The complexity of the pathogenesis hinders the diagnosis and treatment of bipolar disorder (BD). Despite studies finding a correlation between immune function and BD, the causative relationship between the two remains poorly explained.

We investigated the causative relationships between BD (41,917 cases and 371,549 controls) and levels of six types of white blood cells and further evaluated the causative relationships between BD and 731 immune cell traits) using a two-sample Mendelian randomization method, prioritizing the inverse variance weighted approach, based on publicly available GWAS data. Sensitivity analysis was based on MR-Egger intercept method and Cochran's Q test.

We did not find a significant causative relationship between BD and 6 white blood cell traits (FDR > 0.05). However, we found 38 immune cell traits had a causal effect to BD. Among them, 26 immune cell traits increased the risk of BD (OR: 1.01-1.07), including CD4+/CD28+ T cells and CD20+/CD27+ B cells. The remaining 12 including had a protective effect on BD (OR: 0.92-0.99). The backward MR results showed that BD had negative causal effects on 23 immune cell traits (n = 23, OR: 0.79-0.89), which included monocyte, majority of CD4+ T cells, and CD20+ B cells. BD had Positive causal effects 10 immune cell traits (OR: 1.13-1.19), especially CD19+ B cells. The overall causal effect of BD on immune cell traits was significantly higher than the inverse effect (0.011 ± 0.049 vs. 0.001 ± 0.016, p < 0.001).

A complex network of bidirectional causative relationships exists between BD and various phenotypic features of immune cells. These findings provide new insights into the diagnosis and treatment of BD from an immunotherapeutic perspective.

The oral microbiome can influence the growth, development, and regulation of the nervous system through various pathways; however, its relationship with bipolar disorder (BD) remains ambiguous. This study aims to investigate the causal relationship between the oral microbiome and BD through Mendelian randomization (MR) analysis.

Data regarding single nucleotide polymorphisms (SNPs) in GWAS summary statistics for oral microbiota and BD were obtained from two studies: one reported the association between the oral microbiome and the human genome, encompassing both the dorsum of the tongue and saliva microbiomes. The other study investigated the association between BD and the human genome, categorizing BD into BD I and BD II for separate analyses. Thus, three data components were utilized: BD, BD I, and BD II. In this study, GWAS data for saliva and dorsum of the tongue microbiomes were analyzed separately for BD, BD I, and BD II. The inverse variance weighted (IVW) method was used to assess the causal relationship between the oral microbiome and BD. Analyses were conducted only when the number of instrumental variable SNPs was no less than two. The MR-Egger regression and IVW methods were employed to evaluate heterogeneity, whereas the MR-Egger intercept test was utilized to assess pleiotropy. For MR results exhibiting heterogeneity or pleiotropy, sensitivity analyses were performed using the weighted median, simple mode, weighted mode, MR-Egger test, and leave-one-out methods. Furthermore, funnel plots were employed to evaluate publication bias. Reverse MR analysis was also performed to investigate the potential bidirectional interactions between BD and the oral microbiota.

A causal relationship exists between the oral microbiome and BD. The effects of the microbiome from different regions of the oral cavity on BD are variable, with a more pronounced impact noted on BD I. This study identified two overlapping causal relationships shared between BD I and BD II, both exhibiting the same directional influence: ①Salivary s Prevotella aurantiaca SGB 2854 (Taxonomy ID: 596085, species); ② Tongue s Prevotella sp000467895 SGB 1817 (Taxonomy ID: 838, genus). Additionally, there are two overlapping bacteria with opposing directional effects: ① Salivary g Eggerthia (Taxonomy ID: 1279384, genus); ② Salivary s unclassified SGB 2636. Three differential bacteria that exclusively regulate one subtype were identified: ① Salivary s Lachnoanaerobaculum sp000296385 SGB 3537 (Taxonomy ID: 1164882, genus); ② Tongue s unclassified SGB 689; ③ Tongue s unclassified SGB 572. Among these, the genus g Eggerthia in saliva inhibits BD I while promoting BD II; conversely, salivary s unclassified SGB 2636 inhibits BD II while promoting BD I. The analysis of tongue s unclassified SGB 489 and s unclassified SGB 1215 demonstrated pleiotropy without causal significance. The reverse MR analysis identified that BD I may influence four microbial species, including f Leptotrichiaceae (Taxonomy ID: 1129771, family), f Streptococcaceae (Taxonomy ID: 1300, family), s unclassified SGB 1210, and s unclassified SGB 1950. There may be a bidirectional causal relationship between s unclassified SGB 1950 and BD I. Additionally, Reverse Mendel suggested that there was no significant causal relationship between BD and salivary and dorsal tongue microbes.

Our Mendelian randomization results indicate a causal relationship between the oral microbiome and the development of BD. However, the microbial profiles associated with the different subtypes, BD I and BD II, differ significantly; even within the same genus, the direction of influence on BD I and BD II varies, suggesting that the underlying mechanisms for the development of BD I and BD II may differ substantially.

Treatment-resistant depression (TRD) affects one-third of major depressive disorder (MDD) patients. Previous pharmacogenetic studies suggest genetic variation may influence medication response but findings are heterogeneous. We conducted a comprehensive genetic investigation using proxy TRD phenotypes (TRDp) that mirror the treatment options of MDD from UK Biobank primary care records.

Among 15,125 White British MDD patients, we identified TRDp with medication changes (switching or receiving multiple antidepressants [AD]); augmentation therapy (antipsychotics; mood stabilizers; valproate; lithium); or electroconvulsive therapy (ECT). Hospitalized TRDp patients (HOSP-TRDp) were also identified. We conducted genome-wide association analysis, estimated SNP-heritability (hg2), and assessed the genetic burden for nine psychiatric diseases using polygenic risk scores (PRS).

TRDp patients were more often female, unemployed, less educated, and had higher BMI, with hospitalization rates twice as high as non-TRDp. While no credible risk variants emerged, heritability analysis showed significant genetic influence on TRDp (liability hg2 21-24 %), particularly for HOSP-TRDp (28-31 %). TRDp classified by AD changes and augmentation carried an elevated yet varied polygenic burden for MDD, ADHD, BD, and SCZ. Higher BD PRS increased the likelihood of receiving ECT, lithium, and valproate by 1.27-1.80 fold. Patients in the top 10 % PRS relative to the average had a 12-36 % and 24-51 % higher risk of TRDp and HOSP-TRDp, respectively.

Our findings support a significant polygenic basis for TRD, highlighting genetic and phenotypic distinctions from non-TRD. We demonstrate that different TRDp endpoints are enriched with various spectra of psychiatric genetic liability, offering insights into pharmacogenomics and TRD's complex genetic architecture.

Mitochondrial dysfunction has been implicated in the development of mental disorders, yet the underlying mechanisms remain unclear. In this study, we employed summary-data-based Mendelian randomization (SMR) analysis to explore the associations between mitochondrial-related genes and seven common mental disorders across gene expression, DNA methylation, and protein levels.

Summary statistics from genome-wide association studies were used for seven mental disorders, including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, anxiety, bipolar disorder, major depressive disorder, post-traumatic stress disorder, and schizophrenia (SCZ). Instrumental variables associated with 1136 mitochondria-related genes were derived from summary statistics for DNA methylation, gene expression, and protein quantitative trait loci. SMR analyses and colocalization analyses were then conducted across these three biological levels to explore the associations with each of the seven mental disorders.

We identified mitochondria-related genes associated with mental disorders with multi-omics evidence: RMDN1 for ADHD, and ACADVL, ETFA, MMAB, and PPA2 for SCZ. Specifically, an increase of one standard deviation in the level of RMDN1 was linked to a 12 % decrease in the risk of developing ADHD (OR = 0.88, 95 % CI: 0.83-0.94). Increased levels of ETFA (OR = 1.79, 95 % CI: 1.24-2.60) and MMAB (OR = 1.10, 95 % CI: 1.05-1.16) were significantly associated with increased risk of SCZ. Conversely, high levels of ACADVL (OR = 0.50, 95 % CI: 0.33-0.77) and PPA2 (OR = 0.68, 95 % CI: 0.55-0.85) were associated with a reduced risk of SCZ.

These findings suggested that dysfunction in mitochondria-related genes may underlie the molecular mechanisms of ADHD and SCZ, providing novel biomarkers for diagnosis and therapeutic interventions.

Numerous studies have shown an interconnection between the gut microbiota and the brain via the "gut-brain" axis. However, the causal relationships between gut microbiota, lipids, and neuropsychiatric disorders remain unclear. This study aimed to analyze potential associations among gut microbiota, lipids, and neuropsychiatric disorders-including AD, PD, ALS, MS, SCZ, MDD, and BD-using summary data from large-scale GWAS.

Bidirectional Mendelian randomization (MR) with inverse variance weighting (IVW) was the primary method. Supplementary analyses included sensitivity analyses, Steiger tests, and Bayesian weighted MR (BWMR). Mediation analyses used two-step MR (TSMR) and multivariable MR (MVMR).

The analyses revealed 51 positive correlations (risk factors) (β > 0, P < 0.05) and 47 negative correlations (protective factors) (β < 0, P < 0.05) between gut microbiota and neuropsychiatric disorders. In addition, 35 positive correlations (β > 0, P < 0.05) and 22 negative correlations (β < 0, P < 0.05) between lipids and neuropsychiatric disorders were observed. Assessment of reverse causality with the seven neuropsychiatric disorders as exposures and the identified gut microbiota and lipids as outcomes revealed no evidence of reverse causality (P > 0.05). Mediation analysis indicated that the effect of the species Bacteroides plebeius on MDD is partially mediated through the regulation of phosphatidylcholine (16:0_20:4) levels (mediation proportion = 10.9 % [95 % CI = 0.0110-0.2073]).

This study provides evidence of a causal relationship between gut microbiota and neuropsychiatric disorders, suggesting lipids as mediators. These findings offer new insights into the mechanisms by which gut microbiota may influence neuropsychiatric disorders.

The comorbidity between bipolar disorder (BD) and high body mass index (BMI) is well-documented, but their shared genetic architecture remains unclear. Our study aimed to explore this genetic correlation and potential causality.

Utilizing large-scale genome-wide association study (GWAS) summary statistics, we quantified global and local genetic correlations between BD and BMI using linkage disequilibrium score regression (LDSC) and Heritability Estimation from Summary Statistics. Stratified LDSC characterized genetic overlap across functional categories. Cross-trait meta-analyses identified shared risk single nucleotide polymorphisms (SNPs), followed by colocalization analysis using Coloc. Bi-directional Mendelian randomization (MR) assessed causality, while tissue-level SNP heritability enrichment for BD and BMI was evaluated using LDSC-specific expressed genes and Multi-marker Analysis of Genomic Annotation.

We found a genetic correlation between BD and BMI, especially in localized genomic regions. Cross-trait meta-analysis identified 46 significant SNPs shared between BD and BMI, including three novel shared risk SNPs. Colocalization analysis verified two novel SNPs with shared causal variants linked to ITIH1 and TM6SF2 genes. MR analysis demonstrated a causal effect of BD on BMI, but not the reverse. Gene expression data revealed genetic correlation enrichment in five specific brain regions.

This study comprehensively analyzes the genetic correlation between BD and BMI, uncovering shared genetic architecture and identifying novel risk loci. These findings provide new insights into the interplay between BD and BMI, informing the development of diagnostic tools and therapeutic strategies.

The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) created psychosis Biotypes based on neurobiological measurements in a multi-ancestry sample. These Biotypes cut across DSM diagnoses of schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis. Two recently developed post hoc ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Ancestry-Adjusted PRSs (AAPRSs), which allow for PRS analysis of multi-ancestry samples. Applied to schizophrenia PRS, we found the Khera AAPRS method to show superior portability and comparable prediction accuracy as compared with the Ge method. The three Biotypes of psychosis disorders had similar AAPRSs across ancestries. In genomic analysis of Biotypes, 12 genes, and isoforms showed significant genomic associations with specific Biotypes in a Transcriptome-Wide Association Study (TWAS) of genetically regulated expression (GReX) in the adult brain and fetal brain. TWAS inflation was addressed by the inclusion of genotype principal components in the association analyses. Seven of these 12 genes/isoforms satisfied Mendelian Randomization (MR) criteria for putative causality, including four genes TMEM140, ARTN, C1orf115, CYREN, and three transcripts ENSG00000272941, ENSG00000257176, ENSG00000287733. These genes are enriched in the biological pathways of Rearranged during Transfection (RET) signaling, Neural Cell Adhesion Molecule 1 (NCAM1) interactions, and NCAM signaling for neurite out-growth. The specific associations with Biotypes suggest that pharmacological clinical trials and biological investigations might benefit from analyzing Biotypes separately.

Bipolar disorder (BD) affects approximately 2% of the global population, characterized by alternating episodes of mania or hypomania, and depression. It comprises two main types: bipolar I disorder, marked by severe manic episodes, and bipolar II disorder, defined by milder hypomanic episodes. Individuals often experience rapid cycling and significant comorbidities, leading to decreased productivity and increased mortality rates. Early diagnosis and intervention are crucial for better outcomes. Both genetic and environmental factors contribute to BD's etiology, with genetic research promising improved diagnosis, novel therapeutic targets, and societal understanding that may help destigmatize the disorder.

The authors sought to determine whether genetic predispositions to cognitive ability or psychiatric conditions interact with anticholinergic burden (AChB) to impact cognition and brain structure in individuals with psychotic disorders.

Participants with psychosis spectrum disorders (N=1,704) from the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) consortium, 18-65 years of age and representing diverse ancestries, underwent cognitive assessments, structural neuroimaging, genotyping, and a comprehensive medication review. The primary cognitive outcome was the Brief Assessment of Cognition in Schizophrenia (BACS) composite score, and the primary brain structural phenotype was total gray matter volume. AChB scores for scheduled medications were quantified using the CRIDECO Anticholinergic Load Scale. Polygenic scores (PGSs) for cognition, schizophrenia, bipolar disorder, and depression were constructed, and a composite psychiatric PGS was subsequently generated. Linear regression models were used to examine AChB-PGS interactions and their associations with cognitive and brain structure outcomes, adjusting for clinical covariates and multiple testing with false discovery rate. Hypothesis-driven moderated mediation models were used to explore potential association pathways.

Higher AChB was significantly associated with lower BACS performance and reduced gray matter volume. Individuals with higher cognitive PGS values exhibited greater adverse effects of AChB on BACS, while those with lower composite psychiatric PGS values showed more pronounced gray matter volume reductions from AChB. AChB associations with cognitive impairment were partially mediated by reduced gray matter volume and were moderated by composite psychiatric PGS.

Anticholinergic-polygenic interactions significantly impact cognition and brain structure in individuals with psychotic disorders, highlighting a novel gene-by-environment interaction that advances our mechanistic understanding of cognitive impairments in this population.

To elaborate on the origins of the established male-female differences in several brain-related phenotypes, we assessed the patterns of transcriptomic sex biases in the developing and adult human forebrain. We find an abundance of sex differences in expression (sex-DEs) in the prenatal brain, driven by both hormonal and sex-chromosomal factors, and considerable consistency in the sex effects between the developing and adult brain, with little sex-DE exclusive to the adult forebrain. Sex-DE was not enriched in genes associated with brain disorders, consistent with systematic differences in the characteristics of these genes (e.g., constraint). Yet, the genes with persistent sex-DE across the lifespan were overrepresented in disease gene co-regulation networks, pointing to their potential to mediate sex biases in brain phenotypes. Altogether, our work highlights prenatal development as a crucial time point for the establishment of brain sex differences.

Bipolar disorder (BD) is a chronic psychiatric condition characterised by mood episodes and associated structural changes in the central nervous system. Optical coherence tomography (OCT) offers a non-invasive method to assess retinal layer thickness, potentially serving as an endophenotypic biomarker for neurodegeneration. This study aimed to compare retinal thickness among BD patients, their first-degree relatives, and healthy controls to identify structural markers and assess their alignment with existing literature.

Thirty-six BD patients, 30 first-degree relatives, and 38 healthy controls were recruited from Van Yüzüncü Yıl University. Comprehensive ophthalmologic examinations and retinal layer thickness measurements using Spectralis OCT were performed. Retinal layers were analysed at 1 mm, 3 mm, and 6 mm concentric circles per the ETDRS protocol. Peripapillary retinal nerve fibre layer (RNFL) thickness was evaluated across seven regions. Due to significant age differences among groups (p = 0.002), an ANCOVA analysis was used to control for the age effect.

Retinal analysis revealed a significant increase in the inferonasal (NI) nerve fibre layer thickness in BD patients and their first-degree relatives compared to healthy controls (p = 0.008). Optic nerve head analyses showed non-significant thinning in the temporal (T), inferotemporal (TI), and superotemporal (TS) nerve fibre layer thicknesses in BD patients and their relatives compared to healthy controls. The thicknesses of the macular retinal layers did not differ significantly among the groups (p > 0.05).

The observed increase in NI optic nerve fibre layer thickness in BD patients and their first-degree relatives contrasts with the expected thinning reported in previous literature on neurodegeneration in psychiatric disorders. This finding underscores the complexity of structural changes in BD and raises the possibility of alternative pathophysiological mechanisms or methodological considerations influencing retinal measurements. Further research is needed to elucidate these phenomena and their implications for understanding BD.

Psychosis constitutes a cardinal component of schizophrenia and affects nearly fifty percent of those with bipolar disorder. We sought to molecularly characterize psychosis segregating in consanguineous families. Participants from eight multiplex families were evaluated using standardized testing tools. DNA was subjected to exome sequencing followed by Sanger sequencing. Effects of variants were modeled using in-silico tools, while cDNA from a patient's blood sample was analyzed to evaluate the effect of a splice-site variant. Twelve patients in six families were diagnosed with schizophrenia, whereas four patients from two families had psychotic bipolar disorder. Two homozygous rare deleterious variants in INSR (c.2232-7T>G) and NFXL1 (c.1322G>A; p.Cys441Tyr) were identified, which segregated with severe treatment-resistant psychosis/schizophrenia in two families. There were none, or ambiguous findings in the other six families. The predicted deleterious missense variant affected a conserved amino acid, while the intronic variant was predicted to affect splicing. However, cDNA analysis from a patient's blood sample did not reveal an aberrant transcript. Our results indicate that INSR and NFXL1 variants may have a role in psychosis that requires to be investigated further. Lack of molecular diagnosis in some patients suggests the need for genome sequencing to pinpoint the genetic causes.

Polygenic risk scores (PRS) have ushered in a new era in genetic epidemiology, offering insights into individual predispositions to a wide range of diseases. However, despite recent marked enhancements in predictive power, PRS-based models still need to overcome several hurdles before they can be broadly applied in the clinic. Chiefly, they need to achieve sufficient accuracy, easy interpretability and portability across diverse populations. Leveraging trans-ancestry genome-wide association study (GWAS) meta-analysis, we generated novel, diverse summary statistics for 30 medically-related traits and benchmarked the performance of six existing PRS algorithms using UK Biobank. We built an ensemble model using logistic regression to combine outputs of top-performing algorithms and validated it on the diverse eMERGE and PAGE MEC cohorts. It surpassed current state-of-the-art PRS models, with minimal performance drops in external cohorts, indicating good calibration. To enhance predictive accuracy for clinical application, we incorporated easily-accessible clinical characteristics such as age, gender, ancestry and risk factors, creating disease prediction models intended as prospective diagnostic tests, with easily interpretable positive or negative outcomes. After adding clinical characteristics, 12 out of 30 models surpassed 80% AUC. Further, 25 traits exceeded the diagnostic odds ratio (DOR) of five, and 19 traits exceeded DOR of 10 for all ancestry groups, indicating high predictive value. Our PRS model for coronary artery disease identified 55-80 times more true coronary events than rare pathogenic variant models, reinforcing its clinical potential. The polygenic component modulated the effect of high-risk rare variants, stressing the need to consider all genetic components in clinical settings. These findings show that newly developed PRS-based disease prediction models have sufficient accuracy and portability to warrant consideration of being used in the clinic.

Epigenomic profiling of the brain has largely been done on bulk tissues, limiting our understanding of cell type-specific epigenetic changes in disease states. Here, we introduce cell type-specific histone acetylation score (CHAS), a computational tool for inferring cell type-specific signatures in bulk brain H3K27ac profiles. We applied CHAS to >300 H3K27ac chromatin immunoprecipitation sequencing samples from studies of Alzheimer's disease, Parkinson's disease, autism spectrum disorder, schizophrenia, and bipolar disorder in bulk postmortem brain tissue. In addition to recapitulating known disease-associated shifts in cellular proportions, we identified cell type-specific biological insights into brain-disorder-associated regulatory variation. In most cases, genetic risk and epigenetic dysregulation targeted different cell types, suggesting independent mechanisms. For instance, genetic risk of Alzheimer's disease was exclusively enriched within microglia, while epigenetic dysregulation predominantly fell within oligodendrocyte-specific H3K27ac regions. In addition, reanalysis of the original datasets using CHAS enabled identification of biological pathways associated with each neurological and psychiatric disorder at cellular resolution.

The tissues of origin and molecular mechanisms underlying human complex diseases remain incompletely understood. Previous studies have leveraged transcriptomic data to interpret genome-wide association studies (GWASs) for identifying disease-relevant tissues and fine-mapping causal genes. However, according to the central dogma, proteins more directly reflect cellular molecular activities than RNA. Therefore, in this study, we integrated proteomic data with GWAS to identify disease-associated tissues and genes.

We compiled proteomic and paired transcriptomic data for 12,229 genes across 32 human tissues from the GTEx project. Using three tissue inference approaches-S-LDSC, MAGMA, and DESE-we analyzed GWAS data for six representative complex diseases (bipolar disorder, schizophrenia, coronary artery disease, Crohn's disease, rheumatoid arthritis, and type 2 diabetes), with an average sample size of 260 K. We systematically compared disease-associated tissues and genes identified using proteomic versus transcriptomic data.

Tissue-specific protein abundance showed a moderate correlation with RNA expression (mean correlation coefficient = 0.46, 95% CI: 0.42-0.49). Proteomic data accurately identified disease-relevant tissues, such as the association between brain regions and schizophrenia and between coronary arteries and coronary artery disease. Compared to GWAS-based gene association estimates alone, incorporating proteomic data significantly improved gene association detection (AUC difference test, p = 0.0028). Furthermore, proteomic data revealed unique disease-associated genes that were not identified using transcriptomic data, such as the association between bipolar disorder and CREB1.

Integrating proteomic data enables accurate identification of disease-associated tissues and provides irreplaceable advantages in fine-mapping genes for complex diseases.

Innovation in psychiatric therapeutics has stagnated on known mechanisms. Psychiatric genome-wide association studies (GWAS) have identified hundreds of genome-wide significant (GWS) loci that have rapidly advanced our understanding of disease etiology. However, whether these results can be leveraged to improve clinical treatment for specific psychiatric disorders remains poorly understood.

In this proof-of-principal evaluation of GWAS clinical utility, we test whether the targets of drugs used to treat Attention Deficit Hyperactivity Disorder (ADHD), Bipolar Disorder (BiP), Generalized Anxiety Disorder (GAD), Major Depressive Disorder (MDD), Post-Traumatic Stress Disorder (PTSD), Schizophrenia (SCZ), Substance Use Disorders (SUDs), and insomnia (INS), are enriched for GWAS meta-analysis findings.

The genes coding for treatment targets of medications used to SCZ, BiP, MDD, and SUDs (but not ADHD, PTSD, GAD, or INSOM) are enriched for GWS loci identified in their respective GWAS (ORs: 2.78-27.63; all ps <1.15e-3). Enrichment is largely driven by the presence of a GWS locus or loci within a gene coding for a drug target (i.e., proximity matching). Broadly, additional annotation (i.e., functional: Combined Annotation Dependent Depletion [CADD] scores, regulomeDB scores, eQTL, chromatin loop, and gene region; statistical: effect size of genome-wide significant SNPs; Z-score of SNPs; number of drug targets implicated by GWAS), with the exception of weighting by the largest SNP effect size, does not further improve enrichment across disorders. Evaluation of prior smaller GWAS reveal that more recent larger GWAS improve enrichment.

GWAS results may assist in the prioritization of medications for future psychopharmaceutical research.

Individuals with severe mental illnesses (SMI) like schizophrenia, bipolar disorder (BD), and major depressive disorder (MDD) have an increased risk for cardiovascular diseases (CVD), but the causal relationship remains unclear.

Mendelian randomization (MR) was used to investigate the potential causal relationship between SMI and CVD and its five subtypes of disease, coronary heart disease, myocardial infarction, stroke, heart failure, and atrial fibrillation. Subsequently, the MR results of SMI with CVD and its subtypes were meta-analyzed separately. To assess the robustness of the findings, Cochran's Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out analysis were used. Select single nucleotide polymorphisms (SNPs) related to SMI and CVD and their five subtypes (coronary heart disease, myocardial infarction, stroke, heart failure, and atrial fibrillation). Use univariable Mendelian randomization (UVMR) and multivariate Mendelian randomization (MVMR) to assess the causal relationship between these conditions. Conduct a meta-analysis of the MR results of SMI and CVD and their subtypes. Use MR mediation analysis to evaluate the mediating effect of BMI between BD and CVD. Use Cochran's Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out analysis to enhance the robustness of the study.

MR analyses have revealed correlations between schizophrenia and BD with CVD and their subtypes in certain datasets. No significant evidence of an association between MDD and CVD or its subtypes was observed in our MR analyses. After MVMR and MR meta-analysis, no basis for genetically predicted SMI increasing CVD and their subtypes was found. The MR mediation analysis showed that the reduced risk of certain CVDs in BD was partially related to BMI to some extent.

Our MR study did not provide conclusive evidence for a causal association between genetic predisposition to SMI and CVD. Based on the available evidence, it would be more appropriate to consider SMI as potential risk markers for CVD and its subtypes rather than definitive risk factors.

Psychiatric research has shifted from a neuroncentric view to understanding mental disorders as disturbances of heterogeneous brain networks. Oligodendrocyte precursor cells (OPCs)- actively involved in the modulation of neuronal functions - are altered in psychiatric patients, but the extent and related consequences are unclear. This review explores canonical and non-canonical OPC-related pathways in schizophrenia, bipolar disorder, post-traumatic stress disorder, and depression in humans, highlighting potential mechanisms shared across diagnostic entities.

From the very beginnings of our field in the late 18th century, psychiatrists have engaged, often extensively, in "metaphorical brain talk" - rephrasing descriptions of mental processes in unconfirmed brain metaphors (e.g., "diseased working of the brain convolutions"). In the late 19th century, Kraepelin criticized the later developments of such approaches, termed "brain mythology" by the philosopher/psychiatrist Jaspers in 1913. In this essay, I review the history, meaning, and significance of this phenomenon and reach four conclusions. First, this trend has continued to the present day in metaphors such as the "broken brain" and the use of simplistic and empirically poorly supported explanations of psychiatric illness, such as depression being "due to an imbalance of serotonin in the brain." Second, our language stems from the tension in our profession that seeks to be a part of medicine yet declares our main focus as treatment of the mental. We feel more comfortable with the reductionist approach of brain metaphors, which, even though at times self-deceptive, reinforce our commitment to and membership in a brain-based medical specialty. Third, metaphorical brain talk can also be seen as the "promissory note" of our profession, a pledge that the day will come when we can indeed explain accurately to ourselves and to our patients the brain basis of the psychiatric disorders from which they suffer. Finally, moving away from metaphorical brain talk would reflect an increasing maturity of both the research and clinical aspects of our profession.

To understand the pathogenetic mechanisms shared among schizophrenia (SCZ), bipolar disorder (BP), and major depression (MDD), we investigated the pleiotropic mechanisms using large-scale genome-wide and brain transcriptomic data.

We analyzed SCZ, BP, and MDD genome-wide association datasets available from the Psychiatric Genomics Consortium using the PLEIO framework and characterized the pleiotropic loci identified using pathway and tissue enrichment analyses. Pleiotropic and disorder-specific loci were also assessed.

Our pleiotropy-informed genome-wide analysis identified 553 variants that included 192 loci not reaching genome-wide significance in input datasets. These were enriched for five molecular pathways: cadherin signaling (p = 2.18 × 10-8), Alzheimer's disease-amyloid secretase (p = 4 × 10-4), oxytocin receptor-mediated signaling (p = 1.47 × 10-3), metabotropic glutamate receptor group III (p = 5.82 × 10-4) and Wnt signaling (p = 1.61 × 10-11). Pleiotropic loci demonstrated the strongest enrichment in the brain cortex (p = 5.8 × 10-28), frontal cortex (p = 3 × 10-31), and cerebellar hemisphere (p = 9.8 × 10-28). SCZ-BP-MDD pleiotropic variants were also enriched for neurodevelopmental brain transcriptomic profiles related to the second-trimester post-conception (week 21, p = 7.35 × 10-5; week 17, p = 6.36 × 10-4) and first year of life (p = 3.25 × 10-5).

Genetic mechanisms shared among SCZ, BP, and MDD appear to be related to early neuronal development. Because the genetic architecture of psychopathology transcends diagnostic boundaries, pleiotropy-focused analyses can lead to increased gene discovery and novel insights into relevant pathogenic mechanisms.

The concept of Enduring Mental Health (EMH) describes a long-term state in which an individual does not experience mental disorders. As most people encounter mental health issues at some point, this study investigates the prevalence, predictors, and genetic architecture of EMH across childhood.

EMH status was based on longitudinal data from 18,884 Dutch twins assessed at ages 3, 5, 7, 10, and 12 for behavioral and emotional problems. Children were grouped into three categories: EMH, some instances of mental health problems (SIMHP), and many instances of mental health problems (MIMHP). Child and parent level factors including individual polygenic scores were tested for associations with these three categories. A twin model was used to assess the contribution of genetic and environmental factors to EMH.

Thirty-seven percent of the sample had EMH. EMH was associated with parental education (OR(low) =0.77[0.70-0.86]; OR(middle) = 0.88[0.82-0.95]), child academic achievement (OR=1.07[1.03,1.12]), and child wellbeing (OR=1.44[1.35,1.54]), and was weakly associated with some polygenic scores. The twin model estimated that 54% of the variance in EMH was due to genetic factors.

EMH was observed in just over a third of children. Individual differences in EMH were influenced by various sociodemographic factors, mental health-related variables, and genetic predispositions, suggesting that strategies to support EMH will likely require a comprehensive, multifaceted approach.

Membrane palmitoylated protein 6 (MPP6), a membrane skeletal protein, is expressed not only in the peripheral nervous system (PNS) but also in the central nervous system (CNS). In this study, we investigated the localization of MPP6 and its associated protein complexes in the mouse cerebrum, as well as its effects on behavior using MPP6 protein-deficient (Mpp6 -/-) mice. MPP6 was detected in mouse cerebral lysates and synaptic membrane fractions, where it formed protein complexes with other MPP family members, including MPP1, MPP2, and calcium/calmodulin-dependent serine protein kinase (CASK). However, the amounts of these complexes did not differ between Mpp6 -/- and wild-type (Mpp6 +/+) mice. Immunohistochemistry revealed that MPP6 was localized at synapses throughout the cerebrum, particularly in the postsynaptic regions. Ultrastructural analysis showed that synaptic cleft distances and postsynaptic density thickness were slightly reduced in Mpp6 -/- mice compared with Mpp6 +/+ mice. In the elevated plus-maze test, a Mpp6 -/- mouse exhibited unusual behavior not observed in Mpp6 +/+ mice, although there was no statistically significant difference in the time spent in the open and closed arms between the two groups. Locomotor activity measurements revealed that MPP6 -/- mice were more active at midnight and less active from morning to noon than Mpp6 +/+ mice, implying alterations in sleep-wake regulation. These findings suggest that MPP6 plays a role in synaptic function by forming protein complexes with other MPP family members and signaling proteins.

Schizophrenia, bipolar disorder and major depressive disorder - which are the most common adult disorders requiring psychiatric care - contribute substantially to premature mortality and morbidity globally. Treatments for these disorders are suboptimal, there are no diagnostic pathologies or biomarkers and their pathophysiologies are poorly understood. Novel therapeutic and diagnostic approaches are thus badly needed. Given the high heritability of psychiatric disorders, psychiatry has potentially much to gain from the application of genomics to identify molecular risk mechanisms and to improve diagnosis. Recent large-scale, genome-wide association studies and sequencing studies, together with advances in functional genomics, have begun to illuminate the genetic architectures of schizophrenia, bipolar disorder and major depressive disorder and to identify potential biological mechanisms. Genomic findings also point to the aetiological relationships between different diagnoses and to the relationships between adult psychiatric disorders and childhood neurodevelopmental conditions.

Mood and anxiety disorders co-occur and share symptoms, treatments and genetic risk, but it is unclear whether combining them into a single phenotype would better capture genetic variation. The contribution of common genetic variation to these disorders has been investigated using a range of measures; however, the differences in their ability to capture variation remain unclear, while the impact of rare variation is mostly unexplored.

We aimed to explore the contributions of common genetic variation and copy number variations associated with risk of psychiatric morbidity (P-CNVs) to different measures of internalising disorders.

We investigated eight definitions of mood and anxiety disorder, and a combined internalising disorder, derived from self-report questionnaires, diagnostic assessments and electronic healthcare records (EHRs). Association of these definitions with polygenic risk scores (PRSs) of major depressive disorder and anxiety disorder, as well as presence of a P-CNV, was assessed.

The effect sizes of both PRSs and P-CNVs were similar for mood and anxiety disorder. Compared to mood and anxiety disorder, internalising disorder resulted in higher prediction accuracy for PRSs, and increased significance of associations with P-CNVs for most definitions. Comparison across the eight definitions showed that PRSs had higher prediction accuracy and effect sizes for stricter definitions, whereas P-CNVs were more strongly associated with EHR- and self-report-based definitions.

Future studies may benefit from using a combined internalising disorder phenotype, and may need to consider that different phenotype definitions may be more informative depending on whether common or rare variation is studied.

Childhood maltreatment (CM) is associated with psychiatric disorders. The underlying mechanisms are complex and involve genetics.

To investigate the polygenic architecture of CM-exposed individuals across psychiatric conditions and if genetics modulates absolute CM risk in the presence of high-impact risk factors such as parental psychiatric diagnoses.

The population-based case-cohort iPSYCH was used to analyze 13 polygenic scores (PGS) in CM-exposed individuals across 5 psychiatric International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnoses benchmarked against controls. Individuals were stratified into PGS quantiles, and absolute CM risk was calculated using Cox regression. Sex-specific analyses were also performed. Data were analyzed from June 2022 to December 2024.

PGS of phenotypes of psychiatric disorders, CM, educational attainment, and substance use.

PGSs were generated using summary statistics from genome-wide association studies of phenotypes representing psychiatric disorders, CM, educational attainment, and substance use and tested for their association with CM across psychiatric disorders.

This study included 102 856 individuals (mean [SD] age, 22.6 [7.1] years; 54 918 male [53.4%]) 8 to 35 years old. A total of 2179 CM-exposed individuals were analyzed across individuals with attention-deficit/hyperactivity disorder (ADHD; n = 22 674), autism (n = 18 941), schizophrenia (n = 6103), bipolar disorder (n = 3061), depression (n = 28 896), and controls (n = 34 689). PGSs for ADHD and educational attainment were associated with CM across all psychiatric diagnoses. The absolute CM risk was increased in the highest PGS groups, eg, for ADHD, the absolute CM risk was 5.6% in the highest ADHD-PGS quartile whereas it was only 3.3% in the lowest ADHD-PGS quartile (hazard rate ratio quantile 4 vs quantile 1 = 1.81; 95% CI, 1.47-2.22). CM risk was more than twice as high for children with parents with a psychiatric diagnosis (5.7%) than for children with parents without a psychiatric diagnosis (2.5%), but even in the presence of this risk factor, individuals could still be stratified into risk groups based on their genetics. No genetic differences between CM-exposed males and females were observed, but there were striking sex differences in absolute CM risk, which reached 5.6% for females in the highest ADHD-PGS quartile and 2.0% for males.

Results of this case-control study suggest that individuals with high ADHD-PRS and/or low educational attainment-PRS had an associated elevated risk of CM. Extra attention should be given to individuals at high risk for CM across all 5 psychiatric diagnoses, ie, females with a high ADHD-PGS and/or a parent diagnosed with a psychiatric disorder.

24-hour biological rhythms are essential to maintain physiological homeostasis. Disruption of these rhythms increases the risks of multiple diseases. Biological rhythms are known to have a genetic basis formed by core clock genes, but how individual genetic variation shapes the oscillating transcriptome and contributes to human chronophysiology and disease risk is largely unknown. Here, we mapped interactions between temporal gene expression and genotype to identify quantitative trait loci (QTLs) contributing to rhythmic gene expression. These newly identified QTLs were termed as rhythmic QTLs (rhyQTLs), which determine previously unappreciated rhythmic genes in human subpopulations with specific genotypes. Functionally, rhyQTLs and their associated rhythmic genes contribute extensively to essential chronophysiological processes, including bile acid and lipid metabolism. The identification of rhyQTLs sheds light on the genetic mechanisms of gene rhythmicity, offers mechanistic insights into variations in human disease risk, and enables precision chronotherapeutic approaches for patients.

Age at onset of walking is an important early childhood milestone which is used clinically and in public health screening. In this genome-wide association study meta-analysis of age at onset of walking (N = 70,560 European-ancestry infants), we identified 11 independent genome-wide significant loci. SNP-based heritability was 24.13% (95% confidence intervals = 21.86-26.40) with ~11,900 variants accounting for about 90% of it, suggesting high polygenicity. One of these loci, in gene RBL2, co-localized with an expression quantitative trait locus (eQTL) in the brain. Age at onset of walking (in months) was negatively genetically correlated with ADHD and body-mass index, and positively genetically correlated with brain gyrification in both infant and adult brains. The polygenic score showed out-of-sample prediction of 3-5.6%, confirmed as largely due to direct effects in sib-pair analyses, and was separately associated with volume of neonatal brain structures involved in motor control. This study offers biological insights into a key behavioural marker of neurodevelopment.

Substance use disorders (SUDs) and psychiatric disorders frequently co-occur, and their etiology likely reflects both transdiagnostic (i.e., common/shared) and disorder-level (i.e., independent/nonshared) genetic influences. Understanding the genetic influences that are shared and those that operate independently of the shared risk could enhance precision in diagnosis, prevention, and treatment, but this remains underexplored, particularly in non-European ancestry groups.

We applied genomic structural equation modeling to examine the common and independent genetic architecture among SUDs and psychotic, mood, and anxiety disorders using summary statistics from genome-wide association studies (GWAS) conducted in European- (EUR) and African-ancestry (AFR) individuals. To characterize the biological and phenotypic associations, we used FUMA, conducted genetic correlations, and performed phenome-wide association studies (PheWAS).

In EUR individuals, transdiagnostic genetic factors represented SUDs, psychotic, and mood/anxiety disorders, with GWAS identifying two novel lead single-nucleotide polymorphisms (SNPs) for the mood factor. In AFR individuals, genetic factors represented SUDs and psychiatric disorders, and GWAS identified one novel lead SNP for the SUD factor. In EUR individuals, second-order factor models showed phenotypic and genotypic associations with a broad range of physical and mental health traits. Finally, genetic correlations and PheWAS highlighted how common and independent genetic factors for SUD and psychotic disorders were differentially associated with psychiatric, sociodemographic, and medical phenotypes.

Combining transdiagnostic and disorder-level genetic approaches can improve our understanding of co-occurring conditions and increase the specificity of genetic discovery, which is critical for identifying more effective prevention and treatment strategies to reduce the burden of these disorders.

Problematic alcohol use (PAU) adversely affects the clinical course of psychiatric disorders. Genetic studies have suggested that genetic factors underlie the co-occurrence of PAU with psychiatric disorders. This study aimed to elucidate shared genetic architectures, prioritizing genes that disorders may have in common.

Using genome-wide association data of PAU including 435,563 samples from people of European ancestry, this study investigated the genetic relationship between PAU and 11 psychiatric disorders using a bivariate causal mixture model (MiXeR). Local genetic correlation and colocalization analyses were conducted to identify the genomic regions significantly associated with PAU and each psychiatric disorder. Postanalysis included the false discovery rate (FDR) and transcriptome-wide association studies (TWASs), as well as summary-data-based Mendelian randomization to prioritize shared genes by integrating brain transcriptome data.

MiXeR analysis revealed a substantial polygenic overlap (39%-73%) between PAU and psychiatric disorders. Four bivariate genomic regions with high correlations suggest shared causal variants of PAU with major depression and schizophrenia. Within these regions, four and six genes for the PAU-major depression and PAU-schizophrenia pairs, respectively, were mapped by conjunctional FDR analysis. Furthermore, TTC12 and ANKK1 were identified as potential causal genes for PAU and these disorders. The findings were replicated in multi-ancestry analyses of colocalization and TWASs.

Despite the varying degrees of genetic overlap and directions of shared genetic effect correlations, these results imply the presence of shared genetic factors influencing the comorbidity of PAU and psychiatric disorders. Additionally, TTC12 and ANKK1, located near DRD2, may be causally associated with comorbid conditions.

Schizophrenia (SCZ) is a complex psychiatric disorder with a diverse genetic landscape, involving common regulatory variants, rare protein-coding mutations, structural genomic rearrangements, and transcriptional dysregulation. A critical challenge in developing rationally designed therapeutics is understanding how these various factors converge to disrupt cellular networks in the human brain, ultimately contributing to SCZ. Towards this aim, we generated multimodal data, including SCZ-specific protein-protein interactions in stem-cell-derived neuronal models and adult postmortem cortex, integrated with genetic and transcriptomic datasets from individuals with psychiatric disorders. We identified three distinct neuron-specific SCZ protein networks, or modules, significantly enriched for genetic and transcriptional perturbations associated with SCZ. The relevance of these modules was validated through whole-cell proteomics in patient-derived neurons, revealing their disruption in 22q11.2 deletion carriers diagnosed with SCZ. We demonstrated their therapeutic potential by showing that these modules are targets of GSK3 inhibition using phosphoproteomics. Our findings present a foundational model that integrates genetic, transcriptional, and proteomic perturbations in SCZ. This model provides a cohesive framework for understanding how polygenic and multimodal perturbations affect neuronal pathways in the human brain, as well as a data-driven pathway resource for identifying potential drug targets to reverse disruptions observed in these neuronal networks.

Though suicide attempt is the most robust predictor of suicide death, few who attempt go on to die by suicide (<10%), and ∼50% of all suicide deaths occur in the absence of evidence of prior attempts. Risks in this latter group are particularly poorly understood.

Data from the Utah Suicide Mortality Risk Study (USMRS) were used to study underlying polygenic liabilities among suicide deaths without evidence of prior nonfatal suicidal thoughts or behaviors (SD-N) compared to suicide deaths with prior nonfatal suicidality (SD-S).

We used an analysis of covariance design, comparing SD-N to SD-S and to population controls with similar genetic ancestry from the United Kingdom.

We selected 12 source studies to generate descriptive quantitative polygenic scores (PGS) reflecting neuropsychiatric conditions. Analysis of covariance was used to evaluate suicide mortality subsets and controls adjusted for sex, age, and genetic ancestry effects.

Suicide deaths were population-ascertained through a 25-year collaboration with the Utah State Office of the Medical Examiner. Evidence of suicidality was determined from diagnoses and clinical notes, yielding 1,364 SD-N and 1,467 SD-S deaths, compared to 20,368 controls.

The tested PGS spanned 12 psychiatric, neurodevelopmental, and neurodegenerative conditions.

SD-N were significantly more male (82.33% vs. 67.76%) and older at death (47.26 years vs. 41.36 years) than SD-S. Controls were significantly less male than both suicide subsets (43.71%). Genetic ancestry was similar across suicide subsets and controls (% European: 96.77%, 96.81%, and 97.38%). Comparing SD-N to SD-S revealed significantly lower PGS in SD-N for: MDD (p=0.0015), neuroticism (p=0.0016), anxiety (p=0.0048), Alzheimer's (p=0.011), depressed affect (p=0.015), schizophrenia (p=0.020), PTSD (p=0.023), and bipolar disorder (p=0.028). This attenuation in SD-N was particularly pronounced for depressed affect, neuroticism, and Alzheimer's, where PGS were not different from controls. Sex-specific analyses suggested attenuation of PGS in SD-N was driven by males for MDD, anxiety, and PTSD, and by females for bipolar disorder, neuroticism, and Alzheimer's.

SD-N have significantly different genetic liabilities from SD-S, particularly regarding neuropsychiatric conditions. Results have far-reaching implications both for future research and for preventions for those at highest risk of mortality.

What are underlying genetic liabilities related to neuropsychiatric conditions in the roughly half of suicide deaths with no evidence of prior nonfatal suicidal thoughts or behaviors (SD-N), a group that has not previously been accessible for study?

These suicide deaths with no prior nonfatal suicidality showed significantly attenuated underlying polygenic liabilities associated with mental health traditionally thought to be core features of suicide mortality risk, and justifies additional studies of underlying risks associated with non-psychiatric conditions and behaviors.

These differences in underlying liabilities between suicide deaths with and without prior suicidality suggest departure from the traditional mental health risks that have been the focus of suicide risk discovery, and impel new directions for future research and prevention efforts.

Plasma proteins are the potential therapeutic targets for psychiatric disorders due to their important roles in signal transduction. We aimed to explore the plasma protein biomarkers with cross-psychiatric disorders effects. Proteome-wide Mendelian randomization (MR) and colocalization analyses were performed to investigate the potential causal relationship between plasma protein biomarkers and 12 psychiatric disorders and further identify the potential proteins with cross-effects. To assess the directionality and exclude potential reverse causation, Steiger directionality tests and reverse MR analyses were additionally conducted. Then, validation analysis was performed by employing summary data from cross-psychiatric disorder GWAS to validate the cross-psychiatric effects of proteins. Protein-protein interactions were conducted to evaluate the interaction between candidate proteins and druggability assessment was used to prioritize potential drug targets for psychiatric disorders. We identified novel plasma proteins that possessed cross-psychiatric disorder effects, especially BTN2A1 and BTN3A2 associated with major depressive disorder (MDD), schizophrenia (SCZ), and bipolar disorder (BIP); ITIH1, ITIH3, ITIH4 and FES associated with SCZ and BIP, and the cross-effects of these proteins on SCZ and BIP were confirmed by validation analyses. Steiger tests and reverse MR supported causal directionality. Besides, the protein-protein interactions (PPI) analysis indicated cross-effects proteins had significant interaction, especially ITIH1-ITIH3. The druggability assessment prioritized eight proteins, two of which (ITIH3 and NCAM1) has been targeted by antipsychotic drugs. Our findings provided insights into shared biological mechanisms underlying these conditions.

Peripheral (blood-based) biomarkers for psychiatric illness could benefit diagnosis and treatment, but research to date has typically been low throughput, and traditional case-control studies are subject to potential confounds of treatment and other exposures. Large-scale 2-sample mendelian randomization (MR) can examine the potentially causal impact of circulating proteins on neuropsychiatric phenotypes without these confounds.

To identify circulating proteins associated with risk for schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) as well as cognitive task performance (CTP).

In a 2-sample MR design, significant proteomic quantitative trait loci were used as candidate instruments, obtained from 2 large-scale plasma proteomics datasets: the UK Biobank Pharma Proteomics Project (2923 proteins per 34 557 UK individuals) and deCODE Genetics (4719 proteins per 35 559 Icelandic individuals). Data analysis was performed from November 2023 to November 2024.

Genetic influence on circulating levels of proteins in plasma.

Outcome measures were summary statistics drawn from recent large-scale genome-wide association studies for SCZ (67 323 cases and 93 456 controls), BD (40 463 cases and 313 436 controls), MDD (166 773 cases and 507 679 controls), and CTP (215 333 individuals). MR was carried out for each phenotype, and proteins that showed statistically significant (Bonferroni-corrected P < .05) associations from MR analysis were used for pathway, protein-protein interaction, drug target enrichment, and potential druggability analysis for each outcome phenotype separately.

MR analysis revealed 113 Bonferroni-corrected associations (46 novel) involving 91 proteins across the 4 outcome phenotypes. Immune-related proteins, such as interleukins and complement factors, showed pleiotropic effects across multiple outcome phenotypes. Drug target enrichment analysis provided support for repurposing of anti-inflammatory agents for SCZ, amantadine for BD, retinoic acid for MDD, and duloxetine for CTP.

Identifying potentially causal effects of circulating proteins on neuropsychiatric phenotypes suggests potential biomarkers and offers insights for the development of innovative therapeutic strategies. The study also reveals pleiotropic effects of many proteins across different phenotypes, indicating shared etiology among serious psychiatric conditions and cognition.

Many patients with Graves' disease (GD) also suffer from mental disorders in clinical practice, but their causal relationship remains unclear. This study aims to investigate the causal relationship between GD and common mental disorders using a bidirectional Mendelian randomization (MR)approach.

We derived genome-wide association study (GWAS) data for common mental disorders, including major depressive disorder (MDD), anxiety disorders, bipolar disorder, and attention-deficit/hyperactivity disorder (ADHD), from the Psychiatric Genomics Consortium consortium. GWAS data for GD were obtained from the FinnGen consortium. Subsequently, a bidirectional MR analysis was conducted, with the inverse-variance weighted (IVW) methods as the primary MR analysis method. Sensitivity analysis used Cochran's Q test, MR-Egger intercept test, and leave-one-out method.

IVW results in MR demonstrated a positive association between genetic susceptibility to GD and bipolar disorder (OR = 1.073, 95 % CI: 1.042-1.105, p = 2.882 × 10-6). Similar causal estimates were obtained through MR-Egger regression and the weighted median method. Additionally, both Cochran's Q test and MR-Egger intercept test indicated no evidence of heterogeneity or pleiotropy. However, no causal associations were demonstrated between GD and MDD, anxiety disorders, or ADHD. Furthermore, a causal relationship between genetic susceptibility to common mental disorders and GD was not evidenced.

This bidirectional MR study supports the role of GD in the causal association with an increased risk of bipolar disorder, which guides us to pay attention to the mental diseases of GD patients in the clinic.

Both elevated and blunted cortisol responses have been associated with depression. Previous Mendelian randomization (MR) studies have largely ruled out cortisol as a cause of depression. Based on the attenuation hypothesis, this MR study used depression as exposure to assess whether cortisol might be a consequence and therefore a biomarker of depression.

Strong (P < 5 × 10-8) and independent (r2 < 0.001) single nucleotide polymorphisms (SNPs) associated with broadly defined depression (294,322 cases, 741,438 controls) were used as instruments. These were applied to genetic associations with morning, fasting, and random plasma cortisol in the CORtisol NETwork (CORNET) consortium (n = 25,314), METabolic Syndrome in Men (METSIM) study (n = 6667), and Canadian Longitudinal Study on Aging (CLSA) cohort (n = 8299). Multivariable MR, adjusting for childhood maltreatment and major mental disorders, was conducted to address potential horizontal pleiotropy from dichotomous depression. Instruments were also selected by evidence of colocalization with major depressive disorder to address non-specificity.

Using 133 SNPs as instruments, depression was inversely associated with morning plasma cortisol (β per log-odds of genetic liability to depression = -0.107 [95 % CI, -0.181 to -0.032]) in the CORNET consortium. Replication in the METSIM study (β = -0.203 [95 % CI, -0.367 to -0.040]) and CLSA cohort (β = -0.091 [95 % CI, -0.220 to 0.039]) showed consistent but not always significant associations. Multivariable MR and follow-up analysis incorporating colocalization supported these findings.

Consistent with the attenuation hypothesis, blunted cortisol response appeared to be a consequence and potentially a biomarker of depression. Future studies are needed to provide more interpretable effect sizes and validate other biomarker measures.

Post-traumatic stress disorder (PTSD) is a common and disabling psychiatric disorder. PTSD involves multiple brain regions and is often comorbid with other psychiatric disorders, such as major depressive disorder (MDD). Recent genome-wide association studies (GWASs) have identified many PTSD risk loci and transcriptomics studies of postmortem brain have found differentially expressed genes associated with PTSD cases. In this study, we integrated genome-wide measures across modalities to identify convergent molecular effects in the PTSD brain.

We performed tandem mass spectrometry (MS/MS) on a large cohort of donors (N = 66) in two prefrontal cortical areas, dorsolateral prefrontal cortex (DLPFC), and subgenual prefrontal cortex (sgPFC). We also coupled the proteomics data with transcriptomics and microRNA (miRNA) profiling from RNA-seq and small-RNA sequencing, respectively for the same cohort. Additionally, we utilized published GWAS results of multiple psychiatric disorders for integrative analysis.

We found differentially expressed proteins and co-expression protein modules disrupted by PTSD. Integrative analysis with transcriptomics and miRNA data from the same cohort pointed to hsa-mir-589 as a regulatory miRNA responsible for dysregulation of neuronal protein networks for PTSD, including the gamma-aminobutyric acid (GABA) vesicular transporter, SLC32A1. In addition, we identified significant enrichment of risk genes for other psychiatric disorders, such as autism spectrum disorder (ASD) and major depressive disorder (MDD) within PTSD protein co-expression modules, suggesting shared molecular pathology.

We integrated genome-wide measures of mRNA and miRNA expression and proteomics profiling from PTSD, MDD, and control (CON) brains to identify convergent and divergent molecular processes across genomic modalities. We substantially expand the number of differentially expressed genes and proteins in PTSD and identify downregulation of GABAergic processes in the PTSD proteome. This provides a novel framework for future studies integrating proteomic profiling with transcriptomics and non-coding RNAs in the human brain studies.

DNA methylation is an epigenetic modification implicated in psychiatric disorders influenced by both genetic and environmental factors. Methylation risk scores (MRSs) have emerged as a tool for quantifying accumulated epigenetic modifications and assessing the predisposed risk for certain common disorders. This narrative review introduces the MRS application in psychiatric disorders, including schizophrenia (SCZ), bipolar disorder (BD), social anxiety disorder (SAD), and panic disorder (PD), while also discussing the current limitations and ethical considerations in psychiatric research. MRSs are calculated from epigenome-wide association studies (EWASs) for psychiatric disorders in various tissues from blood and brain and reflect methylation patterns associated with the psychiatric disorder risk. MRSs provide a perspective of how the cumulative methylation patterns at specific CpG sites may contribute to the onset of psychiatric disorders. In SCZ and BD, MRSs derived from both blood and brain tissues have shown distinct methylation profiles that differentiate these disorders, particularly in patients with a high genetic SCZ risk. MRSs are also used to assess the impact of environmental factors, such as early-life adversity and chronic stress, on psychiatric outcomes. In SAD and PD, where epigenetic studies are relatively limited, MRSs revealed both shared and distinct epigenetic features between anxiety disorders, with specific methylation changes associated with social avoidance in SAD patients. MRSs can serve as biomarkers, providing a valuable understanding of both genetic predispositions and environmental influences on gene regulation. However, the lack of large-scale EWAS datasets and standardized summary statistics remains as a limitation. To address this issue, this review provides a list of publicly available raw intensity data (IDAT) files from psychiatric epigenetic studies that can help facilitate future research by providing the raw data necessary for conducting independent EWASs and MRS calculations. As the field advances, careful consideration must be given to the ethical implications of MRS applications, particularly in clinical intervention and prevention. While MRSs hold promise for future personalized medicine applications, informing treatment decisions based on an individual's methylation profile, caution is warranted regarding their predictive utility and effect size limitations. This review emphasizes the importance of MRSs in advancing psychiatric research, bridging the gap between genetic risk and environmental factors.

Major Depressive Disorder (MDD) is a common, complex disorder that is a leading cause of disability worldwide and a significant risk factor for suicide. In this study, we have performed the largest molecular analysis of MDD in postmortem human brains (846 samples across 458 individuals) in the subgenual Anterior Cingulate Cortex (sACC) and the Amygdala, two regions central to mood regulation and the pathophysiology of MDD. We found extensive expression differences, particularly at the level of specific transcripts, with prominent enrichment for genes associated with the vesicular functioning, the postsynaptic density, GTPase signaling, and gene splicing. We find associated transcriptional features in 107 of 243 genome-wide significant loci for MDD and, through integrative analyses, highlight convergence of genetic risk, gene expression, and network-based analyses on dysregulated glutamatergic signaling and synaptic vesicular functioning. Together, these results provide an initial mechanistic understanding of MDD and highlight potential targets for novel drug discovery.

The spectrum of infectious risk organisms showing associations with psychiatric traits is expanding. Infectious agents can modulate the risk of psychiatric disorders at different stages of life, such as gestational, childhood, adolescent, and adult periods. Prenatal infection appears to 'prime' the developing brain, whereas infection during childhood or later periods may act as a 'second hit', and these may have synergistic effects on the risk of developing psychiatric diseases. However, neither all the individuals with antecedent infection develop psychiatric disorders, nor do infectious organisms alone lead to psychiatric phenotypes. This suggests modulatory effects of additional host factors. The host genetic background crucially determines differential susceptibility to infection and serves as an important gateway for immune activation and signalling, as well as homeostatic brain functions. Despite the presence of several immune checkpoints and effectors, the infectious organisms disrupt the balance between immune-activating and immune-compensatory mechanisms and contribute to immune dysregulation. This depends substantially on genetic loci encoding immune molecules such as Toll-like receptors, Major Histocompatibility Complex, cytokines/ chemokines and their receptors, complement proteins, and other molecules and elements such as human endogenous retroviruses and gut microbiome that have distinct roles in immune regulation and immune effector functions. Genetic variations within these loci not only influence differential susceptibility to infection but also confer risk to psychiatric disorders. This article highlights a comprehensive overview of the nexus between infections and immune function-related genes and their impact on psychiatric traits. Understanding such interactions will lead to the identification of genetic markers of susceptibility to infection and psychiatric diseases.

Immune dysfunction is implicated in the aetiology of psychiatric, neurodevelopmental, and neurodegenerative conditions, but the issue of causality remains unclear impeding attempts to develop new interventions. Using genomic data on protein and gene expression across blood and brain, we assessed evidence of a potential causal role for 736 immune response-related biomarkers on 7 neuropsychiatric conditions by applying Mendelian randomization (MR) and genetic colocalisation analyses. A systematic three-tier approach, grouping biomarkers based on increasingly stringent criteria, was used to appraise evidence of causality (passing MR sensitivity analyses, colocalisation, False Discovery Rate and Bonferroni thresholds). We provide evidence for a potential causal role of 29 biomarkers for 7 conditions. The identified biomarkers suggest a role of both brain specific and systemic immune response in the aetiology of schizophrenia, Alzheimer's disease, depression, and bipolar disorder. Of the identified biomarkers, 20 are therapeutically tractable, including ACE, TNFRSF17, SERPING1, AGER and CD40, with drugs currently approved or in advanced clinical trials. Based on the largest available selection of plasma immune-response related biomarkers, our study provides insight into possible influential biomarkers for the aetiology of neuropsychiatric conditions. These genetically prioritised biomarkers now require examination to further evaluate causality, their role in the aetiological mechanisms underlying the conditions, and therapeutic potential.