Psychiatric Genomics Consortium Research Articles

Investigating the causal relationship between major depressive disorder and benign prostatic hyperplasia: a bidirectional Mendelian randomisation study.

Evidence from various cohort studies indicate a potential association between depressive disorder and benign prostatic hyperplasia (BPH), yet findings are inconsistent. This study employs bidirectional two-sample Mendelian randomisation (MR) analysis to explore the causal relationship between BPH and major depressive disorder (MDD). Genetic variants strongly associated with MDD were extracted as instrumental variables conducted by the Psychiatric Genomics Consortium (PGC). Two sets of genetic variants associated with BPH were extracted from the recent FinnGen and Medical Research Council-Integrative Epidemiology Unit Consortium of BPH as the discovery and replication stages, respectively. Bidirectional MR analysis employed methods such as inverse variance weighted, MR-Egger, weighted median, maximum likelihood, and weighted mode. The inverse variance weighted method was primarily used to evaluate the causal relationship. MR analysis in both the discovery and replication stages showed a significant causal relationship between MDD and the risk of BPH (discovery stages, odds ratio (OR) = 1.1146, 95% CI 1.0058-1.2353, P = 0.03852; replication stage, OR: 1.0042, 95% CI 1.0019-1.0065, P = 0.0004). No causal relationship was found between BPH and MDD risk in the reverse MR analysis. Our findings highlight a significant association between MDD and an increased risk of BPH development. Further investigation is needed to elucidate the underlying mechanisms linking depression and BPH.

Association between blood mitochondrial DNA copy number and mental disorders: A bidirectional two-sample mendelian randomization study

BackgroundMitochondria is essential for cellular energy production, oxidative stress, and apoptosis. Mitochondrial DNA (mtDNA) encodes essential proteins for mitochondrial function. Although several studies have explored the association between changes in mtDNA copy number (mtDNA-CN) and risk of mental disorders, the results remain debated. This study used a bidirectional two-sample Mendelian randomization (MR) analysis to examine the genetic causality between mtDNA-CN and mental disorders. MethodsGenome-wide association study (GWAS) data for mtDNA-CN were sourced from UK biobank, involving 383,476 European cases. GWAS data for seven mental disorders—attention deficit/hyperactivity disorder, autism spectrum disorder (ASD), schizophrenia, bipolar disorder, major depressive disorder, anxiety, and obsessive-compulsive disorder—were primarily obtained from the Psychiatric Genomics Consortium. Causal associations were assessed using inverse variance weighting, with sensitivity analyses via the weighted median and MR-Egger methods. Reverse MR considered the seven mental disorders as exposures. All analyses were replicated with additional mtDNA-CN GWAS data from 465,809 individuals in the Heart and Ageing Research in Genomic Epidemiology consortium and the UK Biobank. ResultsForward MR observed a 27 % decrease in the risk of ASD per standard deviation increase in genetically determined blood mtDNA-CN (OR = 0.73, 95%CI: 0.58–0.92, p = 0.002), with no causal effects on other disorders. Additionally, reverse MR did not indicate a causal association between any of the mental disorders and mtDNA-CN. Validation analyses corroborated these findings, indicating their robustness. ConclusionsOur study supports the potential causal association between mtDNA-CN and the risk of ASD, suggesting that mtDNA-CN could serve as a promising biomarker for early screening of ASD.

Integrative analysis of transcriptome-wide association study and mRNA expression profile identified risk genes for bipolar disorder

ObjectiveBipolar disorder (BD) is a debilitating neuropsychiatric disorder, which is associated with genetic variation through “vast but mixed” Genome-Wide Association Studies (GWAS). Transcriptome-Wide Association Study (TWAS) is more effective in explaining genetic factors that influence complex diseases and can help identifying risk genes more reliably. So, this study aims to identify potential BD risk genes in pedigrees with TWAS. MethodsWe conducted a TWAS analysis with expression quantitative trait loci (eQTL) analysis on extended BD pedigrees, and the BD genome-wide association study (GWAS) summary data acquired from the Psychiatric Genomics Consortium (PGC). Furthermore, the BD-associated genes identified by TWAS were validated by mRNA expression profiles from the Gene Expression Omnibus (GEO) Datasets (GSE23848 and GSE46416). Functional enrichment and annotation analysis were implemented by RStudio (version 4.2.0). ResultsTWAS identified 362 genes with P value < 0.05, and 18 genes remain significant after Bonferroni correction, such as SEMA3G (PTWAS=1.07 × 10-11), ALOX5AP (PTWAS=3.12 × 10-8), and PLEC (PTWAS=1.27 × 10-7). Further 6 overlapped genes were detected in integrative analysis, such as UQCRB (PTWAS=0.0020, PmRNA=0.0000), TMPRSS9 (PTWAS=0.0405, PmRNA=0.0032), and SNX10 (PTWAS=0.0104, PmRNA=0.0015). Using genes identified by TWAS, Gene Ontology (GO) enrichment analysis identified 40 significant GO terms, such as mitochondrial ATP synthesis coupled electron transport, mitochondrial respiratory, aerobic electron transport chain, oxidative phosphorylation, mitochondrial membrane proteins, and ubiquinone activity. The Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis identified significant 15 pathways for BD, such as Oxidative phosphorylation, endocannabinoids signaling, neurodegeneration, and reactive oxide species. ConclusionsWe found a set of BD-associated genes and pathways, validating the important role of neurodevelopmental abnormalities, inflammatory responses, and mitochondrial dysfunction in the pathology of BD, offering novel information for comprehending the genetic basis of BD.

Association of prescription data with clinical manifestations and polygenic risk scores in patients with bipolar I disorder: An exploratory study

BackgroundWe assessed the association of prescription data with clinical manifestations and polygenic risk scores (PRS) in patients with bipolar I disorder. MethodsWe enrolled 1471 individuals with BID and divided them into several groups according to treatment options and clinical manifestations. BD-PRS of each patient was calculated using the Psychiatric Genomics Consortium data. Data on single nucleotide polymorphisms, clinical manifestations, and prescriptions were extracted from BiGS. ResultsChronicity, suicidality, substance misuse, mixed symptoms, and deterioration of life functioning were significantly more severe in the group that was not prescribed any mood stabilizers (MS). Chronicity, psychotic symptoms, suicidality, and deterioration of life functioning were significantly severe in the group that received two or more antipsychotics (APs). BD-PRS between the group with AP(s) only and that with other treatment options significantly differed. BD-PRS of the group with AP(s) only was significantly lower than that with other treatment options. Our linear regression results showed that high severity of particular clinical aspects, lower BD-PRS, and prescriptions with fewer MSs or more APs were independently associated with poor life functioning. LimitationsThis study had a cross-sectional design, without differentiating the bipolar phase, which could influence our results. ConclusionsPoor life functioning in patients with BID was associated with a high severity of particular clinical aspects, BD-PRS, and prescriptions including fewer MSs or more APs. BD-PRS was significantly higher in the group receiving only AP(s) than that in the groups receiving other drugs.

Bidirectional two-sample Mendelian randomization analysis identifies causal associations between migraine and five psychiatric disorders.

The question of whether a correlation exists between migraine and five psychiatric disorders, including posttraumatic stress disorder (PTSD), major depressive disorder (MDD), anorexia nervosa (AN), bipolar disorder (BIP), and schizophrenia (SCZ), remains a matter of controversy. Hence, this research aims to investigate whether there is a possible association between migraine and five psychiatric disorders. We performed a bidirectional 2-sample Mendelian randomization (MR) analysis to assess the causality between migraine and five psychiatric disorders. Genetic associations of PTSD, MDD, AN, BIP, and SCZ were obtained from the Psychiatric Genomics Consortium (PGC) database and genetic associations of migraine with aura and migraine without aura were obtained from the FinnGen dataset. We used the inverse-variance weighted (IVW), weighted median, weighted mode, MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO), and MR Egger regression methods to evaluate the association of genetically predicted exposure with the risk of outcome. MR demonstrated that MDD was associated with a high risk of migraine without aura (OR = 1.930578, 95% confidence interview (CI): 1.224510, 3.043550, p < 0.05), but BIP was related to a low risk of migraine without aura (OR = 0.758650, 95%CI: 0.639601, 0.899858, p < 0.05). According to the results of reverse MR, migraine with aura was associated with a high risk of BIP (OR = 1.019100, 95%CI: 1.002538, 1.035935, p < 0.05), and migraine without aura was associated with an increased risk of AN (OR = 1.055634, 95%CI: 1.023859, 1.088394, p < 0.05). Our results provide evidence of the potential causal association between migraine and some psychiatric disorders. It may contribute to the prevention of migraine and some psychiatric disorders.

Absence of Causal Relationship Between Levels of Unsaturated Fatty Acids and ADHD: Evidence From Mendelian Randomization Study

Objective: Previous research suggests a potential link between unsaturated fatty acids (UFAs) and ADHD, but the causal relationship remains uncertain. This study aims to investigate the causal association between ADHD and UFAs using Mendelian randomization (MR) analysis. Methods: Summary data from genome-wide association studies were used to estimate the concentration of circulating UFAs, including Monounsaturated Fatty Acids (MUFAs), Polyunsaturated Fatty Acids (PUFAs), Omega-3 PUFAs, Omega-6 PUFAs, Linoleic Acid (LA), and Docosahexaenoic Acid (DHA). Data from the Psychiatric Genomics Consortium, including both childhood and adult ADHD, were respectively used to examine the relationship between genetically predicted UFAs levels and ADHD. Various MR methods, including Inverse-variance weighted (IVW), MR Pleiotropy RESidual Sum and Outlier, MR-Egger, weighted median, and weighted mode, were employed to assess heterogeneity and pleiotropy. Results: The IVW revealed only nominal evidence suggesting a potential causal relationship between genetically predicted PUFAs (OR = 0.92, 95% CI [0.85, 0.99], p = .031), Omega-6 PUFAs (OR = 0.90, 95% CI [0.83, 0.98], p = .020), and LA levels (OR = 0.90, 95% CI [0.82, 0.98], p = .021) with childhood ADHD risk. However, after false discovery rate correction, the p-values for PUFAs, Omega-6 PUFAs, and LA levels all exceeded the threshold for significance. For adult ADHD, we did not find any significant associations between the six circulating UFA levels and adult ADHD. Conclusion: Our findings do not support a causal relationship between UFAs levels and ADHD. This suggests that UFAs supplements may not be effective in improving ADHD symptoms and importantly, it appears that UFAs levels may not have a long-term effect on ADHD.

Defining Suicidal Thought and Behavior Phenotypes for Genetic Studies.

Standardized definitions of suicidality phenotypes, including suicidal ideation (SI), attempt (SA), and death (SD) are a critical step towards improving understanding and comparison of results in suicide research. The complexity of suicidality contributes to heterogeneity in phenotype definitions, impeding evaluation of clinical and genetic risk factors across studies and efforts to combine samples within consortia. Here, we present expert and data-supported recommendations for defining suicidality and control phenotypes to facilitate merging current/legacy samples with definition variability and aid future sample creation. A subgroup of clinician researchers and experts from the Suicide Workgroup of the Psychiatric Genomics Consortium (PGC) reviewed existing PGC definitions for SI, SA, SD, and control groups and generated preliminary consensus guidelines for instrument-derived and international classification of disease (ICD) data. ICD lists were validated in two independent datasets (N = 9,151 and 12,394). Recommendations are provided for evaluated instruments for SA and SI, emphasizing selection of lifetime measures phenotype-specific wording. Recommendations are also provided for defining SI and SD from ICD data. As the SA ICD definition is complex, SA code list recommendations were validated against instrument results with sensitivity (range = 15.4% to 80.6%), specificity (range = 67.6% to 97.4%), and positive predictive values (range = 0.59-0.93) reported. Best-practice guidelines are presented for the use of existing information to define SI/SA/SD in consortia research. These proposed definitions are expected to facilitate more homogeneous data aggregation for genetic and multisite studies. Future research should involve refinement, improved generalizability, and validation in diverse populations.

Druggable genome-wide Mendelian randomization for identifying the role of integrated stress response in therapeutic targets of bipolar disorder

For bipolar disorder (BD), the inconsistency of treatment guidelines and the long phases of pharmacological adjustment remain major challenges. BD is known to be comorbid with many medical and psychiatric conditions and they may share inflammatory and stress-related aetiologies, which could give rise to this association. The integrated stress response (ISR) responds to various stress conditions that lead to alterations in cellular homeostasis. However, as a causative mechanism underlying cognitive deficits and neurodegeneration in a broad range of brain disorders, the impact of ISR on BD is understudied. Mendelian randomization has been widely used to repurpose licensed drugs and discover novel therapeutic targets. Thus, we aimed to identify novel therapeutic targets for BD and analyze their pathophysiological mechanisms, using the summary data-based Mendelian Randomization (SMR) and Bayesian colocalization (COLOC) methods to integrate the summary-level data of the GWAS on BD and the expression quantitative trait locus (eQTL) study in blood. We utilized the GWAS data including 41,917 BD cases and 371,549 controls from the Psychiatric Genomics Consortium and the eQTL data from 31,684 participants of predominantly European ancestry from the eQTLGen consortium. The SMR analysis identified the EIF2B5 gene that was associated with BD due to no linkage but pleiotropy or causality. The COLOC analysis strongly suggested that EIF2B5 and the trait of BD were affected by shared causal variants, and thus were colocalized. Utilizing data in EpiGraphDB we find other putative causal BD genes (EIF2AK4 and GSK3B) to prioritize potential alternative drug targets.

Epigenome-wide association studies identify novel DNA methylation sites associated with PTSD: A meta-analysis of 23 military and civilian cohorts.

The occurrence of post-traumatic stress disorder (PTSD) following a traumatic event is associated with biological differences that can represent the susceptibility to PTSD, the impact of trauma, or the sequelae of PTSD itself. These effects include differences in DNA methylation (DNAm), an important form of epigenetic gene regulation, at multiple CpG loci across the genome. Moreover, these effects can be shared or specific to both central and peripheral tissues. Here, we aim to identify blood DNAm differences associated with PTSD and characterize the underlying biological mechanisms by examining the extent to which they mirror associations across multiple brain regions. As the Psychiatric Genomics Consortium (PGC) PTSD Epigenetics Workgroup, we conducted the largest cross-sectional meta-analysis of epigenome-wide association studies (EWASs) of PTSD to date, involving 5077 participants (2156 PTSD cases and 2921 trauma-exposed controls) from 23 civilian and military studies. PTSD diagnosis assessments were harmonized following the standardized guidelines established by the PGC-PTSD Workgroup. DNAm was assayed from blood using either Illumina HumanMethylation450 or MethylationEPIC (850K) BeadChips. A common QC pipeline was applied. Within each cohort, DNA methylation was regressed on PTSD, sex (if applicable), age, blood cell proportions, and ancestry. An inverse variance-weighted meta-analysis was performed. We conducted replication analyses in tissue from multiple brain regions, neuronal nuclei, and a cellular model of prolonged stress. We identified 11 CpG sites associated with PTSD in the overall meta-analysis (1.44e-09 < p < 5.30e-08), as well as 14 associated in analyses of specific strata (military vs civilian cohort, sex, and ancestry), including CpGs in AHRR and CDC42BPB. Many of these loci exhibit blood-brain correlation in methylation levels and cross-tissue associations with PTSD in multiple brain regions. Methylation at most CpGs correlated with their annotated gene expression levels. This study identifies 11 PTSD-associated CpGs, also leverages data from postmortem brain samples, GWAS, and genome-wide expression data to interpret the biology underlying these associations and prioritize genes whose regulation differs in those with PTSD.

Investigating the impact of poverty on mental illness in the UK Biobank using Mendelian randomization

It is unclear whether poverty and mental illness are causally related. Using UK Biobank and Psychiatric Genomic Consortium data, we examined evidence of causal links between poverty and nine mental illnesses (attention deficit and hyperactivity disorder (ADHD), anorexia nervosa, anxiety disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, post-traumatic stress disorder and schizophrenia). We applied genomic structural equation modelling to derive a poverty common factor from household income, occupational income and social deprivation. Then, using Mendelian randomization, we found evidence that schizophrenia and ADHD causally contribute to poverty, while poverty contributes to major depressive disorder and schizophrenia but decreases the risk of anorexia nervosa. Poverty may also contribute to ADHD, albeit with uncertainty due to unbalanced pleiotropy. The effects of poverty were reduced by approximately 30% when we adjusted for cognitive ability. Further investigations of the bidirectional relationships between poverty and mental illness are warranted, as they may inform efforts to improve mental health for all.

Identification of novel genetic loci associated with major depressive disorder and the hippocampus in a European population using the condFDR method

To identify additional loci associated with depression and the hippocampus (HIP) through genome-wide association study. The depression-related genome-wide association study (GWAS) meta summary data was downloaded from the official website of the Psychiatric Genomics Consortium, which had involved 170 756 cases and 329 443 controls. The left and right hippocampal volume GWAS data sets were downloaded from the UK Biobank, which involved 33 224 participants. The conditional false discovery rate (condFDR) was used to identify novel genetic loci for depression and left and right hippocampal volumes, and a conjunctional false discovery rate (conjFDR) was used to evaluate the enrichment of pleiotropic loci between depression and left and right hippocampal volumes. Respectively, 7, 13, and 12 new loci have been associated with depression, left hippocampal volume and right hippocampal volume, with a significant threshold of condFDR < 0.01. A site of rs1267073 locus was found to be shared by the depression and right hippocampal volume with a threshold of conjFDR < 0.01. Above findings have provided more insights into the genetic mechanisms underlying the volume of hippocampus and the risk for depression. The results may also provide evidence for future clinical trials for treating depression.

Genetic causal relationship between placental weight and autism spectrum disorder: A two-sample Mendelian randomization study

ObjectPrevious research has suggested an association between placental tissue abnormalities and the diagnosis of autism spectrum disorder. This study aims to explore the causal relationship between placental weight and autism spectrum disorder. MethodsThis study employed Mendelian randomization analysis to investigate the potential causal relationship between placental weight and autism spectrum disorder. The study design involved two sample populations, with data for the exposed population sourced from previous studies focusing on PW, and data for the outcome population obtained from the Integrative Psychiatric Research and the Psychiatric Genomics Consortium study. To ensure the robustness of the results, three sensitivity analyses were performed, including heterogeneity testing, pleiotropy testing, and a leave-one-out analysis. The inverse variance weighted method served as the gold standard for the Mendelian randomization analysis. ResultsThe results of the first analysis revealed a significant correlation between an increase in placental weight and an elevated risk of autism spectrum disorder (p = 0.02). Sensitivity analysis detected heterogeneity and outliers. After removing two outlier SNPs in the second round of analysis, the results still supported a genetic causal relationship between placental weight and autism spectrum disorder (p = 0.01). The second-round sensitivity analysis did not reveal any heterogeneity or outliers. ConclusionOur study provides compelling evidence supporting a causal relationship between elevated placental weight and increased risk of autism spectrum disorder. These findings underscore the significance of placental development in the etiology of autism spectrum disorder and propose a potential early predictive indicator for autism spectrum disorder.

Proteome-wide association studies have predicted that the protein abundance of LSM6, GMPPB, ICA1L, and CISD2 is associated with attention-deficit/hyperactivity disorder.

Identification of changes in protein abundance for attention-deficit/hyperactivity disorder (ADHD) is important for potential disease mechanisms and therapeutic study for ADHD. In order to identify candidate proteins that confer risk for ADHD, a proteome-wide association study (PWAS) for ADHD was conducted by integrating two human brain proteome datasets and the ADHD genome-wide association study (GWAS) summary statistics released by the Psychiatric Genomics Consortium (PGC). A total of 11 risk proteins were identified as significant candidates that passed the bonferroni corrected proteome-wide significant (PWS) level. The predicted protein abundance level of LSM6, GMPPB, ICA1L and CISD2 are shown significantly associated with ADHD in both proteome datasets, highlighting their potential role in ADHD pathogenesis. A transcriptome-wide association study (TWAS) of ADHD was also conducted, and 13 genes with predicted expression changes related to ADHD were identified. GMPPB, ICA1L and NAT6 were supported by both TWAS and PWASs analysis. This study uncovers the predicted protein abundance changes that confer risk for ADHD and pinpoints a number of high-confidence protein candidates (e.g. LSM6, GMPPB, ICA1L, CISD2) for further functional exploration studies and drug development targeting these proteins.

Schizophrenia and risk preference: a bidirectional two-sample mendelian randomization study.

Increasing evidence shows that risk preference is associated with schizophrenia. However, the causality and direction of this association are not clear; Therefore, we used Mendelian randomization (MR) to examine the potential bidirectional relationship between risk preference and schizophrenia. Genome-wide association studies (GWAS) summary data on risk preference of 939,908 participants from the UK Biobank and 23andMe were used to identify general risk preference. Data from 320,404 subjects (76,755 cases and 243,649 controls) from The Psychiatric Genomics Consortium were used to identify schizophrenia. The weighted median (WM), the inverse variance weighted (IVW), and the Mendelian randomization-Egger (MR-Egger) methods were used for the MR analysis to estimate the causal effect and detect the directional pleiotropy. The GWAS summary data were respectively from two combined samples, containing 939,908 and 320,404 subjects of European ancestry. Mendelian randomization evidence suggested that risk preference was associated with increased onset of schizophrenia (OR = 2.84, 95CI%: 1.77-4.56, P = 1.58*10 - 5) and that schizophrenia was also associated with raised risk preference (OR = 1.11, 95CI%: 1.07-1.15, P = 7.98*10 - 8). With the use of large-scale GWAS data, robust evidence suggests an interaction between risk preference and schizophrenia. This also indicates that early identification of and intervention for increased risk preference may improve the prognosis of schizophrenia.

Causal association between major depressive disorder and venous thromboembolism: a bidirectional mendelian randomization study.

Major depressive disorder (MDD) and venous thromboembolism (VTE) may be linked in observational studies. However, the causal association remains ambiguous. Therefore, this study investigates the causal associations between them. We performed a two-sample univariable and multivariable bidirectional Mendelian randomization (MR) analysis to evaluate the associations between MDD and VTE. The summary genetic associations of MDD statistics were obtained from the Psychiatric Genomics Consortium and UK Biobank. Information on VTE, deep vein thrombosis (DVT), and pulmonary embolism (PE) were obtained from the FinnGen Biobank. Inverse-variance weighting was used as the main analysis method. Other methods include weighted median, MR-Egger, Simple mode, and Weighted mode. Univariable MR analysis revealed no significant associations between MDD and VTE risk (odds ratio (OR): 0.936, 95% confidence interval (CI): 0.736-1.190, p = 0.590); however, after adjusting the potential relevant polymorphisms of body mass index and education, the multivariable MR analysis showed suggestive evidence of association between them (OR: 1.163, 95% CI: 1.004-1.346, p = 0.044). Univariable MR analysis also revealed significant associations between MDD and PE risk (OR: 1.310, 95% CI: 1.073-1.598, p = 0.008), but the association between them was no longer significant in MVMR analysis (p = 0.072). We found no significant causal effects between MDD and DVT risk in univariable or multivariable MR analyses. There was also no clear evidence showing the causal effects between VTE, PE, or DVT and MDD risk. We provide suggestive genetic evidence to support the causal association between MDD and VTE risk. No causal associations were observed between VTE, PE, or DVT and MDD risk. Further validation of these associations and investigations of potential mechanisms are required.

Evaluating the link between immune characteristics and attention deficit hyperactivity disorder through a bi-directional Mendelian randomization study.

Despite the recognition of attention deficit hyperactivity disorder (ADHD) as a multifaceted neurodevelopmental disorder, its core causes are still ambiguous. The objective of this study was to explore if the traits of circulating immune cells contribute causally to susceptibility to ADHD. By employing a unified GWAS summary data covering 731 immune traits from the GWAS Catalog (accession numbers from GCST0001391 to GCST0002121), our analysis focused on the flow cytometry of lymphocyte clusters, encompassing 3,757 Sardinians, to identify genetically expected immune cells. Furthermore, we obtained summarized GWAS statistics from the Psychiatric Genomics Consortium to evaluate the genetic forecasting of ADHD. The studies employed ADHD2019 (20,183 cases and 35,191 controls from the 2019 GWAS ADHD dataset) and ADHD2022 (38,691 cases and 275,986 controls from the 2022 GWAS ADHD dataset). Through the examination of genome-wide association signals, we identified shared genetic variances between circulating immune cells and ADHD, employing the comprehensive ADHD2022 dataset. We primarily utilized inverse variance weighted (IVW) and weighted median methods in our Mendelian randomization research and sensitivity assessments to evaluate diversity and pleiotropy. After adjusting for false discovery rate (FDR), three distinct immunophenotypes were identified as associated with the risk of ADHD: CD33 in Im MDSC (OR=1.03, CI: 1.01~1.04, P=3.04×10-5, PFDR =0.015), CD8br NKT %T cell (OR=1.08, 95%CI: 1.04~1.12, P=9.33×10-5, PFDR =0.023), and CD8br NKT %lymphocyte (OR=1.08, 95%CI: 1.03~1.12, P=3.59×10-4, PFDR =0.066). Furthermore, ADHD showed no statistical effects on immunophenotypes. It's worth noting that 20 phenotypes exist where ADHD's appearance could diminish 85% of immune cells, including FSC-A in myeloid DC (β= -0.278, 95% CI: 0.616~0.931, P=0.008), CD3 in CD45RA- CD4+ (β= -0.233, 95% CI: 0.654~0.960, P=0.017), CD62L- monocyte AC (β=0.227, 95% CI: 0.038~1.518, P=0.019), CD33 in CD33br HLA DR+ CD14dim (β= -0.331, 95% CI: 0.543~0.950, P=0.020), and CD25 in CD39+ resting Treg (β=0.226, 95% CI: 1.522, P=0.022), and FSC-A in monocytes (β= -0.255, 95% CI: 0.621~0.967, P=0.234), among others. Studies indicate that the immune system's response influences the emergence of ADHD. The findings greatly improve our understanding of the interplay between immune responses and ADHD risk, aiding in the development of treatment strategies from an immunological perspective.

The Contribution of Mosaic Chromosomal Alterations to Schizophrenia

BackgroundMosaic chromosomal alterations are implicated in neuropsychiatric disorders, but the contribution to schizophrenia (SCZ) risk for somatic copy number variations (sCNVs) emerging in early developmental stages has not been fully established. MethodsWe analyzed blood-derived genotype arrays from 9715 patients with SCZ and 28,822 control participants of Chinese descent using a computational tool (MoChA) based on long-range chromosomal information to detect mosaic chromosomal alterations. We focused on probable early developmental sCNVs through stringent filtering. We assessed the burden of sCNVs across varying cell fraction cutoffs, as well as the frequency with which genes were involved in sCNVs. We integrated this data with the PGC (Psychiatric Genomics Consortium) dataset, which comprises 12,834 SCZ cases and 11,648 controls of European descent, and complemented it with genotyping data from postmortem brain tissue of 936 participants (449 cases and 487 controls). ResultsPatients with SCZ had a significantly higher somatic losses detection rate than control participants (1.00% vs. 0.52%; odds ratio = 1.91; 95% CI, 1.47–2.49; two-sided Fisher’s exact test, p = 1.49 × 10−6). Further analysis indicated that the odds ratios escalated proportionately (from 1.91 to 2.78) with the increment in cell fraction cutoffs. Recurrent sCNVs associated with SCZ (odds ratio > 8; Fisher’s exact test, p < .05) were identified, including notable regions at 10q21.1 (ZWINT), 3q26.1 (SLITRK3), 1q31.1 (BRINP3) and 12q21.31-21.32 (MGAT4C and NTS) in the Chinese cohort, and some regions were validated with PGC data. Cross-tissue validation pinpointed somatic losses at loci like 1p35.3-35.2 and 19p13.3-13.2. ConclusionsThe study highlights the significant impact of mosaic chromosomal alterations on SCZ, suggesting their pivotal role in the disorder’s genetic etiology.

The Role of 3' Regulatory Region Flanking Kinectin 1 Gene in Schizophrenia.

Schizophrenia is often associated with volumetric reductions in cortices and expansions in basal ganglia, particularly the putamen. Recent genome-wide association studies have highlighted the significance of variants in the 3' regulatory region adjacent to the kinectin 1 gene (KTN1) in regulating gray matter volume (GMV) of the putamen. This study aimed to comprehensively investigate the involvement of this region in schizophrenia. We analyzed 1136 single-nucleotide polymorphisms (SNPs) covering the entire 3' regulatory region in 4 independent dbGaP samples (4604 schizophrenia patients vs. 4884 healthy subjects) and 3 independent Psychiatric Genomics Consortium samples (107 240 cases vs. 210 203 controls) to identify consistent associations. Additionally, we examined the regulatory effects of schizophrenia-associated alleles on KTN1 mRNA expression in 16 brain areas among 348 subjects, as well as GMVs of 7 subcortical nuclei in 38 258 subjects, and surface areas (SA) and thickness (TH) of the entire cortex and 34 cortical areas in 36 936 subjects. The major alleles (f > 0.5) of 25 variants increased (β > 0) the risk of schizophrenia across 2 to 5 independent samples (8.4 × 10-4 ≤ P ≤ .049). These schizophrenia-associated alleles significantly elevated (β > 0) GMVs of basal ganglia, including the putamen (6.0 × 10-11 ≤ P ≤ 1.1 × 10-4), caudate (8.7 × 10-4 ≤ P ≤ 9.4 × 10-3), pallidum (P = 6.0 × 10-4), and nucleus accumbens (P = 2.7 × 10-5). Moreover, they potentially augmented (β > 0) the SA of posterior cingulate and insular cortices, as well as the TH of frontal (pars triangularis and medial orbitofrontal), parietal (superior, precuneus, and inferior), and temporal (transverse) cortices, but potentially reduced (β < 0) the SA of the whole, frontal (medial orbitofrontal), and temporal (pole, superior, middle, and entorhinal) cortices, as well as the TH of rostral middle frontal and superior frontal cortices (8.9 × 10-4 ≤ P ≤ .050). Our findings identify significant and functionally relevant risk alleles in the 3' regulatory region adjacent to KTN1, implicating their crucial roles in the development of schizophrenia.

Omega-3 fatty acids and major depression: a Mendelian randomization study

Omega-3 fatty acids have been implicated in the aetiology of depressive disorders, though trials supplementing omega-3 to prevent major depressive disorder (MDD) have so far been unsuccessful. Whether this association is causal remains unclear. We used two sample Mendelian randomization (MR) to investigate causality. Genetic variants associated with circulating omega-3 and omega-6 fatty acids in UK Biobank (UKBB, n = 115,078) were selected as exposures. The Psychiatric Genomics Consortium (PGC) genome-wide association studies (GWAS) of MDD (n = 430,775; cases = 116,209; controls = 314,566) and recurrent depression (rMDD, n = 80,933; cases = 17,451; controls = 62,482), were used as outcomes. Multivariable MR (MVMR) models were used to account for biologically correlated lipids, such as high- and low-density cholesterol and triglycerides, and to explore the relative importance of longer-chain omega-3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) using data from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE, n = 8866). Genetic colocalization analyses were used to explore the presence of a shared underlying causal variant between traits. Genetically predicted total omega-3 fatty acids reduced the odds of MDD (ORIVW 0.96 per standard deviation (SD, i.e. 0.22 mmol/l) (95% CIs 0.93–0.98, p = 0.003)). The largest point estimates were observed for eicosapentaenoic acid (EPA), a long-chain omega-3 fatty acid (OREPA 0.92; 95% CI 0.88–0.96; p = 0.0002). The effect of omega-3 fatty acids was robust to MVMR models accounting for biologically correlated lipids. ‘Leave-one-out’ analyses highlighted the FADS gene cluster as a key driver of the effect. Colocalization analyses suggested a shared causal variant using the primary outcome sample, but genomic confounding could not be fully excluded. This study supports a role for omega-3 fatty acids, particularly EPA, in the aetiology of depression, although pleiotropic mechanisms cannot be ruled out. The findings support guidelines highlighting the importance of EPA dose and ratio for MDD and question whether targeted interventions may be superior to universal prevention trials, as modest effect sizes will limit statistical power.

Genetically predicted major depression causally increases the risk of temporomandibular joint disorders.

Observational studies have reported that mental disorders are comorbid with temporomandibular joint disorder (TMD). However, the causal relationship remains uncertain. To clarify the causal relationship between three common mental illnesses and TMD, we conduct this Mendelian Randomization (MR) study. The large-scale genome-wide association studies data of major depression, bipolar disorder and schizophrenia were retrieved from the Psychiatric Genomics Consortium. The summary data of TMD was obtained from the Finn-Gen consortium, including 211,023 subjects of European descent (5,668 cases and 205,355 controls). The main approach utilized was inverse variance weighting (IVW) to evaluate the causal association between the three mental disorders and TMD. Five sensitivity analyses including MR-Egger, Maximum Likelihood, Weighted median, MR. RAPS and MR-PRESSO were used as supplements. We conducted heterogeneity tests and pleiotropic tests to ensure the robustness. As shown by the IVW method, genetically determined major depression was associated with a 1.65-fold risk of TMD (95% CI = 1.10-2.47, p < 0.05). The direction and effect size remained consistent with sensitivity analyses. The odds ratios (ORs) were 1.51 (95% CI = 0.24-9.41, p > 0.05) for MR-Egger, 1.60 (95% CI = 0.98-2.61, p > 0.05) for Weighted median, 1.68 (95% CI = 1.19-2.38, p < 0.05) for Maximum likelihood, 1.56 (95% CI = 1.05-2.33, p < 0.05) for MR. RAPS, and 1.65 (95% CI = 1.10-2.47, p < 0.05) for MR-PRESSO, respectively. No pleiotropy was observed (both P for MR-Egger intercept and Global test >0.05). In addition, the IVW method identified no significant correlation between bipolar disorder, schizophrenia and TMD. Genetic evidence supports a causal relationship between major depression and TMD, instead of bipolar disorder and schizophrenia. These findings emphasize the importance of assessing a patient's depressive status in clinical settings.