Psychiatric Genomics Consortium Research Articles

Depression and the risk of fibromyalgia syndrome: a two-sample Mendelian randomization study

BackgroundFibromyalgia (FM) is a common illness with a wide range of symptoms, mainly manifested by unexplained chronic systemic musculoskeletal pain, sleep disorders and fatigue, sometimes accompanied by cognitive impairment, psychiatric symptoms and autonomic dysfunction. Previous studies have indicated a correlation between depression and the risk of FM; however, it remains uncertain whether this association reflects a causal relationship.MethodsWe evaluated the etiological association between the genetically predicted depression and the risk of developing FM by conducting a two-sample Mendelian Randomization (MR) study. The data on single nucleotide polymorphisms (SNPs) related to depression were obtained from the UK Biobank (UKB) and the Psychiatric Genomics Consortium (PGC) of White British European ancestry, and the data for FM were from the 5th release of the FinnGen study. We adopted the Inverse Variance Weighted (IVW) approach as the principal standard. In order to detect the existence of horizontal pleiotropy and heterogeneity, we adopted the MR-Egger approach as the sensitivity analysis.ResultsIn our MR analysis, 42 depression-related variants were identified as valid instrumental variables (IVs). The IVW approach’s results manifest that there is no etiologic causality between genetically predicted depression and the risk of FM (odds ratio [OR]: 1.673, 95% confidence interval [CI]: 0.852—3.287, P = 0.135). The study did not find any significant heterogeneities or horizontal pleiotropies (P > 0.05).ConclusionsOur results suggest that there is no significant genetic evidence linking depression to an increased risk of FM. However, further research is necessary to investigate the potential relationship and underlying mechanisms between depression and the risk of FM.

Identifying the impact of ARHGAP and MAP gene families on autism spectrum disorders.

The rising incidence of Autism Spectrum Disorder (ASD) has become a major concern, affecting children's psychological well-being and placing a significant strain on healthcare systems. Despite its impact, the etiological mechanisms underpinning ASD remain elusive. This study leveraged dorsolateral prefrontal cortex gene data from 452 individuals of European descent, sourced from the CommonMindConsortium, and examined ASD-related gene expression data from the Gene Expression Omnibus (GEO) database (GSE18123), along with Genome-Wide Association Studies (GWAS) data from the Lundbeck Foundation Integrated Psychiatric Research and Psychiatric Genomics Consortium. Expression quantitative trait loci data were sourced from the GTExv8 database. We employed Transcriptome-Wide Association Studies (TWAS) and Weighted Gene Co-expression Network Analysis (WGCNA) to pinpoint genes within ASD-associated susceptibility gene families (ARHGAP, MAP). Four genes-ARHGAP27, MAPT, ARHGAP19, and MAP1B-were scrutinized, and their biological implications were elucidated through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Protein-Protein Interaction (PPI) analysis and conditional analysis within the TWAS framework helped identify pivotal genes (ARHGAP27, MAPT). A subsequent verification phase involving Mendelian Randomization (MR) evaluated the potential causal links between the identified genes and ASD. The findings revealed no causal association between ARHGAP19, MAP1B, and ASD. In contrast, significant causal relationships were established for ARHGAP27 and MAPT, suggesting that ARHGAP27 may elevate ASD risk as a susceptibility gene, whereas MAPT appears to reduce the risk as a protective gene.

Associations between post-traumatic stress disorder and neurological disorders: A genetic correlation and Mendelian randomization study.

Observational studies have reported a close relationship between post-traumatic stress disorder (PTSD) and neurological disorders, but the existence of a causal link remains uncertain. The aim of this study is to investigate these relationships and potential mediators via Mendelian randomization (MR) analysis. We sourced pooled data for genome-wide association study (GWAS) of PTSD (n = 1,222,882) from the psychiatric genomics consortium. Summary-level data for eight neurological traits were derived from large-scale GWASs. Genetic correlations were computed using linkage disequilibrium (LD) score regression. The inverse variance weighted (IVW) method served as the primary analysis method for MR. We employed a range of sensitivity analysis methods to ensure result robustness. A two-step approach was utilized to ascertain the effects and proportions of mediations. We identified significant genetic associations between PTSD and any dementia, cognitive performance, multiple sclerosis, and migraine. MR analysis revealed a significant association between PTSD and an increased risk of migraine (P = 0.02). This was substantiated by the results of several sensitivity analyses. Notably, the robust association between PTSD and migraine persisted even after adjustment for major depressive disorder and anxiety. Mediation analysis revealed that both alcohol intake frequency and insomnia partially mediated the association between PTSD and migraine. Participants in the MR analysis were of European descent, and verification in other ethnicities was not possible due to data limitations. Our findings indicate a close association between PTSD and migraine. Alcohol intake frequency and insomnia serve as intermediate factors, partially explaining the relationship between PTSD and migraine.

Assessing the validity of a self-reported clinical diagnosis of schizophrenia.

The increasing availability of biobanks is changing the way individuals are identified for genomic research. This study assesses the validity of a self-reported clinical diagnosis of schizophrenia. The study included 1744 clinically-ascertained participants with schizophrenia or schizoaffective disorder depressed-type (SA-D) diagnosed by self-report and/or research interview and 1453 UK Biobank participants with self-reported and/or medical record diagnosis of schizophrenia or SA-D. Unaffected controls included a total of 501,837 participants. We assessed the positive predictive values (PPV) of self-reported clinical diagnoses against research interview and medical record diagnoses. Polygenic risk scores (PRS) and phenotypes relating to demographics, education and employment were compared across diagnostic groups. The variance explained (r2) in schizophrenia PRS for each diagnostic group was compared to samples in the Psychiatric Genomics Consortium (PGC). In the clinically-ascertained participants, the PPV of self-reported schizophrenia for a research diagnosis of schizophrenia was 0.70, which increased to 0.81 after expanding the research diagnosis to schizophrenia or SA-D. In UK Biobank, the PPV of self-reported schizophrenia for a medical record diagnosis was 0.74. Compared to participants who self-reported, participants with a clinically-ascertained research diagnosis were younger and more likely to have a high school qualification. Participants with a medical record diagnosis in UK Biobank were less likely to be employed or have a high school qualification than those who self-reported. Schizophrenia PRS did not differ between participants that had a diagnosis from self-report, research diagnosis or medical records. Polygenic liability r2, for all diagnosis definitions, fell within the distribution of PGC schizophrenia cohorts. Self-reported measures of schizophrenia are justified in genomic research to maximise sample size and reduce the burden of in-depth interviews on participants, although within sample validation of diagnoses is recommended.

Micronutrients and Major Depression: A Mendelian Randomisation Study.

Various vitamins and minerals have been implicated in the aetiology of depression. To estimate the effects of micronutrient exposures on major depressive disorder (MDD) and recurrent depression (rMDD) using Mendelian randomisation (MR), a method using genetic data to estimate causal effects given certain assumptions. We undertook a comprehensive bidirectional MR study of multiple micronutrient exposures on MDD and rMDD. Summary statistics were obtained from the Psychiatric Genomics Consortium (PGC) genome-wide association studies (GWASs) of MDD (cases = 116,209; controls = 314,566) and rMDD (cases = 17,451; controls = 62,482). None of the micronutrients with available genetic instruments were strongly associated with MDD or rMDD using traditional MR methods. However, using methods to increase analytical power by accounting for genetically correlated variants (e.g., cIVW) highlighted five micronutrients with possible causal effects. Point estimates for rMDD were the largest magnitude, with three micronutrients suggestive of a protective effect: serum iron (ORcIVW 0.90 per SD increase; 95% CI 0.85-0.95; p = 0.0003); erythrocyte copper (ORcIVW 0.97; 95% CI 0.95-0.99; p = 0.0004); and 25(OH) vitamin D (ORcIVW 0.81; 0.66-0.99; p = 0.04). Apparent adverse effects of increased selenium on the risk of MDD (ORcIVW 1.03; 95% CI 1.02-1.05; p = 0.0003) and rMDD (ORcIVW 1.08; 95% CI 1.00-1.08; p = 0.06), and serum magnesium on rMDD (ORcIVW 1.21; 1.01-1.44; p = 0.04); were less consistent between methods and may be driven by pleiotropy. Our results suggest weak evidence for a protective effect of iron, copper and 25(OH)D on major depressive outcomes, with mixed evidence for selenium and magnesium. There was no evidence to support a causal effect of any other micronutrients on MDD or rMDD, although genetic instruments were lacking, with insufficient power to detect small but important effects. Future micronutrient supplementation trials should ensure ample statistical power given modest causal effect estimates and consider potential risks of supplementation, as some micronutrient effect estimates suggested potential harm in excess.

Association between vegetable intake and major depressive disorder: results from National Health and Nutrition Examination Survey 2005-2018 and bidirectional two-sample Mendelian randomisation.

This study aimed to evaluate the association between vegetable intake and major depressive disorder (MDD) through cross-sectional analysis and bidirectional two-sample Mendelian randomisation (MR). Cross-sectional analysis was conducted on National Health and Nutrition Examination Survey (NHANES) data from 2005 to 2018 and the corresponding Food Patterns Equivalents Database (FPED). Genome-wide association study (GWAS) data were obtained from UK Biobank and Psychiatric Genomics Consortium (PGC) dataset. Logistic regression analysis was performed after calculating the weights of the samples. Inverse variance weighted, MR-Egger and weighted median methods were used to evaluate the causal effects. A Patient Health Questionnaire-9 score ≥ 10 was considered to indicate MDD. Low vegetable intake was defined as < 2 cups of vegetables per day. 30 861 U.S. adults from NHANES. The GWAS data sample size related to vegetable intake were comprised 448 651 and 435 435 cases respectively, while the GWAS data sample size associated with MDD encompassed 500 199 cases. There were 23 249 (75·33 %) participants with low vegetable intake. The relationship between vegetable intake and MDD was nonlinear. In the multivariate model adjusted for sex, age, education, marital status, poverty income ratio, ethnicity and BMI, participants with low vegetable intake were associated with an increased risk of MDD (OR = 1·53, 95 % CI (1·32, 1·77), P < 0·001). Bidirectional MR showed no causal effects between vegetable intake and MDD. Cross-sectional analysis identified a significant relationship between vegetable intake and MDD, whereas the results from bidirectional two-sample MR did not support a causal role.

Anorexia nervosa polygenic risk, beyond diagnoses: relationship with adolescent disordered eating and behaviors in an Australian female twin population.

It is well established that there is a substantial genetic component to eating disorders (EDs). Polygenic risk scores (PRSs) can be used to quantify cumulative genetic risk for a trait at an individual level. Recent studies suggest PRSs for anorexia nervosa (AN) may also predict risk for other disordered eating behaviors, but no study has examined if PRS for AN can predict disordered eating as a global continuous measure. This study aimed to investigate whether PRS for AN predicted overall levels of disordered eating, or specific lifetime disordered eating behaviors, in an Australian adolescent female population. PRSs were calculated based on summary statistics from the largest Psychiatric Genomics Consortium AN genome-wide association study to date. Analyses were performed using genome-wide complex trait analysis to test the associations between AN PRS and disordered eating global scores, avoidance of eating, objective bulimic episodes, self-induced vomiting, and driven exercise in a sample of Australian adolescent female twins recruited from the Australian Twin Registry (N = 383). After applying the false-discovery rate correction, the AN PRS was significantly associated with all disordered eating outcomes. Findings suggest shared genetic etiology across disordered eating presentations and provide insight into the utility of AN PRS for predicting disordered eating behaviors in the general population. In the future, PRSs for EDs may have clinical utility in early disordered eating risk identification, prevention, and intervention.

Machine learning-driven polygenic risk scores for bipolar disorder, depression, and panic disorder

Polygenic Risk Score (PRS) is a computational tech- nique that uses various genomic data to simultaneously analyze an individ- ual’s genetic risk for particular illnesses or traits. However, the traditional PRS computation has a few weaknesses, including its limited capacity to account for just a portion of trait variance, susceptibility to overfitting, and insufficient ability to discriminate among the larger population. Machine Learning (ML) methods offer a promising alternative to the traditional method by avoiding the problem of overfitting and improving accuracy. This study aims to develop an ML model for improved PRS calculation. We used the summary statistics for three mentals diseases, bipolar, depression, and panic disorder, from the Psychiatric Genomics Consortium (PGC) as a disease reference. We also obtained actual genotype data of individuals from OpenSNP, which includes both case and control samples. This data is used for predicting scores. The suggested approach, called Polygenic Risk Score Neural Network (PRSNN), calculates the PRS using weight vectors that estimate the relevance of each single nucleotide polymorphism (SNP) with a particular phenotype by deep learning model as an alternative to the traditional method. This study aims to develop a machine learning model, called PRSNN, for improved calculation of Polygenic Risk Scores (PRS). The PRSNN method outperforms the conventional method in identifying individuals at risk of mental disease. A novel deep-learning approach, named as PRSNN, is proposed for generating PRSs. The results demonstrate that it outperforms the traditional method of computing PRS for complex diseases. Further upgrades for this tool are required to overcome the current limitations, including lack of validation with external data from different ancestries, which may limit the applicability of the PRSNN method across diverse populations, and the small sample size, which may affect the results.

Polygenic Risk Underlies Youth Psychopathology and Personalized Functional Brain Network Topography.

Functional brain networks are associated with both behavior and genetic factors. To uncover clinically translatable mechanisms of psychopathology, it is critical to define how the spatial organization of these networks relates to genetic risk during development. To determine the relationship between transdiagnostic polygenic risk scores (PRSs), personalized functional brain networks (PFNs), and overall psychopathology (p-factor) during early adolescence. The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing longitudinal cohort study of 21 collection sites across the United States. Here, we conduct a cross-sectional analysis of ABCD baseline data, collected 2017-2018. The ABCD Study ® is a multi-site community-based study. The sample is largely recruited through school systems. Exclusion criteria included severe sensory, intellectual, medical, or neurological issues that interfere with protocol and scanner contraindications. Split-half subsets were used for cross-validation, matched on age, ethnicity, family structure, handedness, parental education, site, sex, and anesthesia exposure. Polygenic risk scores of transdiagnostic genetic factors F1 (PRS-F1) and F2 (PRS-F2) derived from adults in Psychiatric Genomic Consortium and UK Biobanks datasets. PRS-F1 indexes liability for common psychiatric symptoms and disorders related to mood disturbance; PRS-F2 indexes liability for rarer forms of mental illness characterized by mania and psychosis. (1) P-factor derived from bifactor models of youth- and parent-reported mental health assessments. (2) Person-specific functional brain network topography derived from functional magnetic resonance imaging (fMRI) scans. Total participants included 11,873 youths ages 9-10 years old; 5,678 (47.8%) were female, and the mean (SD) age was 9.92 (0.62) years. PFN topography was found to be heritable (N=7,459, 57.06% of vertices h 2 p FDR <0.05, mean h 2 =0.35). PRS-F1 was associated with p-factor (N=5,815, r=0.12, 95% CI [0.09-0.15], p<0.001). Interindividual differences in functional network topography were associated with p-factor (N=7,459, mean r=0.12), PRS-F1 (N=3,982, mean r=0.05), and PRS-F2 (N=3,982, mean r=0.08). Cortical maps of p-factor and PRS-F1 regression coefficients were highly correlated (r=0.7, p=0.003). Polygenic risk for transdiagnostic adulthood psychopathology is associated with both p-factor and heritable PFN topography during early adolescence. These results advance our understanding of the developmental drivers of psychopathology.

Association of air pollutants with psychiatric disorders: a two-sample Mendelian randomization

BackgroundThe link between air pollution and increased risk of psychiatric disorders has been growing in evidence. However, the causal relationship between air pollution and psychiatric disorders remains poorly understood. MethodsSingle-nucleotide polymorphisms associated with air pollutants (including NOx, NO2, PM2.5, PM2.5–10, and PM10) from the UK Biobank were used as instrumental variables. Summary-level data for psychiatric disorders (major depressive disorder, anxiety, bipolar disorder, schizophrenia, post-traumatic stress disorder, attention deficit hyperactivity disorder, autism spectrum disorder, anorexia nervosa, and obsessive-compulsive disorder) were procured from the Psychiatric Genomics Consortium and FinnGen consortium. Two-sample Mendelian randomization (MR) analysis was conducted to analyze the causal associations. ResultsThe MR analysis revealed significant associations between certain air pollutants and specific types of psychiatric disorders. The inverse-variance weighted model of preliminary analysis indicated that genetically predicted NO2 was associated with increased risks of major depressive disorder (odds ratio [OR]: 1.13, 95 % confidence intervals [CI]: 1.00–1.28, P = 0.041), bipolar disorder (OR: 1.26, 95 % CI: 1.00–1.58, P = 0.0497), schizophrenia (OR: 1.57, 95 % CI: 1.23–2.00, P < 0.001), attention deficit hyperactivity disorder (OR: 1.61, 95 % CI: 1.25–2.09, P < 0.001) and autism spectrum disorder (OR: 1.39, 95 % CI: 1.01–1.91, P = 0.044). Genetically predicted PM2.5 showed a positive association with the risk of major depressive disorder (OR: 1.21, 95 % CI: 1.06–1.39, P = 0.006), bipolar disorder (OR: 1.32, 95 % CI: 1.03–1.69, P = 0.030) and attention deficit hyperactivity disorder (OR: 1.57, 95 % CI: 1.16–2.12, P = 0.004). In addition, our results also indicated that NOx (OR: 1.64, 95 % CI: 1.21–2.21, P = 0.0012) and PM10 (OR: 1.70, 95 % CI: 1.23–2.36, P = 0.0014) could increase the risk of attention deficit hyperactivity disorder. ConclusionsThe MR analysis provides evidence for the causality of different air pollutants on specific psychiatric disorders, underscoring the importance of mitigating air pollution to reduce the risk of psychiatric disorders.

Causal effects of pediatric asthma on psychiatric disorders: a bidirectional Mendelian randomization study

Background Previous studies have suggested a potential link between pediatric asthma and psychiatric disorders. However, the causal relationship between pediatric asthma and psychiatric disorders is unclear. Therefore, we used Mendelian randomization to explore causal relationships between pediatric asthma and depression, anxiety disorders, and attention deficit and hyperactivity disorder (ADHD). Methods Genome-wide association studies (GWAS) meta-analyses with the largest possible sample size and independent individuals from European ancestry were selected. The genetic data for depression and anxiety are from FinnGen consortium, while the genetic data for ADHD is from the Psychiatric Genomics Consortium. Inverse variance weighted (IVW) was the main analysis method. The heterogeneity of the instrumental variables (IVs) was assessed using IVW, and the horizontal pleiotropy of the IVs was assessed using MR-Egger. Result The IVW results showed a significant causal relationship between pediatric asthma and depression (OR = 1.08, 95% CI = 1.02–1.15; p = 0.013). However, there is no evidence to suggest a causal relationship between pediatric asthma, anxiety, and ADHD. Reverse MR suggests a significant causal relationship (OR = 1.27, 95% CI [1.14–1.41], p = 9.64E − 06) between ADHD and pediatric asthma using the IVW method. Conclusions Our findings suggest a causal relationship between pediatric asthma and an increased risk of depression. Additionally, we found that ADHD is significantly associated with a higher risk of pediatric asthma.

Relationships between screen time and childhood attention deficit hyperactivity disorder: a Mendelian randomization study.

In previous observational studies and meta-analyses, childhood attention deficit hyperactivity disorder (ADHD) is found to have a significant association with screen time. However, the causal associations between them remain unclear. This study performed a bidirectional two-sample Mendelian randomization (MR) analysis to confirm the causality between screen time and childhood ADHD. Large-scale genome-wide association studies (GWAS) datasets derived from the Psychiatric Genomics Consortium (PGC) and the UK Biobank were used to identify single nucleotide polymorphisms (SNPs) associated with exposure and outcome. Four categories of datasets were selected to represent screen time. The SNPs that are significantly associated with exposure data (P < 5e-08) and have a strong correlation with the exposure in the F-statistic (F > 10) were selected as instrumental variables. This study also used the PhenoScanner V2 database and the LDlink webtool to exclude confounding factors, and the MR-PRESSO method (p < 0.05) was employed to eliminate outliers with bias. Five commonly used methods were employed to assess the interaction and the Inverse Variance Weighted (IVW) method was utilized as the primary basis for determining the MR estimates in this study. The MR analysis revealed that the length of mobile phone use (OR, 1.848; 95% CI, 1.3360-2.5558; p=2.07e-4) and the time spent watching television (OR, 2.104; 95% CI, 1.3958-3.1703; p=3.8e-4) increased the risk of childhood ADHD. Although the causal relationships were exclusively identified through the IVW and weighted median methods, the results retained their statistical significance following correction. In the reverse analysis, no evidence was found to support an effect of childhood ADHD on screen time. The sensitivity analysis conducted on the significant findings revealed no evidence of horizontal pleiotropy or heterogeneity. This study provides some evidence for the causality of screen time and childhood ADHD. Given the limitations of our study, further research is required to comprehensively investigate this relationship.

Evaluating the distinct effects of body mass index at childhood and adulthood on adult major psychiatric disorders.

Children with high body mass index (BMI) are at heightened risk of developing health issues in adulthood, yet the causality between childhood BMI and adult psychiatric disorders remains unclear. Using a life course Mendelian randomization (MR) framework, we investigated the causal effects of childhood and adulthood BMI on adult psychiatric disorders, including Alzheimer's disease, anxiety, major depressive disorder, obsessive-compulsive disorder (OCD), and schizophrenia, using data from the Psychiatric Genomics Consortium and FinnGen study. Childhood BMI was significantly associated with an increased risk of schizophrenia, while adulthood BMI was associated with a decreased risk of OCD and schizophrenia. Multivariable MR analyses indicated a direct causal effect of childhood BMI on schizophrenia, independent of adulthood BMI and lifestyle factors. No evidence of causal associations was found between childhood BMI and other psychiatric outcomes. The sensitivity analyses yielded broadly consistent findings. These findings highlight the critical importance of early-life interventions to mitigate the long-term consequences of childhood adiposity.

Obesity influencing circulating levels of nutrients: Evidence from Mendelian randomization study.

Observational studies have established that obesity is associated with nutritional deficiencies, but the exact causality remains uncertain. Thus, this Mendelian randomization (MR) study aimed to identify the causal associations between obesity and circulating levels of nutrients. Single-nucleotide polymorphisms associated with obesity (body mass index and waist-hip ratio), were extracted from a genome-wide association study of 694,649 European ancestry. Summary-level data for minerals (copper, selenium, zinc, calcium, magnesium, and potassium), and vitamins (folate, vitamins A, C, E, B6, and B12), albumin were obtained from the publicly available integrative epidemiology unit OpenGWAS database psychiatric genomics consortium. Inverse-variance weighted method several sensitivity analyses were conducted. Genetically predicted higher body mass index significantly decreased circulating levels of magnesium (β = -0.07, 95% confidence interval [CI]: -0.10 to -0.03, P = 1.47 × 10-4), folate (β = -0.07, 95% CI: -0.10 to -0.04, P = 5.61 × 10-5), vitamin A (β = -0.11, 95% CI: -0.14 to -0.07, P = 3.10 × 10-9), vitamin E (β = -0.10, 95% CI: -0.13 to -0.06, P = 1.84 × 10-8), albumin (β = -0.15, 95% CI: -0.17 to -0.12, P = 9.89 × 10-28); whereas genetically predicted higher waist-hip ratio decreased circulating levels of magnesium (β = -0.07, 95% CI: -0.11 to -0.02, P = 1.87 × 10-3), folate (β = -0.07, 95% CI: -0.11 to -0.03, P = 9.87 × 10-4), vitamin C (β = -0.08, 95% CI: -0.12 to -0.04, P = 2.40 × 10-4), albumin (β = -0.08, 95% CI: -0.11 to -0.04, P = 3.72 × 10-5). The study supports a causal effect of obesity on lower circulating levels of nutrients. Our findings highlight the necessity of adjuvant nutrients in obesity management.

Investigating the Shared Genetic Architecture Between Psychiatric Disorders and Executive Function

BackgroundEvidence for widespread comorbidity of executive dysfunctions with psychiatric disorders suggests common mechanisms underlying their pathophysiology. However, the shared genetic architectures between psychiatric disorders and executive function (EF) remain poorly understood. MethodsLeveraging large genome-wide association study datasets of European ancestry on bipolar disorder (N = 353,899), major depressive disorder (N = 674,452), and schizophrenia (N = 130,644) from the Psychiatric Genomics Consortium and iPSYCH and a common factor of EF (N = 427,037) from UK Biobank, we systematically investigated the shared genomic architectures between psychiatric disorders and EF with a set of statistical genetic, functional genomic, and gene-level analyses. ResultsOur study demonstrated substantial genetic overlaps and significant genetic correlations between psychiatric disorders and EF. EF showed an estimated 95.9%, 98.1%, and 99.2% of phenotype-influencing variants, as well as 50, 23, and 130 genomic loci shared with bipolar disorder, major depressive disorder, and schizophrenia, respectively. Single nucleotide polymorphism heritability enrichment suggests that the genetic architecture of psychiatric disorders and EF involves the brain’s frontal cortex and prefrontal glutamatergic neurons 1 and 2. Functional genomic analysis of shared variants identified 12 functional regulatory variants that regulate gene expression by affecting the binding affinities of 5 transcription factors. In addition, functional characterization analyses of shared genes revealed potential common biological mechanisms related to synaptic processes and fetal brain development. ConclusionsOur findings provide evidence for extensive shared genetic architectures between psychiatric disorders and EF and have valuable implications for future mechanistic investigations and drug development efforts.

Associations of Polygenic Risk for Depression, Traditional Chinese Medicine Constitution, and Depression: A Population-Based Study in Taiwan.

To comprehensively investigate the risk factors associated with depression, traditional Chinese medicine constitution (TCMC) has been found to be related to depression. However, the underlying mechanism remains unclear. This study examined the association between the concept of unbalanced TCMCs and major depressive disorder (MDD), investigated the overlapping polygenic risks between unbalanced TCMC and MDD, and performed a mediation test to establish potential pathways. In total, 11,030 individuals were recruited from the Taiwan Biobank, and the polygenic risk score (PRS) for MDD for each participant was calculated using the data from the Psychiatric Genomics Consortium. Unbalanced TCMC were classified as yang-deficiency, yin-deficiency, and stasis. The MDD PRS was associated with yang-deficiency odds ratio [OR] per standard deviation increase in standardized (PRS = 1.07, p = 0.0080), yin-deficiency (OR = 1.07, p = 0.0030), and stasis constitution (OR = 1.06, p = 0.0331). Yang-deficiency (OR = 2.07, p < 0.0001) and stasis constitutions (OR = 1.65, p = 0.0015) were associated with an increased risk of MDD. A higher number of unbalanced constitutions was associated with MDD (p < 0.0001). The effect of MDD PRS on MDD was partly mediated by yang-deficiency (10.21%) and stasis (8.41%) constitutions. This study provides evidence for the shared polygenic risk mechanism underlying depression and TCMC and the potential mediating role of TCMC in the polygenic liability for MDD.

Risk of Parkinson's disease and depression severity in different populations: A two-sample Mendelian randomization analysis.

Depression is widely recognized as a common non-motor symptom of Parkinson's disease (PD). Across different studies, the reported prevalence of depression in PD varies widely, ranging from 2.7% to 90%, but it is unclear whether this association is due to genetic or acquired factors. Whether there is a causal relationship remains unknown. The aim of this study was to use a two-sample Mendelian randomization (MR) approach to investigate the causal effect of PD on depression. Analyses were conducted separately for individuals of European and East Asian ancestry using publicly available summary data from genome-wide association studies. Depression was divided into two categories: ever depressed for a whole week and major depressive disorder (MDD). PD data were used as the exposure and were obtained from the International Parkinson's Disease Genomics Consortium and the BioBank Japan PheWeb, while depression data were used as the outcome and were obtained from the ntegrative Epidemiology Unit (IEU) Open GWAS Project(A public GWAS database）and the Psychiatric Genomics Consortium. The influence of PD on depression was assessed using inverse variance weighted (IVW), weighted median, MR-Egger, and weighted mode methods. Heterogeneity and pleiotropy were tested, and the results were validated using FinnGen GWAS data from version R9. In individuals of European ancestry, there was a causal relationship between PD and ever depressed for a whole week (IVW method, odds ratio [OR]=0.990; 95% CI, 0.984-0.996; p=.002), but no causal relationship was observed between PD and MDD (IVW method, OR=0.974; 95% CI, 0.942-1.009; p=.141). In individuals of East Asian ancestry, no causal relationship was observed between PD and ever depressed for a whole week (IVW method, OR=1.001; 95% CI, 0.829-1.209; p=.990) and between PD and MDD (IVW method, OR=1.017; 95% CI, 0.982-1.052; p=.342). The results of the three additional analysis methods were similar to those of the IVW method, and there was no heterogeneity according to Cochran's Q-test. There was no evidence of pleiotropy based on MR-Egger intercept test and MR-PRESSO. The FinnGen validation dataset supported these findings. The results are stable and reliable. The observed increase in depression among PD patients could potentially be attributed to modifiable acquired factors. Consequently, there is an urgent need to strengthen the management of PD patients in order to prevent the development of depression in the future.

Association between psychiatric disorders and irritable bowel syndrome: A bidirectional Mendelian randomization study

ObjectivePrevious studies have suggested that irritable bowel syndrome (IBS) is strongly associated with psychiatric disorders. However, it is unclear whether this association is causal, concomitant, or accidental. Thus, we performed Mendelian randomization (MR) analysis to evaluate the causal effects of several psychiatric disorders on IBS. MethodsSummary data of genome-wide association studies (GWASs) were obtained mainly from the Psychiatric Genomics Consortium (PGC) on individuals of European ancestry and from a recent GWAS on IBS. We used three MR methods, the inverse-variance weighting (IVW), weighted median (WM), and MR-Egger regression (MR-Egger). In addition, two other indicators, namely, the MR-IVW Cochran's Q statistic and MR-Egger intercept, were used to assess heterogeneity and detect directional horizontal pleiotropy, respectively. ResultsHeritability was high for bipolar disorder (81.18 %, 95 % CI = 73.18–148.18 %), schizophrenia (33.88 %, 95 % CI = 33.57–38.19 %), and panic disorder (30.66 %, 95 % CI = 20.74–40.58 %). For other disorders, there was a low liability-scale SNP heritability for major depressive disorder (MDD) (0.67 %, 95 % CI = 0.61–0.73 %), anxiety disorder (7.63 %, 95 % CI = 1.67–13.59 %), PTSD (0.96 %, 95 % CI = 0.12–1.8 %), and IBS (2.44 %, 95 % CI = 2.13–2.75 %). We also observed that schizophrenia had a significant causal effect on IBS according to MR-IVW. Notably, the individual causal estimates of genetic instruments for MDD and schizophrenia were heterogeneous, but no pleiotropic effects were observed. ConclusionsOur analyses revealed the causal effects of MDD and schizophrenia on IBS, a matter that has been subject to debate for decades, and also showed that IBS had causal effects on MDD.

TWAS-GKF: a novel method for causal gene identification in transcriptome-wide association studies with knockoff inference.

Transcriptome-wide association study (TWAS) aims to identify trait-associated genes regulated by significant variants to explore the underlying biological mechanisms at a tissue-specific level. Despite the advancement of current TWAS methods to cover diverse traits, traditional approaches still face two main challenges: (i) the lack of methods that can guarantee finite-sample false discovery rate (FDR) control in identifying trait-associated genes; and (ii) the requirement for individual-level data, which is often inaccessible. To address this challenge, we propose a powerful knockoff inference method termed TWAS-GKF to identify candidate trait-associated genes with a guaranteed finite-sample FDR control. TWAS-GKF introduces the main idea of Ghostknockoff inference to generate knockoff variables using only summary statistics instead of individual-level data. In extensive studies, we demonstrate that TWAS-GKF successfully controls the finite-sample FDR under a pre-specified FDR level across all settings. We further apply TWAS-GKF to identify genes in brain cerebellum tissue from the Genotype-Tissue Expression (GTEx) v8 project associated with schizophrenia (SCZ) from the Psychiatric Genomics Consortium (PGC), and genes in liver tissue related to low-density lipoprotein cholesterol (LDL-C) from the UK Biobank, respectively. The results reveal that the majority of the identified genes are validated by Open Targets Validation Platform. The R package TWAS.GKF is publicly available at https://github.com/AnqiWang2021/TWAS.GKF.

A comprehensive assessment of the association between common drugs and psychiatric disorders using Mendelian randomization and real-world pharmacovigilance database

Medications prescribed for chronic diseases can lead to short-term neuropsychiatric symptoms, but their long-term effects on brain structures and psychiatric conditions remain unclear. We comprehensively analyzed the FDA Adverse Event Reporting System database and conducted drug target Mendelian Randomization (MR) studies on six categories of common drugs, 477 brain imaging-derived phenotypes (IDPs) and eight psychiatric disorders. Genetic instruments were extracted from expression quantitative trait loci (eQTLs) in blood, brain, and other target tissues, protein quantitative trait loci (pQTLs) in blood, and genome-wide association studies (GWAS) of hemoglobin and cholesterol. Summary statistics for brain IDPs, psychiatric disorders, and gut microbiome were obtained from the BIG40, Psychiatric Genomics Consortium, and MiBioGen. A two-step MR and mediation analysis were employed to screen possible mediators of drug-IDP effects from 119 gut microbiota genera and identify their mediation proportions. Among 19 drug classes, six drugs were found to be associated with higher risks of psychiatric adverse events, while 11 drugs were associated with higher risks of gastrointestinal adverse events in the FAERS analysis. We identified ten drug-psychiatric disorder associations, 202 drug-IDP associations, 16 drug-microbiota associations, and four drug-microbiota-IDP causal links. For example, PPARG activation mediated HbA1c reduction caused a higher risk of bipolar disorder (BD) II. Genetically proxied GLP-1R agonists were significantly associated with an increase in the volume of the CA3-head of the right hippocampus and the area of the left precuneus cortex, both of which have been shown to correlate with cognition in previous studies. Common drugs may affect brain structure and risk of psychiatric disorder. Oral medications in particular may exert some of these effects by influencing gut microbiota. This study calls for greater attention to be paid to the neuropsychiatric adverse effects of drugs and encourages drug repurposing. National Natural Science Foundation of China (grant No. 82330035, 82130043, 82172685, and 82001223), National Natural Science Foundation of Hunan Province (grant No. 2021SK1010), and the Science Foundation for Distinguished Young Scholars of Changsha (grant No. kq2209006).