whether the association between childhood adversity and psychosis was mediated by (1) lifetime cannabis use, (2) cannabis potency and (3) frequency of use.

The association between household discord and psychosis was partially mediated by lifetime use of cannabis (indirect effect coef. 0.078, s.e. 0.022, 17%), its potency (indirect effect coef. 0.059, s.e. 0.018, 14%) and by frequency (indirect effect coef. 0.117, s.e. 0.038, 29%). Similar findings were obtained when analyses were restricted to early exposure to household discord.

Harmful patterns of cannabis use mediated the association between specific childhood adversities, like household discord, with later psychosis. Children exposed to particularly challenging environments in their household could benefit from psychosocial interventions aimed at preventing cannabis misuse.

While cannabis use is a well-established risk factor for psychosis, little is known about any association between reasons for first using cannabis (RFUC) and later patterns of use and risk of psychosis.

We used data from 11 sites of the multicentre European Gene-Environment Interaction (EU-GEI) case–control study. 558 first-episode psychosis patients (FEPp) and 567 population controls who had used cannabis and reported their RFUC.

We ran logistic regressions to examine whether RFUC were associated with first-episode psychosis (FEP) case–control status. Path analysis then examined the relationship between RFUC, subsequent patterns of cannabis use, and case–control status.

Controls (86.1%) and FEPp (75.63%) were most likely to report ‘because of friends’ as their most common RFUC. However, 20.1% of FEPp compared to 5.8% of controls reported: ‘to feel better’ as their RFUC (χ2 = 50.97; p < 0.001). RFUC ‘to feel better’ was associated with being a FEPp (OR 1.74; 95% CI 1.03–2.95) while RFUC ‘with friends’ was associated with being a control (OR 0.56; 95% CI 0.37–0.83). The path model indicated an association between RFUC ‘to feel better’ with heavy cannabis use and with FEPp-control status.

Both FEPp and controls usually started using cannabis with their friends, but more patients than controls had begun to use ‘to feel better’. People who reported their reason for first using cannabis to ‘feel better’ were more likely to progress to heavy use and develop a psychotic disorder than those reporting ‘because of friends’.

To identify risk genes whose expression are regulated by the reported risk variants and to explore the potential regulatory mechanism in schizophrenia (SCZ).

We systematically integrated three independent brain expression quantitative traits (eQTLs) (CommonMind, GTEx, and BrainSeq Phase 2, a total of 1039 individuals) and GWAS data (56 418 cases and 78 818 controls), with the use of transcriptome-wide association study (TWAS). Diffusion magnetic resonance imaging was utilized to quantify the integrity of white matter bundles and determine whether polygenic risk of novel genes linked to brain structure was present in patients with first-episode antipsychotic SCZ.

TWAS showed that eight risk genes (CORO7, DDAH2, DDHD2, ELAC2, GLT8D1, PCDHA8, THOC7, and TYW5) reached transcriptome-wide significance (TWS) level. These findings were confirmed by an independent integrative approach (i.e. Sherlock). We further conducted conditional analyses and identified the potential risk genes that driven the TWAS association signal in each locus. Gene expression analysis showed that several TWS genes (including CORO7, DDAH2, DDHD2, ELAC2, GLT8D1, THOC7 and TYW5) were dysregulated in the dorsolateral prefrontal cortex of SCZ cases compared with controls. TWS genes were mainly expressed on the surface of glutamatergic neurons, GABAergic neurons, and microglia. Finally, SCZ cases had a substantially greater TWS genes-based polygenic risk (PRS) compared to controls, and we showed that fractional anisotropy of the cingulum-hippocampus mediates the influence of TWS genes PRS on SCZ.

Our findings identified novel SCZ risk genes and highlighted the importance of the TWS genes in frontal-limbic dysfunctions in SCZ, indicating possible therapeutic targets.

Grey matter (GM) reduction is a consistent observation in established late stages of schizophrenia, but patients in the untreated early stages of illness display an increase as well as a decrease in GM distribution relative to healthy controls (HC). The relative excess of GM may indicate putative compensatory responses, though to date its relevance is unclear.

343 first-episode treatment-naïve patients with schizophrenia (FES) and 342 HC were recruited. Multivariate source-based morphometry was performed to identify covarying ‘networks' of grey matter concentration (GMC). Neurocognitive scores using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and symptom burden using the Positive and Negative Symptoms Scale (PANSS) were obtained. Bivariate linear relationships between GMC and cognition/symptoms were studied.

Compared to healthy subjects, FES had prominently lower GMC in two components; the first consists of the anterior insula, inferior frontal gyrus, anterior cingulate and the second component with the superior temporal gyrus, precuneus, inferior/superior parietal lobule, cuneus, and lingual gyrus. Higher GMC was seen in adjacent areas of the middle and superior temporal gyrus, middle frontal gyrus, inferior parietal cortex and putamen. Greater GMC of this component was associated with lower duration of untreated psychosis, less severe positive symptoms and better performance on cognitive tests.

In untreated stages of schizophrenia, both a distributed lower and higher GMC is observable. While the higher GMC is relatively modest, it occurs across frontoparietal, temporal and subcortical regions in association with reduced illness burden suggesting a compensatory role for higher GMC in the early stages of schizophrenia.

Contrasting the well-described effects of early intervention (EI) services for youth-onset psychosis, the potential benefits of the intervention for adult-onset psychosis are uncertain. This paper aims to examine the effectiveness of EI on functioning and symptomatic improvement in adult-onset psychosis, and the optimal duration of the intervention.

360 psychosis patients aged 26–55 years were randomized to receive either standard care (SC, n = 120), or case management for two (2-year EI, n = 120) or 4 years (4-year EI, n = 120) in a 4-year rater-masked, parallel-group, superiority, randomized controlled trial of treatment effectiveness (Clinicaltrials.gov: NCT00919620). Primary (i.e. social and occupational functioning) and secondary outcomes (i.e. positive and negative symptoms, and quality of life) were assessed at baseline, 6-month, and yearly for 4 years.

Compared with SC, patients with 4-year EI had better Role Functioning Scale (RFS) immediate [interaction estimate = 0.008, 95% confidence interval (CI) = 0.001–0.014, p = 0.02] and extended social network (interaction estimate = 0.011, 95% CI = 0.004–0.018, p = 0.003) scores. Specifically, these improvements were observed in the first 2 years. Compared with the 2-year EI group, the 4-year EI group had better RFS total (p = 0.01), immediate (p = 0.01), and extended social network (p = 0.05) scores at the fourth year. Meanwhile, the 4-year (p = 0.02) and 2-year EI (p = 0.004) group had less severe symptoms than the SC group at the first year.

Specialized EI treatment for psychosis patients aged 26–55 should be provided for at least the initial 2 years of illness. Further treatment up to 4 years confers little benefits in this age range over the course of the study.

Schizophrenia (SZ), bipolar disorder (BD) and depression (D) run in families. This susceptibility is partly due to hundreds or thousands of common genetic variants, each conferring a fractional risk. The cumulative effects of the associated variants can be summarised as a polygenic risk score (PRS). Using data from the EUropean Network of national schizophrenia networks studying Gene-Environment Interactions (EU-GEI) first episode case–control study, we aimed to test whether PRSs for three major psychiatric disorders (SZ, BD, D) and for intelligent quotient (IQ) as a neurodevelopmental proxy, can discriminate affective psychosis (AP) from schizophrenia-spectrum disorder (SSD).

Participants (842 cases, 1284 controls) from 16 European EU-GEI sites were successfully genotyped following standard quality control procedures. The sample was stratified based on genomic ancestry and analyses were done only on the subsample representing the European population (573 cases, 1005 controls). Using PRS for SZ, BD, D, and IQ built from the latest available summary statistics, we performed simple or multinomial logistic regression models adjusted for 10 principal components for the different clinical comparisons.

In case–control comparisons PRS-SZ, PRS-BD and PRS-D distributed differentially across psychotic subcategories. In case–case comparisons, both PRS-SZ [odds ratio (OR) = 0.7, 95% confidence interval (CI) 0.54–0.92] and PRS-D (OR = 1.31, 95% CI 1.06–1.61) differentiated AP from SSD; and within AP categories, only PRS-SZ differentiated BD from psychotic depression (OR = 2.14, 95% CI 1.23–3.74).

Combining PRS for severe psychiatric disorders in prediction models for psychosis phenotypes can increase discriminative ability and improve our understanding of these phenotypes. Our results point towards the potential usefulness of PRSs in specific populations such as high-risk or early psychosis phases.

A history of childhood adversity is associated with psychotic disorder, with an increase in risk according to the number of exposures. However, it is not known why only some exposed individuals go on to develop psychosis. One possibility is pre-existing polygenic vulnerability. Here, we investigated, in the largest sample of first-episode psychosis (FEP) cases to date, whether childhood adversity and high polygenic risk scores for schizophrenia (SZ-PRS) combine synergistically to increase the risk of psychosis, over and above the effect of each alone.

We assigned a schizophrenia-polygenic risk score (SZ-PRS), calculated from the Psychiatric Genomics Consortium (PGC2), to all participants in a sample of 384 FEP patients and 690 controls from the case–control component of the EU-GEI study. Only participants of European ancestry were included in the study. A history of childhood adversity was collected using the Childhood Trauma Questionnaire (CTQ). Synergistic effects were estimated using the interaction contrast ratio (ICR) [odds ratio (OR)exposure and PRS − ORexposure − ORPRS + 1] with adjustment for potential confounders.

There was some evidence that the combined effect of childhood adversities and polygenic risk was greater than the sum of each alone, as indicated by an ICR greater than zero [i.e. ICR 1.28, 95% confidence interval (CI) −1.29 to 3.85]. Examining subtypes of childhood adversities, the strongest synergetic effect was observed for physical abuse (ICR 6.25, 95% CI −6.25 to 20.88).

Our findings suggest possible synergistic effects of genetic liability and childhood adversity experiences in the onset of FEP, but larger samples are needed to increase precision of estimates.

Depression and cardiovascular disease (CVD) are associated with each other but their relationship remains unclear. We aim to determine whether genetic predisposition to depression are causally linked to CVD [including coronary artery disease (CAD), myocardial infarction (MI), stroke and atrial fibrillation (AF)].

Using summary statistics from the largest genome-wide association studies (GWAS) or GWAS meta-analysis of depression (primary analysis: n = 500 199), broad depression (help-seeking behavior for problems with nerves, anxiety, tension or depression; secondary analysis: n = 322 580), CAD (n = 184 305), MI (n = 171 875), stroke (n = 446 696) and AF (n = 1 030 836), genetic correlation was tested between two depression phenotypes and CVD [MI, stroke and AF (not CAD as its correlation was previously confirmed)]. Causality was inferred between correlated traits by Mendelian Randomization analyses.

Both depression phenotypes were genetically correlated with MI (depression: rG = 0.169; p = 9.03 × 10−9; broad depression: rG = 0.123; p = 1 × 10−4) and AF (depression: rG = 0.112; p = 7.80 × 10−6; broad depression: rG = 0.126; p = 3.62 × 10−6). Genetically doubling the odds of depression was causally associated with increased risk of CAD (OR = 1.099; 95% CI 1.031–1.170; p = 0.004) and MI (OR = 1.146; 95% CI 1.070–1.228; p = 1.05 × 10−4). Adjustment for blood lipid levels/smoking status attenuated the causality between depression and CAD/MI. Null causal association was observed for CVD on depression. A similar pattern of results was observed in the secondary analysis for broad depression.

Genetic predisposition to depression may have positive causal roles on CAD/MI. Genetic susceptibility to self-awareness of mood problems may be a strong causal risk factor of CAD/MI. Blood lipid levels and smoking may potentially mediate the causal pathway. Prevention and early diagnosis of depression are important in the management of CAD/MI.

The ‘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.

A total of 817 first episode psychosis patients and 1294 population-based controls completed assessments of general intelligence (IQ), and JTC, and provided blood or saliva samples from which we extracted DNA and computed polygenic risk scores for IQ and schizophrenia.

The estimated proportion of the total effect of case/control differences on JTC mediated by IQ was 79%. Schizophrenia polygenic risk score 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 0.25–0.76, 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 the higher level of psychotic-like experiences (PLEs) 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.

Our findings suggest that the JTC reasoning bias in psychosis might not be a specific cognitive deficit but rather a manifestation or consequence, of general cognitive impairment. Whereas, in the general population, the JTC bias is related to PLEs, independent of IQ. The work has the potential to inform interventions targeting cognitive biases in early psychosis.

The etiology of depression remains poorly understood. Changes in blood lipid levels were reported to be associated with depression and suicide, however study findings were mixed.

We performed a two-sample Mendelian randomisation (MR) analysis to investigate the causal relationship between blood lipids and depression phenotypes, based on large-scale GWAS summary statistics (N = 188 577/480 359 for lipid/depression traits respectively). Five depression-related phenotypes were included, namely major depression (MD; from PGC), depressive symptoms (DS; from SSGAC), longest duration and number of episodes of low mood, and history of deliberate self-harm (DSH)/suicide (from UK Biobank). MR was conducted with inverse-variance weighted (MR-IVW), Egger and Generalised Summary-data-based MR (GSMR) methods.

There was consistent evidence that triglyceride (TG) is causally associated with DS (MR-IVW β for one-s.d. increase in TG = 0.0346, 95% CI 0.0114–0.0578), supported by MR-IVW and GSMR and multiple r2 clumping thresholds. We also observed relatively consistent associations of TG with DSH/suicide (MR-Egger OR = 2.514, CI 1.579–4.003). There was moderate evidence for positive associations of TG with MD and the number of episodes of low mood. For HDL-c, we observed moderate evidence for causal associations with DS and MD. LDL-c and TC did not show robust causal relationships with depression phenotypes, except for weak evidence that LDL-c is inversely related to DSH/suicide. We did not detect significant associations when depression phenotypes were treated as exposures.

This study provides evidence to a causal relationship between TG, and to a lesser extent, altered cholesterol levels with depression phenotypes. Further studies on its mechanistic basis and the effects of lipid-lowering therapies are warranted.

Increasing evidence suggests psychosis may be more meaningfully viewed in dimensional terms rather than as discrete categorical states and that specific symptom clusters may be identified. If so, particular risk factors and premorbid factors may predict these symptom clusters.

(i) To explore, using principal component analysis, whether specific factors for psychotic symptoms can be isolated. (ii) To establish the predictors of the different symptom factors using multiple regression techniques.

One hundred and eighty-nine inpatients with psychotic illness were recruited and information on family history, premorbid factors and current symptoms obtained from them and their mothers.

Seven distinct symptom components were identified. Regression analysis failed to identify any developmental predictors of depression or mania. Delusions/hallucinations were predicted by a family history of schizophrenia and by poor school functioning in spite of normal premorbid IQ (F = 6.5; P < 0.001); negative symptoms by early onset of illness, developmental delay and a family history of psychosis (F = 4.1; P = 0.04). Interestingly disorganisation was predicted by the combination of family history of bipolar disorder and low premorbid IQ (F = 4.9; P = 0.003), and paranoia by obstetric complications (OCs) and poor school functioning (F = 4.2; P = 0.01).

Delusions and hallucinations, negative symptoms and paranoia all appeared to have a developmental origin though they were associated with different childhood problems. On the other hand, neither mania nor depression was associated with childhood dysfunction. Our most striking finding was that disorganisation appeared to arise when a familial predisposition to mania was compounded by low premorbid IQ.

Daily use of high-potency cannabis has been reported to carry a high risk for developing a psychotic disorder. However, the evidence is mixed on whether any pattern of cannabis use is associated with a particular symptomatology in first-episode psychosis (FEP) patients.

We analysed data from 901 FEP 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. We used item response modelling 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 were evaluated using linear mixed-effects models analyses.

In 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 0.14–0.56). Moreover, negative symptoms were more common among patients who never used cannabis compared with those with any pattern of use (B = −0.22; 95% CI −0.37 to −0.07). In controls, psychotic experiences were associated with current use of cannabis but not with the extent of lifetime use. Neither patients nor controls presented differences in depressive dimension related to cannabis use.

Our findings provide the first large-scale evidence that FEP patients with a history of daily use of high-potency cannabis present with more positive and less negative symptoms, compared with those who never used cannabis or used low-potency types.

Risk prediction algorithms have long been used in health research and practice (e.g. prediction of cardiovascular disease and diabetes). However, similar tools have not been developed for mental health. For example, for psychotic disorders, attempts to sum environmental risk are rare, unsystematic and dictated by available data. In light of this, we sought to develop a valid, easy to use measure of the aggregate environmental risk score (ERS) for psychotic disorders.

We reviewed the literature to identify well-replicated and validated environmental risk factors for psychosis that combine a significant effect and large-enough prevalence. Pooled estimates of relative risks were taken from the largest available meta-analyses. We devised a method of scoring the level of exposure to each risk factor to estimate ERS. Relative risks were rounded as, due to the heterogeneity of the original studies, risk effects are imprecisely measured.

Six 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 simulated data showed that most of the population would be at low/moderate risk with a small minority at increased environmental risk for psychosis.

This is the first systematic approach to develop an aggregate measure of environmental risk for psychoses in asymptomatic individuals. This can be used as a continuous measure of liability to disease; mostly relevant to areas where the original studies took place. Its predictive ability will improve with the collection of additional, population-specific data.

Little is known about long-term employment outcomes for patients with first-episode schizophrenia-spectrum (FES) disorders who received early intervention services.

We compared the 10-year employment trajectory of patients with FES who received early intervention services with those who received standard care. Factors differentiating the employment trajectories were explored.

Patients with FES (N = 145) who received early intervention services in Hong Kong between 1 July 2001 and 30 June 2002 were matched with those who entered standard care 1 year previously. We used hierarchical clustering analysis to explore the 10-year employment clusters for both groups. We used the mixed model test to compare cluster memberships and piecewise regression analysis to compare the employment trajectories of the two groups.

There were significantly more patients who received the early intervention service in the good employment cluster (early intervention: N = 98 [67.6%]; standard care: N = 76 [52.4%]; P = 0.009). In the poor employment cluster, there was a significant difference in the longitudinal pattern between early intervention and standard care for years 1–5 (P < 0.0001). The number of relapses during the first 3 years, months of full-time employment during the first year and years of education were significant in differentiating the clusters of the early intervention group.

Results suggest there was an overall long-term benefit of early intervention services on employment. However, the benefit was not sustained for all patients. Personalisation of the duration of the early intervention service with a focus on relapse prevention and early vocational reintegration should be considered for service enhancement.

Major depressive disorder (MDD) is a leading cause of disability worldwide and influenced by both environmental and genetic factors. Genetic studies of MDD have focused on common variants and have been constrained by the heterogeneity of clinical symptoms.

We sequenced the exome of 77 cases and 245 controls of Han Chinese ancestry and scanned their brain. Burden tests of rare variants were performed first to explore the association between genes/pathways and MDD. Secondly, parallel Independent Component Analysis was conducted to investigate genetic underpinnings of gray matter volume (GMV) changes of MDD.

Two genes (CSMD1, p = 5.32×10−6; CNTNAP5, p = 1.32×10−6) and one pathway (Neuroactive Ligand Receptor Interactive, p = 1.29×10−5) achieved significance in burden test. In addition, we identified one pair of imaging-genetic components of significant correlation (r = 0.38, p = 9.92×10−6). The imaging component reflected decreased GMV in cases and correlated with intelligence quotient (IQ). IQ mediated the effects of GMV on MDD. The genetic component enriched in two gene sets, namely Singling by G-protein coupled receptors [false discovery rate (FDR) q = 3.23×10−4) and Alzheimer Disease Up (FDR q = 6.12×10−4).

Both rare variants analysis and imaging–genetic analysis found evidence corresponding with the neuroinflammation and synaptic plasticity hypotheses of MDD. The mediation of IQ indicates that genetic component may act on MDD through GMV alteration and cognitive impairment.