Background Schizophrenia (SCZ) and bipolar disorder (BD) are severe psychiatric conditions with overlapping clinical presentations, genetic risk factors, and brain network dysfunction. Whether alterations in large-scale intrinsic brain networks reflect shared or disorder-specific genetic influences remains poorly understood. Clarifying this distinction is essential for refining etiological models and improving diagnostic precision.Methods Genome-wide inferred statistics (GWIS) were applied to decompose the genetic architecture of SCZ and BD into shared and unique components. Using resting-state network (RSN) data from the UK Biobank, functional connectivity (FC) and structural connectivity (SC) were extracted as neuroimaging phenotypes. Causal inference approaches were subsequently employed to infer potential directional relationships between brain network connectivity and each disorder.Results Analyses revealed both common and distinct patterns of brain network connectivity associated with SCZ and BD. Notably, SC within the default mode network (DMN) exhibited opposing effects across the two disorders, suggesting divergent structural underpinnings despite clinical overlap. Additionally, SC within the limbic network (LN) and frontotemporal control network demonstrated potential causal relationships with both conditions, implicating these circuits astransdiagnostic neural substrates.Conclusion These findings illuminate the shared and disorder-specific genetic and neural architecture underlying SCZ and BD. Integrating genome-wide genetic methods with large-scale neuroimaging data offers a powerful framework for disentangling psychiatric comorbidity and may inform more targeted diagnostic criteria and individualized treatment strategies.

BackgroundADHD and anxiety disorders often co-occur, sharing symptoms and dysfunctions, yet the underlying mechanisms remain elusive.MethodsTo explore the shared and distinct genetic variations between ADHD and anxiety disorders, we applied Mendelian randomization (MR) analysis to ADHD, anxiety disorders, and three socioeconomic factors: income, educational attainment (EA), and intelligence. MR analysis utilized genome-wide association study summary datasets (anxiety disorder: 7,016 cases and 14,745 controls; ADHD: 38,691 cases and 275,986 controls; EA: 766,345 participants; intelligence: 146,808 participants; household income: 392,422 participants), with inverse-variance weighting as the primary method.ResultsOur MR analysis revealed no discernible genetic-level causal effect between ADHD and anxiety disorders (p > 0.77). Additionally, the independent variables for ADHD (25 SNPs) and anxiety disorders (18 SNPs) did not overlap, highlighting the genetic distinction between the two conditions. Higher income (p < 0.002) and EA (p < 0.005) were found to serve as protective factors for both ADHD and anxiety disorders. Genetic predisposition to higher income (86 SNPs) and EA (457 SNPs) were identified as a potential common protective factors for both conditions. Lastly, genetic predisposition to higher intelligence was found to potentially guard against ADHD (p < 0.001) but not against anxiety disorders (p > 0.55).ConclusionOur findings indicate that the shared symptoms observed between ADHD and anxiety disorders are more likely influenced by genetic predispositions related to socioeconomic factors rather than by the genetic predispositions specific to the disorders themselves.

Background 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. Method 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. Results 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. Conclusion 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.

The gut microbiota alterations interact with the pathogenesis and progression of chronic kidney disease (CKD). Probiotics have received wide attention as a potential management in CKD. We investigated the effects of Lactobacillus paracasei N1115 (LP N1115) on intestinal microbiota and related short-chain fatty acids (SCFAs) in end stage kidney disease patients on peritoneal dialysis (PD) in a single-center, prospective, randomized, double-blind, placebo-controlled study. The patients were randomly allocated into two groups. The intervention group (n = 38, PR group) was given the probiotics (two bags) containing fructooligosaccharide (FOS) (additive amount > 80%), maltosaccharin, and LP N1115 (additive amount > 3 x 10(10) CFU/bag) every day whereas the control group (n = 19, PL group) received placebo (two bags) containing only pregelatinized starch and lactose, both for 12 weeks. In addition to collecting fecal samples for 16S rRNA gene high-throughput sequencing and SCFAs analysis, gastrointestinal (GI) symptoms were also assessed at baseline and after the intervention. Probiotics administration caused significant changes in the composition of gut microbiota, as indicated by increased abundance of beneficial bacteria (Firmicutes), decreased Bacteroidetes, and opportunistic pathogens (Fusobacterium, Bilophila) (p < 0.05). However, there was no significant difference in intestinal microbial diversity. SCFAs levels increased in PR group although the change was not statistically significant between the two groups (P > 0.05). In addition, probiotics administration could effectively reduce GI symptoms, particularly in dyspepsia and constipation (p < 0.05). Together, the results suggest that probiotics administration caused significant changes in the composition of gut microbiota and also could effectively reduce GI symptoms, particularly in dyspepsia and constipation in PD patients. Trial registration: This study was registered with the Chinese Clinical Trial Registry (Trial registration number: ChiCTR-INR-17011718; Date of the first registration: 21/06/2017).

Schizophrenia is a mental disorder constituting different symptom clusters. Its high heterogeneity in both pathophysiology and clinical manifestations hampered effective prevention and treatment. It has long been recognized that one of the core features of schizophrenia is its intellectual decline. Using the proxy-phenotype method (PPM), we tried to identify core genes, the expression of which in the dorsal lateral prefrontal cortex (DLPFC) showed a genetic dependence between intelligence (IT) and schizophrenia (SCZ). The result revealed ten genes of genetic dependence in their genetic expression in DLPFC between IT and schizophrenia. Further, a clustering analysis using the expression matrix of these ten genes identified four biotypes in our patient group. Subsequent phenotypic profiling of these four biotypes indicated a significant difference in working memory capacity, the gray matter volume (GMV) of five brain regions (lLimbicA_TempPole_2, rLimbicA_TempPole_2 rLimbicB_OFC_1, rContA_IPS_1 and rContB_PFClv_1), structural network and psychopathology. An in-vitro investigation of the biological functions of these core genes indicated their potentially critical role in neuronal growth, especially in dendritic spines. Our current study employed a novel statistical approach to identify the core genes associated with IT and explore the possibility of using the expression knowledge of these core genes to reduce the heterogeneity of schizophrenia. The results pinpointed one biotype that exhibited significant deficits in working memory, GMV in limbic and prefrontal areas, and also showed psychopathology of core negative symptom and worse outcomes.

Background: Since the early clinical efficacy of antipsychotics has not yet been well perceived, this study sought to decide whether the efficacy of antipsychotics at week 2 can predict subsequent responses at week 6 and identify how such predictive capacities vary among different antipsychoticsand psychotic symptoms. Methods: A total of 3010 patients with schizophrenia enrolled in a randomized controlled trial (RCT) and received a 6-week treatment with one antipsychotic drug randomly chosen from five atypical antipsychotics (risperidone 2-6 mg/d, olanzapine 5-20 mg/d, quetiapine 400-750 mg/d, aripiprazole 10-30 mg/d, and ziprasidone 80-160 mg/d) and two typical antipsychotics (perphenazine 20-60 mg/d and haloperidol 6-20 mg/d). Early efficacy was defined as the reduction rate using the Positive and Negative Syndrome Scale (PANSS) total score at week 2. With cut-offs at 50% reduction, logistic regression, receiver operating characteristic (ROC) and random forests were adopted. Results: The reduction rate of PANSS total score and improvement of psychotic symptoms at week 2 enabled subsequent responses to 7 antipsychotics to be predicted, in which improvements in delusions, lack of judgment and insight, unusual thought content, and suspiciousness/ persecution were endowed with the greatest weight. Conclusion: It is robust enough to clinically predict treatment responses to antipsychotics at week 6 using the reduction rate of PANSS total score and symptom relief at week 2. Psychiatric clinicians had better determine whether to switch the treatment plan by the first 2 weeks.

AbstractBackgroundDeficits in event-related potential (ERP) including duration mismatch negativity (MMN) and P3a have been demonstrated widely in chronic schizophrenia (SZ) but inconsistent findings were reported in first-episode patients. Psychotropic medications and diagnosis might contribute to different findings on MMN/P3a ERP in first-episode patients. The present study examined MMN and P3a in first episode drug naïve SZ and bipolar disorder (BPD) patients and explored the relationships among ERPs, neurocognition and global functioning.MethodsTwenty SZ, 24 BPD and 49 age and sex-matched healthy controls were enrolled in this study. Data of clinical symptoms [Positive and Negative Symptoms Scale (PANSS), Young Manic Rating Scale (YMRS), Hamilton Depression Rating Scale (HAMD)], neurocognition [Wechsler Adult Intelligence Scale (WAIS), Cattell's Culture Fair Intelligence Test (CCFT), Delay Matching to Sample (DMS), Rapid Visual Information Processing (RVP)], and functioning [Functioning Assessment Short Test (FAST)] were collected. P3a and MMN were elicited using a passive auditory oddball paradigm.ResultsSignificant MMN and P3a deficits and impaired neurocognition were found in both SZ and BPD patients. In SZ, MMN was significantly correlated with FAST (r = 0.48) and CCFT (r = −0.31). In BPD, MMN was significantly correlated with DMS (r = −0.54). For P3a, RVP and FAST scores were significant predictors in SZ, whereas RVP, WAIS and FAST were significant predictors in BPD.ConclusionsThe present study found deficits in MMN, P3a, neurocognition in drug naïve SZ and BPD patients. These deficits appeared to link with levels of higher-order cognition and functioning.

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1134/S0022476623020178

The results generated from large psychiatric genomic consortia show us some new vantage points to understand the pathophysiology of psychiatric disorders. We explored the potential of integrating the transcription output of the core gene underlying the commonality of psychiatric disorders with a clustering algorithm to redefine psychiatric disorders. Our results showed that an extended MHC region was associated with the common factor of schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) at the level of genomic significance, with rs7746199 (P = 4.905e-08), a cis-eQTL to the gene ZNF391, pinpointed as a potential causal variant driving the signals in the region. Gene expression pattern of ZNF391 in the brain led to the emergence of 3 biotypes, independent of disorder. The 3 biotypes performed significantly differently in working memory and demonstrated different gray matter volumes in the right inferior frontal orbital gyrus (RIFOG), with a partial causal pathway arising from ZNF391 to RIFOG to working memory. Our study illustrates the potential of a trans-diagnostic, top-down approach in understanding the commonality of psychiatric disorders.

Schizophrenia is a complex disorder associated with aberrant brain functional connectivity. This study aims to demonstrate the relation of heterogeneous symptomatology in this disorder to distinct brain connectivity patterns within the triple-network model. The study sample comprised 300 first-episode antipsychotic-naive patients with schizophrenia (FES) and 301 healthy controls (HCs). At baseline, resting-state functional magnetic resonance imaging data were captured for each participant, and concomitant neurocognitive functions were evaluated outside the scanner. Clinical information of 49 FES in the discovery dataset were reevaluated at a 6-week follow-up. Differential features between FES and HCs were selected from triple-network connectivity profiles. Cutting-edge unsupervised machine learning algorithms were used to define patient subtypes. Clinical and cognitive variables were compared between patient subgroups. Two FES subgroups with differing triple-network connectivity profiles were identified in the discovery dataset and confirmed in an independent hold-out cohort. One patient subgroup appearing to have more severe clinical symptoms was distinguished by salience network (SN)-centered hypoconnectivity, which was associated with greater impairments in sustained attention. The other subgroup exhibited hyperconnectivity and manifested greater deficits in cognitive flexibility. The SN-centered hypoconnectivity subgroup had more persistent negative symptoms at the 6-week follow-up than the hyperconnectivity subgroup. The present study illustrates that clinically relevant cognitive subtypes of schizophrenia may be associated with distinct differences in connectivity in the triple-network model. This categorization may foster further analysis of the effects of therapy on these network connectivity patterns, which may help to guide therapeutic choices to effectively reach personalized treatment goals.

BACKGROUND:Understanding the patterns of treatment response is critical for the treatment of patients with schizophrenia; one way to achieve this is through using a longitudinal dynamic process study design. AIMS:This study aims to explore the response trajectory of antipsychotics and compare the treatment responses of seven different antipsychotics over 6 weeks in patients with schizoprenia (trial registration: Chinese Clinical Trials Registry Identifier: ChiCTR-TRC-10000934). METHOD:Data were collected from a multicentre, randomised open-label clinical trial. Patients were evaluated with the Positive and Negative Syndrome Scale (PANSS) at baseline and follow-up at weeks 2, 4 and 6. Trajectory groups were classified by the method of k-means cluster modelling for longitudinal data. Trajectory analyses were also employed for the seven antipsychotic groups. RESULTS:The early treatment response trajectories were classified into a high-trajectory group of better responders and a low-trajectory group of worse responders. The results of trajectory analysis showed differences compared with the classification method characterised by a 50% reduction in PANSS scores at week 6. A total of 349 patients were inconsistently grouped by the two methods, with a significant difference in the composition ratio of treatment response groups using these two methods (χ2 = 43.37, P < 0.001). There was no differential contribution of high- and low trajectories to different drugs (χ2 = 12.52, P = 0.051); olanzapine and risperidone, which had a larger proportion in the >50% reduction at week 6, performed better than aripiprazole, quetiapine, ziprasidone and perphenazine. CONCLUSIONS:The trajectory analysis of treatment response to schizophrenia revealed two distinct trajectories. Comparing the treatment responses to different antipsychotics through longitudinal analysis may offer a new perspective for evaluating antipsychotics.

OBJECTIVE:To compare longitudinal metabolic effects of 7 antipsychotics, including body mass index (BMI), waist circumference (WC), blood pressure (BP), glucose, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C); to investigate risk factors for metabolic syndrome (MetS); and to make recommendations on frequency and timing of monitoring metabolic measurements. METHODS:This randomized, open-label, pharmacologic trial was conducted among patients with schizophrenia (DSM-IV) in 32 hospitals across China. Patients were randomly assigned to 7 groups and assessed at baseline, 2, 4, and 6 weeks. Linear mixed-effect models were used to assess changes of metabolic measures over time. Multivariable logistic regression analysis was performed to investigate the risk factors for MetS. RESULTS:In total, 2,550 (718 drug-naïve) of 2,774 patients finished the study between July 6, 2010, and November 30, 2011. We found significant (P < .05) changes for BMI, WC, TG, and LDL-C, with TG and LDL-C reaching a plateau. Interactions between baseline metabolic condition and changes over time were observed for BMI (χ² = 43.11, P < .001), WC (χ² = 36.34, P < .001), systolic BP (χ² = 11.92, P = .002), glucose (χ² = 6.09, P = .01), and TG (χ² = 6.01, P = .01). Antipsychotics generally had greater adverse effects on patients who were initially screened as metabolically normal. After controlling for other associated factors, we found that antipsychotics resulted in differing risk for incident MetS, with a similar pattern to findings in other populations: olanzapine (odds ratio [OR] = 3.36, P < .001) > quetiapine (OR = 3.29, P < .001) > perphenazine (OR = 2.73, P = .007) > risperidone (OR = 2.21, P = .02) > aripiprazole (OR = 1.74, P = .15) ≈ haloperidol (OR = 1.75, P = .22) ≈ ziprasidone (OR = 1, reference). CONCLUSIONS:Metabolic traits should be monitored frequently in early stages of antipsychotic treatment due to rapid and substantial changes. Clinicians should not assume low risk for patients with normal metabolic parameters at baseline. TRIAL REGISTRATION:Chinese Clinical Trial Registry identifier: ChiCTR-TRC-10000934.

By the mixed-ligand approach, a new coordination polymer with the chemical formula [Cd(bpydb)(bimb)(H2O)](H2O)(3)(1, H(2)bpydb = 4,4 '-([2,4 '-bipyridine]-2 ',6 '-diyl)dibenzoic acid, bimb = 5,5 '-bis(1H-benzo[d]imidazol-2-yl)-2,2 '-bipyridine) is successfully prepared via a hydrothermal reaction of Cd(NO3)4H(2)O, H(2)bpydb, and bimb in the presence of NaOH as the pH modulator. Furthermore, we use an ultrasonic method to obtain nanoparticles of1with an average thickness of 126 nm. To evaluate the antiinflammatory effect of the compound against inflammatory metrorrhagia, the activation of NF-kappa b signal pathway is measured by RT-PCR. Furthermore, ELSA is used in this research to detect the IL-1 beta and IL-18 content.

Predicting antidepressant response has been a clinical challenge for mood disorder. Although several genome-wide association studies have suggested a number of genetic variants to be associated with antidepressant response, the sample sizes are small and the results are difficult to replicate. Previous animal studies have shown that knockout of the serotonin receptor 7 gene (HTR7) resulted in an antidepressant-like phenotype, suggesting it was important to antidepressant action. In this report, in the first stage, we used a cost-effective pooled-sequencing strategy to sequence the entire HTR7 gene and its regulatory regions to investigate the association of common variants in HTR7 and clinical response to four selective serotonin reuptake inhibitors (SSRIs: citalopram, paroxetine, fluoxetine and sertraline) in a retrospective cohort mainly consisting of subjects with bipolar disorder (n = 359). We found 80 single-nucleotide polymorphisms (SNPs) with false discovery rate < 0.05 associated with response to paroxetine. Among the significant SNPs, rs7905446 (T/G), which is located at the promoter region, also showed nominal significance (P < 0.05) in fluoxetine group. GG/TG genotypes for rs7905446 and female gender were associated with better response to two SSRIs (paroxetine and fluoxetine). In the second stage, we replicated this association in two independent prospective samples of SSRI-treated patients with major depressive disorder: the MARS (n = 253, P = 0.0169) and GENDEP studies (n = 432, P = 0.008). The GG/TG genotypes were consistently associated with response in all three samples. Functional study of rs7905446 showed greater activity of the G allele in regulating expression of HTR7. The G allele displayed higher luciferase activity in two neuronal-related cell lines, and estrogen treatment decreased the activity of only the G allele. Electrophoretic mobility shift assay suggested that the G allele interacted with CCAAT/enhancer-binding protein beta transcription factor (TF), while the T allele did not show any interaction with any TFs. Our results provided novel pharmacogenomic evidence to support the role of HTR7 in association with antidepressant response.

Antipsychotic-induced metabolic disturbance (AIMD) is a common adverse effect of antipsychotics with genetics partly underpinning variation in susceptibility among schizophrenia patients. Melanocortin4 receptor (MC4R) gene, one of the candidate genes for AIMD, has been under-studied in the Chinese patients. We conducted a pharmacogenetic study in a large cohort of Chinese patients with schizophrenia. In this study, we investigated the genetic variation of MC4R in Chinese population by genotyping two SNPs (rs489693 and rs17782313) in 1,991 Chinese patients and examined association of these variants with the metabolic effects that were often observed to be related to AIMD. Metabolic measures, including body mass index (BMI), waist circumference (WC), glucose, triglyceride, high-density lipoprotein (HDL), and low-density lipoprotein (LDL) levels were assessed at baseline and after 6-week antipsychotic treatment. We found that interaction of SNP×medication status (drug-naïve/medicated) was significantly associated with BMI, WC, and HDL change %, respectively. Both SNPs were significantly associated with baseline BMI and WC in the medicated group. Moderate association of rs489693 with WC, Triglyceride, and HDL change % were observed in the whole sample. In the drug-naïve group, we found recessive effects of rs489693 on BMI gain more than 7%, WC and Triglyceride change %, with AA incurring more metabolic adverse effects. In conclusion, the association between rs489693 and the metabolic measures is ubiquitous but moderate. Rs17782313 is less involved in AIMD. Two SNPs confer risk of AIMD to patients treated with different antipsychotics in a similar way.

Background Schizophrenia is a complex mental disorder with recent evidence suggesting a critical immune component underpinning its pathophysiology. Two-sample Mendelian randomization (MR) provided an opportunity to probe the immune changes in schizophrenia by harnessing the increasing availability of summary-level data from large GWAS consortia. Objective To map the extensive immune response of schizophrenia in terms of cytokines/chemokines and to explore the effect of cytokines induced by schizophrenia (SCZ-induced cytokines) on the brain structure and function Sources and methods Using the summary-level data generated from GWAS of schizophrenia, cytokines in the peripheral blood and imaging-derived phenotypes (IDPs), we performed two rounds of two-sample MR analysis; the identified cytokines from first round of analysis (schizophrenia => cytokines) were modeled for its underlying structure and subsequent clustering analysis further grouped SCZ-induced cytokines based on their genetic similarities. The multi-phenotype summary statistics of each cytokine module were then used as instrumental variables (IVs) for the second round of MR analysis to detect their effect on brain structure and function. Results The first round of MR analysis identified nine cytokines, the highlight of which includes IL18 (OR = 1.292, P = 8.37 × 10 −42 ) and TNFa (OR = 0.721, P = 7.33 × 10 −6 ), to be causally associated with schizophrenia. These SCZ-induced cytokines could be clustered into three modules. The second round of MR analysis (cytokine module => IDPs) indicated that module B (SCGFb-IP10-CTACK-IL6) significantly increased the level of IDPs including IDP_T1_SIENAX_peripheral_grey_normalised_volume (β = 0.0453, P = 4.40×10 10 ), IDP_dMRI_TBSS_MD_Posterior_corona_radiata_R (β= 0.0584, P = 8.89× 10 −16 ) and IDP_dMRI_TBSS_MD_Cingulum_hippocampus_R (β = 0.0563, P = 9.88× 10 −15 ), with module C (IL18-GROa-TNFa) increasing the level of IDP_dMRI_TBSS_L2_Posterior_thalamic_radiation_R (β= 0.0341, P = 2.67× 10 −6 ). Conclusion Our study, for the first time, mapped the causal link from schizophrenia to the comprehensive immune responses, and the findings suggest immune networks play a role in pathophysiology of schizophrenia by mediating the deviations of total gray matter volume and white matter fibers possibly in the mesolimbic system.

Background Previous studies have inferred a strong genetic component in schizophrenia. However, the genetic variants involved in the susceptibility to schizophrenia remain unclear. Aims To detect potential gene pathways and networks associated with schizophrenia, and to explore the relationship between common and rare variants in these pathways and abnormal white matter integrity in schizophrenia. Method The analysis included 100 first-episode treatment-naive patients with schizophrenia and 140 healthy controls. A network-based analysis was carried out on the data collected from the Psychiatric Genomics Consortium Phase I (PGC-I). Based on our genome-wide association study and whole-exome sequencing data-sets, we performed a gene-set analysis to detect associations between the combining effects of common and rare genetic variants and abnormal white matter integrity in schizophrenia. Results Patients had significantly reduced functional anisotropy in the left and right anterior cingulate cortex, left and right precuneus and extra-nuclear (t = 4.61-5.10, P-FDR < 0.01), compared with controls. Generated from co-expression network analysis of the PGC-1 summary statistics of schizophrenia, a subnetwork of 207 genes associated with schizophrenia was identified (P < 0.01), and 176 genes were co-expressed in four gene modules. Functional enrichment analysis for genes in each module revealed that the yellow module was enriched with highly co-expressed, innate immune response genes. Furthermore, rare variants of enriched genes in the yellow module were associated with reduced functional anisotropy in the left anterior cingulate cortex (P = 0.006; P-adjusted = 0.024) in patients only. Conclusions The pathogenesis of schizophrenia may be substantially influenced by genes involved in the immune system, via both pathway and network. Declaration of interests None.

A key feature of Major Depressive Disorder (MDD) is anhedonia, which is a predictor of response to antidepressant treatment. In order to shed light on its genetic underpinnings, we conducted a Genome-Wide Association Study (GWAS) followed by investigation of biological pathway enrichment using the anhedonia dimension for 796 patients with MDD in the GENDEP. The GWAS identified 19 SNPs associated at genome-wide significance with the top one being an intronic SNP (rs9392549) in PRPF4B (pre-mRNA processing factor 4B) located on chromosome 6 (P= 2.07×10 −9) while gene set enrichment analysis returned one gene ontology term, axon cargo transport (GO: 0008088) with a nominal P value (1.15×10 −5). Furthermore, on exploratory analysis, the genetic association was positively correlated with that of Parkinson's disease (rg=0.803) and negatively correlated with that of nucleus accumbens gray matter volume (rg=-0.649). We found some markers significantly associated with anhedonia, and some suggestive findings of related pathways and biological functions, which could be further investigated in other studies.

Dear Editor,Schizophrenia is a chronic and debilitating brain disorder,which has a strong genetic component with heritability ranging from 66%to 85%[1, 2]. Currently, antipsychotic drugs remain the most effective treatment for the psychotic