Revealing the complex genetic architecture of obsessive-compulsive disorder using meta-analysis

Transcriptomic Evaluation of a Stress Vulnerability Network Using Single-Cell RNA Sequencing in Mouse Prefrontal Cortex

BACKGROUND

Increased vulnerability to stress is a major risk factor for several mood disorders, including major depressive disorder. Although cellular and molecular mechanisms associated with depressive behaviors following stress have been identified, little is known about the mechanisms that confer the vulnerability that predisposes individuals to future damage from chronic stress.

METHODS

We used multisite in vivo neurophysiology in freely behaving male and female C57BL/6 mice (n = 12) to measure electrical brain network activity previously identified as indicating a latent stress vulnerability brain state. We combined this neurophysiological approach with single-cell RNA sequencing of the prefrontal cortex to identify distinct transcriptomic differences between groups of mice with inherent high and low stress vulnerability.

RESULTS

We identified hundreds of differentially expressed genes (padjusted < .05) across 5 major cell types in animals with high and low stress vulnerability brain network activity. This unique analysis revealed that GABAergic (gamma-aminobutyric acidergic) neuron gene expression contributed most to the network activity of the stress vulnerability brain state. Upregulation of mitochondrial and metabolic pathways also distinguished high and low vulnerability brain states, especially in inhibitory neurons. Importantly, genes that were differentially regulated with vulnerability network activity significantly overlapped (above chance) with those identified by genome-wide association studies as having single nucleotide polymorphisms significantly associated with depression as well as genes more highly expressed in postmortem prefrontal cortex of patients with major depressive disorder.

CONCLUSIONS

This is the first study to identify cell types and genes involved in a latent stress vulnerability state in the brain.

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

BACKGROUND

Mitochondria 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.

METHODS

Genome-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.

RESULTS

Forward 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.

CONCLUSIONS

Our 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.

Application of polygenic scores to a deeply phenotyped sample enriched for substance use disorders reveals extensive pleiotropy with psychiatric and somatic traits

Co-occurring psychiatric, medical, and substance use disorders (SUDs) are common, but the complex pathways leading to such comorbidities are poorly understood. A greater understanding of genetic influences on this phenomenon could inform precision medicine efforts. We used the Yale-Penn dataset, a cross-sectional sample enriched for individuals with SUDs, to examine pleiotropic effects of genetic liability for psychiatric and somatic traits. Participants completed an in-depth interview that provides information on demographics, environment, medical illnesses, and psychiatric and SUDs. Polygenic scores (PGS) for psychiatric disorders and somatic traits were calculated in European-ancestry (EUR; n = 5691) participants and, when discovery datasets were available, for African-ancestry (AFR; n = 4918) participants. Phenome-wide association studies (PheWAS) were then conducted. In AFR participants, the only PGS with significant associations was bipolar disorder (BD), all of which were with substance use phenotypes. In EUR participants, PGS for major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), schizophrenia (SCZ), body mass index (BMI), coronary artery disease (CAD), and type 2 diabetes (T2D) all showed significant associations, the majority of which were with phenotypes in the substance use categories. For instance, PGSMDD was associated with over 200 phenotypes, 15 of which were depression-related (e.g., depression criterion count), 55 of which were other psychiatric phenotypes, and 126 of which were substance use phenotypes; and PGSBMI was associated with 138 phenotypes, 105 of which were substance related. Genetic liability for psychiatric and somatic traits is associated with numerous phenotypes across multiple categories, indicative of the broad genetic liability of these traits.

A burden of rare copy number variants in obsessive-compulsive disorder

Current genetic research on obsessive-compulsive disorder (OCD) supports contributions to risk specifically from common single nucleotide variants (SNVs), along with rare coding SNVs and small insertion-deletions (indels). The contribution to OCD risk from rare copy number variants (CNVs), however, has not been formally assessed at a similar scale. Here we describe an analysis of rare CNVs called from genotype array data in 2248 deeply phenotyped OCD cases and 3608 unaffected controls from Sweden and Norway. Cases carry an elevated burden of CNVs ≥30 kb in size (OR = 1.12, P = 1.77 × 10-3). The excess rate of these CNVs in cases versus controls was around 0.07 (95% CI 0.02-0.11, P = 2.58 × 10-3). This signal was largely driven by CNVs overlapping protein-coding regions (OR = 1.19, P = 3.08 × 10-4), particularly deletions impacting loss-of-function intolerant genes (pLI >0.995, OR = 4.12, P = 2.54 × 10-5). We did not identify any specific locus where CNV burden was associated with OCD case status at genome-wide significance, but we noted non-random recurrence of CNV deletions in cases (permutation P = 2.60 × 10-3). In cases where sufficient clinical data were available (n = 1612) we found that carriers of neurodevelopmental duplications were more likely to have comorbid autism (P < 0.001), and that carriers of deletions overlapping neurodevelopmental genes had lower treatment response (P = 0.02). The results demonstrate a contribution of rare CNVs to OCD risk, and suggest that studies of rare coding variation in OCD would have increased power to identify risk genes if this class of variation were incorporated into formal tests.

Genomic structural equation modeling reveals latent phenotypes in the human cortex with distinct genetic architecture

Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to map the genetic architecture of cortical surface area (SA) and cortical thickness (CT) for 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance, which we are denoting genetically informed brain networks (GIBNs). Genomic SEM can fit a multivariate GWAS from summary statistics for each of the GIBNs, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA identified 6 GIBNs and CT identified 4 GIBNs, although sensitivity analyses indicated that other structures were plausible. The multivariate GWASs of the GIBNs identified 74 genome-wide significant (GWS) loci (p < 5 × 10-8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of GIBN GWASs found that SA-derived GIBNs had a positive genetic correlation with bipolar disorder (BPD), and cannabis use disorder, indicating genetic predisposition to a larger SA in the specific GIBN is associated with greater genetic risk of these disorders. A negative genetic correlation was observed between attention deficit hyperactivity disorder (ADHD) and major depressive disorder (MDD). CT GIBNs displayed a negative genetic correlation with alcohol dependence. Even though we observed model instability in our application of genomic SEM to high-dimensional data, jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across neuroimaging phenotypes offers new insights into the genetics of cortical structure and relationships to psychopathology.

Experience-dependent grooming microstructure alterations and gastrointestinal dysfunction in the SAPAP3 knockout mouse model of compulsive behaviour

BACKGROUND

Compulsive- and anxiety-like behaviour can be efficiently modelled in SAPAP3 knockout (KO) mice, a preclinical model of relevance to obsessive-compulsive disorder (OCD). Although there is emerging evidence in the clinical literature of gastrointestinal dysfunction in OCD, no previous studies have investigated gut function in preclinical models of relevance to OCD. Similarly, the effects of voluntary exercise (EX) or environmental enrichment (EE) have not yet been explored in this context.

METHOD

We comprehensively phenotyped the SAPAP3 KO mouse model, including the assessment of grooming microstructure, anxiety- and depressive-like behaviour, and gastrointestinal function. Mice were exposed to either standard housing (SH), exercise (EX, provided by giving mice access to running wheels), or environmental enrichment (EE) for 4 weeks to investigate the effects of enriched housing conditions in this animal model relevant to OCD.

FINDINGS

Our study is the first to assess grooming microstructure, perseverative locomotor activity, and gastrointestinal function in SAPAP3 KO mice. We are also the first to report a sexually dimorphic effect of grooming in young-adult SAPAP3 KO mice; along with changes to grooming patterning and indicators of gut dysfunction, which occurred in the absence of gut dysbiosis in this model. Overall, we found no beneficial effects of voluntary exercise or environmental enrichment interventions in this mouse model; and unexpectedly, we revealed a deleterious effect of wheel-running exercise on grooming behaviour. We suspect that the detrimental effects of experimental housing in our study may be indicative of off-target effects of stress-a conclusion that warrants further investigation into the effects of chronic stress in this preclinical model of compulsive behaviour.

Mendelian Randomization Study Supports Genetic Liability to Obsessive-Compulsive Disorder Associated With the Risk of Alzheimer's Disease

BACKGROUND

Observational studies have suggested that obsessive-compulsive disorder (OCD) may be associated with Alzheimer's disease (AD). However, whether OCD is a causal risk factor for AD remains unclear. This study aimed to assess the causal effect of OCD on AD risk by performing a two-sample Mendelian randomization (MR) analysis.

METHODS

Genome-wide association summary statistics were obtained for OCD, comprising 2688 cases and 7037 controls, as well as for AD, including 21,982 cases and 41,944 controls from Kunkle et al.'s study, and 39,918 cases and 358,140 controls from Wightman et al.'s study. On the basis of two diverse thresholds, OCD-associated genetic variants were screened as instrumental variables (IVs) for subsequent MR analyses. Inverse variance weighed was the primary MR method. MR-Egger, weighted median, and weighted mode were used as supplementary MR methods. Various sensitivity tests assessed the reliability of MR results.

RESULTS

On the basis of strict IV selecting thresholds, inverse-variance weighted (IVW) identified significant causal associations between genetic liability to OCD and increased risk of AD in two different sources ((i) Kunkle et al.: odds ratio [OR] = 1.070, 95% confidence interval [CI]: 1.015-1.127, p = 0.012; (ii) Wightman et al. 0.012; (iii) Wightman et al.: OR = 1.051, 95% CI: 1.014-1.090, p = 0.007). Three other supplementary MR methods yielded similar results to IVWs (OR > 1). Furthermore, all results were replicated in MR analyses based on lenient IV selecting thresholds. The sensitivity tests indicated that MR results were stable and not affected by significant horizontal pleiotropy.

CONCLUSIONS

This comprehensive MR study suggests that genetic liability to OCD is a causal risk factor for AD. Early intervention in patients with OCD may be beneficial in preventing future AD progression.

Neuroticism and posttraumatic stress disorder: A Mendelian randomization analysis

OBJECTIVE

Epidemiological studies revealed an unestablished association between neuroticism and posttraumatic stress disorder (PTSD) and we conducted mendelian randomization (MR) analyses to examine whether neuroticism clusters of worry, depressed affect, and sensitivity to environmental stress and adversity (SESA) were involved in the development of PTSD.

METHOD

We obtained data on three neuroticism clusters, PTSD, and nine other psychiatric disorders from genome-wide association studies summary statistics and employed univariable, multivariable, and mediation MR analyses to explore causal associations among them.

RESULTS

Neuroticism clusters were linked with PTSD (depressed affect (odds ratio [OR]: 2.94 [95% confidence interval: 2.21-3.92]); SESA (2.69 [1.95-3.71]; worry (1.81 [1.37-2.99])). Neuroticism clusters were also associated with psychiatric disorders, with the depressed effect on panic disorder (PD) (2.60 [1.14-5.91]), SESA on anorexia nervosa (AN) (2.77 [1.95-3.94]) and schizophrenia (2.55 [1.99-3.25]), worry on major depressive disorder (MDD) (2.58 [2.19-3.05]). In multivariable MR, only the SESA-PTSD association remained (2.60 [2.096, 3.107]) while worry-PTSD and depressed affect-PTSD associations attenuated to nonsignificance. Mediation MR analyses suggested that PD mediated 3.76% of the effect of depressed effect on PTSD and AN mediated 10.33% of the effect of SESA on PTSD.

CONCLUSION

Delving deeper into neuroticism clusters, we comprehensively understand the role of neuroticism in PTSD.

Nine Neuroimaging-AI Endophenotypes Unravel Disease Heterogeneity and Partial Overlap across Four Brain Disorders: A Dimensional Neuroanatomical Representation

Disease heterogeneity poses a significant challenge for precision diagnostics. Recent work leveraging artificial intelligence has offered promise to dissect this heterogeneity by identifying complex intermediate brain phenotypes, herein called dimensional neuroimaging endophenotypes (DNEs). We advance the argument that these DNEs capture the degree of expression of respective neuroanatomical patterns measured, offering a dimensional neuroanatomical representation for studying disease heterogeneity and similarities of neurologic and neuropsychiatric diseases. We investigate the presence of nine such DNEs derived from independent yet harmonized studies on Alzheimer's disease (AD1-2)1, autism spectrum disorder (ASD1-3)2, late-life depression (LLD1-2)3, and schizophrenia (SCZ1-2)4, in the general population of 39,178 participants in the UK Biobank study. Phenome-wide associations revealed prominent associations between the nine DNEs and phenotypes related to the brain and other human organ systems. This phenotypic landscape aligns with the SNP-phenotype genome-wide associations, revealing 31 genomic loci associated with the nine DNEs (Bonferroni corrected P-value < 5×10-8/9). The DNEs exhibited significant genetic correlations, colocalization, and causal relationships with multiple human organ systems and chronic diseases. A causal effect (odds ratio=1.25 [1.11, 1.40], P-value=8.72×10-4) was established from AD2, characterized by focal medial temporal lobe atrophy, to AD. The nine DNEs, along with their polygenic risk scores, significantly enhanced the predictive accuracy for 14 systemic disease categories, particularly for conditions related to mental health and the central nervous system, as well as mortality outcomes. These findings underscore the potential of the nine DNEs to capture the expression of disease-related brain phenotypes in individuals of the general population and to relate such measures with genetics, lifestyle factors, and chronic diseases. All results are publicly available at https://labs-laboratory.com/medicine/.

Assortative mating across nine psychiatric disorders is consistent and persistent over cultures and generations

Emerging evidence has shown that assortative mating (AM) is a key factor that shapes the landscape of complex human traits. It can increase the overall prevalence of disorders, influence occurrences of comorbidities, and bias estimation of genetic architectures. However, there is lack of large-scale studies to examine the cultural differences and the generational trends of AM for psychiatric disorders. Here, using national registry datasets, we conduct the largest scale of AM analyses on nine psychiatric disorders, with up to 1.4 million mated cases and 6 million matched controls. We performed meta-analyses on AM estimates from Taiwan, Denmark, and Sweden, to examine the potential impact of cultural differences. Generational changes for people born after 1930s were investigated as well. We found that AM of psychiatric disorders are consistent across nations and persistent over generations, with a small proportion of disorders showing generational changes of AM. Our results provide additional insight into the mechanisms of AM across psychiatric disorders and have evident implications on the estimation of the genetic architectures of psychiatric disorders.

Phenome-Wide Association Study of Latent Autoimmune Diabetes from a Southern Mexican Population Implicates rs7305229 with Plasmatic Anti-Glutamic Acid Decarboxylase Autoantibody (GADA) Levels

Latent autoimmune diabetes in adults (LADA) is characterized by the presence of glutamate decarboxylase autoantibodies (GADA). LADA has intermediate features between type 1 diabetes and type 2 diabetes. In addition, genetic risk factors for both types of diabetes are present in LADA. Nonetheless, evidence about the genetics of LADA in non-European populations is scarce. This study aims to perform a genome-wide association study with a phenome-wide association study of LADA in a southeastern Mexican population. We included 59 patients diagnosed with LADA from a previous study and 3121 individuals without diabetes from the MxGDAR/ENCODAT database. We utilized the GENESIS package in R to perform the genome-wide association study (GWAS) of LADA and PLINK for the phenome-wide association study (PheWAS) of LADA features. Nine polymorphisms reach the nominal association level (1 × 10-5) in the GWAS. The PheWAS showed that rs7305229 is genome-wide and associated with serum GADA levels in our sample (p = 1.84 × 10-8). rs7305229 is located downstream of the FAIM2 gene; previous reports associate FAIM2 variants with childhood obesity, body mass index, body adiposity measures, lymphocyte CD8+ activity, and anti-thyroid peroxidase antibodies. Our findings reveal that rs7305229 affects the GADA levels in patients with LADA from southeastern Mexico. More studies are needed to determine if this risk genotype exists in other populations with LADA.

A population-based study of familial coaggregation and shared genetic etiology of psychiatric and gastrointestinal disorders

BACKGROUND

It has been proposed that having a psychiatric disorder could increase the risk of developing a gastrointestinal disorder, and vice versa. The role of familial coaggregation and shared genetic loading between psychiatric and gastrointestinal disorders remains unclear.

METHODS

This study used the Taiwan National Health Insurance Research Database; 4,504,612 individuals born 1970-1999 with parental information, 51,664 same-sex twins, and 3,322,959 persons with full-sibling(s) were enrolled. Genotyping was available for 106,796 unrelated participants from the Taiwan Biobank. A logistic regression model was used to examine the associations of individual history, affected relatives, and polygenic risk scores (PRS) for schizophrenia (SCZ), bipolar disorder (BPD), major depressive disorder (MDD), and obsessive-compulsive disorder (OCD), with the risk of peptic ulcer disease (PUD), gastroesophageal reflux disease (GERD), irritable bowel syndrome (IBS), and inflammatory bowel disease (IBD), and vice versa.

RESULTS

Here we show that parental psychiatric disorders are associated with gastrointestinal disorders. Full-siblings of psychiatric cases have an increased risk of gastrointestinal disorders except for SCZ/BPD and IBD; the magnitude of coaggregation is higher in same-sex twins than in full-siblings. The results of bidirectional analyses mostly remain unchanged. PRS for SCZ, MDD, and OCD are associated with IBS, PUD/GERD/IBS/IBD, and PUD/GERD/IBS, respectively. PRS for PUD, GERD, IBS, and IBD are associated with MDD, BPD/MDD, SCZ/BPD/MDD, and BPD, respectively.

CONCLUSIONS

There is familial coaggregation and shared genetic etiology between psychiatric and gastrointestinal comorbidity. Individuals with psychiatric disorder-affected relatives or with higher genetic risk for psychiatric disorders should be monitored for gastrointestinal disorders, and vice versa.

Blood metabolites, neurocognition and psychiatric disorders: a Mendelian randomization analysis to investigate causal pathways

BACKGROUND

Neurocognitive dysfunction is observationally associated with the risk of psychiatric disorders. Blood metabolites, which are readily accessible, may become highly promising biomarkers for brain disorders. However, the causal role of blood metabolites in neurocognitive function, and the biological pathways underlying their association with psychiatric disorders remain unclear.

METHODS

To explore their putative causalities, we conducted bidirectional two-sample Mendelian randomization (MR) using genetic variants associated with 317 human blood metabolites (nmax = 215,551), g-Factor (an integrated index of multiple neurocognitive tests with nmax = 332,050), and 10 different psychiatric disorders (n = 9,725 to 807,553) from the large-scale genome-wide association studies of European ancestry. Mediation analysis was used to assess the potential causal pathway among the candidate metabolite, neurocognitive trait and corresponding psychiatric disorder.

RESULTS

MR evidence indicated that genetically predicted acetylornithine was positively associated with g-Factor (0.035 standard deviation units increase in g-Factor per one standard deviation increase in acetylornithine level; 95% confidence interval, 0.021 to 0.049; P = 1.15 × 10-6). Genetically predicted butyrylcarnitine was negatively associated with g-Factor (0.028 standard deviation units decrease in g-Factor per one standard deviation increase in genetically proxied butyrylcarnitine; 95% confidence interval, -0.041 to -0.015; P = 1.31 × 10-5). There was no evidence of associations between genetically proxied g-Factor and metabolites. Furthermore, the mediation analysis via two-step MR revealed that the causal pathway from acetylornithine to bipolar disorder was partly mediated by g-Factor, with a mediated proportion of 37.1%. Besides, g-Factor mediated the causal pathway from butyrylcarnitine to schizophrenia, with a mediated proportion of 37.5%. Other neurocognitive traits from different sources provided consistent findings.

CONCLUSION

Our results provide genetic evidence that acetylornithine protects against bipolar disorder through neurocognitive abilities, while butyrylcarnitine has an adverse effect on schizophrenia through neurocognition. These findings may provide insight into interventions at the metabolic level for risk of neurocognitive and related disorders.

Causal association between mitochondrial function and psychiatric disorders: Insights from a bidirectional two-sample Mendelian randomization study

BACKGROUND

Previous observational studies have suggested that there appears to be a close association between mitochondrial function and psychiatric disorders, but whether a causal role exists remains unclear.

METHODS

We extracted genetic instruments for 67 mitochondrial-related proteins and 10 psychiatric disorders from publicly available genome-wide association studies, and employed five distinct MR methods and false discovery rate correction to detect causal associations between them. Additionally, we conducted a series of sensitivity tests and additional model analysis to ensure the robustness of the results. For potential causal associations, we further performed reverse MR analyses to assess the impact of reverse causality.

RESULTS

We identified a total of 2 significant causal associations and 24 suggestive causal associations. Specifically, Phenylalanine-tRNA ligase was found to increase the risk of Alzheimer's disease, while Mitochondrial glutamate carrier 2 decreased the risk of autism spectrum disorder. Furthermore, there was no evidence of significant pleiotropy, heterogeneity, or reverse causality.

LIMITATIONS

This study was limited to individuals of European ancestry, and the conclusions drawn are merely revelatory.

CONCLUSION

This study provides novel insights into the relationship between mitochondria and psychiatric disorders, as well as the pathogenesis and treatment strategies for psychiatric disorders.

Multitrait analysis to decipher the intertwined genetic architecture of neuroanatomical phenotypes and psychiatric disorders

BACKGROUND

There is increasing evidence of shared genetic factors between psychiatric disorders and brain magnetic resonance imaging (MRI) phenotypes. However, deciphering the joint genetic architecture of these outcomes has proven challenging, and new approaches are needed to infer potential genetic structure underlying those phenotypes. Multivariate analyses is arising as a meaningful approach to reveal links between MRI phenotypes and psychiatric disorders missed by univariate approaches.

METHODS

We first conducted univariate and multivariate genome-wide association studies (GWAS) for nine MRI-derived brain volume phenotypes in 20K UK Biobank participants. We next performed various complementary enrichment analyses to assess whether and how univariate and multitrait approaches can distinguish disorder-associated and non-disorder-associated variants from six psychiatric disorders: bipolarity, attention-deficit/hyperactivity disorder (ADHD), autism, schizophrenia, obsessive-compulsive disorder, and major depressive disorder. Finally, we conducted a clustering analysis of top associated variants based on their MRI multitrait association using an optimized k-medoids approach.

RESULTS

Univariate MRI GWAS displayed only negligible genetic correlation with psychiatric disorders, while multitrait GWAS identified multiple new associations and showed significant enrichment for variants related to both ADHD and schizophrenia. Clustering analyses further detected two clusters displaying not only enrichment for association with ADHD and schizophrenia, but also consistent direction of effects. Functional annotation analyses of those clusters pointed to multiple potential mechanisms, suggesting in particular a role of neurotrophins pathways on both MRI and schizophrenia.

CONCLUSIONS

Our results show that multitrait association signature can be used to infer genetically-driven latent MRI variables associated with psychiatric disorders, opening paths for future biomarker development.

Cognitive processing speed and accuracy are intrinsically different in genetic architecture and brain phenotypes

Since the birth of cognitive science, researchers have used reaction time and accuracy to measure cognitive ability. Although recognition of these two measures is often based on empirical observations, the underlying consensus is that most cognitive behaviors may be along two fundamental dimensions: cognitive processing speed (CPS) and cognitive processing accuracy (CPA). In this study, we used genomic-wide association studies (GWAS) data from 14 cognitive traits to show the presence of those two factors and revealed the specific neurobiological basis underlying them. We identified that CPS and CPA had distinct brain phenotypes (e.g. white matter microstructure), neurobiological bases (e.g. postsynaptic membrane), and developmental periods (i.e. late infancy). Moreover, those two factors showed differential associations with other health-related traits such as screen exposure and sleep status, and a significant causal relationship with psychiatric disorders such as major depressive disorder and schizophrenia. Utilizing an independent cohort from the Adolescent Brain Cognitive Development (ABCD) study, we also uncovered the distinct contributions of those two factors on the cognitive development of young adolescents. These findings reveal two fundamental factors underlying various cognitive abilities, elucidate the distinct brain structural fingerprint and genetic architecture of CPS and CPA, and hint at the complex interrelationship between cognitive ability, lifestyle, and mental health.

Distinct cognitive and functional connectivity features from healthy cohorts can identify clinical obsessive-compulsive disorder

Improving diagnostic accuracy of obsessive-compulsive disorder (OCD) using models of brain imaging data is a key goal of the field, but this objective is challenging due to the limited size and phenotypic depth of clinical datasets. Leveraging the phenotypic diversity in large non-clinical datasets such as the UK Biobank (UKBB), offers a potential solution to this problem. Nevertheless, it remains unclear whether classification models trained on non-clinical populations will generalise to individuals with clinical OCD. This question is also relevant for the conceptualisation of OCD; specifically, whether the symptomology of OCD exists on a continuum from normal to pathological. Here, we examined a recently published "meta-matching" model trained on functional connectivity data from five large normative datasets (N=45,507) to predict cognitive, health and demographic variables. Specifically, we tested whether this model could classify OCD status in three independent clinical datasets (N=345). We found that the model could identify out-of-sample OCD individuals. Notably, the most predictive functional connectivity features mapped onto known cortico-striatal abnormalities in OCD and correlated with genetic brain expression maps previously implicated in the disorder. Further, the meta-matching model relied upon estimates of cognitive functions, such as cognitive flexibility and inhibition, to successfully predict OCD. These findings suggest that variability in non-clinical brain and behavioural features can discriminate clinical OCD status. These results support a dimensional and transdiagnostic conceptualisation of the brain and behavioural basis of OCD, with implications for research approaches and treatment targets.

Direct targets of MEF2C are enriched for genes associated with schizophrenia and cognitive function and are involved in neuron development and mitochondrial function

Myocyte Enhancer Factor 2C (MEF2C) is a transcription factor that plays a crucial role in neurogenesis and synapse development. Genetic studies have identified MEF2C as a gene that influences cognition and risk for neuropsychiatric disorders, including autism spectrum disorder (ASD) and schizophrenia (SCZ). Here, we investigated the involvement of MEF2C in these phenotypes using human-derived neural stem cells (NSCs) and glutamatergic induced neurons (iNs), which represented early and late neurodevelopmental stages. For these cellular models, MEF2C function had previously been disrupted, either by direct or indirect mutation, and gene expression assayed using RNA-seq. We integrated these RNA-seq data with MEF2C ChIP-seq data to identify dysregulated direct target genes of MEF2C in the NSCs and iNs models. Several MEF2C direct target gene-sets were enriched for SNP-based heritability for intelligence, educational attainment and SCZ, as well as being enriched for genes containing rare de novo mutations reported in ASD and/or developmental disorders. These gene-sets are enriched in both excitatory and inhibitory neurons in the prenatal and adult brain and are involved in a wide range of biological processes including neuron generation, differentiation and development, as well as mitochondrial function and energy production. We observed a trans expression quantitative trait locus (eQTL) effect of a single SNP at MEF2C (rs6893807, which is associated with IQ) on the expression of a target gene, BNIP3L. BNIP3L is a prioritized risk gene from the largest genome-wide association study of SCZ and has a function in mitophagy in mitochondria. Overall, our analysis reveals that either direct or indirect disruption of MEF2C dysregulates sets of genes that contain multiple alleles associated with SCZ risk and cognitive function and implicates neuron development and mitochondrial function in the etiology of these phenotypes.

Executive Function and Impulsivity Predict Distinct Genetic Variance in Internalizing Problems, Externalizing Problems, Thought Disorders, and Compulsive Disorders: A Genomic Structural Equation Modeling Study

Individual differences in self-control predict many health and life outcomes. Building on twin literature, we used genomic structural equation modeling to test the hypothesis that genetic influences on executive function and impulsivity predict independent variance in mental health and other outcomes. The impulsivity factor (comprising urgency, lack of premeditation, and other facets) was only modestly genetically correlated with low executive function (rg =.13). Controlling for impulsivity, low executive function was genetically associated with increased internalizing (βg =.15), externalizing (βg =.13), thought disorders (βg =.38), compulsive disorders (βg =.22), and chronotype (βg =.11). Controlling for executive function, impulsivity was positively genetically associated with internalizing (βg =.36), externalizing (βg =.55), body mass index (βg =.26), and insomnia (βg =.35), and negatively genetically associated with compulsive disorders (βg = -.17). Executive function and impulsivity were both genetically correlated with general cognitive ability and educational attainment. This work suggests that executive function and impulsivity are genetically separable and show independent associations with mental health.

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.

Genome-Wide Association Study of Obsessive-Compulsive Symptoms including 33,943 individuals from the general population

While 1-2% of individuals meet the criteria for a clinical diagnosis of obsessive-compulsive disorder (OCD), many more (~13-38%) experience subclinical obsessive-compulsive symptoms (OCS) during their life. To characterize the genetic underpinnings of OCS and its genetic relationship to OCD, we conducted the largest genome-wide association study (GWAS) meta-analysis of parent- or self-reported OCS to date (N = 33,943 with complete phenotypic and genome-wide data), combining the results from seven large-scale population-based cohorts from Sweden, the Netherlands, England, and Canada (including six twin cohorts and one cohort of unrelated individuals). We found no genome-wide significant associations at the single-nucleotide polymorphism (SNP) or gene-level, but a polygenic risk score (PRS) based on the OCD GWAS previously published by the Psychiatric Genetics Consortium (PGC-OCD) was significantly associated with OCS (Pfixed = 3.06 × 10-5). Also, one curated gene set (Mootha Gluconeogenesis) reached Bonferroni-corrected significance (Ngenes = 28, Beta = 0.79, SE = 0.16, Pbon = 0.008). Expression of genes in this set is high at sites of insulin mediated glucose disposal. Dysregulated insulin signaling in the etiology of OCS has been suggested by a previous study describing a genetic overlap of OCS with insulin signaling-related traits in children and adolescents. We report a SNP heritability of 4.1% (P = 0.0044) in the meta-analyzed GWAS, and heritability estimates based on the twin cohorts of 33-43%. Genetic correlation analysis showed that OCS were most strongly associated with OCD (rG = 0.72, p = 0.0007) among all tested psychiatric disorders (N = 11). Of all 97 tested phenotypes, 24 showed a significant genetic correlation with OCS, and 66 traits showed concordant directions of effect with OCS and OCD. OCS have a significant polygenic contribution and share genetic risk with diagnosed OCD, supporting the hypothesis that OCD represents the extreme end of widely distributed OCS in the population.

Shared Genetic Architecture Between Schizophrenia and Anorexia Nervosa: A Cross-trait Genome-Wide Analysis

BACKGROUND AND HYPOTHESIS

Schizophrenia (SCZ) and anorexia nervosa (AN) are 2 severe and highly heterogeneous disorders showing substantial familial co-aggregation. Genetic factors play a significant role in both disorders, but the shared genetic etiology between them is yet to be investigated.

STUDY DESIGN

Using summary statistics from recent large genome-wide association studies on SCZ (Ncases = 53 386) and AN (Ncases = 16 992), a 2-sample Mendelian randomization analysis was conducted to explore the causal relationship between SCZ and AN. MiXeR was employed to quantify their polygenic overlap. A conditional/conjunctional false discovery rate (condFDR/conjFDR) framework was adopted to identify loci jointly associated with both disorders. Functional annotation and enrichment analyses were performed on the shared loci.

STUDY RESULTS

We observed a cross-trait genetic enrichment, a suggestive bidirectional causal relationship, and a considerable polygenic overlap (Dice coefficient = 62.2%) between SCZ and AN. The proportion of variants with concordant effect directions among all shared variants was 69.9%. Leveraging overlapping genetic associations, we identified 6 novel loci for AN and 33 novel loci for SCZ at condFDR <0.01. At conjFDR <0.05, we identified 10 loci jointly associated with both disorders, implicating multiple genes highly expressed in the cerebellum and pituitary and involved in synapse organization. Particularly, high expression of the shared genes was observed in the hippocampus in adolescence and orbitofrontal cortex during infancy.

CONCLUSIONS

This study provides novel insights into the relationship between SCZ and AN by revealing a shared genetic component and offers a window into their complex etiology.

Dynamic convergence of autism disorder risk genes across neurodevelopment

Over a hundred risk genes underlie risk for autism spectrum disorder (ASD) but the extent to which they converge on shared downstream targets to increase ASD risk is unknown. To test the hypothesis that cellular context impacts the nature of convergence, here we apply a pooled CRISPR approach to target 29 ASD loss-of-function genes in human induced pluripotent stem cell (hiPSC)-derived neural progenitor cells, glutamatergic neurons, and GABAergic neurons. Two distinct approaches (gene-level and network-level analyses) demonstrate that convergence is greatest in mature glutamatergic neurons. Convergent effects are dynamic, varying in strength, composition, and biological role between cell types, increasing with functional similarity of the ASD genes examined, and driven by cell-type-specific gene co-expression patterns. Stratification of ASD genes yield targeted drug predictions capable of reversing gene-specific convergent signatures in human cells and ASD-related behaviors in zebrafish. Altogether, convergent networks downstream of ASD risk genes represent novel points of individualized therapeutic intervention.

Exploring the genetic and socioeconomic interplay between ADHD and anxiety disorders using Mendelian randomization

Background

ADHD and anxiety disorders often co-occur, sharing symptoms and dysfunctions, yet the underlying mechanisms remain elusive.

Methods

To explore the shared and distinct genetic variations between ADHD and anxiety disorders, we applied Mendelian randomization (MR) analysis to ADHD, anxiety disorders, and three socioeconomic factors: income, educational attainment (EA), and intelligence. MR analysis utilized genome-wide association study summary datasets (anxiety disorder: 7,016 cases and 14,745 controls; ADHD: 38,691 cases and 275,986 controls; EA: 766,345 participants; intelligence: 146,808 participants; household income: 392,422 participants), with inverse-variance weighting as the primary method.

Results

Our MR analysis revealed no discernible genetic-level causal effect between ADHD and anxiety disorders (p > 0.77). Additionally, the independent variables for ADHD (25 SNPs) and anxiety disorders (18 SNPs) did not overlap, highlighting the genetic distinction between the two conditions. Higher income (p < 0.002) and EA (p < 0.005) were found to serve as protective factors for both ADHD and anxiety disorders. Genetic predisposition to higher income (86 SNPs) and EA (457 SNPs) were identified as a potential common protective factors for both conditions. Lastly, genetic predisposition to higher intelligence was found to potentially guard against ADHD (p < 0.001) but not against anxiety disorders (p > 0.55).

Conclusion

Our findings indicate that the shared symptoms observed between ADHD and anxiety disorders are more likely influenced by genetic predispositions related to socioeconomic factors rather than by the genetic predispositions specific to the disorders themselves.

The genetics of trichotillomania and excoriation disorder: A systematic review

BACKGROUND

Trichotillomania (TTM) and excoriation disorder (ED) are impairing obsessive-compulsive related disorders that are common in the general population and for which there are no clear first-line medications, highlighting the need to better understand the underlying biology of these disorders to inform treatments. Given the importance of genetics in obsessive-compulsive disorder (OCD), evaluating genetic factors underlying TTM and ED may advance knowledge about the pathophysiology of these body-focused repetitive behaviors.

AIM

In this systematic review, we summarize the available evidence on the genetics of TTM and ED and highlight gaps in the field warranting further research.

METHOD

We systematically searched Embase, PsycInfo, PubMed, Medline, Scopus, and Web of Science for original studies in genetic epidemiology (family or twin studies) and molecular genetics (candidate gene and genome-wide) published up to June 2023.

RESULTS

Of the 3536 records identified, 109 studies were included in this review. These studies indicated that genetic factors play an important role in the development of TTM and ED, some of which may be shared across the OCD spectrum, but there are no known high-confidence specific genetic risk factors for either TTM or ED.

CONCLUSIONS

Our review underscores the need for additional genome-wide research conducted on the genetics of TTM and ED, for instance, genome-wide association and whole-genome/whole-exome DNA sequencing studies. Recent advances in genomics have led to the discovery of risk genes in several psychiatric disorders, including related conditions such as OCD, but to date, TTM and ED have remained understudied.

Gene × environment effects and mediation involving adverse childhood events, mood and anxiety disorders, and substance dependence

Adverse childhood events (ACEs) contribute to the development of mood and anxiety disorders and substance dependence. However, the extent to which these effects are direct or indirect and whether genetic risk moderates them is unclear. We examined associations among ACEs, mood/anxiety disorders and substance dependence in 12,668 individuals (44.9% female, 42.5% African American/Black, 42.1% European American/white). Using latent variables for each phenotype, we modelled direct and indirect associations of ACEs with substance dependence, mediated by mood/anxiety disorders (the forward or 'self-medication' model) and of ACEs with mood/anxiety disorders, mediated by substance dependence (the reverse or 'substance-induced' model). In a subsample, we tested polygenic scores for the substance dependence and mood/anxiety disorder factors as moderators in the mediation models. Although there were significant indirect paths in both directions, mediation by mood/anxiety disorders (the forward model) was greater than that by substance dependence (the reverse model). Greater genetic risk for substance use disorders was associated with a weaker direct association between ACEs and substance dependence in both ancestry groups (reflecting gene × environment interactions) and a weaker indirect association in European-ancestry individuals (reflecting moderated mediation). We found greater evidence that substance dependence reflects self-medication of mood/anxiety disorders than that mood/anxiety disorders are substance induced. Among individuals at higher genetic risk for substance dependence, ACEs were less associated with that outcome. Following exposure to ACEs, multiple pathways appear to underlie the associations between mood/anxiety disorders and substance dependence. Specification of these pathways could inform individually targeted prevention and treatment approaches.

Seven psychiatric traits and the risk of increased carotid intima-media thickness: a Mendelian randomization study

Background

Numerous observational studies have suggested an association between psychiatric traits and carotid intima-media thickness (cIMT). However, whether these associations have a causal relationship remains unknown, largely due to issues of reverse causality and potential confounders. This study aims to elucidate the potential causal role of psychiatric traits in the risk of arterial injury as measured by cIMT.

Methods

We utilized instrumental variables for attention deficit/hyperactivity disorder (ADHD, n = 226,534), bipolar disorder (n = 353,899), major depressive disorder (n = 142,646), post-traumatic stress disorder (n = 174,494), obsessive-compulsive disorder (n = 9,725), autism spectrum disorder (n = 173,773), and anxiety disease (n = 17,310), derived from the largest corresponding genome-wide association studies (GWAS). Summary statistics for cIMT associations were obtained from a meta-analysis combining GWAS data from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortia (n = 71,128) and the UK Biobank study (n = 45,185). The inverse-variance weighted method served as the primary analytical tool, supplemented by additional statistical methods in the secondary analyses to corroborate the findings. Adjustments were made according to the Bonferroni correction threshold.

Results

The Mendelian randomization analyses indicated a suggestive causal link between genetically predicted ADHD and cIMT (beta = 0.05; 95% confidence interval, 0.01-0.09; p = 0.018). Sensitivity analyses largely concurred with this finding. However, no significant associations were found between other psychiatric traits and cIMT.

Conclusions

This study provides insights into the risk effect of ADHD on cIMT, suggesting that arteriopathy and potential associated complications should be considered during the treatment and monitoring of patients with ADHD.

Examining the Genetic Links between Clusters of Immune-mediated Diseases and Psychiatric Disorders

Importance

Autoimmune and autoinflammatory diseases have been linked to psychiatric disorders in the phenotypic and genetic literature. However, a comprehensive model that investigates the association between a broad range of psychiatric disorders and immune-mediated disease in a multivariate framework is lacking.

Objective

This study aims to establish a factor structure based on the genetic correlations of immune-mediated diseases and investigate their genetic relationships with clusters of psychiatric disorders.

Design Setting and Participants

We utilized Genomic Structural Equation Modeling (Genomic SEM) to establish a factor structure of 11 immune-mediated diseases. Genetic correlations between these immune factors were examined with five established factors across 13 psychiatric disorders representing compulsive, schizophrenia/bipolar, neurodevelopmental, internalizing, and substance use disorders. We included GWAS summary statistics of individuals of European ancestry with sample sizes from 1,223 cases for Addison's disease to 170,756 cases for major depressive disorder.

Main Outcomes and Measures

Genetic correlations between psychiatric and immune-mediated disease factors and traits to determine genetic overlap. We develop and validate a new heterogeneity metric, Q Factor , that quantifies the degree to which factor correlations are driven by more specific pairwise associations. We also estimate residual genetic correlations between pairs of psychiatric disorders and immune-mediated diseases.

Results

A four-factor model of immune-mediated diseases fit the data well and described a continuum from autoimmune to autoinflammatory diseases. The four factors reflected autoimmune, celiac, mixed pattern, and autoinflammatory diseases. Analyses revealed seven significant factor correlations between the immune and psychiatric factors, including autoimmune and mixed pattern diseases with the internalizing and substance use factors, and autoinflammatory diseases with the compulsive, schizophrenia/bipolar, and internalizing factors. Additionally, we find evidence of divergence in associations within factors as indicated by Q Factor . This is further supported by 14 significant residual genetic correlations between individual psychiatric disorders and immune-mediated diseases.

Conclusion and Relevance

Our results revealed genetic links between clusters of immune-mediated diseases and psychiatric disorders. Current analyses indicate that previously described relationships between specific psychiatric disorders and immune-mediated diseases often capture broader pathways of risk sharing indexed by our genomic factors, yet are more specific than a general association across all psychiatric disorders and immune-mediated diseases.

Polygenic scores for psychiatric traits mediate the impact of multigenerational history for depression on offspring psychopathology

A family history of depression is a well-documented risk factor for offspring psychopathology. However, the genetic mechanisms underlying the intergenerational transmission of depression remain unclear. We used genetic, family history, and diagnostic data from 11,875 9-10 year-old children from the Adolescent Brain Cognitive Development study. We estimated and investigated the children's polygenic scores (PGSs) for 30 distinct traits and their association with a family history of depression (including grandparents and parents) and the children's overall psychopathology through logistic regression analyses. We assessed the role of polygenic risk for psychiatric disorders in mediating the transmission of depression from one generation to the next. Among 11,875 multi-ancestry children, 8,111 participants had matching phenotypic and genotypic data (3,832 female [47.2%]; mean (SD) age, 9.5 (0.5) years), including 6,151 [71.4%] of European ancestry). Greater PGSs for depression (estimate = 0.129, 95% CI = 0.070-0.187) and bipolar disorder (estimate = 0.109, 95% CI = 0.051-0.168) were significantly associated with higher family history of depression (Bonferroni-corrected P < .05). Depression PGS was the only PGS that significantly associated with both family risk and offspring's psychopathology, and robustly mediated the impact of family history of depression on several youth psychopathologies including anxiety disorders, suicidal ideation, and any psychiatric disorder (proportions mediated 1.39%-5.87% of the total effect on psychopathology; FDR-corrected P < .05). These findings suggest that increased polygenic risk for depression partially mediates the associations between family risk for depression and offspring psychopathology, showing a genetic basis for intergenerational transmission of depression. Future approaches that combine assessments of family risk with polygenic profiles may offer a more accurate method for identifying children at elevated risk.

Neurobiology of Obsessive-Compulsive Disorder from Genes to Circuits: Insights from Animal Models

Obsessive-compulsive disorder (OCD) is a chronic, severe psychiatric disorder that has been ranked by the World Health Organization as one of the leading causes of illness-related disability, and first-line interventions are limited in efficacy and have side-effect issues. However, the exact pathophysiology underlying this complex, heterogeneous disorder remains unknown. This scenario is now rapidly changing due to the advancement of powerful technologies that can be used to verify the function of the specific gene and dissect the neural circuits underlying the neurobiology of OCD in rodents. Genetic and circuit-specific manipulation in rodents has provided important insights into the neurobiology of OCD by identifying the molecular, cellular, and circuit events that induce OCD-like behaviors. This review will highlight recent progress specifically toward classic genetic animal models and advanced neural circuit findings, which provide theoretical evidence for targeted intervention on specific molecular, cellular, and neural circuit events.

Dynamic stress- and inflammatory-based regulation of psychiatric risk loci in human neurons

The prenatal environment can alter neurodevelopmental and clinical trajectories, markedly increasing risk for psychiatric disorders in childhood and adolescence. To understand if and how fetal exposures to stress and inflammation exacerbate manifestation of genetic risk for complex brain disorders, we report a large-scale context-dependent massively parallel reporter assay (MPRA) in human neurons designed to catalogue genotype x environment (GxE) interactions. Across 240 genome-wide association study (GWAS) loci linked to ten brain traits/disorders, the impact of hydrocortisone, interleukin 6, and interferon alpha on transcriptional activity is empirically evaluated in human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons. Of ~3,500 candidate regulatory risk elements (CREs), 11% of variants are active at baseline, whereas cue-specific CRE regulatory activity range from a high of 23% (hydrocortisone) to a low of 6% (IL-6). Cue-specific regulatory activity is driven, at least in part, by differences in transcription factor binding activity, the gene targets of which show unique enrichments for brain disorders as well as co-morbid metabolic and immune syndromes. The dynamic nature of genetic regulation informs the influence of environmental factors, reveals a mechanism underlying pleiotropy and variable penetrance, and identifies specific risk variants that confer greater disorder susceptibility after exposure to stress or inflammation. Understanding neurodevelopmental GxE interactions will inform mental health trajectories and uncover novel targets for therapeutic intervention.

Genetic Correlates of Treatment-Resistant Depression: Insights from Polygenic Scores Across Cognitive, Temperamental, and Sleep Traits in the All of US cohort

Background

Treatment-resistant depression (TRD) is a major challenge in mental health, affecting a significant number of patients and leading to considerable economic and social burdens. The etiological factors contributing to TRD are complex and not fully understood.

Objective

To investigate the genetic factors associated with TRD using polygenic scores (PGS) across various traits, and to explore their potential role in the etiology of TRD using large-scale genomic data from the All of Us Research Program (AoU).

Methods

Data from 292,663 participants in the AoU were analyzed using a case-cohort design. Treatment resistant depression (TRD), treatment responsive Major Depressive Disorder (trMDD), and all others who have no formal diagnosis of Major Depressive Disorder (non-MDD) were identified through diagnostic codes and prescription patterns. Polygenic scores (PGS) for 61 unique traits from seven domains were used and logistic regressions were conducted to assess associations between PGS and TRD. Finally, Cox proportional hazard models were used to explore the predictive value of PGS for progression rate from the diagnostic event of Major Depressive Disorder (MDD) to TRD.

Results

In the discovery set (104128 non-MDD, 16640 trMDD, and 4177 TRD), 44 of 61 selected PGS were found to be significantly associated with MDD, regardless of treatment responsiveness. Eleven of them were found to have stronger associations with TRD than with trMDD, encompassing PGS from domains in education, cognition, personality, sleep, and temperament. Genetic predisposition for insomnia and specific neuroticism traits were associated with increased TRD risk (OR range from 1.05 to 1.15), while higher education and intelligence scores were protective (ORs 0.88 and 0.91, respectively). These associations are consistent across two other independent sets within AoU (n = 104,388 and 63,330). Among 28,964 individuals tracked over time, 3,854 developed TRD within an average of 944 days (95% CI: 883 ~ 992 days) after MDD diagnosis. All eleven previously identified and replicated PGS were found to be modulating the conversion rate from MDD to TRD. Thus, those having higher education PGS would experiencing slower conversion rates than those who have lower education PGS with hazard ratios in 0.79 (80th versus 20th percentile, 95% CI: 0.74 ~ 0.85). Those who had higher insomnia PGS experience faster conversion rates than those who had lower insomnia PGS, with hazard ratios in 1.21 (80th versus 20th percentile, 95% CI: 1.13 ~ 1.30).

Conclusions

Our results indicate that genetic predisposition related to neuroticism, cognitive function, and sleep patterns play a significant role in the development of TRD. These findings underscore the importance of considering genetic and psychosocial factors in managing and treating TRD. Future research should focus on integrating genetic data with clinical outcomes to enhance our understanding of pathways leading to treatment resistance.

The risk of Alzheimer's disease and cognitive impairment characteristics in eight mental disorders: A UK Biobank observational study and Mendelian randomization analysis

INTRODUCTION

The cognitive impairment patterns and the association with Alzheimer's disease (AD) in mental disorders remain poorly understood.

METHODS

We analyzed data from 486,297 UK Biobank participants, categorizing them by mental disorder history to identify the risk of AD and the cognitive impairment characteristics. Causation was further assessed using Mendelian randomization (MR).

RESULTS

AD risk was higher in individuals with bipolar disorder (BD; hazard ratio [HR] = 2.37, P < 0.01) and major depressive disorder (MDD; HR = 1.63, P < 0.001). MR confirmed a causal link between BD and AD (ORIVW = 1.098), as well as obsessive-compulsive disorder (OCD) and AD (ORIVW = 1.050). Cognitive impairments varied, with BD and schizophrenia showing widespread deficits, and OCD affecting complex task performance.

DISCUSSION

Observational study and MR provide consistent evidence that mental disorders are independent risk factors for AD. Mental disorders exhibit distinct cognitive impairment prior to dementia, indicating the potential different mechanisms in AD pathogenesis. Early detection of these impairments in mental disorders is crucial for AD prevention.

HIGHLIGHTS

This is the most comprehensive study that investigates the risk and causal relationships between a history of mental disorders and the development of Alzheimer's disease (AD), alongside exploring the cognitive impairment characteristics associated with different mental disorders. Individuals with bipolar disorder (BD) exhibited the highest risk of developing AD (hazard ratio [HR] = 2.37, P < 0.01), followed by those with major depressive disorder (MDD; HR = 1.63, P < 0.001). Individuals with schizophrenia (SCZ) showed a borderline higher risk of AD (HR = 2.36, P = 0.056). Two-sample Mendelian randomization (MR) confirmed a causal association between BD and AD (ORIVW = 1.098, P < 0.05), as well as AD family history (proxy-AD, ORIVW = 1.098, P < 0.001), and kept significant after false discovery rate correction. MR also identified a nominal significant causal relationship between the obsessive-compulsive disorder (OCD) spectrum and AD (ORIVW = 1.050, P < 0.05). Individuals with SCZ, BD, and MDD exhibited impairments in multiple cognitive domains with distinct patterns, whereas those with OCD showed only slight declines in complex tasks.

Discrimination between healthy participants and people with panic disorder based on polygenic scores for psychiatric disorders and for intermediate phenotypes using machine learning

OBJECTIVE

Panic disorder is a modestly heritable condition. Currently, diagnosis is based only on clinical symptoms; identifying objective biomarkers and a more reliable diagnostic procedure is desirable. We investigated whether people with panic disorder can be reliably diagnosed utilizing combinations of multiple polygenic scores for psychiatric disorders and their intermediate phenotypes, compared with single polygenic score approaches, by applying specific machine learning techniques.

METHODS

Polygenic scores for 48 psychiatric disorders and intermediate phenotypes based on large-scale genome-wide association studies (n = 7556-1,131,881) were calculated for people with panic disorder (n = 718) and healthy controls (n = 1717). Discrimination between people with panic disorder and healthy controls was based on the 48 polygenic scores using five methods for classification: logistic regression, neural networks, quadratic discriminant analysis, random forests and a support vector machine. Differences in discrimination accuracy (area under the curve) due to an increased number of polygenic score combinations and differences in the accuracy across five classifiers were investigated.

RESULTS

All five classifiers performed relatively well for distinguishing people with panic disorder from healthy controls by increasing the number of polygenic scores. Of the 48 polygenic scores, the polygenic score for anxiety UK Biobank was the most useful for discrimination by the classifiers. In combinations of two or three polygenic scores, the polygenic score for anxiety UK Biobank was included as one of polygenic scores in all classifiers. When all 48 polygenic scores were used in combination, the greatest areas under the curve significantly differed among the five classifiers. Support vector machine and logistic regression had higher accuracy than quadratic discriminant analysis and random forests. For each classifier, the greatest area under the curve was 0.600 ± 0.030 for logistic regression (polygenic score combinations N = 14), 0.591 ± 0.039 for neural networks (N = 9), 0.603 ± 0.033 for quadratic discriminant analysis (N = 10), 0.572 ± 0.039 for random forests (N = 25) and 0.617 ± 0.041 for support vector machine (N = 11). The greatest areas under the curve at the best polygenic score combination significantly differed among the five classifiers. Random forests had the lowest accuracy among classifiers. Support vector machine had higher accuracy than neural networks.

CONCLUSIONS

These findings suggest that increasing the number of polygenic score combinations up to approximately 10 effectively improved the discrimination accuracy and that support vector machine exhibited greater accuracy among classifiers. However, the discrimination accuracy for panic disorder, when based solely on polygenic score combinations, was found to be modest.

Dissecting the genetic and causal relationship between sleep-related traits and common brain disorders

BACKGROUND

There is a profound connection between abnormal sleep patterns and brain disorders, suggesting a shared influential association. However, the shared genetic basis and potential causal relationships between sleep-related traits and brain disorders are yet to be fully elucidated.

METHODS

Utilizing linkage disequilibrium score regression (LDSC) and bidirectional two-sample univariable Mendelian Randomization (UVMR) analyses with large-scale GWAS datasets, we investigated the genetic correlations and causal associations across six sleep traits and 24 prevalent brain disorders. Additionally, a multivariable Mendelian Randomization (MVMR) analysis evaluated the cumulative effects of various sleep traits on each brain disorder, complemented by genetic loci characterization to pinpoint pertinent genes and pathways.

RESULTS

LDSC analysis identified significant genetic correlations in 66 out of 144 (45.8 %) pairs between sleep-related traits and brain disorders, with the most pronounced correlations observed in psychiatric disorders (66 %, 48/72). UVMR analysis identified 29 causal relationships (FDR<0.05) between sleep traits and brain disorders, with 19 associations newly discovered according to our knowledge. Notably, major depression, attention-deficit/hyperactivity disorder, bipolar disorder, cannabis use disorder, and anorexia nervosa showed bidirectional causal relations with sleep traits, especially insomnia's marked influence on major depression (IVW beta 0.468, FDR = 5.24E-09). MVMR analysis revealed a nuanced interplay among various sleep traits and their impact on brain disorders. Genetic loci characterization underscored potential genes, such as HOXB2, while further enrichment analyses illuminated the importance of synaptic processes in these relationships.

CONCLUSIONS

This study provides compelling evidence for the causal relationships and shared genetic backgrounds between common sleep-related traits and brain disorders.

Distinct Longitudinal Brain White Matter Microstructure Changes and Associated Polygenic Risk of Common Psychiatric Disorders and Alzheimer's Disease in the UK Biobank

Background

During the course of adulthood and aging, white matter (WM) structure and organization are characterized by slow degradation processes such as demyelination and shrinkage. An acceleration of such aging processes has been linked to the development of a range of diseases. Thus, an accurate description of healthy brain maturation, particularly in terms of WM features, is fundamental to the understanding of aging.

Methods

We used longitudinal diffusion magnetic resonance imaging to provide an overview of WM changes at different spatial and temporal scales in the UK Biobank (UKB) (n = 2678; agescan 1 = 62.38 ± 7.23 years; agescan 2 = 64.81 ± 7.1 years). To examine the genetic overlap between WM structure and common clinical conditions, we tested the associations between WM structure and polygenic risk scores for the most common neurodegenerative disorder, Alzheimer's disease, and common psychiatric disorders (unipolar and bipolar depression, anxiety, obsessive-compulsive disorder, autism, schizophrenia, attention-deficit/hyperactivity disorder) in longitudinal (n = 2329) and cross-sectional (n = 31,056) UKB validation data.

Results

Our findings indicate spatially distributed WM changes across the brain, as well as distributed associations of polygenic risk scores with WM. Importantly, brain longitudinal changes reflected genetic risk for disorder development better than the utilized cross-sectional measures, with regional differences giving more specific insights into gene-brain change associations than global averages.

Conclusions

We extend recent findings by providing a detailed overview of WM microstructure degeneration on different spatial levels, helping to understand fundamental brain aging processes. Further longitudinal research is warranted to examine aging-related gene-brain associations.

Obsessive-Compulsive Disorder in Children and Adolescents

Obsessive-compulsive disorder (OCD) in children and adolescents is a neurobehavioral condition that can lead to functional impairment in multiple domains and decreased quality of life. We review the clinical presentation, diagnostic considerations, and common comorbidities of pediatric OCD. An overview of the biological and psychological models of OCD is provided along with a discussion of developmental considerations in youth. We also describe evidence-based treatments for OCD in childhood and adolescence, including cognitive behavioral therapy (CBT) with exposure and response prevention (ERP) and pharmacotherapy. Finally, research evaluating the delivery of CBT in different formats and modalities is discussed, and we conclude with suggestions for future research directions.

Mendelian randomization analyses reveal causal relationships between brain functional networks and risk of psychiatric disorders

Dysfunction of brain resting-state functional networks has been widely reported in psychiatric disorders. However, the causal relationships between brain resting-state functional networks and psychiatric disorders remain largely unclear. Here we perform bidirectional two-sample Mendelian randomization (MR) analyses to investigate the causalities between 191 resting-state functional magnetic resonance imaging (rsfMRI) phenotypes (n = 34,691 individuals) and 12 psychiatric disorders (n = 14,307 to 698,672 individuals). Forward MR identified 8 rsfMRI phenotypes causally associated with the risk of psychiatric disorders. For example, the increase in the connectivity of motor, subcortical-cerebellum and limbic network was associated with lower risk of autism spectrum disorder. In adddition, increased connectivity in the default mode and central executive network was associated with lower risk of post-traumatic stress disorder and depression. Reverse MR analysis revealed significant associations between 4 psychiatric disorders and 6 rsfMRI phenotypes. For instance, the risk of attention-deficit/hyperactivity disorder increases the connectivity of the attention, salience, motor and subcortical-cerebellum network. The risk of schizophrenia mainly increases the connectivity of the default mode and central executive network and decreases the connectivity of the attention network. In summary, our findings reveal causal relationships between brain functional networks and psychiatric disorders, providing important interventional and therapeutic targets for psychiatric disorders at the brain functional network level.

Early Developmental Origins of Cortical Disorders Modeled in Human Neural Stem Cells

The implications of the early phases of human telencephalic development, involving neural stem cells (NSCs), in the etiology of cortical disorders remain elusive. Here, we explored the expression dynamics of cortical and neuropsychiatric disorder-associated genes in datasets generated from human NSCs across telencephalic fate transitions in vitro and in vivo. We identified risk genes expressed in brain organizers and sequential gene regulatory networks across corticogenesis revealing disease-specific critical phases, when NSCs are more vulnerable to gene dysfunctions, and converging signaling across multiple diseases. Moreover, we simulated the impact of risk transcription factor (TF) depletions on different neural cell types spanning the developing human neocortex and observed a spatiotemporal-dependent effect for each perturbation. Finally, single-cell transcriptomics of newly generated autism-affected patient-derived NSCs in vitro revealed recurrent alterations of TFs orchestrating brain patterning and NSC lineage commitment. This work opens new perspectives to explore human brain dysfunctions at the early phases of development.

Bidirectional causal relational between frailty and mental illness: a two-sample Mendelian randomization study

Background

Frailty has been associated with mental illness (MI) observational studies, but the causal relationship between these factors remains uncertain. We aimed to assess the bidirectional causality between frailty and MI by two-sample Mendelian randomization (MR) analyses.

Methods

To investigate the causal relationship among them, summary statistics of frailty index (FI) and six types of MI: anxiety, depression, affective disorder, mania, schizophrenia, and obsessive-compulsive disorder (OCD) were included in this MR study. This MR analysis was performed using inverse variance weighting (IVW), MR-Egger regression, and weighted median. The stability of the results was evaluated using Cochran's Q test, MR-Egger intercept test, Funnel Plots, and leave-one-out analysis.

Results

Genetic predisposition to FI was significantly associated with increased anxiety (odds ratio [OR] = 1.62, 95% confidence interval [CI] 1.13-2.33, P = 8.18E-03), depression (OR = 1.88, 95% CI 1.30-2.71, P = 8.21E-04), affective disorder (OR = 1.70, 95% CI 1.28-2.27, P = 2.57E-04). However, our study findings do not demonstrate a causal relationship between FI and mania (OR = 1.02, 95% CI 0.99-1.06, P = 2.20E-01), schizophrenia (OR = 1.02, 95% CI 0.07-0.86, P = 9.28E-01). In particular, although the IVW results suggest a potential causal relationship between FI and OCD (OR = 0.64, 95% CI 0.07-0.86, P = 2.85E-02), the directions obtained from the three methods we employed ultimately show inconsistency. Therefore, the result must be interpreted with caution. The results of the reverse MR analysis indicated a statistically significant and causal relationship between anxiety (OR = 1.06, 95% CI 1.01-1.11, P = 2.00E-02), depression (OR = 1.14, 95% CI 1.04-1.26, P = 7.99E-03), affective disorder (OR = 1.15, 95% CI 1.09-1.21, P = 3.39E-07), and schizophrenia (OR = 1.02, 95% CI 1.01-1.04, P = 1.70E-03) with FI. However, our findings do not provide support for a link between mania (OR = 1.46, 95% CI 0.79-2.72, P = 2.27E-01), OCD (OR = 1.01, 95% CI 1.00-1.02, P = 2.11E-01) and an increased risk of FI.

Conclusion

The MR results suggest a potential bidirectional causal relationship between FI and anxiety, depression, and affective disorder. Schizophrenia was found to be associated with a higher risk of FI. The evidence was insufficient to support a causal relationship between Fl and other Ml. These findings offer new insights into the development of effective management strategies for frailty and MI.

Similarities and differences between eating disorders and obsessive-compulsive disorder in childhood and adolescence: a systematic review

Background

The developmental age, comprising childhood and adolescence, constitutes an extremely important phase of neurodevelopment during which various psychiatric disorders can emerge. Obsessive-Compulsive Disorder (OCD) and Eating Disorders (ED) often manifest during this critical developmental period sharing similarities but also differences in psychopathology, neurobiology, and etiopathogenesis. The aim of this study is to focus on clinical, genetic and neurobiological similarities and differences in OCD and ED.

Methods

This study is based on a PubMed/MEDLINE and Cochrane Central Register for Controlled Trial (CENTRAL). The research adhered to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).

Results

The aforementioned search yielded an initial collection of 335 articles, published from 1968 to September 2023. Through the application of inclusion and exclusion criteria, a total of 324 articles were excluded, culminating in a final selection of 10 articles.

Conclusions

Our findings showed both differences and similarities between OCD and ED. Obsessive-compulsive (OC) symptoms are more prevalent in ED characterized by a binge/purge profile than in those with a restrictive profile during developmental age. OC symptomatology appears to be a common dimension in both OCD and ED. When presents, OC symptomatology, exhibits transversal characteristic alterations in the anterior cingulate cortex and poorer cognitive flexibility. These correlations could be highlighted by genetic overlaps between disorders. A comprehensive definition, integrating psychopathological and neurobiological aspects could significantly aid treatment selection and thereby influence the prognosis of these patients.

Large-scale whole-exome sequencing of neuropsychiatric diseases and traits in 350,770 adults

While numerous genomic loci have been identified for neuropsychiatric conditions, the contribution of protein-coding variants has yet to be determined. Here we conducted a large-scale whole-exome-sequencing study to interrogate the impact of protein-coding variants on 46 neuropsychiatric diseases and 23 traits in 350,770 adults from the UK Biobank. Twenty new genes were associated with neuropsychiatric diseases through coding variants, among which 16 genes had impacts on the longitudinal risks of diseases. Thirty new genes were associated with neuropsychiatric traits, with SYNGAP1 showing pleiotropic effects across cognitive function domains. Pairwise estimation of genetic correlations at the coding-variant level highlighted shared genetic associations among pairs of neurodegenerative diseases and mental disorders. Lastly, a comprehensive multi-omics analysis suggested that alterations in brain structures, blood proteins and inflammation potentially contribute to the gene-phenotype linkages. Overall, our findings characterized a compendium of protein-coding variants for future research on the biology and therapeutics of neuropsychiatric phenotypes.

Causal associations between chronic viral hepatitis and psychiatric disorders: a Mendelian randomization study

Background

There may be an interaction between viral hepatitis and psychiatric disorders during disease progression. Herein, we conducted Mendelian randomization (MR) to explore the causal associations and mediators between viral hepatitis and psychiatric disorders.

Methods

Genome-wide association studies summary data for viral hepatitis [including chronic hepatitis B (CHB) and chronic hepatitis C (CHC)] and psychiatric disorders (including depression, anxiety, schizophrenia, obsessive-compulsive disorder, bipolar disorder, and post-traumatic stress disorder) were obtained. Two-sample MR was performed to assess the causal associations between viral hepatitis and psychiatric disorders. Further, a mediation analysis was conducted to evaluate the potential mediators. Inverse-variance weighted, MR-Egger, and weighted median were used as the main methods, while a sensitivity analysis was performed to evaluate pleiotropy and heterogeneity.

Results

There was no causal effect of CHB/CHC on psychiatric disorders, as well as psychiatric disorders on CHB. However, schizophrenia presented a causal effect on increased CHC risk [odds ratio (OR)=1.378, 95%CI: 1.012-1.876]. Further, a mediation analysis identified coffee consumption and body mass index as mediators in the effect of schizophrenia on CHC, mediating 3.75% (95%CI: 0.76%-7.04%) and 0.94% (95%CI: 0.00%-1.70%) proportion, respectively.

Conclusion

We revealed that schizophrenia patients faced a high risk of CHC, and insufficient coffee consumption and underweight could mediate the causal effect of schizophrenia on CHC. The prevention of hepatitis C might be a beneficial strategy for patients with schizophrenia. The right amount of nutrition supplements and coffee consumption might be part of a beneficial lifestyle in preventing the high CHC risk in patients with schizophrenia.

Massively parallel characterization of regulatory elements in the developing human cortex

Nucleotide changes in gene regulatory elements are important determinants of neuronal development and diseases. Using massively parallel reporter assays in primary human cells from mid-gestation cortex and cerebral organoids, we interrogated the cis-regulatory activity of 102,767 open chromatin regions, including thousands of sequences with cell type-specific accessibility and variants associated with brain gene regulation. In primary cells, we identified 46,802 active enhancer sequences and 164 variants that alter enhancer activity. Activity was comparable in organoids and primary cells, suggesting that organoids provide an adequate model for the developing cortex. Using deep learning we decoded the sequence basis and upstream regulators of enhancer activity. This work establishes a comprehensive catalog of functional gene regulatory elements and variants in human neuronal development.

Polygenic architecture of brain structure and function, behaviors, and psychopathologies in children

The human brain undergoes structural and functional changes during childhood, a critical period in cognitive and behavioral development. Understanding the genetic architecture of the brain development in children can offer valuable insights into the development of the brain, cognition, and behaviors. Here, we integrated brain imaging-genetic-phenotype data from over 8,600 preadolescent children of diverse ethnic backgrounds using multivariate statistical techniques. We found a low-to-moderate level of SNP-based heritability in most IDPs, which is lower compared to the adult brain. Using sparse generalized canonical correlation analysis (SGCCA), we identified several covariation patterns among genome-wide polygenic scores (GPSs) of 29 traits, 7 different modalities of brain imaging-derived phenotypes (IDPs), and 266 cognitive and psychological phenotype data. In structural MRI, significant positive associations were observed between total grey matter volume, left ventral diencephalon volume, surface area of right accumbens and the GPSs of cognition-related traits. Conversely, negative associations were found with the GPSs of ADHD, depression and neuroticism. Additionally, we identified a significant positive association between educational attainment GPS and regional brain activation during the N-back task. The BMI GPS showed a positive association with fractional anisotropy (FA) of connectivity between the cerebellum cortex and amygdala in diffusion MRI, while the GPSs for educational attainment and cannabis use were negatively associated with the same IDPs. Our GPS-based prediction models revealed substantial genetic contributions to cognitive variability, while the genetic basis for many mental and behavioral phenotypes remained elusive. This study delivers a comprehensive map of the relationships between genetic profiles, neuroanatomical diversity, and the spectrum of cognitive and behavioral traits in preadolescence.

Genetic variants for head size share genes and pathways with cancer

The size of the human head is highly heritable, but genetic drivers of its variation within the general population remain unmapped. We perform a genome-wide association study on head size (N = 80,890) and identify 67 genetic loci, of which 50 are novel. Neuroimaging studies show that 17 variants affect specific brain areas, but most have widespread effects. Gene set enrichment is observed for various cancers and the p53, Wnt, and ErbB signaling pathways. Genes harboring lead variants are enriched for macrocephaly syndrome genes (37-fold) and high-fidelity cancer genes (9-fold), which is not seen for human height variants. Head size variants are also near genes preferentially expressed in intermediate progenitor cells, neural cells linked to evolutionary brain expansion. Our results indicate that genes regulating early brain and cranial growth incline to neoplasia later in life, irrespective of height. This warrants investigation of clinical implications of the link between head size and cancer.

Association of mental disorders with sepsis: a bidirectional Mendelian randomization study

Background

Substantial research evidence supports the correlation between mental disorders and sepsis. Nevertheless, the causal connection between a particular psychological disorder and sepsis remains unclear.

Methods

For investigating the causal relationships between mental disorders and sepsis, genetic variants correlated with mental disorders, including anorexia nervosa (AN), attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), panic disorder (PD), posttraumatic stress disorder (PTSD), schizophrenia (SCZ), and tourette syndrome (TS), were all extracted from the Psychiatric Genomics Consortium (PGC). The causal estimates and direction between these mental disorders and sepsis were evaluated employing a two-sample bidirectional MR strategy. The inverse variance weighted (IVW) method was the primary approach utilized. Various sensitivity analyses were performed to confirm the validity of the causal effect. Meta-analysis, multivariable MR, and mediation MR were conducted to ensure the credibility and depth of this research.

Results

The presence of AN was in relation to a greater likelihood of sepsis (OR 1.08, 95% CI 1.02-1.14; p = 0.013). A meta-analysis including validation cohorts supported this observation (OR 1.06, 95% CI 1.02-1.09). None of the investigated mental disorders appeared to be impacted when sepsis was set as the exposure factor. Even after adjusting for confounding factors, AN remained statistically significant (OR 1.08, 95% CI 1.02-1.15; p = 0.013). Mediation analysis indicated N-formylmethionine levels (with a mediated proportion of 7.47%), cystatin D levels (2.97%), ketogluconate Metabolism (17.41%) and N10-formyl-tetrahydrofolate biosynthesis (20.06%) might serve as mediators in the pathogenesis of AN-sepsis.

Conclusion

At the gene prediction level, two-sample bidirectional MR analysis revealed that mental disorder AN had a causal association with an increased likelihood of sepsis. In addition, N-formylmethionine levels, cystatin D levels, ketogluconate metabolism and N10-formyl-tetrahydrofolate biosynthesis may function as potential mediators in the pathophysiology of AN-sepsis. Our research may contribute to the investigation of novel therapeutic strategies for mental illness and sepsis.

Genetically predicted fatty liver disease and risk of psychiatric disorders: A mendelian randomization study

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) and alcohol-related liver disease (ArLD) constitute the primary forms of chronic liver disease, and their incidence is progressively increasing with changes in lifestyle habits. Earlier studies have documented a correlation between the occurrence and development of prevalent mental disorders and fatty liver.

AIM

To investigate the correlation between fatty liver and mental disorders, thus necessitating the implementation of a mendelian randomization (MR) study to elucidate this association.

METHODS

Data on NAFLD and ArLD were retrieved from the genome-wide association studies catalog, while information on mental disorders, including Alzheimer's disease, schizophrenia, anxiety disorder, attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder, multiple personality disorder, obsessive-compulsive disorder (OCD), post-traumatic stress disorder (PTSD), and schizophrenia was acquired from the psychiatric genomics consortium. A two-sample MR method was applied to investigate mediators in significant associations.

RESULTS

After excluding weak instrumental variables, a causal relationship was identified between fatty liver disease and the occurrence and development of some psychiatric disorders. Specifically, the findings indicated that ArLD was associated with a significantly elevated risk of developing ADHD (OR: 5.81, 95%CI: 5.59-6.03, P < 0.01), bipolar disorder (OR: 5.73, 95%CI: 5.42-6.05, P = 0.03), OCD (OR: 6.42, 95%CI: 5.60-7.36, P < 0.01), and PTSD (OR: 5.66, 95%CI: 5.33-6.01, P < 0.01). Meanwhile, NAFLD significantly increased the risk of developing bipolar disorder (OR: 55.08, 95%CI: 3.59-845.51, P < 0.01), OCD (OR: 61.50, 95%CI: 6.69-565.45, P < 0.01), and PTSD (OR: 52.09, 95%CI: 4.24-639.32, P < 0.01).

CONCLUSION

Associations were found between genetic predisposition to fatty liver disease and an increased risk of a broad range of psychiatric disorders, namely bipolar disorder, OCD, and PTSD, highlighting the significance of preventive measures against psychiatric disorders in patients with fatty liver disease.

Chromatin accessibility during human first-trimester neurodevelopment

The human brain develops through a tightly organized cascade of patterning events, induced by transcription factor expression and changes in chromatin accessibility. Although gene expression across the developing brain has been described at single-cell resolution1, similar atlases of chromatin accessibility have been primarily focused on the forebrain2-4. Here we describe chromatin accessibility and paired gene expression across the entire developing human brain during the first trimester (6-13 weeks after conception). We defined 135 clusters and used multiomic measurements to link candidate cis-regulatory elements to gene expression. The number of accessible regions increased both with age and along neuronal differentiation. Using a convolutional neural network, we identified putative functional transcription factor-binding sites in enhancers characterizing neuronal subtypes. We applied this model to cis-regulatory elements linked to ESRRB to elucidate its activation mechanism in the Purkinje cell lineage. Finally, by linking disease-associated single nucleotide polymorphisms to cis-regulatory elements, we validated putative pathogenic mechanisms in several diseases and identified midbrain-derived GABAergic neurons as being the most vulnerable to major depressive disorder-related mutations. Our findings provide a more detailed view of key gene regulatory mechanisms underlying the emergence of brain cell types during the first trimester and a comprehensive reference for future studies related to human neurodevelopment.

Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder

Post-traumatic stress disorder (PTSD) genetics are characterized by lower discoverability than most other psychiatric disorders. The contribution to biological understanding from previous genetic studies has thus been limited. We performed a multi-ancestry meta-analysis of genome-wide association studies across 1,222,882 individuals of European ancestry (137,136 cases) and 58,051 admixed individuals with African and Native American ancestry (13,624 cases). We identified 95 genome-wide significant loci (80 new). Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators (for example, GRIA1, GRM8 and CACNA1E), developmental, axon guidance and transcription factors (for example, FOXP2, EFNA5 and DCC), synaptic structure and function genes (for example, PCLO, NCAM1 and PDE4B) and endocrine or immune regulators (for example, ESR1, TRAF3 and TANK). Additional top genes influence stress, immune, fear and threat-related processes, previously hypothesized to underlie PTSD neurobiology. These findings strengthen our understanding of neurobiological systems relevant to PTSD pathophysiology, while also opening new areas for investigation.