A transcriptome-wide association study implicates specific pre- and post-synaptic abnormalities in schizophrenia

Integrating human endogenous retroviruses into transcriptome-wide association studies highlights novel risk factors for major psychiatric conditions

Human endogenous retroviruses (HERVs) are repetitive elements previously implicated in major psychiatric conditions, but their role in aetiology remains unclear. Here, we perform specialised transcriptome-wide association studies that consider HERV expression quantified to precise genomic locations, using RNA sequencing and genetic data from 792 post-mortem brain samples. In Europeans, we identify 1238 HERVs with expression regulated in cis, of which 26 represent expression signals associated with psychiatric disorders, with ten being conditionally independent from neighbouring expression signals. Of these, five are additionally significant in fine-mapping analyses and thus are considered high confidence risk HERVs. These include two HERV expression signatures specific to schizophrenia risk, one shared between schizophrenia and bipolar disorder, and one specific to major depressive disorder. No robust signatures are identified for autism spectrum conditions or attention deficit hyperactivity disorder in Europeans, or for any psychiatric trait in other ancestries, although this is likely a result of relatively limited statistical power. Ultimately, our study highlights extensive HERV expression and regulation in the adult cortex, including in association with psychiatric disorder risk, therefore providing a rationale for exploring neurological HERV expression in complex neuropsychiatric traits.

Genetically Regulated Gene Expression in the Brain Associated With Chronic Pain: Relationships With Clinical Traits and Potential for Drug Repurposing

BACKGROUND

Chronic pain is a common, poorly understood condition. Genetic studies including genome-wide association studies have identified many relevant variants, which have yet to be translated into full understanding of chronic pain. Transcriptome-wide association studies using transcriptomic imputation methods such as S-PrediXcan can help bridge this genotype-phenotype gap.

METHODS

We carried out transcriptomic imputation using S-PrediXcan to identify genetically regulated gene expression associated with multisite chronic pain in 13 brain tissues and whole blood. Then, we imputed genetically regulated gene expression for over 31,000 Mount Sinai BioMe participants and performed a phenome-wide association study to investigate clinical relationships in chronic pain-associated gene expression changes.

RESULTS

We identified 95 experiment-wide significant gene-tissue associations (p < 7.97 × 10-7), including 36 unique genes and an additional 134 gene-tissue associations reaching within-tissue significance, including 53 additional unique genes. Of the 89 unique genes in total, 59 were novel for multisite chronic pain and 18 are established drug targets. Chronic pain genetically regulated gene expression for 10 unique genes was significantly associated with cardiac dysrhythmia, metabolic syndrome, disc disorders/dorsopathies, joint/ligament sprain, anemias, and neurologic disorder phecodes. Phenome-wide association study analyses adjusting for mean pain score showed that associations were not driven by mean pain score.

CONCLUSIONS

We carried out the largest transcriptomic imputation study of any chronic pain trait to date. Results highlight potential causal genes in chronic pain development and tissue and direction of effect. Several gene results were also drug targets. Phenome-wide association study results showed significant associations for phecodes including cardiac dysrhythmia and metabolic syndrome, thereby indicating potential shared mechanisms.

The schizophrenia syndrome, circa 2024: What we know and how that informs its nature

With new data about different aspects of schizophrenia being continually generated, it becomes necessary to periodically revisit exactly what we know. Along with a need to review what we currently know about schizophrenia, there is an equal imperative to evaluate the construct itself. With these objectives, we undertook an iterative, multi-phase process involving fifty international experts in the field, with each step building on learnings from the prior one. This review assembles currently established findings about schizophrenia (construct, etiology, pathophysiology, clinical expression, treatment) and posits what they reveal about its nature. Schizophrenia is a heritable, complex, multi-dimensional syndrome with varying degrees of psychotic, negative, cognitive, mood, and motor manifestations. The illness exhibits a remitting and relapsing course, with varying degrees of recovery among affected individuals with most experiencing significant social and functional impairment. Genetic risk factors likely include thousands of common genetic variants that each have a small impact on an individual's risk and a plethora of rare gene variants that have a larger individual impact on risk. Their biological effects are concentrated in the brain and many of the same variants also increase the risk of other psychiatric disorders such as bipolar disorder, autism, and other neurodevelopmental conditions. Environmental risk factors include but are not limited to urban residence in childhood, migration, older paternal age at birth, cannabis use, childhood trauma, antenatal maternal infection, and perinatal hypoxia. Structural, functional, and neurochemical brain alterations implicate multiple regions and functional circuits. Dopamine D-2 receptor antagonists and partial agonists improve psychotic symptoms and reduce risk of relapse. Certain psychological and psychosocial interventions are beneficial. Early intervention can reduce treatment delay and improve outcomes. Schizophrenia is increasingly considered to be a heterogeneous syndrome and not a singular disease entity. There is no necessary or sufficient etiology, pathology, set of clinical features, or treatment that fully circumscribes this syndrome. A single, common pathophysiological pathway appears unlikely. The boundaries of schizophrenia remain fuzzy, suggesting the absence of a categorical fit and need to reconceptualize it as a broader, multi-dimensional and/or spectrum construct.

Spatiotemporal Mapping and Molecular Basis of Whole-brain Circuit Maturation

Brain development is highly dynamic and asynchronous, marked by the sequential maturation of functional circuits across the brain. The timing and mechanisms driving circuit maturation remain elusive due to an inability to identify and map maturing neuronal populations. Here we create DevATLAS (Developmental Activation Timing-based Longitudinal Acquisition System) to overcome this obstacle. We develop whole-brain mapping methods to construct the first longitudinal, spatiotemporal map of circuit maturation in early postnatal mouse brains. Moreover, we uncover dramatic impairments within the deep cortical layers in a neurodevelopmental disorders (NDDs) model, demonstrating the utility of this resource to pinpoint when and where circuit maturation is disrupted. Using DevATLAS, we reveal that early experiences accelerate the development of hippocampus-dependent learning by increasing the synaptically mature granule cell population in the dentate gyrus. Finally, DevATLAS enables the discovery of molecular mechanisms driving activity-dependent circuit maturation.

Psychosis Endophenotypes: A Gene-Set-Specific Polygenic Risk Score Analysis

BACKGROUND AND HYPOTHESIS

Endophenotypes can help to bridge the gap between psychosis and its genetic predispositions, but their underlying mechanisms remain largely unknown. This study aims to identify biological mechanisms that are relevant to the endophenotypes for psychosis, by partitioning polygenic risk scores into specific gene sets and testing their associations with endophenotypes.

STUDY DESIGN

We computed polygenic risk scores for schizophrenia and bipolar disorder restricted to brain-related gene sets retrieved from public databases and previous publications. Three hundred and seventy-eight gene-set-specific polygenic risk scores were generated for 4506 participants. Seven endophenotypes were also measured in the sample. Linear mixed-effects models were fitted to test associations between each endophenotype and each gene-set-specific polygenic risk score.

STUDY RESULTS

After correction for multiple testing, we found that a reduced P300 amplitude was associated with a higher schizophrenia polygenic risk score of the forebrain regionalization gene set (mean difference per SD increase in the polygenic risk score: -1.15 µV; 95% CI: -1.70 to -0.59 µV; P = 6 × 10-5). The schizophrenia polygenic risk score of forebrain regionalization also explained more variance of the P300 amplitude (R2 = 0.032) than other polygenic risk scores, including the genome-wide polygenic risk scores.

CONCLUSIONS

Our finding on reduced P300 amplitudes suggests that certain genetic variants alter early brain development thereby increasing schizophrenia risk years later. Gene-set-specific polygenic risk scores are a useful tool to elucidate biological mechanisms of psychosis and endophenotypes, offering leads for experimental validation in cellular and animal models.

Human forebrain organoid-based multi-omics analyses of PCCB as a schizophrenia associated gene linked to GABAergic pathways

Identifying genes whose expression is associated with schizophrenia (SCZ) risk by transcriptome-wide association studies (TWAS) facilitates downstream experimental studies. Here, we integrated multiple published datasets of TWAS, gene coexpression, and differential gene expression analysis to prioritize SCZ candidate genes for functional study. Convergent evidence prioritized Propionyl-CoA Carboxylase Subunit Beta (PCCB), a nuclear-encoded mitochondrial gene, as an SCZ risk gene. However, the PCCB's contribution to SCZ risk has not been investigated before. Using dual luciferase reporter assay, we identified that SCZ-associated SNPs rs6791142 and rs35874192, two eQTL SNPs for PCCB, showed differential allelic effects on transcriptional activities. PCCB knockdown in human forebrain organoids (hFOs) followed by RNA sequencing analysis revealed dysregulation of genes enriched with multiple neuronal functions including gamma-aminobutyric acid (GABA)-ergic synapse. The metabolomic and mitochondrial function analyses confirmed the decreased GABA levels resulted from inhibited tricarboxylic acid cycle in PCCB knockdown hFOs. Multielectrode array recording analysis showed that PCCB knockdown in hFOs resulted into SCZ-related phenotypes including hyper-neuroactivities and decreased synchronization of neural network. In summary, this study utilized hFOs-based multi-omics analyses and revealed that PCCB downregulation may contribute to SCZ risk through regulating GABAergic pathways, highlighting the mitochondrial function in SCZ.

Investigating the shared genetic architecture between schizophrenia and body mass index

Evidence for reciprocal comorbidity of schizophrenia (SCZ) and body mass index (BMI) has grown in recent years. However, little is known regarding the shared genetic architecture or causality underlying the phenotypic association between SCZ and BMI. Leveraging summary statistics from the hitherto largest genome-wide association study (GWAS) on each trait, we investigated the genetic overlap and causal associations of SCZ with BMI. Our study demonstrated a genetic correlation between SCZ and BMI, and the correlation was more evident in local genomic regions. The cross-trait meta-analysis identified 27 significant SNPs shared between SCZ and BMI, most of which had the same direction of influence on both diseases. Mendelian randomization analysis showed the causal association of SCZ with BMI, but not vice versa. Combining the gene expression information, we found that the genetic correlation between SCZ and BMI is enriched in six regions of brain, led by the brain frontal cortex. Additionally, 34 functional genes and 18 specific cell types were found to have an impact on both SCZ and BMI within these regions. Taken together, our comprehensive genome-wide cross-trait analysis suggests a shared genetic basis including pleiotropic loci, tissue enrichment, and shared function genes between SCZ and BMI. This work provides novel insights into the intrinsic genetic overlap of SCZ and BMI, and highlights new opportunities and avenues for future investigation.

Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions

Schizophrenia is a neurodevelopmental brain disorder whose genetic risk is associated with shifting clinical phenomena across the life span. We investigated the convergence of putative schizophrenia risk genes in brain coexpression networks in postmortem human prefrontal cortex (DLPFC), hippocampus, caudate nucleus, and dentate gyrus granule cells, parsed by specific age periods (total N = 833). The results support an early prefrontal involvement in the biology underlying schizophrenia and reveal a dynamic interplay of regions in which age parsing explains more variance in schizophrenia risk compared to lumping all age periods together. Across multiple data sources and publications, we identify 28 genes that are the most consistently found partners in modules enriched for schizophrenia risk genes in DLPFC; twenty-three are previously unidentified associations with schizophrenia. In iPSC-derived neurons, the relationship of these genes with schizophrenia risk genes is maintained. The genetic architecture of schizophrenia is embedded in shifting coexpression patterns across brain regions and time, potentially underwriting its shifting clinical presentation.

Human forebrain organoids-based multi-omics analyses reveal PCCB's regulation on GABAergic system contributing to schizophrenia

Identifying genes whose expression is associated with schizophrenia (SCZ) risk by transcriptome-wide association studies (TWAS) facilitates downstream experimental studies. Here, we integrated multiple published datasets of TWAS (including FUSION, PrediXcan, summary-data-based Mendelian randomization (SMR), joint-tissue imputation approach with Mendelian randomization (MR-JTI)), gene coexpression, and differential gene expression analysis to prioritize SCZ candidate genes for functional study. Convergent evidence prioritized Propionyl-CoA Carboxylase Subunit Beta ( PCCB ), a nuclear-encoded mitochondrial gene, as an SCZ risk gene. However, the PCCB ’s contribution to SCZ risk has not been investigated before. Using dual luciferase reporter assay, we identified that SCZ-associated SNP rs35874192, an eQTL SNP for PCCB , showed differential allelic effects on transcriptional activities. PCCB knockdown in human forebrain organoids (hFOs) followed by RNA-seq revealed dysregulation of genes enriched with multiple neuronal functions including gamma-aminobutyric acid (GABA)-ergic synapse, as well as genes dysregulated in postmortem brains of SCZ patients or in cerebral organoids derived from SCZ patients. The metabolomic and mitochondrial function analyses confirmed the deceased GABA levels resulted from reduced tricarboxylic acid cycle in PCCB knockdown hFOs. Multielectrode array recording analysis showed that PCCB knockdown in hFOs resulted into SCZ-related phenotypes including hyper-neuroactivities and decreased synchronization of neural network. In summary, this study utilized hFOs-based multi-omics data and revealed that PCCB downregulation may contribute to SCZ risk through regulating GABAergic system, highlighting the mitochondrial function in SCZ.

Analysis of networks in the dorsolateral prefrontal cortex in chronic schizophrenia: Relevance of altered immune response

The dorsolateral prefrontal cortex (DLPFC) has a crucial role in cognitive functioning and negative symptoms in schizophrenia. However, limited information of altered protein networks is available in this region in schizophrenia. We performed a proteomic analysis using single-shot liquid chromatography-tandem mass spectrometry of grey matter of postmortem DLPFC in chronic schizophrenia subjects (n = 20) and unaffected subjects (n = 20) followed by bioinformatic analysis to identify altered protein networks in schizophrenia (PXD024939 identifier in ProteomeXchange repository). Our results displayed a proteome profile in the DLPFC of 1989 proteins. 43 proteins were found significantly altered in schizophrenia. Analysis of this panel showed an enrichment of biological processes implicated in vesicle-mediated transport, processing and antigen presentation via MHC class II, intracellular transport and selenium metabolism. The enriched identified pathways were MHC class II antigen presentation, vesicle-mediated transport, Golgi ER retrograde transport, Nef mediated CD8 downregulation and the immune system. All these enriched categories were found to be downregulated. Furthermore, our network analyses showed crosstalk between proteins involved in MHC class II antigen presentation, membrane trafficking, Golgi-to-ER retrograde transport, Nef-mediated CD8 downregulation and the immune system with only one module built by 13 proteins. RAB7A showed eight interactions with proteins of all these pathways. Our results provide an altered molecular network involved in immune response in the DLPFC in schizophrenia with a central role of RAB7A. These results suggest that RAB7A or other proteins of this network could be potential targets for novel pharmacological strategies in schizophrenia for improving cognitive and negative symptoms.

BrainGENIE: The Brain Gene Expression and Network Imputation Engine

In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. To address this problem, we developed a computational method called the Brain Gene Expression and Network-Imputation Engine (BrainGENIE) that leverages peripheral-blood transcriptomes to predict brain tissue-specific gene-expression levels. Paired blood-brain transcriptomic data collected by the Genotype-Tissue Expression (GTEx) Project was used to train BrainGENIE models to predict gene-expression levels in ten distinct brain regions using whole-blood gene-expression profiles. The performance of BrainGENIE was compared to PrediXcan, a popular method for imputing gene expression levels from genotypes. BrainGENIE significantly predicted brain tissue-specific expression levels for 2947-11,816 genes (false-discovery rate-adjusted p < 0.05), including many transcripts that cannot be predicted significantly by a transcriptome-imputation method such as PrediXcan. BrainGENIE recapitulated measured diagnosis-related gene-expression changes in the brain for autism, bipolar disorder, and schizophrenia better than direct correlations from blood and predictions from PrediXcan. We developed a convenient software toolset for deploying BrainGENIE, and provide recommendations for how best to implement models. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.

Non-additive effects of schizophrenia risk genes reflect convergent downstream function

Genetic studies of schizophrenia (SCZ) reveal a complex polygenic risk architecture comprised of hundreds of risk variants, the majority of which are common in the population at-large and confer only modest increases in disorder risk. Precisely how genetic variants with individually small predicted effects on gene expression combine to yield substantial clinical impacts in aggregate is unclear. Towards this, we previously reported that the combinatorial perturbation of four SCZ risk genes ("eGenes", whose expression is regulated by common variants) resulted in gene expression changes that were not predicted by individual perturbations, being most non-additive among genes associated with synaptic function and SCZ risk. Now, across fifteen SCZ eGenes, we demonstrate that non-additive effects are greatest within groups of functionally similar eGenes. Individual eGene perturbations reveal common downstream transcriptomic effects ("convergence"), while combinatorial eGene perturbations result in changes that are smaller than predicted by summing individual eGene effects ("sub-additive effects"). Unexpectedly, these convergent and sub-additive downstream transcriptomic effects overlap and constitute a large proportion of the genome-wide polygenic risk score, suggesting that functional redundancy of eGenes may be a major mechanism underlying non-additivity. Single eGene perturbations likewise fail to predict the magnitude or directionality of cellular phenotypes resulting from combinatorial perturbations. Overall, our results indicate that polygenic risk cannot be extrapolated from experiments testing one risk gene at a time and must instead be empirically measured. By unravelling the interactions between complex risk variants, it may be possible to improve the clinical utility of polygenic risk scores through more powerful prediction of symptom onset, clinical trajectory, and treatment response, or to identify novel targets for therapeutic intervention.

Epigenetic Targets in Schizophrenia Development and Therapy

Schizophrenia is regarded as a neurodevelopmental disorder with its course progressing throughout life. However, the aetiology and development of schizophrenia are still under investigation. Several data suggest that the dysfunction of epigenetic mechanisms is known to be involved in the pathomechanism of this mental disorder. The present article revised the epigenetic background of schizophrenia based on the data available in online databases (PubMed, Scopus). This paper focused on the role of epigenetic regulation, such as DNA methylation, histone modifications, and interference of non-coding RNAs, in schizophrenia development. The article also reviewed the available data related to epigenetic regulation that may modify the severity of the disease as a possible target for schizophrenia pharmacotherapy. Moreover, the effects of antipsychotics on epigenetic malfunction in schizophrenia are discussed based on preclinical and clinical results. The obtainable data suggest alterations of epigenetic regulation in schizophrenia. Moreover, they also showed the important role of epigenetic modifications in antipsychotic action. There is a need for more data to establish the role of epigenetic mechanisms in schizophrenia therapy. It would be of special interest to find and develop new targets for schizophrenia therapy because patients with schizophrenia could show little or no response to current pharmacotherapy and have treatment-resistant schizophrenia.

Mendelian Randomization Study Using Dopaminergic Neuron-Specific eQTL Identifies Novel Risk Genes for Schizophrenia

Multiple integrative studies have been performed to identify the potential target genes of the non-coding schizophrenia (SCZ) risk variants. However, all the integrative studies used expression quantitative trait loci (eQTL) data from bulk tissues. Considering the cell type-specific regulatory effect of many genetic variants, it is important to conduct integrative studies using cell type-specific eQTL data. Here, we conduct a Mendelian randomization (MR) study by integrating genome-wide associations of SCZ (74,776 cases and 101,023 controls) and eQTL data (N = 215) from dopaminergic neurons, which were differentiated from human-induced pluripotent stem cell (iPSC) lines. For eQTL from young post-mitotic dopaminergic neurons (differentiation of iPSC for 30 days, D30), we identified 34 genes whose genetically regulated expression in dopaminergic neurons may have a causal role in SCZ. Among which, ARL3 showed the most significant associations with SCZ. For eQTL from more mature dopaminergic neurons (D52), we identified 37 potential SCZ causal genes, and ARL3 and GNL3 showed the most significant associations. Only 12 genes showed significant associations with SCZ in both D30 and D52 eQTL datasets, indicating the time point-specific genetic regulatory effects in young post-mitotic dopaminergic neurons and more mature dopaminergic neurons. Comparing the results from dopaminergic neurons with bulk brain tissues prioritized 2 high-confidence risk genes, including DDHD2 and GALNT10. Our study identifies multiple risk genes whose genetically regulated expression in dopaminergic neurons may have a causal role in SCZ. Further mechanistic investigation will provide pivotal insights into SCZ pathophysiology.

Multiomic prioritisation of risk genes for anorexia nervosa

BACKGROUND

Anorexia nervosa (AN) is a psychiatric disorder associated with marked morbidity. Whilst AN genetic studies could identify novel treatment targets, integration of functional genomics data, including transcriptomics and proteomics, would assist to disentangle correlated signals and reveal causally associated genes.

METHODS

We used models of genetically imputed expression and splicing from 14 tissues, leveraging mRNA, protein, and mRNA alternative splicing weights to identify genes, proteins, and transcripts, respectively, associated with AN risk. This was accomplished through transcriptome, proteome, and spliceosome-wide association studies, followed by conditional analysis and finemapping to prioritise candidate causal genes.

RESULTS

We uncovered 134 genes for which genetically predicted mRNA expression was associated with AN after multiple-testing correction, as well as four proteins and 16 alternatively spliced transcripts. Conditional analysis of these significantly associated genes on other proximal association signals resulted in 97 genes independently associated with AN. Moreover, probabilistic finemapping further refined these associations and prioritised putative causal genes. The gene WDR6, for which increased genetically predicted mRNA expression was correlated with AN, was strongly supported by both conditional analyses and finemapping. Pathway analysis of genes revealed by finemapping identified the pathway regulation of immune system process (overlapping genes = MST1, TREX1, PRKAR2A, PROS1) as statistically overrepresented.

CONCLUSIONS

We leveraged multiomic datasets to genetically prioritise novel risk genes for AN. Multiple-lines of evidence support that WDR6 is associated with AN, whilst other prioritised genes were enriched within immune related pathways, further supporting the role of the immune system in AN.

Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

Cognitive Impairment Associated with Schizophrenia: From Pathophysiology to Treatment

Cognitive impairment is a core feature of schizophrenia and a major contributor to poor functional outcomes. Methods for assessment of cognitive dysfunction in schizophrenia are now well established. In addition, there has been increasing appreciation in recent years of the additional role of social cognitive impairment in driving functional outcomes and of the contributions of sensory-level dysfunction to higher-order impairments. At the neurochemical level, acute administration of N-methyl-d-aspartate receptor (NMDAR) antagonists reproduces the pattern of neurocognitive dysfunction associated with schizophrenia, encouraging the development of treatments targeted at both NMDAR and its interactome. At the local-circuit level, an auditory neurophysiological measure, mismatch negativity, has emerged both as a veridical index of NMDAR dysfunction and excitatory/inhibitory imbalance in schizophrenia and as a critical biomarker for early-stage translational drug development. Although no compounds have yet been approved for treatment of cognitive impairment associated with schizophrenia, several candidates are showing promise in early-phase testing.

Genome-wide Mendelian randomization identifies actionable novel drug targets for psychiatric disorders

Psychiatric disorders impose tremendous economic burden on society and are leading causes of disability worldwide. However, only limited drugs are available for psychiatric disorders and the efficacy of most currently used drugs is poor for many patients. To identify novel therapeutic targets for psychiatric disorders, we performed genome-wide Mendelian randomization analyses by integrating brain-derived molecular quantitative trait loci (mRNA expression and protein abundance quantitative trait loci) of 1263 actionable proteins (targeted by approved drugs or drugs in clinical phase of development) and genetic findings from large-scale genome-wide association studies (GWASs). Using transcriptome data, we identified 25 potential drug targets for psychiatric disorders, including 12 genes for schizophrenia, 7 for bipolar disorder, 7 for depression, and 1 (TIE1) for attention deficit and hyperactivity. We also identified 10 actionable drug targets by using brain proteome data, including 4 (HLA-DRB1, CAMKK2, P2RX7, and MAPK3) for schizophrenia, 1 (PRKCB) for bipolar disorder, 6 (PSMB4, IMPDH2, SERPINC1, GRIA1, P2RX7 and TAOK3) for depression. Of note, MAPK3 and HLA-DRB1 were supported by both transcriptome and proteome-wide MR analyses, suggesting that these two proteins are promising therapeutic targets for schizophrenia. Our study shows the power of integrating large-scale GWAS findings and transcriptomic and proteomic data in identifying actionable drug targets. Besides, our findings prioritize actionable novel drug targets for development of new therapeutics and provide critical drug-repurposing opportunities for psychiatric disorders.

Excitatory Dysfunction Drives Network and Calcium Handling Deficits in 16p11.2 Duplication Schizophrenia Induced Pluripotent Stem Cell-Derived Neurons

BACKGROUND

Schizophrenia (SCZ) is a debilitating psychiatric disorder with a large genetic contribution; however, its neurodevelopmental substrates remain largely unknown. Modeling pathogenic processes in SCZ using human induced pluripotent stem cell-derived neurons (iNs) has emerged as a promising strategy. Copy number variants confer high genetic risk for SCZ, with duplication of the 16p11.2 locus increasing the risk 14.5-fold.

METHODS

To dissect the contribution of induced excitatory neurons (iENs) versus GABAergic (gamma-aminobutyric acidergic) neurons (iGNs) to SCZ pathophysiology, we induced iNs from CRISPR (clustered regularly interspaced short palindromic repeats)-Cas9 isogenic and SCZ patient-derived induced pluripotent stem cells and analyzed SCZ-related phenotypes in iEN monocultures and iEN/iGN cocultures.

RESULTS

In iEN/iGN cocultures, neuronal firing and synchrony were reduced at later, but not earlier, stages of in vitro development. These were fully recapitulated in iEN monocultures, indicating a primary role for iENs. Moreover, isogenic iENs showed reduced dendrite length and deficits in calcium handling. iENs from 16p11.2 duplication-carrying patients with SCZ displayed overlapping deficits in network synchrony, dendrite outgrowth, and calcium handling. Transcriptomic analysis of both iEN cohorts revealed molecular markers of disease related to the glutamatergic synapse, neuroarchitecture, and calcium regulation.

CONCLUSIONS

Our results indicate the presence of 16p11.2 duplication-dependent alterations in SCZ patient-derived iENs. Transcriptomics and cellular phenotyping reveal overlap between isogenic and patient-derived iENs, suggesting a central role of glutamatergic, morphological, and calcium dysregulation in 16p11.2 duplication-mediated pathogenesis. Moreover, excitatory dysfunction during early neurodevelopment is implicated as the basis of SCZ pathogenesis in 16p11.2 duplication carriers. Our results support network synchrony and calcium handling as outcomes directly linked to this genetic risk variant.

Functional impairment of cortical AMPA receptors in schizophrenia

Clinical and preclinical studies suggest that some of the behavioral alterations observed in schizophrenia (SZ) may be mechanistically linked to synaptic dysfunction of glutamatergic signaling. Recent genetic and proteomic studies suggest alterations of cortical glutamate receptors of the AMPA-type (AMPARs), which are the predominant ligand-gated ionic channels of fast transmission at excitatory synapses. The impact of gene and protein alterations on the electrophysiological activity of AMPARs is not known in SZ. In this proof of principle work, using human postmortem brain synaptic membranes isolated from the dorsolateral prefrontal cortex (DLPFC), we combined electrophysiological analysis from microtransplanted synaptic membranes (MSM) with transcriptomic (RNA-Seq) and label-free proteomics data in 10 control and 10 subjects diagnosed with SZ. We observed in SZ a reduction in the amplitude of AMPARs currents elicited by kainate, an agonist of AMPARs that blocks the desensitization of the receptor. This reduction was not associated with protein abundance but with a reduction in kainate's potency to activate AMPARs. Electrophysiologically-anchored dataset analysis (EDA) was used to identify synaptosomal proteins that linearly correlate with the amplitude of the AMPARs responses, gene ontology functional annotations were then used to determine protein-protein interactions. Protein modules associated with positive AMPARs current increases were downregulated in SZ, while protein modules that were upregulated in SZ were associated with decreased AMPARs currents. Our results indicate that transcriptomic and proteomic alterations, frequently observed in the DLPFC in SZ, converge at the synaptic level producing a functional electrophysiological impairment of AMPARs.

Attenuated transcriptional response to pro-inflammatory cytokines in schizophrenia hiPSC-derived neural progenitor cells

Maternal immune activation (MIA) during prenatal development is an environmental risk factor for psychiatric disorders including schizophrenia (SZ). Converging lines of evidence from human and animal model studies suggest that elevated cytokine levels in the maternal and fetal compartments are an important indication of the mechanisms driving this association. However, there is variability in susceptibility to the psychiatric risk conferred by MIA, likely influenced by genetic factors. How MIA interacts with a genetic profile susceptible to SZ is challenging to test in animal models. To address this gap, we examined whether differential gene expression responses occur in forebrain-lineage neural progenitor cells (NPCs) derived from human induced pluripotent stem cells (hiPSC) generated from three individuals with a diagnosis of schizophrenia and three healthy controls. Following acute (24 h) treatment with either interferon-gamma (IFNγ; 25 ng/μl) or interleukin (IL)-1β (10 ng/μl), we identified, by RNA sequencing, 3380 differentially expressed genes (DEGs) in the IFNγ-treated control lines (compared to untreated controls), and 1980 DEGs in IFNγ-treated SZ lines (compared to untreated SZ lines). Out of 4137 genes that responded significantly to IFNγ across all lines, 1223 were common to both SZ and control lines. The 2914 genes that appeared to respond differentially to IFNγ treatment in SZ lines were subjected to a further test of significance (multiple testing correction applied to the interaction effect between IFNγ treatment and SZ diagnosis), yielding 359 genes that passed the significance threshold. There were no differentially expressed genes in the IL-1β-treatment conditions after Benjamini-Hochberg correction. Gene set enrichment analysis however showed that IL-1β impacts immune function and neuronal differentiation. Overall, our data suggest that a) SZ NPCs show an attenuated transcriptional response to IFNγ treatment compared to controls; b) Due to low IL-1β receptor expression in NPCs, NPC cultures appear to be less responsive to IL-1β than IFNγ; and c) the genes differentially regulated in SZ lines - in the face of a cytokine challenge - are primarily associated with mitochondrial, "loss-of-function", pre- and post-synaptic gene sets. Our findings particularly highlight the role of early synaptic development in the association between maternal immune activation and schizophrenia risk.

Potential diagnostic biomarkers for schizophrenia

Schizophrenia (SCH) is a complex and severe mental disorder with high prevalence, disability, mortality and carries a heavy disease burden, the lifetime prevalence of SCH is around 0.7%-1.0%, which has a profound impact on the individual and society. In the clinical practice of SCH, key problems such as subjective diagnosis, experiential treatment, and poor overall prognosis are still challenging. In recent years, some exciting discoveries have been made in the research on objective biomarkers of SCH, mainly focusing on genetic susceptibility genes, metabolic indicators, immune indices, brain imaging, electrophysiological characteristics. This review aims to summarize the biomarkers that may be used for the prediction and diagnosis of SCH.

Structural and Functional Deviations of the Hippocampus in Schizophrenia and Schizophrenia Animal Models

Schizophrenia is a grave neuropsychiatric disease which frequently onsets between the end of adolescence and the beginning of adulthood. It is characterized by a variety of neuropsychiatric abnormalities which are categorized into positive, negative and cognitive symptoms. Most therapeutical strategies address the positive symptoms by antagonizing D2-dopamine-receptors (DR). However, negative and cognitive symptoms persist and highly impair the life quality of patients due to their disabling effects. Interestingly, hippocampal deviations are a hallmark of schizophrenia and can be observed in early as well as advanced phases of the disease progression. These alterations are commonly accompanied by a rise in neuronal activity. Therefore, hippocampal formation plays an important role in the manifestation of schizophrenia. Furthermore, studies with animal models revealed a link between environmental risk factors and morphological as well as electrophysiological abnormalities in the hippocampus. Here, we review recent findings on structural and functional hippocampal abnormalities in schizophrenic patients and in schizophrenia animal models, and we give an overview on current experimental approaches that especially target the hippocampus. A better understanding of hippocampal aberrations in schizophrenia might clarify their impact on the manifestation and on the outcome of this severe disease.

Schizophrenia Genomics: Convergence on Synaptic Development, Adult Synaptic Plasticity, or Both?

Large-scale genomic studies of schizophrenia have identified hundreds of genetic loci conferring risk to the disorder. This progress offers an important route toward defining the biological basis of the condition and potentially developing new treatments. In this review, we discuss insights from recent genome-wide association study, copy number variant, and exome sequencing analyses of schizophrenia, together with functional genomics data from the pre- and postnatal brain, in relation to synaptic development and function. These data provide strong support for the view that synaptic dysfunction within glutamatergic and GABAergic (gamma-aminobutyric acidergic) neurons of the cerebral cortex, hippocampus, and other limbic structures is a central component of schizophrenia pathophysiology. Implicated genes and functional genomic data suggest that disturbances in synaptic connectivity associated with susceptibility to schizophrenia begin in utero but continue throughout development, with some alleles conferring risk to the disorder through direct effects on synaptic function in adulthood. This model implies that novel interventions for schizophrenia could include broad preventive approaches aimed at enhancing synaptic health during development as well as more targeted treatments aimed at correcting synaptic function in affected adults.

Bioinformatics and Network-based Approaches for Determining Pathways, Signature Molecules, and Drug Substances connected to Genetic Basis of Schizophrenia etiology

Knowledge of heterogeneous etiology and pathophysiology of schizophrenia (SZP) is reasonably inadequate and non-deterministic due to its inherent complexity and underlying vast dynamics related to genetic mechanisms. The evolution of large-scale transcriptome-wide datasets and subsequent development of relevant, robust technologies for their analyses show promises toward elucidating the genetic basis of disease pathogenesis, its early risk prediction, and predicting drug molecule targets for therapeutic intervention. In this research, we have scrutinized the genetic basis of SZP through functional annotation and network-based system biology approaches. We have determined 96 overlapping differentially expressed genes (DEGs) from 2 microarray datasets and subsequently identified their interconnecting networks to reveal transcriptome signatures like hub proteins (FYN, RAD51, SOCS3, XIAP, AKAP13, PIK3C2A, CBX5, GATA3, EIF3K, and CDKN2B), transcription factors and miRNAs. In addition, we have employed gene set enrichment to highlight significant gene ontology (e.g., positive regulation of microglial cell activation) and relevant pathways (such as axon guidance and focal adhesion) interconnected to the genes associated with SZP. Finally, we have suggested candidate drug substances like Luteolin HL60 UP as a possible therapeutic target based on these key molecular signatures.

A protein-centric view of in vitro biological model systems for schizophrenia

Schizophrenia (SCZ) is a severe brain disorder, characterized by psychotic, negative, and cognitive symptoms, affecting 1% of the population worldwide. The precise etiology of SCZ is still unknown; however, SCZ has a high heritability and is associated with genetic, environmental, and social risk factors. Even though the genetic contribution is indisputable, the discrepancies between transcriptomics and proteomics in brain tissues are consistently challenging the field to decipher the disease pathology. Here we provide an overview of the state of the art of neuronal two-dimensional and three-dimensional model systems that can be combined with proteomics analyses to decipher specific brain pathology and detection of alternative entry points for drug development.

Proteome-wide Association Study Provides Insights Into the Genetic Component of Protein Abundance in Psychiatric Disorders

BACKGROUND

Genome-wide association studies have identified multiple risk variants for psychiatric disorders. Nevertheless, how the risk variants confer risk of psychiatric disorders remains largely unknown.

METHODS

We performed proteome-wide association studies to identify genes whose cis-regulated protein abundance change in the human brain were associated with psychiatric disorders.

RESULTS

By integrating genome-wide associations of four common psychiatric disorders and two independent brain proteomes (n = 376 and n = 152, respectively) from the dorsolateral prefrontal cortex, we identified 61 genes (including 48 genes for schizophrenia, 12 genes for bipolar disorder, 5 genes for depression, and 2 genes for attention-deficit/hyperactivity disorder) whose genetically regulated protein abundance levels were associated with risk of psychiatric disorders. Comparison with transcriptome-wide association studies identified 18 overlapping genes that showed significant associations with psychiatric disorders at both proteome-wide and transcriptome-wide levels, suggesting that genetic risk variants likely confer risk of psychiatric disorders by regulating messenger RNA expression and protein abundance of these genes.

CONCLUSIONS

Our study not only provides new insights into the genetic component of protein abundance in psychiatric disorders but also highlights several high-confidence risk proteins (including CNNM2 and CTNND1) for schizophrenia and depression. These high-confidence risk proteins represent promising therapeutic targets for future drug development.

Genetic architecture of schizophrenia: a review of major advancements

Schizophrenia is a severe psychiatric disorder with high heritability. Consortia efforts and technological advancements have led to a substantial increase in knowledge of the genetic architecture of schizophrenia over the past decade. In this article, we provide an overview of the current understanding of the genetics of schizophrenia, outline remaining challenges, and summarise future directions of research. World-wide collaborations have resulted in genome-wide association studies (GWAS) in over 56 000 schizophrenia cases and 78 000 controls, which identified 176 distinct genetic loci. The latest GWAS from the Psychiatric Genetics Consortium, available as a pre-print, indicates that 270 distinct common genetic loci have now been associated with schizophrenia. Polygenic risk scores can currently explain around 7.7% of the variance in schizophrenia case-control status. Rare variant studies have implicated eight rare copy-number variants, and an increased burden of loss-of-function variants in SETD1A, as increasing the risk of schizophrenia. The latest exome sequencing study, available as a pre-print, implicates a burden of rare coding variants in a further nine genes. Gene-set analyses have demonstrated significant enrichment of both common and rare genetic variants associated with schizophrenia in synaptic pathways. To address current challenges, future genetic studies of schizophrenia need increased sample sizes from more diverse populations. Continued expansion of international collaboration will likely identify new genetic regions, improve fine-mapping to identify causal variants, and increase our understanding of the biology and mechanisms of schizophrenia.

Targeting synaptic plasticity in schizophrenia: insights from genomic studies

Patients with schizophrenia experience cognitive dysfunction and negative symptoms that do not respond to current drug treatments. Historical evidence is consistent with the hypothesis that these deficits are due, at least in part, to altered cortical synaptic plasticity (the ability of synapses to strengthen or weaken their activity), making this an attractive pathway for therapeutic intervention. However, while synaptic transmission and plasticity is well understood in model systems, it has been challenging to identify specific therapeutic targets for schizophrenia. New information is emerging from genomic findings, which converge on synaptic plasticity and provide a new window on the neurobiology of schizophrenia. Translating this information into therapeutic advances will require a multidisciplinary and collaborative approach.

Schizophrenia risk alleles often affect the expression of many genes and each gene may have a different effect on the risk: A mediation analysis

Variants identified by genome-wide association studies (GWAS) are often expression quantitative trait loci (eQTLs), suggesting they are proxies or are themselves regulatory. Across many data sets, analyses show that variants often affect multiple genes. Lacking data on many tissue types, developmental time points, and homogeneous cell types, the extent of this one-to-many relationship is underestimated. This raises questions on whether a disease eQTL target gene explains the genetic association or is a bystander and puts into question the direction of expression effect of on the risk, since the many variants-regulated genes may have opposing effects, imperfectly balancing each other. We used two brain gene expression data sets (CommonMind and BrainSeq) for mediation analysis of schizophrenia-associated variants. We confirm that eQTL target genes often mediate risk but the direction in which expression affects risk is often different from that in which the risk allele changes expression. Of 38 mediator genes significant in both data sets 33 showed consistent mediation direction (Chi² test p = 6 × 10^-6 ). One might expect that the expression would correlate with the risk allele in the same direction it correlates with the disease. For 15 of these 33 (45%), however, the expression change associated with the risk allele was protective, suggesting the likely presence of other target genes with overriding effects. Our results identify specific risk mediating genes and suggest caution in interpreting the biological consequences of targeted modifications of gene expression, as not all eQTL targets may be relevant to disease while those that are, might have different from expected directions.

Cross-platform validation of neurotransmitter release impairments in schizophrenia patient-derived NRXN1-mutant neurons

Heterozygous NRXN1 deletions constitute the most prevalent currently known single-gene mutation associated with schizophrenia, and additionally predispose to multiple other neurodevelopmental disorders. Engineered heterozygous NRXN1 deletions impaired neurotransmitter release in human neurons, suggesting a synaptic pathophysiological mechanism. Utilizing this observation for drug discovery, however, requires confidence in its robustness and validity. Here, we describe a multicenter effort to test the generality of this pivotal observation, using independent analyses at two laboratories of patient-derived and newly engineered human neurons with heterozygous NRXN1 deletions. Using neurons transdifferentiated from induced pluripotent stem cells that were derived from schizophrenia patients carrying heterozygous NRXN1 deletions, we observed the same synaptic impairment as in engineered NRXN1-deficient neurons. This impairment manifested as a large decrease in spontaneous synaptic events, in evoked synaptic responses, and in synaptic paired-pulse depression. Nrxn1-deficient mouse neurons generated from embryonic stem cells by the same method as human neurons did not exhibit impaired neurotransmitter release, suggesting a human-specific phenotype. Human NRXN1 deletions produced a reproducible increase in the levels of CASK, an intracellular NRXN1-binding protein, and were associated with characteristic gene-expression changes. Thus, heterozygous NRXN1 deletions robustly impair synaptic function in human neurons regardless of genetic background, enabling future drug discovery efforts.

Integrative Analyses Followed by Functional Characterization Reveal TMEM180 as a Schizophrenia Risk Gene

Recent large-scale integrative analyses (including Transcriptome-Wide Association Study [TWAS] and Summary-data-based Mendelian Randomization [SMR]) have identified multiple genes whose cis-regulated expression changes may confer risk of schizophrenia. Nevertheless, expression quantitative trait loci (eQTL) data and genome-wide associations used for integrative analyses were mainly from populations of European ancestry, resulting in potential missing of pivotal biological insights in other continental populations due to population heterogeneity. Here we conducted TWAS and SMR integrative analyses using blood eQTL (from 162 subjects) and GWAS data (22 778 cases and 35 362 controls) of schizophrenia in East Asian (EAS) populations. Both TWAS (P = 2.89 × 10-14) and SMR (P = 6.04 × 10-5) analyses showed that decreased TMEM180 mRNA expression was significantly associated with risk of schizophrenia. We further found that TMEM180 was significantly down-regulated in the peripheral blood of schizophrenia cases compared with controls (P = 8.63 × 10-4 in EAS sample), and its expression was also significantly lower in the brain tissues of schizophrenia cases compared with controls (P = 1.87 × 10-5 in European sample from PsychENCODE). Functional explorations suggested that Tmem180 knockdown affected neurodevelopment, ie, proliferation and differentiation of neural stem cells. RNA sequencing showed that pathways regulated by Tmem180 were significantly enriched in brain development and synaptic transmission. In conclusion, our study provides convergent lines of evidence for the involvement of TMEM180 in schizophrenia, and highlights the potential and importance of resource integration and sharing at this big data era in bio-medical research.

An Integrative Transcriptome-Wide Analysis of Amyotrophic Lateral Sclerosis for the Identification of Potential Genetic Markers and Drug Candidates

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative neuromuscular disease. Although genome-wide association studies (GWAS) have successfully identified many variants significantly associated with ALS, it is still difficult to characterize the underlying biological mechanisms inducing ALS. In this study, we performed a transcriptome-wide association study (TWAS) to identify disease-specific genes in ALS. Using the largest ALS GWAS summary statistic (n = 80,610), we identified seven novel genes using 19 tissue reference panels. We conducted a conditional analysis to verify the genes’ independence and to confirm that they are driven by genetically regulated expressions. Furthermore, we performed a TWAS-based enrichment analysis to highlight the association of important biological pathways, one in each of the four tissue reference panels. Finally, utilizing a connectivity map, a database of human cell expression profiles cultured with bioactive small molecules, we discovered functional associations between genes and drugs to identify 15 bioactive small molecules as potential drug candidates for ALS. We believe that, by integrating the largest ALS GWAS summary statistic with gene expression to identify new risk loci and causal genes, our study provides strong candidates for molecular basis experiments in ALS.

Transcriptome-wide association study reveals two genes that influence mismatch negativity

Mismatch negativity (MMN) is a differential electrophysiological response measuring cortical adaptability to unpredictable stimuli. MMN is consistently attenuated in patients with psychosis. However, the genetics of MMN are uncharted, limiting the validation of MMN as a psychosis endophenotype. Here, we perform a transcriptome-wide association study of 728 individuals, which reveals 2 genes (FAM89A and ENGASE) whose expression in cortical tissues is associated with MMN. Enrichment analyses of neurodevelopmental expression signatures show that genes associated with MMN tend to be overexpressed in the frontal cortex during prenatal development but are significantly downregulated in adulthood. Endophenotype ranking value calculations comparing MMN and three other candidate psychosis endophenotypes (lateral ventricular volume and two auditory-verbal learning measures) find MMN to be considerably superior. These results yield promising insights into sensory processing in the cortex and endorse the notion of MMN as a psychosis endophenotype.

Core transcriptional networks in Williams syndrome: IGF1-PI3K-AKT-mTOR, MAPK and actin signaling at the synapse echo autism

Gene networks for disorders of social behavior provide the mechanisms critical for identifying therapeutic targets and biomarkers. Large behavioral phenotypic effects of small human deletions make the positive sociality of Williams syndrome (WS) ideal for determining transcriptional networks for social dysfunction currently based on DNA variations for disorders such as autistic spectrum disorder (ASD) and schizophrenia (SCHZ). Consensus on WS networks has been elusive due to the need for larger cohort size, sensitive genome-wide detection and analytic tools. We report a core set of WS network perturbations in a cohort of 58 individuals (34 with typical, 6 atypical deletions and 18 controls). Genome-wide exon-level expression arrays robustly detected changes in differentially expressed gene (DEG) transcripts from WS deleted genes that ranked in the top 11 of 12 122 transcripts, validated by quantitative reverse transcription PCR, RNASeq and western blots. WS DEG's were strictly dosed in the full but not the atypical deletions that revealed a breakpoint position effect on non-deleted CLIP2, a caveat for current phenotypic mapping based on copy number variants. Network analyses tested the top WS DEG's role in the dendritic spine, employing GeneMANIA to harmonize WS DEGs with comparable query gene-sets. The results indicate perturbed actin cytoskeletal signaling analogous to the excitatory dendritic spines. Independent protein-protein interaction analyses of top WS DEGs generated a 100-node graph annotated topologically revealing three interacting pathways, MAPK, IGF1-PI3K-AKT-mTOR/insulin and actin signaling at the synapse. The results indicate striking similarity of WS transcriptional networks to genome-wide association study-based ASD and SCHZ risk suggesting common network dysfunction for these disorders of divergent sociality.

Multi-omics analyses of cognitive traits and psychiatric disorders highlights brain-dependent mechanisms

Integrating findings from genome-wide association studies with molecular datasets can develop insight into the underlying functional mechanisms responsible for trait-associated genetic variants. We have applied the principles of Mendelian randomization (MR) to investigate whether brain-derived gene expression (n = 1194) may be responsible for mediating the effect of genetic variants on eight cognitive and psychological outcomes (attention deficit hyperactivity disorder (ADHD), Alzheimer's disease, bipolar disorder, depression, intelligence, insomnia, neuroticism and schizophrenia). Transcriptome-wide analyses identified 83 genes associated with at least one outcome (PBonferroni < 6.72 × 10-6), with multiple-trait colocalization also implicating changes to brain-derived DNA methylation at nine of these loci. Comparing effects between outcomes identified evidence of enrichment which may reflect putative causal relationships, such as an inverse relationship between genetic liability towards schizophrenia risk and cognitive ability in later life. Repeating these analyses in whole blood (n = 31 684), we replicated 58.2% of brain-derived effects (based on P < 0.05). Finally, we undertook phenome-wide evaluations at associated loci to investigate pleiotropic effects with 700 complex traits. This highlighted pleiotropic loci such as FURIN (initially implicated in schizophrenia risk (P = 1.05 × 10-7)) which had evidence of an effect on 28 other outcomes, as well as genes which may have a more specific role in disease pathogenesis (e.g. SLC12A5 which only provided evidence of an effect on depression (P = 7.13 × 10-10)). Our results support the utility of whole blood as a valuable proxy for informing initial target identification but also suggest that gene discovery in a tissue-specific manner may be more informative. Finally, non-pleiotropic loci highlighted by our study may be of use for therapeutic translational endeavours.

Local gene regulation in radial glia: Lessons from across the nervous system

Radial glial cells (RGCs) are progenitors of the cerebral cortex which produce both neurons and glia during development. Given their central role in development, RGC dysfunction can result in diverse neurodevelopmental disorders. RGCs have an elongated bipolar morphology that spans the entire radial width of the cortex and ends in basal endfeet connected to the pia. The basal process and endfeet are important for proper guidance of migrating neurons and are implicated in signaling. However, endfeet must function at a great distance from the cell body. This spatial separation suggests a role for local gene regulation in endfeet. Endfeet contain a local transcriptome enriched for cytoskeletal and signaling factors. These localized mRNAs are actively transported from the cell body and can be locally translated in endfeet. Yet, studies of local gene regulation in RGC endfeet are still in their infancy. Here, we draw comparisons of RGCs with foundational work in anatomically and phylogenetically related cell types, neurons and astrocytes. Our review highlights a striking overlap in the types of RNAs localized, as well as principles of local translation between these three cell types. Thus, studies in neurons, astrocytes and RGCs can mutually inform an understanding of RNA localization across the nervous system.

nMAGMA: a network-enhanced method for inferring risk genes from GWAS summary statistics and its application to schizophrenia

MOTIVATION

Annotating genetic variants from summary statistics of genome-wide association studies (GWAS) is crucial for predicting risk genes of various disorders. The multimarker analysis of genomic annotation (MAGMA) is one of the most popular tools for this purpose, where MAGMA aggregates signals of single nucleotide polymorphisms (SNPs) to their nearby genes. In biology, SNPs may also affect genes that are far away in the genome, thus missed by MAGMA. Although different upgrades of MAGMA have been proposed to extend gene-wise variant annotations with more information (e.g. Hi-C or eQTL), the regulatory relationships among genes and the tissue specificity of signals have not been taken into account.

RESULTS

We propose a new approach, namely network-enhanced MAGMA (nMAGMA), for gene-wise annotation of variants from GWAS summary statistics. Compared with MAGMA and H-MAGMA, nMAGMA significantly extends the lists of genes that can be annotated to SNPs by integrating local signals, long-range regulation signals (i.e. interactions between distal DNA elements), and tissue-specific gene networks. When applied to schizophrenia (SCZ), nMAGMA is able to detect more risk genes (217% more than MAGMA and 57% more than H-MAGMA) that are involved in SCZ compared with MAGMA and H-MAGMA, and more of nMAGMA results can be validated with known SCZ risk genes. Some disease-related functions (e.g. the ATPase pathway in Cortex) are also uncovered in nMAGMA but not in MAGMA or H-MAGMA. Moreover, nMAGMA provides tissue-specific risk signals, which are useful for understanding disorders with multitissue origins.

Genome-Wide Association Studies of Schizophrenia and Bipolar Disorder in a Diverse Cohort of US Veterans

BACKGROUND

Schizophrenia (SCZ) and bipolar disorder (BIP) are debilitating neuropsychiatric disorders, collectively affecting 2% of the world's population. Recognizing the major impact of these psychiatric disorders on the psychosocial function of more than 200 000 US Veterans, the Department of Veterans Affairs (VA) recently completed genotyping of more than 8000 veterans with SCZ and BIP in the Cooperative Studies Program (CSP) #572.

METHODS

We performed genome-wide association studies (GWAS) in CSP #572 and benchmarked the predictive value of polygenic risk scores (PRS) constructed from published findings. We combined our results with available summary statistics from several recent GWAS, realizing the largest and most diverse studies of these disorders to date.

RESULTS

Our primary GWAS uncovered new associations between CHD7 variants and SCZ, and novel BIP associations with variants in Sortilin Related VPS10 Domain Containing Receptor 3 (SORCS3) and downstream of PCDH11X. Combining our results with published summary statistics for SCZ yielded 39 novel susceptibility loci including CRHR1, and we identified 10 additional findings for BIP (28 326 cases and 90 570 controls). PRS trained on published GWAS were significantly associated with case-control status among European American (P < 10-30) and African American (P < .0005) participants in CSP #572.

CONCLUSIONS

We have demonstrated that published findings for SCZ and BIP are robustly generalizable to a diverse cohort of US veterans. Leveraging available summary statistics from GWAS of global populations, we report 52 new susceptibility loci and improved fine-mapping resolution for dozens of previously reported associations.

Multimodal hippocampal and amygdala subfield volumetry in polygenic risk for Alzheimer's disease

Preclinical models of Alzheimer's disease (AD) suggest that volumetric reductions in medial temporal lobe (MTL) structures manifest before clinical onset. AD polygenic risk scores (PRSs) are further linked to reduced MTL volumes (the hippocampus/amygdala); however, the relationship between the PRS and specific subregions remains unclear. We determine the relationship between the AD-PRSs and MTL subregions in a large sample of young participants (N = 730, aged 22–35 years) using a multimodal (T1w/T2w) approach. We first demonstrate that the PRSs for the hippocampus/amygdala predict their respective volumes and specific hippocampal subregions (pFDR < 0.05). We further observe negative relationships between the AD-PRSs and whole hippocampal/amygdala volumes. Critically, we demonstrate novel associations between the AD-PRSs and specific hippocampal subfields such as CA1 (β = −0.096, pFDR = 0.045) and the fissure (β = −0.101, pFDR = 0.041). We provide evidence that the AD-PRS is linked to specific MTL subfields decades before AD onset. This may help inform preclinical models of AD risk, providing additional specificity for intervention and further insight into mechanisms by which common AD variants confer susceptibility.

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Polygenic risk for Alzheimer's disease (AD-PRS) explains significant proportion of AD.

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AD-PRS also linked to hippocampus and amygdala volume.

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AD-PRS is negatively associated with specific hippocampal subfields.

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Polygenic AD models help us understand genetic contributions to medial temporal lobe nuclei.

Novel genes involved in the genetic architecture of temperament in Brahman cattle

Cattle temperament is a complex and economically relevant trait. The objective of this study was to identify genomic regions and genes associated with cattle temperament. From a Brahman cattle population of 1,370 animals evaluated for temperament traits (Exit velocity-EV, Pen Score-PS, Temperament Score-TS), two groups of temperament-contrasting animals were identified based on their EV-average values ±1/2 standard deviation (SD). To be considered in the calm group, the EV of females ranged between 0.16–1.82 m/s (n = 50) and the EV of males ranged between 0.4–1.56 m/s (n = 48). Females were classified as temperamental if their EV ranged between 3.13–7.66 m/s (n = 46) and males were classified as temperamental if their EV ranged between 3.05–10.83 m/s (n = 45). Selected animals were genotyped using a total of 139,376 SNPs (GGP-HD-150K), evaluated for their association with EV. The Genome-Wide Association analysis (GWAS) identified fourteen SNPs: rs135340276, rs134895560, rs110190635, rs42949831, rs135982573, rs109393235, rs109531929, rs135087545, rs41839733, rs42486577, rs136661522, rs110882543, rs110864071, rs109722627, (P<8.1E-05), nine of them were located on intergenic regions, harboring seventeen genes, of which only ACER3, VRK2, FANCL and SLCO3A1 were considered candidate associated with bovine temperament due to their reported biological functions. Five SNPs were located at introns of the NRXN3, EXOC4, CACNG4 and SLC9A4 genes. The indicated candidate genes are implicated in a wide range of behavioural phenotypes and complex cognitive functions. The association of the fourteen SNPs on bovine temperament traits (EV, PS and TS) was evaluated; all these SNPs were significant for EV; only some were associated with PS and TS. Fourteen SNPs were associated with EV which allowed the identification of twenty-one candidate genes for Brahman temperament. From a functional point of view, the five intronic SNPs identified in this study, are candidates to address control of bovine temperament, further investigation will probe their role in expression of this trait.

Translating insights from neuropsychiatric genetics and genomics for precision psychiatry

The primary aim of precision medicine is to tailor healthcare more closely to the needs of individual patients. This requires progress in two areas: the development of more precise treatments and the ability to identify patients or groups of patients in the clinic for whom such treatments are likely to be the most effective. There is widespread optimism that advances in genomics will facilitate both of these endeavors. It can be argued that of all medical specialties psychiatry has most to gain in these respects, given its current reliance on syndromic diagnoses, the minimal foundation of existing mechanistic knowledge, and the substantial heritability of psychiatric phenotypes. Here, we review recent advances in psychiatric genomics and assess the likely impact of these findings on attempts to develop precision psychiatry. Emerging findings indicate a high degree of polygenicity and that genetic risk maps poorly onto the diagnostic categories used in the clinic. The highly polygenic and pleiotropic nature of psychiatric genetics will impact attempts to use genomic data for prediction and risk stratification, and also poses substantial challenges for conventional approaches to gaining biological insights from genetic findings. While there are many challenges to overcome, genomics is building an empirical platform upon which psychiatry can now progress towards better understanding of disease mechanisms, better treatments, and better ways of targeting treatments to the patients most likely to benefit, thus paving the way for precision psychiatry.

Meta-Analysis of Transcriptomic Data of Dorsolateral Prefrontal Cortex and of Peripheral Blood Mononuclear Cells Identifies Altered Pathways in Schizophrenia

Schizophrenia (SCZ) is a psychiatric disorder characterized by both positive and negative symptoms, including cognitive dysfunction, decline in motivation, delusion and hallucinations. Antipsychotic agents are currently the standard of care treatment for SCZ. However, only about one-third of SCZ patients respond to antipsychotic medications. In the current study, we have performed a meta-analysis of publicly available whole-genome expression datasets on Brodmann area 46 of the brain dorsolateral prefrontal cortex in order to prioritize potential pathways underlying SCZ pathology. Moreover, we have evaluated whether the differentially expressed genes in SCZ belong to specific subsets of cell types. Finally, a cross-tissue comparison at both the gene and functional level was performed by analyzing the transcriptomic pattern of peripheral blood mononuclear cells of SCZ patients. Our study identified a robust disease-specific set of dysfunctional biological pathways characterizing SCZ patients that could in the future be exploited as potential therapeutic targets.

Novel Approaches for Identifying the Molecular Background of Schizophrenia

Recent advances in psychiatric genetics have led to the discovery of dozens of genomic loci associated with schizophrenia. However, a gap exists between the detection of genetic associations and understanding the underlying molecular mechanisms. This review describes the basic approaches used in the so-called post-GWAS studies to generate biological interpretation of the existing population genetic data, including both molecular (creation and analysis of knockout animals, exploration of the transcriptional effects of common variants in human brain cells) and computational (fine-mapping of causal variability, gene set enrichment analysis, partitioned heritability analysis) methods. The results of the crucial studies, in which these approaches were used to uncover the molecular and neurobiological basis of the disease, are also reported.

Understanding the genetics of neuropsychiatric disorders: the potential role of genomic regulatory blocks

Recent genome-wide association studies have identified numerous loci associated with neuropsychiatric disorders. The majority of these are in non-coding regions, and are commonly assigned to the nearest gene along the genome. However, this approach neglects the three-dimensional organisation of the genome, and the fact that the genome contains arrays of extremely conserved non-coding elements termed genomic regulatory blocks (GRBs), which can be utilized to detect genes under long-range developmental regulation. Here we review a GRB-based approach to assign loci in non-coding regions to potential target genes, and apply it to reanalyse the results of one of the largest schizophrenia GWAS (SWG PGC, 2014). We further apply this approach to GWAS data from two related neuropsychiatric disorders-autism spectrum disorder and bipolar disorder-to show that it is applicable to developmental disorders in general. We find that disease-associated SNPs are overrepresented in GRBs and that the GRB model is a powerful tool for linking these SNPs to their correct target genes under long-range regulation. Our analysis identifies novel genes not previously implicated in schizophrenia and corroborates a number of predicted targets from the original study. The results are available as an online resource in which the genomic context and the strength of enhancer-promoter associations can be browsed for each schizophrenia-associated SNP.