Defining the Genetic, Genomic, Cellular, and Diagnostic Architectures of Psychiatric Disorders

Relationship Between Problematic Alcohol Use and Various Psychiatric Disorders: A Genetically Informed Study

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Phenotypic complexities of rare heterozygous neurexin-1 deletions

Given the large number of genes significantly associated with risk for neuropsychiatric disorders, a critical unanswered question is the extent to which diverse mutations-sometimes affecting the same gene-will require tailored therapeutic strategies. Here we consider this in the context of rare neuropsychiatric disorder-associated copy number variants (2p16.3) resulting in heterozygous deletions in NRXN1, which encodes a presynaptic cell-adhesion protein that serves as a critical synaptic organizer in the brain. Complex patterns of NRXN1 alternative splicing are fundamental to establishing diverse neurocircuitry, vary between the cell types of the brain and are differentially affected by unique (non-recurrent) deletions1. We contrast the cell-type-specific effect of patient-specific mutations in NRXN1 using human-induced pluripotent stem cells, finding that perturbations in NRXN1 splicing result in divergent cell-type-specific synaptic outcomes. Through distinct loss-of-function (LOF) and gain-of-function (GOF) mechanisms, NRXN1+/- deletions cause decreased synaptic activity in glutamatergic neurons, yet increased synaptic activity in GABAergic neurons. Reciprocal isogenic manipulations causally demonstrate that aberrant splicing drives these changes in synaptic activity. For NRXN1 deletions, and perhaps more broadly, precision medicine will require stratifying patients based on whether their gene mutations act through LOF or GOF mechanisms, to achieve individualized restoration of NRXN1 isoform repertoires by increasing wild-type and/or ablating mutant isoforms. Given the increasing number of mutations predicted to engender both LOF and GOF mechanisms in brain disorders, our findings add nuance to future considerations of precision medicine.

Categorical misalignment: Making autism(s) in big data biobanking

The opaque relationship between biology and behavior is an intractable problem for psychiatry, and it increasingly challenges longstanding diagnostic categorizations. While various big data sciences have been repeatedly deployed as potential solutions, they have so far complicated more than they have managed to disentangle. Attending to categorical misalignment, this article proposes one reason why this is the case: Datasets have to instantiate clinical categories in order to make biological sense of them, and they do so in different ways. Here, I use mixed methods to examine the role of the reuse of big data in recent genomic research on autism spectrum disorder (ASD). I show how divergent regimes of psychiatric categorization are innately encoded within commonly used datasets from MSSNG and 23andMe, contributing to a rippling disjuncture in the accounts of autism that this body of research has produced. Beyond the specific complications this dynamic introduces for the category of autism, this paper argues for the necessity of critical attention to the role of dataset reuse and recombination across human genomics and beyond.

Transdiagnostic Polygenic Risk Models for Psychopathology and Comorbidity: Cross-Ancestry Analysis in the All of Us Research Program

Psychiatric disorders exhibit substantial genetic overlap, raising questions about the utility of transdiagnostic genetic risk models. Using data from the All of Us Research Program (N=102,091), we evaluated common psychiatric genetic (CPG) factor-based polygenic risk scores (PRSs) compared to standard disorder-specific PRSs. The CPG PRS consistently outperformed disorder-specific scores in predicting individual disorder risk, explaining 1.07 to 24.6 times more phenotypic variance across 11 psychiatric conditions. Meanwhile, many disorder-specific PRSs retained independent but smaller contributions, highlighting the complementary nature of shared and disorder-specific genetic risk. While alternative multi-factor models improved model fit, the CPG PRS provided comparable or superior predictive performance across most disorders, including overall comorbidity burden. Cross-ancestry analyses however revealed notable limitations of European-centric GWAS datasets for other populations due to ancestral differences in genetic architecture. These findings underscore the potential value of transdiagnostic PRSs for psychiatric genetics while highlighting the need for more equitable genetic risk models.

Autism spectrum disorder risk genes have convergent effects on transcription and neuronal firing patterns in primary neurons

Autism spectrum disorder (ASD) is a highly heterogenous neurodevelopmental disorder with numerous genetic risk factors. Notably, a disproportionate number of risk genes encode transcription regulators including transcription factors and proteins that regulate chromatin. Here, we tested the function of nine such ASD-linked transcription regulators by depleting them in primary cultured neurons. We then defined the resulting gene expression disruptions using RNA-sequencing and tested effects on neuronal firing using multielectrode array recordings. We identified shared gene expression signatures across many ASD risk genes that converged on disruption of critical synaptic genes. Fitting with this, we detected drastic disruptions to neuronal firing throughout neuronal maturation. Together, these findings provide evidence that multiple ASD-linked transcriptional regulators disrupt transcription of synaptic genes and have convergent effects on neuronal firing that may contribute to enhanced ASD risk.

Psychiatric Genetics in Clinical Practice: Essential Knowledge for Mental Health Professionals

OBJECTIVE

The authors provide recommendations on incorporating recent advances in psychiatric genetics into clinical practice for mental health clinicians.

METHOD

The International Society for Psychiatric Genetics Education Committee met monthly to come to a consensus on priority topics in psychiatric genetics. Topics were then assigned to small teams of subspecialty experts to summarize the current knowledge base and create an illustrative clinical case. Topics included, familial aggregation, common and rare genetic variants, epigenetics, gene-environment interactions, pharmacogenomics, genetic counseling, and ethical and social implications. Each section was reviewed and revised by all committee members and then finalized by the Committee Chair.

RESULTS

Key findings highlight the importance of understanding the genetic architecture of psychiatric disorders, the potential applications of genetic information in risk assessment, diagnosis, treatment selection, and patient education, as well as the ethical and social considerations surrounding the use of genetic data. The committee emphasizes the need for a nuanced approach that integrates genetic factors with environmental and experiential factors in a holistic model of care.

CONCLUSION

As psychiatric genetics continues to evolve rapidly, mental health clinicians must stay informed about the latest findings and their clinical implications. Ongoing education, collaboration with genetics professionals, and effective communication strategies are crucial to harness the power of genetics while avoiding potential pitfalls such as genetic determinism and stigma. The committee recommends a balanced perspective that recognizes the complex interplay of genetic and non-genetic factors in shaping mental health outcomes.

Estimating Disorder Probability Based on Polygenic Prediction Using the BPC Approach

Polygenic Scores (PGSs) summarize an individual's genetic propensity for a given trait in a single value based on SNP effect sizes derived from Genome-Wide Association Study (GWAS) results. Methods have been developed that apply Bayesian approaches to improve the prediction accuracy of PGSs through optimization of estimated effect sizes. While these methods are generally well-calibrated for continuous traits (implying the predicted values are, on average, equal to the true trait values), they are not well-calibrated for binary disorder traits in ascertained samles. This is a problem because well-calibrated PGSs are needed to reliably compute the absolute disorder probability for an individual to facilitate future clinical implementation. Here, we introduce the Bayesian polygenic score Probability Conversion (BPC) approach, which computes an individual's predicted disorder probability using GWAS summary statistics, an existing Bayesian PGS method (e.g., PRScs, SBayesR), the individual's genotype data, and a prior disorder probability (which can be specified flexibly, based on e.g., literature, small reference samples, or prior elicitation). The BPC approach transforms the PGS to its underlying liability scale, computes the variances of the PGS in cases and controls, and applies Bayes' Theorem to compute the absolute disorder probability; it is practical in its application as it does not require a tuning sample with both genotype and phenotype data. We applied the BPC approach to extensive simulated data and empirical data of nine disorders. The BPC approach yielded well-calibrated results that were consistently better than the results of another recently published approach.

Is There a Core Deficit in Autism Spectrum Disorder? An Analysis of CPEP-3 Assessment Data from 543 Children With Autism

Identifying a "core deficit" is essential for early detection and intervention in developmental disorders among children. However, the presence of a core deficit within autism spectrum disorder (ASD) continues to be unclear. Therefore, the purpose of this study was to examine the possibility of the core deficit in autism spectrum disorders. This study evaluated 543 children diagnosed with ASD by using Chinese version of the Psychoeducational Profile-Third Edition (CPEP-3). Structural equation modeling (SEM) was used to construct single-factor models (assuming the presence of a core deficit) and a multi-factor model (assuming the absence of core deficits) based on the assessed data, and then to compare the fit of the two types of models. Assessments revealed developmental delays and adaptive challenges among the children with ASD. The single-factor model assuming the "motor" domain as the "core deficit" showed a superior fit (CFI = 0.86, AIC = 356.47, ECVI = 0.66) than other single-factor models. The multi-factor model, which assumes no core deficit, provided a better fit and greater predictive accuracy (CFI = 0.87, AIC = 351.94, ECVI = 0.65) than all single-factor models. ASD is characterized by widespread developmental delays and adaptive challenges. While motor impairment may serve as an effective predictor of these issues, it does not fully account for the diverse and complex symptomatology observed in children with ASD. The symptoms in these children likely arise from multiple factors, which are not adequately explained by a single core deficit model.

Transcriptomic and genetic analysis suggests a role for mitochondrial dysregulation in schizophrenia

Schizophrenia is an often devastating disorder characterized by persistent and idiopathic cognitive deficits, delusions and hallucinations. Schizophrenia has been associated with impaired nervous system development and an excitation/inhibition imbalance in the prefrontal cortex. On a molecular level, schizophrenia is moderately heritable and genetically complex. Hundreds of risk genes have been identified, spanning a heterogeneous landscape dominated by loci that confer relatively small risk. Bioinformatic analyses of genetic associations point to a limited set of neurons, mainly excitatory cortical neurons, but other analyses suggest the importance of astrocytes and microglia. To understand different cell type roles in schizophrenia and reveal novel cell-type specific aetiologically relevant perturbations in schizophrenia, our study integrated genetic analysis with single nucleus RNA-seq of 536,618 nuclei from postmortem samples of dorsal prefrontal cortex (Brodmann Area 8/9) of 43 cases with schizophrenia and 42 neurotypical controls. We found no significant difference in cell type abundance. Gene expression in excitatory layer 2-3 intra-telencephalic neurons had the greatest number of differentially expressed transcripts and, together with excitatory deep layer intra-telencephalic neurons, conferred most of the genetic risk for schizophrenia. Most differential expression of genes was found in specific cell types and was dominated by down-regulated transcripts. Down-regulated transcripts were enriched in gene sets including transmembrane transport, mitochondrial function, protein folding, and cell-cell signaling whereas up-regulated transcripts were enriched in gene sets related to RNA processing, including RNA splicing in neurons. Co-regulation network analysis identified 40 schizophrenia-relevant programs across 13 cell types. A gene program largely shared between neuronal subtypes, astrocytes, and oligodendrocytes was significantly enriched for schizophrenia risk, supporting an aetiological role for perturbed protein modification, ion transport, and mitochondrial function. These results were largely consistent with cell-type expression quantitative trait locus and transcriptome-wide association analyses. Moreover, single-cell RNA sequencing results, most prominently mitochondrial dysfunction, had multiple points of convergence with proteomic and long-read RNA sequencing results from samples from the same donors. Our study integrates genetic analysis with transcriptomics to reveal novel cell-type specific aetiologically relevant perturbations in schizophrenia.

Association of Polygenic Risk for Psychiatric Disorders with Cardiometabolic Disease

IMPORTANCE

Clinical diagnoses of psychiatric disorders are associated with cardiometabolic diseases (CMDs) such as type 2 diabetes and ischemic heart diseases. Studying how genetic liability for psychiatric disorders relate to CMD risk will offer novel insight into the relationship between psychiatric disorders and CMDs.

OBJECTIVE

To evaluate the associations between psychiatric polygenic risk scores (PRSs) and clinically diagnosed CMDs while accounting for cross-disorder pleiotropy.

DESIGN SETTING AND PARTICIPANTS

This study computed PRSs for attention deficit-hyperactivity disorder (ADHD), major depressive disorder (MDD), anxiety disorder, post-traumatic stress disorder (PTSD), bipolar disorder, and schizophrenia. The analysis was conducted in three population-based Northern European cohorts: the Swedish Twin Registry (STR, N=17,378 genotyped samples), the Estonian Biobank (EstBB, N=208,383), and the Norwegian Mother, Father and Child Cohort Study (MoBa, N=129,398). Associations between psychiatric PRSs and clinical diagnoses of 10 major CMDs (including metabolic diseases such as hyperlipidemia, obesity, and type 2 diabetes, and cardiovascular diseases such as hypertensive disease, arteriosclerosis, ischemic heart disease, heart failure, thromboembolic disease, cerebrovascular disease, and arrhythmias) were estimated using models that mutually adjusted for all psychiatric PRSs. Supplementary analyses were performed by additionally controlling for self-reported body mass index (BMI). A discordant twin-pair analysis was conducted in the STR (N=70,619) to assess the association between self-reported lifetime MDD and subsequent CMD risk while adjusting for familial factors shared between monozygotic and dizygotic co-twins.

MAIN OUTCOMES AND MEASURES

Psychiatric PRSs were constructed based on both all available genetic risk variants and genome-wide significant risk variants from large-scale GWASs. Clinical diagnoses of psychiatric disorders and CMDs were ascertained through electronic health records (with primary care records used exclusively in the EstBB). Lifetime self-reported MDD in the STR was assessed via the Composite International Diagnostic Interview Short Form.

RESULTS

PRSs for ADHD and MDD were associated with increased risk of all CMDs. The ADHD PRS showed stronger associations with metabolic disease, whereas the MDD PRS showed stronger associations with cardiovascular diseases. PRSs for anxiety disorder, PTSD, and bipolar disorder showed only limited associations with CMDs, while increased levels of schizophrenia PRSs were associated with decreased risk of CMDs. These associations remained after adjustment for BMI. Finally, twins endorsing lifetime MDD were found to have an increased risk of subsequent CMD diagnoses compared to their unexposed co-twins.

CONCLUSIONS AND RELEVANCE

PRSs for ADHD and MDD showed robust associations with risk of CMDs and self-reported MDD was associated with subsequent CMD risk even after adjusting for familial factors shared between co-twins. These findings provide robust evidence for genetic overlap between ADHD and MDD with CMDs.

A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework

The heterogeneity of psychiatric disorders makes researching disorder-specific neurobiological markers an ill-posed problem. Here, we face the need for disease stratification models by presenting a generalizable multivariate normative modelling framework for characterizing brain morphology, applied to bipolar disorder (BD). We used deep autoencoders in an anomaly detection framework, combined for the first time with a confounder removal step that integrates training and external validation. The model was trained with healthy control (HC) data from the human connectome project and applied to multi-site external data of HC and BD individuals. We found that brain deviating scores were greater, more heterogeneous, and with increased extreme values in the BD group, with volumes prominently from the basal ganglia, hippocampus, and adjacent regions emerging as significantly deviating. Similarly, individual brain deviating maps based on modified z scores expressed higher abnormalities occurrences, but their overall spatial overlap was lower compared to HCs. Our generalizable framework enabled the identification of brain deviating patterns differing between the subject and the group levels, a step forward towards the development of more effective and personalized clinical decision support systems and patient stratification in psychiatry.

Transcriptional enhancers in human neuronal differentiation provide clues to neuronal disorders

Genome-wide association studies (GWASs) have identified thousands of variants associated with complex phenotypes, including neuropsychiatric disorders. To better understand their pathogenesis, it is necessary to identify the functional roles of these variants, which are largely located in non-coding DNA regions. Here, we employ a human mesencephalic neuronal cell differentiation model, LUHMES, with sensitive and high-resolution methods to discover enhancers (NET-CAGE), perform DNA conformation analysis (Capture Hi-C) to link enhancers to their target genes, and finally validate selected interactions. We expand the number of known enhancers active in differentiating human LUHMES neurons to 47,350, and find overlap with GWAS variants for Parkinson's disease and schizophrenia. Our findings reveal a fine-tuned regulation of human neuronal differentiation, even between adjacent developmental stages; provide a valuable resource for further studies on neuronal development, regulation, and disorders; and emphasize the importance of exploring the vast regulatory potential of non-coding DNA and enhancers.

Genomics-driven integrative analysis highlights immune-related plasma proteins for psychiatric disorders

BACKGROUND

Genome-wide association studies (GWAS) have identified numerous variants associated with psychiatric disorders. However, it remains largely unknown on how GWAS risk variants contribute to psychiatric disorders.

METHODS

Through integrating two largest, publicly available, independent protein quantitative trait loci datasets of plasma protein and nine large-scale GWAS summary statistics of psychiatric disorders, we first performed proteome-wide association study (PWAS) to identify psychiatric disorders-associated plasma proteins, followed by enrichment analysis to reveal the underlying biological processes and pathways. Then, we conducted Mendelian randomization (MR) and Bayesian colocalization (COLOC) analyses, with both discovery and parallel replication datasets, to further identify protein-disorder pairs with putatively causal relationships. We finally prioritized the potential drug targets using Drug Gene Interaction Database.

RESULTS

PWAS totally identified 112 proteins, which were significantly enriched in biological processes relevant to immune regulation and response to stimulus including regulation of immune system process (adjusted P = 1.69 × 10-7) and response to external stimulus (adjusted P = 4.13 × 10-7), and viral infection related pathways, including COVID-19 (adjusted P = 2.94 × 10-2). MR and COLOC analysis further identified 26 potentially causal protein-disorder pairs in both discovery and replication analysis. Notably, eight protein-coding genes were immune-related, such as IRF3, CSK, and ACE, five among 16 druggable genes were reported to interact with drugs, including ACE, CSK, PSMB4, XPNPEP1, and MICB.

CONCLUSIONS

Our findings highlighted the immunological hypothesis and identified potentially causal plasma proteins for psychiatric disorders, providing biological insights into the pathogenesis and benefit the development of preventive or therapeutic drugs for psychiatric disorders.

Role of immature choroid plexus in the pathology of model mice and human iPSC-derived organoids with autism spectrum disorder

During gestation, the choroid plexus (ChP) produces protein-rich cerebrospinal fluid and matures prior to brain development. It is assumed that ChP dysfunction has a profound effect on developmental neuropsychiatric disorders, such as autism spectrum disorder (ASD). However, the mechanisms linking immature ChP to the onset of ASD remain unclear. Here, we find that ChP-specific CAMDI-knockout mice develop an immature ChP alongside decreased multiciliogenesis and expression of differentiation marker genes following disruption of the cerebrospinal fluid barrier. These mice exhibit ASD-like behaviors, including anxiety and impaired socialization. Additionally, the administration of metformin, an FDA-approved drug, before the social critical period achieves ChP maturation and restores social behaviors. Furthermore, both the ASD model mice and organoids derived from patients with ASD developed an immature ChP. These results propose the involvement of an immature ChP in the pathogenesis of ASD and suggest the targeting of functional maturation of the ChP as a therapeutic strategy for ASD.

Connecting genomic results for psychiatric disorders to human brain cell types and regions reveals convergence with functional connectivity

Identifying cell types and brain regions critical for psychiatric disorders and brain traits is essential for targeted neurobiological research. By integrating genomic insights from genome-wide association studies with a comprehensive single-cell transcriptomic atlas of the adult human brain, we prioritized specific neuronal clusters significantly enriched for the SNP-heritabilities for schizophrenia, bipolar disorder, and major depressive disorder along with intelligence, education, and neuroticism. Extrapolation of cell-type results to brain regions reveals the whole-brain impact of schizophrenia genetic risk, with subregions in the hippocampus and amygdala exhibiting the most significant enrichment of SNP-heritability. Using functional MRI connectivity, we further confirmed the significance of the central and lateral amygdala, hippocampal body, and prefrontal cortex in distinguishing schizophrenia cases from controls. Our findings underscore the value of single-cell transcriptomics in understanding the polygenicity of psychiatric disorders and suggest a promising alignment of genomic, transcriptomic, and brain imaging modalities for identifying common biological targets.

Transcriptomic Evidence Reveals the Dysfunctional Mechanism of Synaptic Plasticity Control in ASD

BACKGROUND/OBJECTIVES

A prominent endophenotype in Autism Spectrum Disorder (ASD) is the synaptic plasticity dysfunction, yet the molecular mechanism remains elusive. As a prototype, we investigate the postsynaptic signal transduction network in glutamatergic neurons and integrate single-cell nucleus transcriptomics data from the Prefrontal Cortex (PFC) to unveil the malfunction of translation control.

METHODS

We devise an innovative and highly dependable pipeline to transform our acquired signal transduction network into an mRNA Signaling-Regulatory Network (mSiReN) and analyze it at the RNA level. We employ Cell-Specific Network Inference via Integer Value Programming and Causal Reasoning (CS-NIVaCaR) to identify core modules and Cell-Specific Probabilistic Contextualization for mRNA Regulatory Networks (CS-ProComReN) to quantitatively reveal activated sub-pathways involving MAPK1, MKNK1, RPS6KA5, and MTOR across different cell types in ASD.

RESULTS

The results indicate that specific pivotal molecules, such as EIF4EBP1 and EIF4E, lacking Differential Expression (DE) characteristics and responsible for protein translation with long-term potentiation (LTP) or long-term depression (LTD), are dysregulated. We further uncover distinct activation patterns causally linked to the EIF4EBP1-EIF4E module in excitatory and inhibitory neurons.

CONCLUSIONS

Importantly, our work introduces a methodology for leveraging extensive transcriptomics data to parse the signal transduction network, transforming it into mSiReN, and mapping it back to the protein level. These algorithms can serve as potent tools in systems biology to analyze other omics and regulatory networks. Furthermore, the biomarkers within the activated sub-pathways, revealed by identifying convergent dysregulation, illuminate potential diagnostic and prognostic factors in ASD.

Retrospective evaluation of novel serum inflammatory biomarkers in first-episode psychiatric disorders: diagnostic potential and immune dysregulation

Background

This study assessed the diagnostic capabilities of eight inflammatory biomarkers in first-episode schizophrenia (SCZ), bipolar disorder (BD), and depression (D), examining their differential expression across these psychiatric disorders. The markers studied include neutrophils/lymphocyte ratio (NLR), aggregate index of systemic inflammation (AISI), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), lymphocytes/high-density lipoprotein (HDL) ratio (LHR), monocytes/HDL ratio (MHR), neutrophils/HDL ratio (NHR), and platelets/HDL ratio (PHR).

Methods

We conducted a retrospective observational study involving 335 individuals with SCZ, 68 with BD, 202 with D, and 282 healthy controls (C) to evaluate hematologic parameters from untreated patients and controls.

Results

Significant differences in biomarker levels were found between patient groups and controls. Logistic regression analysis indicated that NHR and MHR (p < 0.001), as well as LHR and NLR (p < 0.01), were predictive factors for SCZ. MHR was a predictive factor for BD (p < 0.05). NHR (p < 0.01) and MHR (p < 0.001) were predictive factors for distinguishing between D and C. The area under the curve (AUC) value of the NHR + MHR + NLR composite index model for the SCZ group was 0.846 (p < 0.001). In the BD group, the AUC value for the MHR was 0.816 (p < 0.001). The D group's combined AUC value of NHR + MHR was 0.824 (p < 0.001).

Conclusion

This study highlights the diagnostic value of inflammatory biomarkers in distinguishing SCZ, BD, and D based on their differential expression.

Stimulating Wnt signaling reveals context-dependent genetic effects on gene regulation in primary human neural progenitors

Gene regulatory effects have been difficult to detect at many non-coding loci associated with brain-related traits, likely because some genetic variants have distinct functions in specific contexts. To explore context-dependent gene regulation, we measured chromatin accessibility and gene expression after activation of the canonical Wnt pathway in primary human neural progenitors (n = 82 donors). We found that TCF/LEF motifs and brain-structure-associated and neuropsychiatric-disorder-associated variants were enriched within Wnt-responsive regulatory elements. Genetically influenced regulatory elements were enriched in genomic regions under positive selection along the human lineage. Wnt pathway stimulation increased detection of genetically influenced regulatory elements/genes by 66%/53% and enabled identification of 397 regulatory elements primed to regulate gene expression. Stimulation also increased identification of shared genetic effects on molecular and complex brain traits by up to 70%, suggesting that genetic variant function during neurodevelopmental patterning can lead to differences in adult brain and behavioral traits.

Biospecimens in the HEALthy Brain and Child Development (HBCD) Study: Rationale and protocol

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The longitudinal collection of biological samples from over 7000 birthing parents and their children within the HBCD study enables research on pre- and postnatal exposures (e.g., substance use, toxicants, nutrition), and biological processes (e.g., genetics, epigenetic signatures, proteins, metabolites) on neurobehavioral developmental outcomes. The following biosamples are collected from the birthing parent: 1) blood (i.e., whole blood, serum, plasma, buffy coat, and dried blood spots) during pregnancy, 2) nail clippings during pregnancy and one month postpartum, 3) urine during pregnancy, and 4) saliva during pregnancy and at in-person postnatal assessments. The following samples are collected from the child at in-person study assessments: 1) saliva, 2) stool, and 3) urine. Additionally, placenta tissue, cord blood, and cord tissue are collected by a subset of HBCD sites. Here, we describe the rationale for the collection of these biospecimens, their current and potential future uses, the collection protocol, and collection success rates during piloting. This information will assist research teams in the planning of future studies utilizing this collection of biological samples.

A multi-regional human brain atlas of chromatin accessibility and gene expression facilitates promoter-isoform resolution genetic fine-mapping

Brain region- and cell-specific transcriptomic and epigenomic features are associated with heritability for neuropsychiatric traits, but a systematic view, considering cortical and subcortical regions, is lacking. Here, we provide an atlas of chromatin accessibility and gene expression profiles in neuronal and non-neuronal nuclei across 25 distinct human cortical and subcortical brain regions from 6 neurotypical controls. We identified extensive gene expression and chromatin accessibility differences across brain regions, including variation in alternative promoter-isoform usage and enhancer-promoter interactions. Genes with distinct promoter-isoform usage across brain regions were strongly enriched for neuropsychiatric disease risk variants. Moreover, we built enhancer-promoter interactions at promoter-isoform resolution across different brain regions and highlighted the contribution of brain region-specific and promoter-isoform-specific regulation to neuropsychiatric disorders. Including promoter-isoform resolution uncovers additional distal elements implicated in the heritability of diseases, thereby increasing the power to fine-map risk genes. Our results provide a valuable resource for studying molecular regulation across multiple regions of the human brain and underscore the importance of considering isoform information in gene regulation.

Shared genetic factors between osteoarthritis and cardiovascular disease may underlie common etiology

Osteoarthritis is one of the most common musculoskeletal diseases and increases the risk of severe cardiovascular disease, like heart attack and stroke. In some individuals, osteoarthritis and cardiovascular disease will co-occur. This co-occurrence might be due to shared risk factors, for example high age, lifestyle factors and/or a shared genetic liability for the two diseases. Here, we show that the correlation between osteoarthritis and cardiovascular disease can be explained by shared genetic factors, independent of high age and body weight, and also likely independent of lifestyle factors, like smoking and physical activity level. Findings suggest that genetic factors that are shared for osteoarthritis and cardiovascular disease may contribute to both diseases. Thus, the prevailing idea that osteoarthritis is predominantly a risk factor for cardiovascular disease is challenged. Our findings imply that the current diagnostic boundaries between these diseases may need to be re-evaluated.

The genetic landscape of neuro-related proteins in human plasma

Understanding the genetic basis of neuro-related proteins is essential for dissecting the molecular basis of human behavioural traits and the disease aetiology of neuropsychiatric disorders. Here the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,000 individuals for 184 neuro-related proteins in human plasma. The analysis identified 125 cis-regulatory protein quantitative trait loci (cis-pQTL) and 164 trans-pQTL. The mapped pQTL capture on average 50% of each protein's heritability. At the cis-pQTL, multiple proteins shared a genetic basis with human behavioural traits such as alcohol and food intake, smoking and educational attainment, as well as neurological conditions and psychiatric disorders such as pain, neuroticism and schizophrenia. Integrating with established drug information, the causal inference analysis validated 52 out of 66 matched combinations of protein targets and diseases or side effects with available drugs while suggesting hundreds of repurposing and new therapeutic targets.

Mutational landscape of risk variants in comorbid depression and obesity: a next-generation sequencing approach

Major depression (MD) and obesity are complex genetic disorders that are frequently comorbid. However, the study of both diseases concurrently remains poorly addressed and therefore the underlying genetic mechanisms involved in this comorbidity remain largely unknown. Here we examine the contribution of common and rare variants to this comorbidity through a next-generation sequencing (NGS) approach. Specific genomic regions of interest in MD and obesity were sequenced in a group of 654 individuals from the PISMA-ep epidemiological study. We obtained variants across the entire frequency spectrum and assessed their association with comorbid MD and obesity, both at variant and gene levels. We identified 55 independent common variants and a burden of rare variants in 4 genes (PARK2, FGF21, HIST1H3D and RSRC1) associated with the comorbid phenotype. Follow-up analyses revealed significantly enriched gene-sets associated with biological processes and pathways involved in metabolic dysregulation, hormone signaling and cell cycle regulation. Our results suggest that, while risk variants specific to the comorbid phenotype have been identified, the genes functionally impacted by the risk variants share cell biological processes and signaling pathways with MD and obesity phenotypes separately. To the best of our knowledge, this is the first study involving a targeted sequencing approach toward the study of the comorbid MD and obesity. The framework presented here allowed a deep characterization of the genetics of the co-occurring MD and obesity, revealing insights into the mutational and functional profile that underlies this comorbidity and contributing to a better understanding of the relationship between these two disabling disorders.

Genetic testing in psychiatry, the perceptions of healthcare workers and patients: a mini review

Background

Genetic testing in psychiatry has gained attention, raising questions about its application and impact. Understanding stakeholders' perspectives, including healthcare providers and patients, is vital for informed policy development. The aim of this systematic review was to focus on the perceptions and concerns of patients and healthcare workers in psychiatry regarding the use of genetic testing.

Methods

We conducted a systematic review following PRISMA guidelines, for the period 1/2/2014, to 1/1/2024, via PubMed and Embase databases identifying 50 articles in total. After excluding duplicates (n = 12), 38 articles went through screening. After careful full-text article assessment for eligibility and applying the inclusion and exclusion criteria, only fifteen (n = 15) of the articles were included.

Results

Among 15 selected studies involving 3,156 participants (2,347 healthcare professionals; 809 patients), thematic analysis identified four primary themes: Organizational-implementation concerns, Ethical Considerations, Concerns on changes in clinical praxis, and Legal implications. Despite these concerns, seven out of eleven studies indicated that healthcare workers viewed genetic testing in psychiatry positively. Patients' perspectives varied, with two of the four studies reflecting positive attitudes. No pervasive negative sentiment was observed.

Conclusion

Our review highlights the multidimensional perspectives of healthcare professionals and patients surrounding the application of genetic testing in psychiatry. These considerations need to be addressed to facilitate the implementation of genetic testing in clinical praxis in psychiatry. Further research is needed for validation of the results and to guide policies and clinicians in the integration of genetic testing into mental healthcare practice.

Upregulated solute-carrier family genes in the hippocampus of schizophrenia can be rescued by antipsychotic medications

BACKGROUND AND HYPOTHESIS

Our previous studies have found that functional changes in the hippocampal circuit from dentate gyrus (DG) to cornu ammonis 3 and 1 (CA3, CA1) are highly associated with schizophrenia (SZ). However, no studies have explored the genetic expression across the three and two human hippocampal subfields (DG-CA3-CA1 and CA3-CA1) between subjects with SZ and healthy controls (CT).

STUDY DESIGN

We matched cohorts between CT (n = 13) and SZ (n = 13). Among SZ, 6 subjects were on antipsychotics (AP) while 7 were off AP. We combined RNA-seq data from all three and two hippocampal subfields and performed differentially expressed gene analyses across DG-CA3-CA1 and CA3-CA1 affected by either SZ or AP.

STUDY RESULTS

We found that differentially expressed genes (DEGs) from effects of SZ and AP across DG-CA3-CA1 and CA3-CA1 were highly associated with gene ontology terms related to hormonal and immune signaling, cellular mitosis and apoptosis, ion and amino acid transports, and protein modification and degradation. Additionally, we found that multiple genes related to solute-carrier family and immune signaling were significantly upregulated across DG-CA3-CA1 and CA3-CA1 in patients with SZ relative to CT, and AP consistently and robustly repressed the expression of these upregulated genes in the DG-CA3-CA1 and CA3-CA1 from subjects with SZ.

CONCLUSIONS

Together, these data suggest that the upregulated solute-carrier family genes in the hippocampus might have important roles in the pathophysiology of SZ, and that AP may reduce the symptoms of psychosis in SZ via rescuing the solute-carrier gene expression.

Distal-to-proximal etiologically relevant variables associated with the general (p) and specific factors of psychopathology

BACKGROUND

The general factor of psychopathology, often denoted as p, captures the common variance among a broad range of psychiatric symptoms. Specific factors are co-modeled based on subsets of closely related symptoms. This paper investigated the extent to which wide-ranging genetic, personal, and environmental etiologically relevant variables are associated with p and specific psychopathology factors.

METHODS

Using data from four waves (ages 11-19) of TRAILS, we modeled a bifactor model of p and four specific factors [internalizing, externalizing, ADHD, Autism Spectrum Disorder (ASD)]. Next, we examined the associations of 19 etiologically relevant variables with these psychology factors using path models that organized the variables according to the distal-to-proximal risk principle.

RESULTS

Collectively, the etiologically relevant factors, including temperament traits, accounted for 55% of p's variance, 46% in ADHD, 35% in externalizing, 19% in internalizing, and 7% in ASD. The low 7% is due to insufficient unique variance in ASD indicators that load more strongly on p. Excluding temperament, variables accounted for 29% variance in p, 9% ADHD, 14% EXT, 7% INT, and 4% ASD. Most etiologically relevant factors were generic, predicting p. In addition, we identified effects on specific factors in addition to effects on p (e.g., parental SES, executive functioning); only effects on specific factors (e.g., parental rejection); opposite effects on different factors [e.g., diurnal cortisol (high INT but low EXT, p); developmental delay (high ASD and p but low EXT)]. Frustration, family functioning, parental psychopathology, executive functioning, and fearfulness had strong effects on p.

CONCLUSIONS

(1) Strong generic effects on p suggest that etiologically relevant factors and psychopathology tend to cluster in persons. (2) While many factors predict p, additional as well as opposite effects on specific factors indicate the relevance of specific psychopathology factors in understanding mental disorder. (3) High frustration, neurodevelopmental problems, and a disadvantaged family environment primarily characterize p.

Intron retention as an excellent marker for diagnosing depression and for discovering new potential pathways for drug intervention

Background

Peripheral inflammation is often associated with depressive disorders, and immunological biomarkers of depression remain a focus of investigation.

Methods

We performed RNA-seq analysis of RNA transcripts of human peripheral blood mononuclear cells from a case-control study including subjects with self-reported depression in the pre-symptomatic state of major depressive disorder and analyzed differentially expressed genes (DEGs) and the frequency of intron retention (IR) using rMATS.

Results

Among the statistically significant DEGs identified, the 651 upregulated DEGs were particularly enriched in the term "bacterial infection and phagocytosis", whereas the 820 downregulated DEGs were enriched in the terms "antigen presentation" and "T-cell proliferation and maturation". We also analyzed 158 genes for which the IR was increased (IncIR) and 211 genes for which the IR was decreased (DecIR) in the depressed subjects. Although the Gene Ontology terms associated with IncIR and DecIR were very similar to those of the up- and downregulated genes, respectively, IR genes appeared to be particularly enriched in genes with sensor functions, with a preponderance of the term "ciliary assembly and function". The observation that IR genes specifically interact with innate immunity genes suggests that immune-related genes, as well as cilia-related genes, may be excellent markers of depression. Re-analysis of previously published RNA-seq data from patients with MDD showed that common IR genes, particularly our predicted immune- and cilia-related genes, are commonly detected in populations with different levels of depression, providing validity for using IR to detect depression.

Conclusion

Depression was found to be associated with activation of the innate immune response and relative inactivation of T-cell signaling. The DEGs we identified reflect physiological demands that are controlled at the transcriptional level, whereas the IR results reflect a more direct mechanism for monitoring protein homeostasis. Accordingly, an alteration in IR, namely IncIR or DecIR, is a stress response, and intron-retained transcripts are sensors of the physiological state of the cytoplasm. The results demonstrate the potential of relative IR as a biomarker for the immunological stratification of depressed patients and the utility of IR for the discovery of novel pathways involved in recovery from depression.

Inhibitory neuron links the causal relationship from air pollution to psychiatric disorders: a large multi-omics analysis

Psychiatric disorders are severe health challenges that exert a heavy public burden. Air pollution has been widely reported as related to psychiatric disorder risk, but their casual association and pathological mechanism remained unclear. Herein, we systematically investigated the large genome-wide association studies (6 cohorts with 1,357,645 samples), single-cell RNA (26 samples with 157,488 cells), and bulk-RNAseq (1595 samples) datasets to reveal the genetic causality and biological link between four air pollutants and nine psychiatric disorders. As a result, we identified ten positive genetic correlations between air pollution and psychiatric disorders. Besides, PM2.5 and NO2 presented significant causal effects on schizophrenia risk which was robust with adjustment of potential confounders. Besides, transcriptome-wide association studies identified the shared genes between PM2.5/NO2 and schizophrenia. We then discovered a schizophrenia-derived inhibitory neuron subtype with highly expressed shared genes and abnormal synaptic and metabolic pathways by scRNA analyses and confirmed their abnormal level and correlations with the shared genes in schizophrenia patients in a large RNA-seq cohort. Comprehensively, we discovered robust genetic causality between PM2.5, NO2, and schizophrenia and identified an abnormal inhibitory neuron subtype that links schizophrenia pathology and PM2.5/NO2 exposure. These discoveries highlight the schizophrenia risk under air pollutants exposure and provide novel mechanical insights into schizophrenia pathology, contributing to pollutant-related schizophrenia risk control and therapeutic strategies development.

Graphical Abstract

A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework

The heterogeneity of psychiatric disorders makes researching disorder-specific neurobiological markers an ill-posed problem. Here, we face the need for disease stratification models by presenting a generalizable multivariate normative modelling framework for characterizing brain morphology, applied to bipolar disorder (BD). We employed deep autoencoders in an anomaly detection framework, combined with a confounder removal step integrating training and external validation. The model was trained with healthy control (HC) data from the human connectome project and applied to multi-site external data of HC and BD individuals. We found that brain deviating scores were greater, more heterogeneous, and with increased extreme values in the BD group, with volumes prominently from the basal ganglia, hippocampus and adjacent regions emerging as significantly deviating. Similarly, individual brain deviating maps based on modified z scores expressed higher abnormalities occurrences, but their overall spatial overlap was lower compared to HCs. Our generalizable framework enabled the identification of subject- and group-level brain normative-deviating patterns, a step forward towards the development of more effective and personalized clinical decision support systems and patient stratification in psychiatry.

Genetics of cell-type-specific post-transcriptional gene regulation during human neurogenesis

The function of some genetic variants associated with brain-relevant traits has been explained through colocalization with expression quantitative trait loci (eQTL) conducted in bulk postmortem adult brain tissue. However, many brain-trait associated loci have unknown cellular or molecular function. These genetic variants may exert context-specific function on different molecular phenotypes including post-transcriptional changes. Here, we identified genetic regulation of RNA editing and alternative polyadenylation (APA) within a cell-type-specific population of human neural progenitors and neurons. More RNA editing and isoforms utilizing longer polyadenylation sequences were observed in neurons, likely due to higher expression of genes encoding the proteins mediating these post-transcriptional events. We also detected hundreds of cell-type-specific editing quantitative trait loci (edQTLs) and alternative polyadenylation QTLs (apaQTLs). We found colocalizations of a neuron edQTL in CCDC88A with educational attainment and a progenitor apaQTL in EP300 with schizophrenia, suggesting that genetically mediated post-transcriptional regulation during brain development leads to differences in brain function.

Schizophrenia genomics: genetic complexity and functional insights

Determining the causes of schizophrenia has been a notoriously intractable problem, resistant to a multitude of investigative approaches over centuries. In recent decades, genomic studies have delivered hundreds of robust findings that implicate nearly 300 common genetic variants (via genome-wide association studies) and more than 20 rare variants (via whole-exome sequencing and copy number variant studies) as risk factors for schizophrenia. In parallel, functional genomic and neurobiological studies have provided exceptionally detailed information about the cellular composition of the brain and its interconnections in neurotypical individuals and, increasingly, in those with schizophrenia. Taken together, these results suggest unexpected complexity in the mechanisms that drive schizophrenia, pointing to the involvement of ensembles of genes (polygenicity) rather than single-gene causation. In this Review, we describe what we now know about the genetics of schizophrenia and consider the neurobiological implications of this information.

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.

Effect of pharmacogenomic testing on the clinical treatment of patients with depressive disorder: A randomized clinical trial

BACKGROUND

Pharmacotherapy is one of the primary treatment modalities for depression. However, there is considerable variability in the individual response to antidepressant medications. Personalized medicine guided by pharmacogenomic testing may hold promise in addressing this issue.

METHODS

In this study, 665 depressive patients were randomly enrolled into two groups: the pharmacogenomic testing group (n = 333) and the control group (n = 332). In the testing group, participants underwent pharmacogenomic testing, and clinicians customized the treatment plan with the result, while the control group relied solely on clinicians' experience. The primary outcomes were the proportion of remission and response, assessed with Hamilton Depression Rating Scale (HDRS). The secondary outcomes included changes in HDRS scores over time and frequency of adverse drug reactions by the participants.

RESULTS

At week 8, the pharmacogenomic testing group showed significantly higher remission rates (24.0 % v.s. 15.1 %; RR = 1.117; P = 0.007) and response rates (39.3 % v.s. 25.7 %; RR = 1.225; P < 0.001) compared to the control group. By week 12, the pharmacogenomic testing group continued to demonstrate significant advantages in remission (31.0 % v.s. 20.0 %; RR = 1.159; P = 0.003) and response (48.7 % v.s. 37.3 %; RR = 1.224; P = 0.006). Additionally, adverse drug reactions were less frequent in the pharmacogenomic testing group.

LIMITATIONS

This study is not blind to clinicians and it's a single-center study.

CONCLUSIONS

Pharmacogenomic testing-guided drug therapy can provide greater assistance in the treatment of depression.

'Almost nothing is firmly established': A History of Heredity and Genetics in Mental Health Science

Background

For more than a century, scientists have tried to find the key to causation of mental ill health in heredity and genetics. The difficulty of finding clear and actionable answers in our genes has not stopped them looking. This history offers important context to understanding mental health science today.

Methods

This article explores the main themes in research on genetics and inheritance in psychiatry from the second half of the nineteenth century to the present day, to address the question: what is the history of genetics as a causative explanation in mental health science? We take a critical historical approach to the literature, interrogating primary and secondary material for the light it brings to the research question, while considering the social and historical context.

Results

We begin with the statistics gathered in asylums and used to 'prove' the importance of heredity in mental ill health. We then move through early twentieth century Mendelian models of mental inheritance, the eugenics movement, the influence of social psychiatry, new classifications and techniques of the postwar era, the Human Genome Project and Genome Wide Association Studies (GWAS) and epigenetics. Setting these themes in historical context shows that this research was often popular because of wider social, political and cultural issues, which impacted the views of scientists just as they did those of policymakers, journalists and the general public.

Conclusions

We argue that attempting to unpick this complex history is essential to the modern ethics of mental health and genetics, as well as helping to focus our efforts to better understand causation in mental ill-health.For a succinct timeline of the history of psychiatric genetics, alongside the history of other proposed causes for mental ill-health, visit: https://historyofcauses.co.uk/.

Whole genome sequencing study of identical twins discordant for psychosis

Monozygotic (MZ) twins are often thought to have identical genomes, but recent work has shown that early post-zygotic events can result in a spectrum of DNA variants that are different between MZ twins. Such variants may explain phenotypic discordance and contribute to disease etiology. Here we performed whole genome sequencing in 17 pairs of MZ twins discordant for a psychotic disorder (schizophrenia, schizoaffective disorder or bipolar disorder). We examined various classes of rare variants that are discordant within a twin pair. We identified four genes harboring rare, predicted deleterious missense variants that were private to an affected individual in the cohort. Variants in FOXN1 and FLOT2 would have been categorized as damaging from recent schizophrenia and bipolar exome sequencing studies. Additionally, we identified four rare genic copy number variants (CNVs) private to an affected sample, two of which overlapped genes that have shown evidence for association with schizophrenia or bipolar disorder. One such CNV was a 3q29 duplication previously implicated in autism and developmental delay. We have performed the largest MZ twin study for discordant psychotic phenotypes to date. These findings warrant further investigation using other analytical approaches.

Micronutrient-Associated Single Nucleotide Polymorphism and Mental Health: A Mendelian Randomization Study

PURPOSE

Previous studies have demonstrated the link between micronutrients and mental health. However, it remains uncertain whether this connection is causal. We aim to investigate the potential causal effects of micronutrients on mental health based on linkage disequilibrium score (LDSC) regression and Mendelian randomization (MR) analysis.

METHODS

Utilizing publicly available genome-wide association study (GWAS) summary datasets, we performed LDSC and MR analysis to identify candidate micronutrients with potential causal effects on mental health. Single nucleotide polymorphisms (SNPs) significantly linked with candidate micronutrients with a genome-wide significance level (p < 5 × 10-8) were selected as instrumental variables (IVs). To estimate the causal effect of candidate micronutrients on mental health, we employed inverse variance weighted (IVW) regression. Additionally, two sensitivity analyses, MR-Egger and weighted median, were performed to validate our results.

RESULTS

We found evidence supporting significant causal associations between micronutrients and mental health. LDSC detected several candidate micronutrients, including serum iron (genetic correlation = -0.134, p = 0.032) and vitamin C (genetic correlation = -0.335, p < 0.001) for attention-deficit/hyperactivity disorder (ADHD), iron-binding capacity (genetic correlation = 0.210, p = 0.037) for Alzheimer's disease (AD), and vitamin B12 (genetic correlation = -0.178, p = 0.044) for major depressive disorder (MDD). Further MR analysis suggested a potential causal relationship between vitamin B12 and MDD (b = -0.139, p = 0.009). There was no significant heterogeneity or pleiotropy, indicating the validity of the findings.

CONCLUSION

In this study, we identified underlying causal relationships between micronutrients and mental health. Notably, more research is necessary to clarify the underlying biological mechanisms by which micronutrients affect mental health.

Perspective: the evolution of hormones and person perception-a quantitative genetic framework

Evolutionary biology provides a unifying theory for testing hypotheses about the relationship between hormones and person perception. Person perception usually receives attention from the perspective of sexual selection. However, because person perception is one trait in a suite regulated by hormones, univariate approaches are insufficient. In this Perspectives article, quantitative genetics is presented as an important but underutilized framework for testing evolutionary hypotheses within this literature. We note tacit assumptions within the current literature on psychiatric genetics, which imperil the interpretation of findings thus far. As regulators of a diverse manifold of traits, hormones mediate tradeoffs among an array of functions. Hormonal pleiotropy also provides the basis of correlational selection, a process whereby selection on one trait in a hormone-mediated suite generates selection on the others. This architecture provides the basis for conflicts between sexual and natural selection within hormone-mediated suites. Due to its role in person perception, psychiatric disorders, and reproductive physiology, the sex hormone estrogen is highlighted as an exemplar here. The implications of this framework for the evolution of person perception are discussed. Empirical quantification of selection on traits within hormone-mediated suites remains an important gap in this literature with great potential to illuminate the fundamental nature of psychiatric disorders.

Improved functional mapping of complex trait heritability with GSA-MiXeR implicates biologically specific gene sets

While genome-wide association studies are increasingly successful in discovering genomic loci associated with complex human traits and disorders, the biological interpretation of these findings remains challenging. Here we developed the GSA-MiXeR analytical tool for gene set analysis (GSA), which fits a model for the heritability of individual genes, accounting for linkage disequilibrium across variants and allowing the quantification of partitioned heritability and fold enrichment for small gene sets. We validated the method using extensive simulations and sensitivity analyses. When applied to a diverse selection of complex traits and disorders, including schizophrenia, GSA-MiXeR prioritizes gene sets with greater biological specificity compared to standard GSA approaches, implicating voltage-gated calcium channel function and dopaminergic signaling for schizophrenia. Such biologically relevant gene sets, often with fewer than ten genes, are more likely to provide insights into the pathobiology of complex diseases and highlight potential drug targets.

Recapitulation of Perturbed Striatal Gene Expression Dynamics of Donors' Brains With Ventral Forebrain Organoids Derived From the Same Individuals With Schizophrenia

OBJECTIVE

Schizophrenia is a brain disorder that originates during neurodevelopment and has complex genetic and environmental etiologies. Despite decades of clinical evidence of altered striatal function in affected patients, studies examining its cellular and molecular mechanisms in humans are limited. To explore neurodevelopmental alterations in the striatum associated with schizophrenia, the authors established a method for the differentiation of induced pluripotent stem cells (iPSCs) into ventral forebrain organoids (VFOs).

METHODS

VFOs were generated from postmortem dural fibroblast-derived iPSCs of four individuals with schizophrenia and four neurotypical control individuals for whom postmortem caudate genotypes and transcriptomic data were profiled in the BrainSeq neurogenomics consortium. Individuals were selected such that the two groups had nonoverlapping schizophrenia polygenic risk scores (PRSs).

RESULTS

Single-cell RNA sequencing analyses of VFOs revealed differences in developmental trajectory between schizophrenia and control individuals in which inhibitory neuronal cells from the patients exhibited accelerated maturation. Furthermore, upregulated genes in inhibitory neurons in schizophrenia VFOs showed a significant overlap with upregulated genes in postmortem caudate tissue of individuals with schizophrenia compared with control individuals, including the donors of the iPSC cohort.

CONCLUSIONS

The findings suggest that striatal neurons derived from high-PRS individuals with schizophrenia carry abnormalities that originated during early brain development and that the VFO model can recapitulate disease-relevant cell type-specific neurodevelopmental phenotypes in a dish.

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.

Selective vulnerability of the ventral hippocampus-prelimbic cortex axis parvalbumin interneuron network underlies learning deficits of fragile X mice

High-penetrance mutations affecting mental health can involve genes ubiquitously expressed in the brain. Whether the specific patterns of dysfunctions result from ubiquitous circuit deficits or might reflect selective vulnerabilities of targetable subnetworks has remained unclear. Here, we determine how loss of ubiquitously expressed fragile X mental retardation protein (FMRP), the cause of fragile X syndrome, affects brain networks in Fmr1y/- mice. We find that in wild-type mice, area-specific knockout of FMRP in the adult mimics behavioral consequences of area-specific silencing. By contrast, the functional axis linking the ventral hippocampus (vH) to the prelimbic cortex (PreL) is selectively affected in constitutive Fmr1y/- mice. A chronic alteration in late-born parvalbumin interneuron networks across the vH-PreL axis rescued by VIP signaling specifically accounts for deficits in vH-PreL theta-band network coherence, ensemble assembly, and learning functions of Fmr1y/- mice. Therefore, vH-PreL axis function exhibits a selective vulnerability to loss of FMRP in the vH or PreL, leading to learning and memory dysfunctions in fragile X mice.

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.

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

Inferring causal cell types of human diseases and risk variants from candidate regulatory elements

The heritability of human diseases is extremely enriched in candidate regulatory elements (cRE) from disease-relevant cell types. Critical next steps are to infer which and how many cell types are truly causal for a disease (after accounting for co-regulation across cell types), and to understand how individual variants impact disease risk through single or multiple causal cell types. Here, we propose CT-FM and CT-FM-SNP, two methods that leverage cell-type-specific cREs to fine-map causal cell types for a trait and for its candidate causal variants, respectively. We applied CT-FM to 63 GWAS summary statistics (average N = 417K) using nearly one thousand cRE annotations, primarily coming from ENCODE4. CT-FM inferred 81 causal cell types with corresponding SNP-annotations explaining a high fraction of trait SNP-heritability (~2/3 of the SNP-heritability explained by existing cREs), identified 16 traits with multiple causal cell types, highlighted cell-disease relationships consistent with known biology, and uncovered previously unexplored cellular mechanisms in psychiatric and immune-related diseases. Finally, we applied CT-FM-SNP to 39 UK Biobank traits and predicted high confidence causal cell types for 2,798 candidate causal non-coding SNPs. Our results suggest that most SNPs impact a phenotype through a single cell type, and that pleiotropic SNPs target different cell types depending on the phenotype context. Altogether, CT-FM and CT-FM-SNP shed light on how genetic variants act collectively and individually at the cellular level to impact disease risk.

Subtypes of Depressed Youth Admitted for Inpatient Psychiatric Care: A Latent Profile Analysis

Depressed youth frequently present with comorbid symptoms. Comorbidity is related to a poorer prognosis, including treatment resistance, academic problems, risk of suicide, and overall impairment. Studies examining the latent structure of depression support the notion of multiple presentations of depressed youth; however, it is unclear how these presentations are represented among acutely impaired youth. Participants (n = 457) in this naturalistic study were admitted to a psychiatric inpatient unit (Mean age = 14.33 years, SD = 1.94;76% female;46.6% Black/African-American). Selected subscales from the parent-report Behavior Assessment System for Children, Second Edition, were utilized as indicators in a latent profile analysis. Subgroups were validated based on their relationships with meaningful clinical correlates (e.g., family factors, discharge diagnosis) and further described by their associations with demographic variables. A five-class model provided the best balance of fit and parsimony. Subtypes of depressed youth included Predominantly Depressed (39.1%), Oppositional (28.2%), Severely Disruptive (12.3%), Anxious-Oppositional (11.6%), and Anxious-Withdrawn (8.8%). Comorbid symptoms were present in four of the five classes (60.9% of sample). High levels of externalizing symptoms were a prominent clinical feature associated with three classes (52.1% of the sample). Construct validity of the respective classes was demonstrated by differential association with clinical correlates, family characteristics, and demographics. Findings suggest that depressed youth presenting for acute inpatient psychiatric care displayed varied clinical presentations. The identified latent groups aligned with existing research reflecting comorbidity with anxiety, inattention, and externalizing disorders. Findings underscore the need for an increased clinical appreciation of comorbidity and encourage more targeted and effective prevention and treatment strategies.

Unraveling COVID-19 relationship with anxiety disorders and symptoms using genome-wide data

BACKGROUND

There is still a limited understanding of the dynamics contributing to the comorbidity of COVID-19 and anxiety outcomes.

METHODS

To dissect the pleiotropic mechanisms contributing to COVID-19/anxiety comorbidity, we used genome-wide data from UK Biobank (up to 420,531 participants), FinnGen Project (up to 329,077 participants), Million Veteran Program (175,163 participants), and COVID-19 Host Genetics Initiative (up to 122,616 cases and 2,475,240 controls). Specifically, we assessed global and local genetic correlation and genetically inferred effects linking COVID-19 outcomes (infection, hospitalization, and severe respiratory symptoms) to anxiety disorders and symptoms.

RESULTS

We observed a strong genetic correlation of anxiety disorder with COVID-19 positive status (rg = 0.35, p = 2×10-4) and COVID-19 hospitalization (rg = 0.31, p = 7.2×10-4). Among anxiety symptoms, "Tense, sore, or aching muscles during worst period of anxiety" was genetically correlated with COVID-19 positive status (rg = 0.33, p = 0.001), while "Frequent trouble falling or staying asleep during worst period of anxiety" was genetically correlated with COVID-19 hospitalization (rg = 0.24, p = 0.004). Through a latent causal variable analysis, we observed that COVID-19 outcomes have statistically significant genetic causality proportion (gcp) on anxiety symptoms (e.g., COVID-19 positive status→"Recent easy annoyance or irritability" │gcp│ = 0.18, p = 6.72×10-17). Conversely, anxiety disorders appear to have a possible causal effect on COVID-19 (│gcp│ = 0.38, p = 3.17×10-9). Additionally, we also identified multiple loci with evidence of local genetic correlation between anxiety and COVID-19. These appear to be related to genetic effects shared with lung function, brain morphology, alcohol and tobacco use, and hematologic parameters.

CONCLUSIONS

This study provided insights into the pleiotropic mechanisms linking COVID-19 and anxiety outcomes, suggesting differences between dynamics related to anxiety disorders and those related to anxiety symptoms.

Mental illness and cardiovascular health: observational and polygenic score analyses in a population-based cohort study

BACKGROUND

Individuals with serious mental illness have a markedly shorter life expectancy. A major contributor to premature death is cardiovascular disease (CVD). We investigated associations of (genetic liability for) depressive disorder, bipolar disorder and schizophrenia with a range of CVD traits and examined to what degree these were driven by important confounders.

METHODS

We included participants of the Dutch Lifelines cohort (N = 147 337) with information on self-reported lifetime diagnosis of depressive disorder, bipolar disorder, or schizophrenia and CVD traits. Employing linear mixed-effects models, we examined associations between mental illness diagnoses and CVD, correcting for psychotropic medication, demographic and lifestyle factors. In a subsample (N = 73 965), we repeated these analyses using polygenic scores (PGSs) for the three mental illnesses.

RESULTS

There was strong evidence that depressive disorder diagnosis is associated with increased arrhythmia and atherosclerosis risk and lower heart rate variability, even after confounder adjustment. Positive associations were also found for the depression PGSs with arrhythmia and atherosclerosis. Bipolar disorder was associated with a higher risk of nearly all CVD traits, though most diminished after adjustment. The bipolar disorder PGSs did not show any associations. While the schizophrenia PGSs was associated with increased arrhythmia risk and lower heart rate variability, schizophrenia diagnosis was not. All mental illness diagnoses were associated with lower blood pressure and a lower risk of hypertension.

CONCLUSIONS

Our study shows widespread associations of (genetic liability to) mental illness (primarily depressive disorder) with CVD, even after confounder adjustment. Future research should focus on clarifying potential causal pathways between mental illness and CVD.

Dissecting the biology of feeding and eating disorders

Feeding and eating disorders (FEDs) are heterogenous and characterized by varying patterns of dysregulated eating and weight. Genome-wide association studies (GWASs) are clarifying their underlying biology and their genetic relationship to other psychiatric and metabolic/anthropometric traits. Genetic research on anorexia nervosa (AN) has identified eight significant loci and uncovered genetic correlations implicating both psychiatric and metabolic/anthropometric risk factors. Careful explication of these metabolic contributors may be key to developing effective and enduring treatments for devastating, life-altering, and frequently lethal illnesses. We discuss clinical phenomenology, genomics, phenomics, intestinal microbiota, and functional genomics and propose a path that translates variants to genes, genes to pathways, and pathways to metabolic outcomes to advance the science and eventually treatment of FEDs.

Systematic exploration of a decade of publications on psychiatric genetics in Latin America

Psychiatric disorders have a great impact in terms of mortality, morbidity, and disability across the lifespan. Considerable effort has been devoted to understanding their complex and heterogeneous genetic architecture, including diverse ancestry populations. Our aim was to review the psychiatric genetics research published with Latin American populations from 2010 to 2019, and classify it according to country of origin, type of analysis, source of funding, and other variables. We found that most publications came from Brazil, Mexico, and Colombia. Also, local funds are generally not large enough for genome-wide studies in Latin America, with the exception of Brazil and Mexico; larger studies are often done in collaboration with international partners, mostly funded by US agencies. In most of the larger studies, the participants are individuals of Latin American ancestry living in the United States, which limits the potential for exploring the complex gene-environment interaction. Family studies, traditionally strong in Latin America, represent about 30% of the total research publications. Scarce local resources for research in Latin America have probably been an important limitation for conducting bigger and more complex studies, contributing to the reduced representation of these populations in global psychiatric genetics studies. Increasing diversity must be a goal to improve generalizability and applicability in clinical settings.

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.