Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia

Heterozygosity for neurodevelopmental disorder-associated TRIO variants yields distinct deficits in behavior, neuronal development, and synaptic transmission in mice

Genetic variants in TRIO are associated with neurodevelopmental disorders (NDDs) including schizophrenia (SCZ), autism spectrum disorder (ASD) and intellectual disability. TRIO uses its two guanine nucleotide exchange factor (GEF) domains to activate GTPases (GEF1: Rac1 and RhoG; GEF2: RhoA) that control neuronal development and connectivity. It remains unclear how discrete TRIO variants differentially impact these neurodevelopmental events. Here, we investigate how heterozygosity for NDD-associated Trio variants - +/K1431M (ASD), +/K1918X (SCZ), and +/M2145T (bipolar disorder, BPD) - impact mouse behavior, brain development, and synapse structure and function. Heterozygosity for different Trio variants impacts motor, social, and cognitive behaviors in distinct ways that align with clinical phenotypes in humans. Trio variants differentially impact head and brain size with corresponding changes in dendritic arbors of motor cortex layer 5 pyramidal neurons (M1 L5 PNs). Although neuronal structure was only modestly altered in the Trio variant heterozygotes, we observe significant changes in synaptic function and plasticity. We also identified distinct changes in glutamate synaptic release in +/K1431M and +/M2145T cortico-cortical synapses. The TRIO K1431M GEF1 domain has impaired ability to promote GTP exchange on Rac1, but +/K1431M mice exhibit increased Rac1 activity, associated with increased levels of the Rac1 GEF Tiam1. Acute Rac1 inhibition with NSC23766 rescued glutamate release deficits in +/K1431M variant cortex. Our work reveals that discrete NDD-associated Trio variants yield overlapping but distinct phenotypes in mice, demonstrates an essential role for Trio in presynaptic glutamate release, and underscores the importance of studying the impact of variant heterozygosity in vivo.

Discovering functional interactions among schizophrenia-risk genes by combining behavioral genetics with cell biology

Despite much progress in identifying risk genes for polygenic brain disorders, their core pathogenic mechanisms remain poorly understood. In particular, functions of many proteins encoded by schizophrenia risk genes appear diverse and unrelated, complicating the efforts to establish the causal relationship between genes and behavior. Using various mouse lines, recent studies indicate that alterations of parvalbumin-positive (PV+) GABAergic interneurons can lead to schizophrenia-like behavior. PV+ interneurons display fast spiking and contribute to excitation-inhibition balance and network oscillations via feedback and feedforward inhibition. Here, we first summarize different lines of genetically modified mice that display motor, cognitive, emotional, and social impairments used to model schizophrenia and related mental disorders. We highlight ten genes, encoding either a nuclear, cytosolic, or membrane protein. Next, we discuss their functional relationship in regulating fast spiking and other aspects of PV+ interneurons and in the context of other domains of schizophrenia. Future investigations combining behavioral genetics and cell biology should elucidate functional relationships among risk genes to identify the core pathogenic mechanisms underlying polygenic brain disorders.

Genetic Implication of Specific Glutamatergic Neurons of the Prefrontal Cortex in the Pathophysiology of Schizophrenia

Background

The prefrontal cortex (PFC) has been strongly implicated in the pathophysiology of schizophrenia. Here, we combined high-resolution single-nuclei RNA sequencing data from the human PFC with large-scale genomic data for schizophrenia to identify constituent cell populations likely to mediate genetic liability to the disorder.

Methods

Gene expression specificity values were calculated from a single-nuclei RNA sequencing dataset comprising 84 cell populations from the human PFC, spanning gestation to adulthood. Enrichment of schizophrenia common variant liability and burden of rare protein-truncating coding variants were tested in genes with high expression specificity for each cell type. We also explored schizophrenia common variant associations in relation to gene expression across the developmental trajectory of implicated neurons.

Results

Common risk variation for schizophrenia was prominently enriched in genes with high expression specificity for a population of mature layer 4 glutamatergic neurons emerging in infancy. Common variant liability to schizophrenia increased along the developmental trajectory of this neuronal population. Fine-mapped genes at schizophrenia genome-wide association study risk loci had significantly higher expression specificity than other genes in these neurons and in a population of layer 5/6 glutamatergic neurons. People with schizophrenia had a higher rate of rare protein-truncating coding variants in genes expressed by cells of the PFC than control individuals, but no cell population was significantly enriched above this background rate.

Conclusions

We identified a population of layer 4 glutamatergic PFC neurons likely to be particularly affected by common variant genetic risk for schizophrenia, which may contribute to disturbances in thalamocortical connectivity in the condition.

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

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

Accurate identification of genes associated with brain disorders by integrating heterogeneous genomic data into a Bayesian framework

BACKGROUND

Genome-wide association studies (GWAS) have revealed many brain disorder-associated SNPs residing in the noncoding genome, rendering it a challenge to decipher the underlying pathogenic mechanisms.

METHODS

Here, we present an unsupervised Bayesian framework to identify disease-associated genes by integrating risk SNPs with long-range chromatin interactions (iGOAT), including SNP-SNP interactions extracted from ∼500,000 patients and controls from the UK Biobank, and enhancer-promoter interactions derived from multiple brain cell types at different developmental stages.

FINDINGS

The application of iGOAT to three psychiatric disorders and three neurodegenerative/neurological diseases predicted sets of high-risk (HRGs) and low-risk (LRGs) genes for each disorder. The HRGs were enriched in drug targets, and exhibited higher expression during prenatal brain developmental stages than postnatal stages, indicating their potential to affect brain development at an early stage. The HRGs associated with Alzheimer's disease were found to share genetic architecture with schizophrenia, bipolar disorder and major depressive disorder according to gene co-expression module analysis and rare variants analysis. Comparisons of this method to the eQTL-based method, the TWAS-based method, and the gene-level GWAS method indicated that the genes identified by our method are more enriched in known brain disorder-related genes, and exhibited higher precision. Finally, the method predicted 205 risk genes not previously reported to be associated with any brain disorder, of which one top-risk gene, MLH1, was experimentally validated as being schizophrenia-associated.

INTERPRETATION

iGOAT can successfully leverage epigenomic data, phenotype-genotype associations, and protein-protein interactions to advance our understanding of brain disorders, thereby facilitating the development of new therapeutic approaches.

FUNDING

The work was funded by the National Key Research and Development Program of China (2024YFF1204902), the Natural Science Foundation of China (82371482), Guangzhou Science and Technology Research Plan (2023A03J0659) and Natural Science Foundation of Guangdong (2024A1515011363).

Enhancing schizophrenia phenotype prediction from genotype data through knowledge-driven deep neural network models

This article explores deep learning model design, drawing inspiration from the omnigenic model and genetic heterogeneity concepts, to improve schizophrenia prediction using genotype data. It introduces an innovative three-step approach leveraging neural networks' capabilities to efficiently handle genetic interactions. A locally connected network initially routes input data from variants to their corresponding genes. The second step employs an Encoder-Decoder to capture relationships among identified genes. The final model integrates knowledge from the first two and incorporates a parallel component to consider the effects of additional genes. This expansion enhances prediction scores by considering a larger number of genes. Trained models achieved an average AUC of 0.83, surpassing other genotype-trained models and matching gene expression dataset-based approaches. Additionally, tests on held-out sets reported an average sensitivity of 0.72 and an accuracy of 0.76, aligning with schizophrenia heritability predictions. Moreover, the study addresses genetic heterogeneity challenges by considering diverse population subsets.

Accelerated Cortical Thinning in Schizophrenia Is Associated With Rare and Common Predisposing Variation to Schizophrenia and Neurodevelopmental Disorders

BACKGROUND

Schizophrenia is a highly heritable disorder characterized by increased cortical thinning throughout the life span. Studies have reported a shared genetic basis between schizophrenia and cortical thickness. However, no genes whose expression is related to abnormal cortical thinning in schizophrenia have been identified.

METHODS

We conducted linear mixed models to estimate the rates of accelerated cortical thinning across 68 regions from the Desikan-Killiany atlas in individuals with schizophrenia compared with healthy control participants from a large longitudinal sample (ncases = 169 and ncontrols = 298, ages 16-70 years). We studied the correlation between gene expression data from the Allen Human Brain Atlas and accelerated thinning estimates across cortical regions. Finally, we explored the functional and genetic underpinnings of the genes that contribute most to accelerated thinning.

RESULTS

We found a global pattern of accelerated cortical thinning in individuals with schizophrenia compared with healthy control participants. Genes underexpressed in cortical regions that exhibit this accelerated thinning were downregulated in several psychiatric disorders and were enriched for both common and rare disrupting variation for schizophrenia and neurodevelopmental disorders. In contrast, none of these enrichments were observed for baseline cross-sectional cortical thickness differences.

CONCLUSIONS

Our findings suggest that accelerated cortical thinning, rather than cortical thickness alone, serves as an informative phenotype for neurodevelopmental disruptions in schizophrenia. We highlight the genetic and transcriptomic correlates of this accelerated cortical thinning, emphasizing the need for future longitudinal studies to elucidate the role of genetic variation and the temporal-spatial dynamics of gene expression in brain development and aging in schizophrenia.

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.

Bayesian Rare Variant Analysis Identifies Novel Schizophrenia Putative Risk Genes

The genetics of schizophrenia is so complex that it involves both common variants and rare variants. Rare variant association studies of schizophrenia are challenging because statistical methods for rare variant analysis are under-powered due to the rarity of rare variants. The recent Schizophrenia Exome meta-analysis (SCHEMA) consortium, the largest consortium in this field to date, has successfully identified 10 schizophrenia risk genes from ultra-rare variants by burden test, while more risk genes remain to be discovered by more powerful rare variant association test methods. In this study, we use a recently developed Bayesian rare variant association method that is powerful for detecting sparse rare risk variants that implicates 88 new candidate risk genes associated with schizophrenia from the SCHEMA case-control sample. These newly identified genes are significantly enriched in autism risk genes and GO enrichment analysis indicates that new candidate risk genes are involved in mechanosensory behavior, regulation of cell size, neuron projection morphogenesis, and plasma-membrane-bounded cell projection morphogenesis, that may provide new insights on the etiology of schizophrenia.

Rare variants analyses suggest novel cleft genes in the African population

Non-syndromic orofacial clefts (NSOFCs) are common birth defects with a complex etiology. While over 60 common risk loci have been identified, they explain only a small proportion of the heritability for NSOFCs. Rare variants have been implicated in the missing heritability. Thus, our study aimed to identify genes enriched with nonsynonymous rare coding variants associated with NSOFCs. Our sample included 814 non-syndromic cleft lip with or without palate (NSCL/P), 205 non-syndromic cleft palate only (NSCPO), and 2150 unrelated control children from Nigeria, Ghana, and Ethiopia. We conducted a gene-based analysis separately for each phenotype using three rare-variants collapsing models: (1) protein-altering (PA), (2) missense variants only (MO); and (3) loss of function variants only (LOFO). Subsequently, we utilized relevant transcriptomics data to evaluate associated gene expression and examined their mutation constraint using the gnomeAD database. In total, 13 genes showed suggestive associations (p = E-04). Among them, eight genes (ABCB1, ALKBH8, CENPF, CSAD, EXPH5, PDZD8, SLC16A9, and TTC28) were consistently expressed in relevant mouse and human craniofacial tissues during the formation of the face, and three genes (ABCB1, TTC28, and PDZD8) showed statistically significant mutation constraint. These findings underscore the role of rare variants in identifying candidate genes for NSOFCs.

Detecting outliers in case-control cohorts for improving deep learning networks on Schizophrenia prediction

This study delves into the intricate genetic and clinical aspects of Schizophrenia, a complex mental disorder with uncertain etiology. Deep Learning (DL) holds promise for analyzing large genomic datasets to uncover new risk factors. However, based on reports of non-negligible misdiagnosis rates for SCZ, case-control cohorts may contain outlying genetic profiles, hindering compelling performances of classification models. The research employed a case-control dataset sourced from the Swedish populace. A gene-annotation-based DL architecture was developed and employed in two stages. First, the model was trained on the entire dataset to highlight differences between cases and controls. Then, samples likely to be misclassified were excluded, and the model was retrained on the refined dataset for performance evaluation. The results indicate that SCZ prevalence and misdiagnosis rates can affect case-control cohorts, potentially compromising future studies reliant on such datasets. However, by detecting and filtering outliers, the study demonstrates the feasibility of adapting DL methodologies to large-scale biological problems, producing results more aligned with existing heritability estimates for SCZ. This approach not only advances the comprehension of the genetic background of SCZ but also opens doors for adapting DL techniques in complex research for precision medicine in mental health.

The cliff edge model of the evolution of schizophrenia: Mathematical, epidemiological, and genetic evidence

How has schizophrenia, a condition that significantly reduces an individual's evolutionary fitness, remained common across generations and cultures? Numerous theories about the evolution of schizophrenia have been proposed, most of which are not consistent with modern epidemiological and genetic evidence. Here, we briefly review this evidence and explore the cliff edge model of schizophrenia. It suggests that schizophrenia is the extreme manifestation of a polygenic trait or a combination of traits that, within a normal range of variation, confer cognitive, linguistic, and/or social advantages. Only beyond a certain threshold, these traits precipitate the onset of schizophrenia and reduce fitness. We provide the first mathematical model of this qualitative concept and show that it requires only very weak positive selection of the underlying trait(s) to explain today's schizophrenia prevalence. This prediction, along with expectations about the effect size of schizophrenia risk alleles, are surprisingly well matched by empirical evidence. The cliff edge model predicts a dynamic change of selection of risk alleles, which explains the contradictory findings of evolutionary genetic studies.

Mutations in the postsynaptic density signaling hub TNIK disrupt PSD signaling in human models of neurodevelopmental disorders

A large number of synaptic proteins have been recurrently associated with complex brain disorders. One of these proteins, the Traf and Nck interacting kinase (TNIK), is a postsynaptic density (PSD) signaling hub, with many variants reported in neurodevelopmental disorder (NDD) and psychiatric disease. While rodent models of TNIK dysfunction have abnormal spontaneous synaptic activity and cognitive impairment, the role of mutations found in patients with TNIK protein deficiency and TNIK protein kinase activity during early stages of neuronal and synapse development has not been characterized. Here, using hiPSC-derived excitatory neurons, we show that TNIK mutations dysregulate neuronal activity in human immature synapses. Moreover, the lack of TNIK protein kinase activity impairs MAPK signaling and protein phosphorylation in structural components of the PSD. We show that the TNIK interactome is enriched in NDD risk factors and TNIK lack of function disrupts signaling networks and protein interactors associated with NDD that only partially overlap to mature mouse synapses, suggesting a differential role of TNIK in immature synapsis in NDD.

The expanded spectrum of human disease associated with GREB1L likely includes complex congenital heart disease

OBJECTIVE

GREB1L has been linked prenatally to Potter's sequence, as well as less severe anomalies of the kidney, uterus, inner ear, and heart. The full phenotypic spectrum is unknown. The purpose of this study was to characterize known and novel pre- and postnatal phenotypes associated with GREB1L.

METHODS

We solicited cases from the Fetal Sequencing Consortium, screened a population-based genomic database, and conducted a comprehensive literature search to identify disease cases associated with GREB1L. We present a detailed phenotypic spectrum and molecular changes.

RESULTS

One hundred twenty-seven individuals with 51 unique pathogenic or likely pathogenic GREB1L variants were identified. 24 (47%) variants were associated with isolated kidney anomalies, 19 (37%) with anomalies of multiple systems, including one case of hypoplastic left heart syndrome, five (10%) with isolated sensorineural hearing loss, two (4%) with isolated uterine agenesis; and one (2%) with isolated tetralogy of Fallot.

CONCLUSION

GREB1L may cause complex congenital heart disease (CHD) in humans. Clinicians should consider GREB1L testing in the setting of CHD, and cardiac screening in the setting of GREB1L variants.

Rare Variants Analyses Suggest Novel Cleft Genes in the African Population

Non-syndromic orofacial clefts (NSOFCs) are common birth defects with a complex etiology. While over 60 common risk loci have been identified, they explain only a small proportion of the heritability for NSOFC. Rare variants have been implicated in the missing heritability. Thus, our study aimed to identify genes enriched with nonsynonymous rare coding variants associated with NSOFCs. Our sample included 814 non-syndromic cleft lip with or without palate (NSCL/P), 205 non-syndromic cleft palate only (NSCPO), and 2150 unrelated control children from Nigeria, Ghana, and Ethiopia. We conducted a gene-based analysis separately for each phenotype using three rare-variants collapsing models: (1) protein-altering (PA), (2) missense variants only (MO); and (3) loss of function variants only (LOFO). Subsequently, we utilized relevant transcriptomics data to evaluate associated gene expression and examined their mutation constraint using the gnomeAD database. In total, 13 genes showed suggestive associations (p = E-04). Among them, eight genes (ABCB1, ALKBH8, CENPF, CSAD, EXPH5, PDZD8, SLC16A9, and TTC28) were consistently expressed in relevant mouse and human craniofacial tissues during the formation of the face, and three genes (ABCB1, TTC28, and PDZD8) showed statistically significant mutation constraint. These findings underscore the role of rare variants in identifying candidate genes for NSOFCs.

SATB2 organizes the 3D genome architecture of cognition in cortical neurons

The DNA-binding protein SATB2 is genetically linked to human intelligence. We studied its influence on the three-dimensional (3D) epigenome by mapping chromatin interactions and accessibility in control versus SATB2-deficient cortical neurons. We find that SATB2 affects the chromatin looping between enhancers and promoters of neuronal-activity-regulated genes, thus influencing their expression. It also alters A/B compartments, topologically associating domains, and frequently interacting regions. Genes linked to SATB2-dependent 3D genome changes are implicated in highly specialized neuronal functions and contribute to cognitive ability and risk for neuropsychiatric and neurodevelopmental disorders. Non-coding DNA regions with a SATB2-dependent structure are enriched for common variants associated with educational attainment, intelligence, and schizophrenia. Our data establish SATB2 as a cell-type-specific 3D genome modulator, which operates both independently and in cooperation with CCCTC-binding factor (CTCF) to set up the chromatin landscape of pyramidal neurons for cognitive processes.

How brain structure-function decoupling supports individual cognition and its molecular mechanism

Functional signals emerge from the structural network, supporting multiple cognitive processes through underlying molecular mechanism. The link between human brain structure and function is region-specific and hierarchical across the neocortex. However, the relationship between hierarchical structure-function decoupling and the manifestation of individual behavior and cognition, along with the significance of the functional systems involved, and the specific molecular mechanism underlying structure-function decoupling remain incompletely characterized. Here, we used the structural-decoupling index (SDI) to quantify the dependency of functional signals on the structural connectome using a significantly larger cohort of healthy subjects. Canonical correlation analysis (CCA) was utilized to assess the general multivariate correlation pattern between region-specific SDIs across the whole brain and multiple cognitive traits. Then, we predicted five composite cognitive scores resulting from multivariate analysis using SDIs in primary networks, association networks, and all networks, respectively. Finally, we explored the molecular mechanism related to SDI by investigating its genetic factors and relationship with neurotransmitter receptors/transporters. We demonstrated that structure-function decoupling is hierarchical across the neocortex, spanning from primary networks to association networks. We revealed better performance in cognition prediction is achieved by using high-level hierarchical SDIs, with varying significance of different brain regions in predicting cognitive processes. We found that the SDIs were associated with the gene expression level of several receptor-related terms, and we also found the spatial distributions of four receptors/transporters significantly correlated with SDIs, namely D2, NET, MOR, and mGluR5, which play an important role in the flexibility of neuronal function. Collectively, our findings corroborate the association between hierarchical macroscale structure-function decoupling and individual cognition and provide implications for comprehending the molecular mechanism of structure-function decoupling. PRACTITIONER POINTS: Structure-function decoupling is hierarchical across the neocortex, spanning from primary networks to association networks. High-level hierarchical structure-function decoupling contributes much more than low-level decoupling to individual cognition. Structure-function decoupling could be regulated by genes associated with pivotal receptors that are crucial for neuronal function flexibility.

Genome-wide DNA methylation risk scores for schizophrenia derived from blood and brain tissues further explain the genetic risk in patients stratified by polygenic risk scores for schizophrenia and bipolar disorder

BACKGROUND

Genetic and environmental factors contribute to the pathogenesis of schizophrenia (SZ) and bipolar disorder (BD). Among genetic risk groups stratified by combinations of Polygenic Risk Score (PRS) deciles for SZ, BD and SZ versus BD, genetic SZ risk groups had high SZ risk and prominent cognitive impairments. Furthermore, epigenetic alterations are implicated in these disorders. However, it was unclear whether DNA Methylation Risk Scores (MRSs) for SZ risk derived from blood and brain tissues were associated with SZ risk, particularly the PRS-stratified genetic SZ risk group.

METHODS

Epigenome-wide association studies (EWASs) of SZ risk in whole blood were preliminarily conducted between 66 SZ patients and 30 healthy controls (HCs) and among genetic risk groups (individuals with low genetic risk for SZ and BD in HCs (n=30) and in SZ patients (n=11), genetic BD risk in SZ patients (n=25) and genetic SZ risk in SZ patients (n=30)) stratified by combinations of PRSs for SZ, BD and SZ versus BD. Next, differences in MRSs based on independent EWASs of SZ risk in whole blood, postmortem frontal cortex (FC) and superior temporal gyrus (STG) were investigated among our case‒control and PRS-stratified genetic risk status groups.

RESULTS

Among case‒control and genetic risk status groups, 33 and 351 genome-wide significant differentially methylated positions (DMPs) associated with SZ were identified, respectively, many of which were hypermethylated. Compared with the low genetic risk in HCs group, the genetic SZ risk in SZ group had 39 genome-wide significant DMPs, while the genetic BD risk in SZ group had only six genome-wide significant DMPs. The MRSs for SZ risk derived from whole blood, FC and STG were higher in our SZ patients than in HCs in whole blood and were particularly higher in the genetic SZ risk in SZ group than in the low genetic risk in HCs and genetic BD risk in SZ groups. Conversely, the MRSs for SZ risk based on our whole-blood EWASs among genetic risk groups were also associated with SZ in the FC and STG. There were no correlations between the MRSs and PRSs.

CONCLUSIONS

These findings suggest that the MRS is a potential genetic marker in understanding SZ, particularly in patients with a genetic SZ risk.

An evolutionary perspective on complex neuropsychiatric disease

The forces of evolution-mutation, selection, migration, and genetic drift-shape the genetic architecture of human traits, including the genetic architecture of complex neuropsychiatric illnesses. Studying these illnesses in populations that are diverse in genetic ancestry, historical demography, and cultural history can reveal how evolutionary forces have guided adaptation over time and place. A fundamental truth of shared human biology is that an allele responsible for a disease in anyone, anywhere, reveals a gene critical to the normal biology underlying that condition in everyone, everywhere. Understanding the genetic causes of neuropsychiatric disease in the widest possible range of human populations thus yields the greatest possible range of insight into genes critical to human brain development. In this perspective, we explore some of the relationships between genes, adaptation, and history that can be illuminated by an evolutionary perspective on studies of complex neuropsychiatric disease in diverse populations.

Functional analysis of a novel splice site variant in the ASAH1 gene

BACKGROUND

Acid ceramidase (ACDase) deficiency is an ultrarare autosomal recessive lysosomal disorder caused by pathogenic N-acylsphingosine amidohydrolase (ASAH1) variants. It presents with either Farber disease (FD) or spinal muscular atrophy with progressive myoclonic epilepsy (SMA-PME).

OBJECTIVE

The study aims to identify a novel splice site variant in a hydrops fetus that causes ASAH1-related disorder, aid genetic counseling, and accurate prenatal diagnosis.

METHODS

We report a case of hydrops fetalis with a novel homozygous mutation in ASAH1 inherited from non-consanguineous parents. We performed copy number variation sequencing (CNV-Seq) and whole exome sequencing (WES) on the fetus and family, respectively. Minigene splicing analyses were conducted to confirm the pathogenic variants.

RESULTS

WES data revealed a splice site variant of the ASAH1 (c.458-2A>T), which was predicted to affect RNA splicing. Minigene splicing analyses found that the c.458-2A>T variant abolished the canonical splicing of intron 6, thereby activating two cryptic splicing products (c.456_458ins56bp and c.458_503del).

CONCLUSIONS

Overall, we identified a novel splice site variant in the mutational spectrum of ASAH1 and its aberrant effect on splicing. These findings highlight the importance of ultrasonic manifestation and family history of fetal hydrops during ASAH1-related disorders and could also aid genetic counseling and accurate prenatal diagnosis. To the best of our knowledge, this is the shortest-lived account of ASAH1-related disorders in utero with severe hydrops fetalis.

Social isolation-induced transcriptomic changes in mouse hippocampus impact the synapse and show convergence with human genetic risk for neurodevelopmental phenotypes

Early life stress (ELS) can impact brain development and is a risk factor for neurodevelopmental disorders such as schizophrenia. Post-weaning social isolation (SI) is used to model ELS in animals, using isolation stress to disrupt a normal developmental trajectory. We aimed to investigate how SI affects the expression of genes in mouse hippocampus and to investigate how these changes related to the genetic basis of neurodevelopmental phenotypes. BL/6J mice were exposed to post-weaning SI (PD21-25) or treated as group-housed controls (n = 7-8 per group). RNA sequencing was performed on tissue samples from the hippocampus of adult male and female mice. Four hundred and 1,215 differentially-expressed genes (DEGs) at a false discovery rate of < 0.05 were detected between SI and control samples for males and females respectively. DEGS for both males and females were significantly overrepresented in gene ontologies related to synaptic structure and function, especially the post-synapse. DEGs were enriched for common variant (SNP) heritability in humans that contributes to risk of neuropsychiatric disorders (schizophrenia, bipolar disorder) and to cognitive function. DEGs were also enriched for genes harbouring rare de novo variants that contribute to autism spectrum disorder and other developmental disorders. Finally, cell type analysis revealed populations of hippocampal astrocytes that were enriched for DEGs, indicating effects in these cell types as well as neurons. Overall, these data suggest a convergence between genes dysregulated by the SI stressor in the mouse and genes associated with neurodevelopmental disorders and cognitive phenotypes in humans.

Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection

The genetic architecture of human diseases and complex traits has been extensively studied, but little is known about the relationship of causal disease effect sizes between proximal SNPs, which have largely been assumed to be independent. We introduce a new method, LD SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs, depending on their allele frequencies, LD, and functional annotations; LDSPEC produced robust estimates in simulations across various genetic architectures. We applied LDSPEC to 70 diseases and complex traits from the UK Biobank (average N=306K), meta-analyzing results across diseases/traits. We detected significantly nonzero effect correlations for proximal SNP pairs (e.g., -0.37±0.09 for low-frequency positive-LD 0-100bp SNP pairs) that decayed with distance (e.g., -0.07±0.01 for low-frequency positive-LD 1-10kb), varied with allele frequency (e.g., -0.15±0.04 for common positive-LD 0-100bp), and varied with LD between SNPs (e.g., +0.12±0.05 for common negative-LD 0-100bp) (because we consider derived alleles, positive-LD and negative-LD SNP pairs may yield very different results). We further determined that SNP pairs with shared functions had stronger effect correlations that spanned longer genomic distances, e.g., -0.37±0.08 for low-frequency positive-LD same-gene promoter SNP pairs (average genomic distance of 47kb (due to alternative splicing)) and -0.32±0.04 for low-frequency positive-LD H3K27ac 0-1kb SNP pairs. Consequently, SNP-heritability estimates were substantially smaller than estimates of the sum of causal effect size variances across all SNPs (ratio of 0.87±0.02 across diseases/traits), particularly for certain functional annotations (e.g., 0.78±0.01 for common Super enhancer SNPs)-even though these quantities are widely assumed to be equal. We recapitulated our findings via forward simulations with an evolutionary model involving stabilizing selection, implicating the action of linkage masking, whereby haplotypes containing linked SNPs with opposite effects on disease have reduced effects on fitness and escape negative selection.

Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection

The genetic architecture of human diseases and complex traits has been extensively studied, but little is known about the relationship of causal disease effect sizes between proximal SNPs, which have largely been assumed to be independent. We introduce a new method, LD SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs, depending on their allele frequencies, LD, and functional annotations; LDSPEC produced robust estimates in simulations across various genetic architectures. We applied LDSPEC to 70 diseases and complex traits from the UK Biobank (average N=306K), meta-analyzing results across diseases/traits. We detected significantly nonzero effect correlations for proximal SNP pairs (e.g., -0.37±0.09 for low-frequency positive-LD 0-100bp SNP pairs) that decayed with distance (e.g., -0.07±0.01 for low-frequency positive-LD 1-10kb), varied with allele frequency (e.g., -0.15±0.04 for common positive-LD 0-100bp), and varied with LD between SNPs (e.g., +0.12±0.05 for common negative-LD 0-100bp) (because we consider derived alleles, positive-LD and negative-LD SNP pairs may yield very different results). We further determined that SNP pairs with shared functions had stronger effect correlations that spanned longer genomic distances, e.g., -0.37±0.08 for low-frequency positive-LD same-gene promoter SNP pairs (average genomic distance of 47kb (due to alternative splicing)) and -0.32±0.04 for low-frequency positive-LD H3K27ac 0-1kb SNP pairs. Consequently, SNP-heritability estimates were substantially smaller than estimates of the sum of causal effect size variances across all SNPs (ratio of 0.87±0.02 across diseases/traits), particularly for certain functional annotations (e.g., 0.78±0.01 for common Super enhancer SNPs)-even though these quantities are widely assumed to be equal. We recapitulated our findings via forward simulations with an evolutionary model involving stabilizing selection, implicating the action of linkage masking, whereby haplotypes containing linked SNPs with opposite effects on disease have reduced effects on fitness and escape negative selection.

Non-synaptic function of the autism spectrum disorder-associated gene SYNGAP1 in cortical neurogenesis

Genes involved in synaptic function are enriched among those with autism spectrum disorder (ASD)-associated rare genetic variants. Dysregulated cortical neurogenesis has been implicated as a convergent mechanism in ASD pathophysiology, yet it remains unknown how 'synaptic' ASD risk genes contribute to these phenotypes, which arise before synaptogenesis. Here, we show that the synaptic Ras GTPase-activating (RASGAP) protein 1 (SYNGAP1, a top ASD risk gene) is expressed within the apical domain of human radial glia cells (hRGCs). In a human cortical organoid model of SYNGAP1 haploinsufficiency, we find dysregulated cytoskeletal dynamics that impair the scaffolding and division plane of hRGCs, resulting in disrupted lamination and accelerated maturation of cortical projection neurons. Additionally, we confirmed an imbalance in the ratio of progenitors to neurons in a mouse model of Syngap1 haploinsufficiency. Thus, SYNGAP1-related brain disorders may arise through non-synaptic mechanisms, highlighting the need to study genes associated with neurodevelopmental disorders (NDDs) in diverse human cell types and developmental stages.

Genome-wide significant risk loci for mood disorders in the Old Order Amish founder population

Genome-wide association studies (GWAS) of mood disorders in large case-control cohorts have identified numerous risk loci, yet pathophysiological mechanisms remain elusive, primarily due to the very small effects of common variants. We sought to discover risk variants with larger effects by conducting a genome-wide association study of mood disorders in a founder population, the Old Order Amish (OOA, n = 1,672). Our analysis revealed four genome-wide significant risk loci, all of which were associated with >2-fold relative risk. Quantitative behavioral and neurocognitive assessments (n = 314) revealed effects of risk variants on sub-clinical depressive symptoms and information processing speed. Network analysis suggested that OOA-specific risk loci harbor novel risk-associated genes that interact with known neuropsychiatry-associated genes via gene interaction networks. Annotation of the variants at these risk loci revealed population-enriched, non-synonymous variants in two genes encoding neurodevelopmental transcription factors, CUX1 and CNOT1. Our findings provide insight into the genetic architecture of mood disorders and a substrate for mechanistic and clinical studies.

New insights into the structure and function of CNNM proteins

Magnesium (Mg2+ ) is the most abundant divalent cation in cells and plays key roles in almost all biological processes. CBS-pair domain divalent metal cation transport mediators (CNNMs) are a newly characterized class of Mg2+ transporters present throughout biology. Originally discovered in bacteria, there are four CNNM proteins in humans, which are involved in divalent cation transport, genetic diseases, and cancer. Eukaryotic CNNMs are composed of four domains: an extracellular domain, a transmembrane domain, a cystathionine-β-synthase (CBS)-pair domain, and a cyclic nucleotide-binding homology domain. The transmembrane and CBS-pair core are the defining features of CNNM proteins with over 20 000 protein sequences known from over 8000 species. Here, we review the structural and functional studies of eukaryotic and prokaryotic CNNMs that underlie our understanding of their regulation and mechanism of ion transport. Recent structures of prokaryotic CNNMs confirm the transmembrane domain mediates ion transport with the CBS-pair domain likely playing a regulatory role through binding divalent cations. Studies of mammalian CNNMs have identified new binding partners. These advances are driving progress in understanding this deeply conserved and widespread family of ion transporters.

A single-cell genomic atlas for maturation of the human cerebellum during early childhood

Inflammation early in life is a clinically established risk factor for autism spectrum disorders and schizophrenia, yet the impact of inflammation on human brain development is poorly understood. The cerebellum undergoes protracted postnatal maturation, making it especially susceptible to perturbations contributing to the risk of developing neurodevelopmental disorders. Here, using single-cell genomics of postmortem cerebellar brain samples, we characterized the postnatal development of cerebellar neurons and glia in 1- to 5-year-old children, comparing individuals who had died while experiencing inflammation with those who had died as a result of an accident. Our analyses revealed that inflammation and postnatal cerebellar maturation are associated with extensive, overlapping transcriptional changes primarily in two subtypes of inhibitory neurons: Purkinje neurons and Golgi neurons. Immunohistochemical analysis of a subset of these postmortem cerebellar samples revealed no change to Purkinje neuron soma size but evidence for increased activation of microglia in those children who had experienced inflammation. Maturation-associated and inflammation-associated gene expression changes included genes implicated in neurodevelopmental disorders. A gene regulatory network model integrating cell type-specific gene expression and chromatin accessibility identified seven temporally specific gene networks in Purkinje neurons and suggested that inflammation may be associated with the premature down-regulation of developmental gene expression programs.

Genes positively regulated by Mef2c in cortical neurons are enriched for common genetic variation associated with IQ and educational attainment

The myocyte enhancer factor 2 C (MEF2C) gene encodes a transcription factor important for neurogenesis and synapse development and contains common variants associated with intelligence (IQ) and educational attainment (EA). Here, we took gene expression data from the mouse cortex of a Mef2c mouse model with a heterozygous DNA binding-deficient mutation of Mef2c (Mef2c-het) and combined these data with MEF2C ChIP-seq data from cortical neurons and single-cell data from the mouse brain. This enabled us to create a set of genes that were differentially regulated in Mef2c-het mice, represented direct target genes of MEF2C and had elevated in expression in cortical neurons. We found this gene-set to be enriched for genes containing common genetic variation associated with IQ and EA. Genes within this gene-set that were down-regulated, i.e. have reduced expression in Mef2c-het mice versus controls, were specifically significantly enriched for both EA and IQ associated genes. These down-regulated genes were enriched for functionality in the adenylyl cyclase signalling system, which is known to positively regulate synaptic transmission and has been linked to learning and memory. Within the adenylyl cyclase signalling system, three genes regulated by MEF2C, CRHR1, RGS6, and GABRG3, are associated at genome-wide significant levels with IQ and/or EA. Our results indicate that genetic variation in MEF2C and its direct target genes within cortical neurons contribute to variance in cognition within the general population, and the molecular mechanisms involved include the adenylyl cyclase signalling system's role in synaptic function.

Brain-region-specific changes in neurons and glia and dysregulation of dopamine signaling in Grin2a mutant mice

A genetically valid animal model could transform our understanding of schizophrenia (SCZ) disease mechanisms. Rare heterozygous loss-of-function (LoF) mutations in GRIN2A, encoding a subunit of the NMDA receptor, greatly increase the risk of SCZ. By transcriptomic, proteomic, and behavioral analyses, we report that heterozygous Grin2a mutant mice show (1) large-scale gene expression changes across multiple brain regions and in neuronal (excitatory and inhibitory) and non-neuronal cells (astrocytes and oligodendrocytes), (2) evidence of hypoactivity in the prefrontal cortex (PFC) and hyperactivity in the hippocampus and striatum, (3) an elevated dopamine signaling in the striatum and hypersensitivity to amphetamine-induced hyperlocomotion (AIH), (4) altered cholesterol biosynthesis in astrocytes, (5) a reduction in glutamatergic receptor signaling proteins in the synapse, and (6) an aberrant locomotor pattern opposite of that induced by antipsychotic drugs. These findings reveal potential pathophysiologic mechanisms, provide support for both the "hypo-glutamate" and "hyper-dopamine" hypotheses of SCZ, and underscore the utility of Grin2a-deficient mice as a genetic model of SCZ.

Genomic findings in schizophrenia and their implications

There has been substantial progress in understanding the genetics of schizophrenia over the past 15 years. This has revealed a highly polygenic condition with the majority of the currently explained heritability coming from common alleles of small effect but with additional contributions from rare copy number and coding variants. Many specific genes and loci have been implicated that provide a firm basis upon which mechanistic research can proceed. These point to disturbances in neuronal, and particularly synaptic, functions that are not confined to a small number of brain regions and circuits. Genetic findings have also revealed the nature of schizophrenia's close relationship to other conditions, particularly bipolar disorder and childhood neurodevelopmental disorders, and provided an explanation for how common risk alleles persist in the population in the face of reduced fecundity. Current genomic approaches only potentially explain around 40% of heritability, but only a small proportion of this is attributable to robustly identified loci. The extreme polygenicity poses challenges for understanding biological mechanisms. The high degree of pleiotropy points to the need for more transdiagnostic research and the shortcomings of current diagnostic criteria as means of delineating biologically distinct strata. It also poses challenges for inferring causality in observational and experimental studies in both humans and model systems. Finally, the Eurocentric bias of genomic studies needs to be rectified to maximise benefits and ensure these are felt across diverse communities. Further advances are likely to come through the application of new and emerging technologies, such as whole-genome and long-read sequencing, to large and diverse samples. Substantive progress in biological understanding will require parallel advances in functional genomics and proteomics applied to the brain across developmental stages. For these efforts to succeed in identifying disease mechanisms and defining novel strata they will need to be combined with sufficiently granular phenotypic data.

A cytoskeleton-membrane interaction conserved in fast-spiking neurons controls movement, emotion, and memory

The pathogenesis of schizophrenia is believed to involve combined dysfunctions of many proteins including microtubule-associated protein 6 (MAP6) and Kv3.1 voltage-gated K+ (Kv) channel, but their relationship and functions in behavioral regulation are often not known. Here we report that MAP6 stabilizes Kv3.1 channels in parvalbumin-positive (PV+ ) fast-spiking GABAergic interneurons, regulating behavior. MAP6-/- and Kv3.1-/- mice display similar hyperactivity and avoidance reduction. Their proteins colocalize in PV+ interneurons and MAP6 deletion markedly reduces Kv3.1 protein level. We further show that two microtubule-binding modules of MAP6 bind the Kv3.1 tetramerization domain with high affinity, maintaining the channel level in both neuronal soma and axons. MAP6 knockdown by AAV-shRNA in the amygdala or the hippocampus reduces avoidance or causes hyperactivity and recognition memory deficit, respectively, through elevating projection neuron activity. Finally, knocking down Kv3.1 or disrupting the MAP6-Kv3.1 binding in these brain regions causes avoidance reduction and hyperactivity, consistent with the effects of MAP6 knockdown. Thus, disrupting this conserved cytoskeleton-membrane interaction in fast-spiking neurons causes different degrees of functional vulnerability in various neural circuits.

TULP4, a novel E3 ligase gene, participates in neuronal migration as a candidate in schizophrenia

BACKGROUND

TUB-like protein 4 (TULP4) is one of the distant members of tubby family proteins, whose function remains largely unknown. In the present study, we intend to identify the role of TULP4 in schizophrenia from human samples and animal models.

METHODS

Whole-exome sequencing was used to detect the four schizophrenia families collected. In different cell lines, the effects of identified variants in TULP4 gene on its expression and localization were analyzed. Knockdown models in utero and adult mice were employed to investigate the role of Tulp4 on neuronal migration and schizophrenia-related behavior. Subsequently, co-IP assays were used to search for proteins that interact with TULP4 and the effects of mutants on the molecular function of TULP4.

RESULTS

For the first time, we identified five rare variants in TULP4 from schizophrenia families, of which three significantly reduced TULP4 protein expression. Knockdown the expression of Tulp4 delayed neuronal migration during embryological development and consequently triggered abnormal behaviors in adult mice, including impaired sensorimotor gating and cognitive dysfunction. Furthermore, we confirmed that TULP4 is involved in the formation of a novel E3 ligase through interaction with CUL5-ELOB/C-RNF7 and the three deleterious variants affected the binding amount of TULP4 and CUL5 to a certain extent.

CONCLUSIONS

Together, we believe TULP4 plays an important role in neurodevelopment and subsequent schizophrenic-related phenotypes through its E3 ubiquitin ligase function.

Pathogenic Mis-splicing of CPEB4 in Schizophrenia

BACKGROUND

Schizophrenia (SCZ) is caused by an interplay of polygenic risk and environmental factors, which may alter regulators of gene expression leading to pathogenic misexpression of SCZ risk genes. The CPEB family of RNA-binding proteins (CPEB1-4) regulates translation of target RNAs (approximately 40% of overall genes). We previously identified CPEB4 as a key dysregulated translational regulator in autism spectrum disorder (ASD) because its neuronal-specific microexon (exon 4) is mis-spliced in ASD brains, causing underexpression of numerous ASD risk genes. The genetic factors and pathogenic mechanisms shared between SCZ and ASD led us to hypothesize CPEB4 mis-splicing in SCZ leading to underexpression of multiple SCZ-related genes.

METHODS

We performed MAGMA-enrichment analysis on Psychiatric Genomics Consortium genome-wide association study data and analyzed RNA sequencing data from the PsychENCODE Consortium. Reverse transcriptase polymerase chain reaction and Western blot were performed on postmortem brain tissue, and the presence/absence of antipsychotics was assessed through toxicological analysis. Finally, mice with mild overexpression of exon 4-lacking CPEB4 (CPEB4Δ4) were generated and analyzed biochemically and behaviorally.

RESULTS

First, we found enrichment of SCZ-associated genes for CPEB4-binder transcripts. We also found decreased usage of CPEB4 microexon in SCZ probands, which was correlated with decreased protein levels of CPEB4-target SCZ-associated genes only in antipsychotic-free individuals. Interestingly, differentially expressed genes fit those reported for SCZ, specifically in the SCZ probands with decreased CPEB4-microexon inclusion. Finally, we demonstrated that mice with mild overexpression of CPEB4Δ4 showed decreased protein levels of CPEB4-target SCZ genes and SCZ-linked behaviors.

CONCLUSIONS

We identified aberrant CPEB4 splicing and downstream misexpression of SCZ risk genes as a novel etiological mechanism in SCZ.

Neurite outgrowth deficits caused by rare PLXNB1 mutation in pediatric bipolar disorder

Pediatric bipolar disorder (PBD) is a severe mood dysregulation condition that affects 0.5-1% of children and teens in the United States. It is associated with recurrent episodes of mania and depression and an increased risk of suicidality. However, the genetics and neuropathology of PBD are largely unknown. Here, we used a combinatorial family-based approach to characterize cellular, molecular, genetic, and network-level deficits associated with PBD. We recruited a PBD patient and three unaffected family members from a family with a history of psychiatric illnesses. Using resting-state functional magnetic resonance imaging (rs-fMRI), we detected altered resting-state functional connectivity in the patient as compared to an unaffected sibling. Using transcriptomic profiling of patient and control induced pluripotent stem cell (iPSC)-derived telencephalic organoids, we found aberrant signaling in the molecular pathways related to neurite outgrowth. We corroborated the presence of neurite outgrowth deficits in patient iPSC-derived cortical neurons and identified a rare homozygous loss-of-function PLXNB1 variant (c.1360C>C; p.Ser454Arg) responsible for the deficits in the patient. Expression of wild-type PLXNB1, but not the variant, rescued neurite outgrowth in patient neurons, and expression of the variant caused the neurite outgrowth deficits in cortical neurons from PlxnB1 knockout mice. These results indicate that dysregulated PLXNB1 signaling may contribute to an increased risk of PBD and other mood dysregulation-related disorders by disrupting neurite outgrowth and functional brain connectivity. Overall, this study established and validated a novel family-based combinatorial approach for studying cellular and molecular deficits in psychiatric disorders and identified dysfunctional PLXNB1 signaling and neurite outgrowth as potential risk factors for PBD.

Using brain cell-type-specific protein interactomes to interpret neurodevelopmental genetic signals in schizophrenia

Genetics have nominated many schizophrenia risk genes and identified convergent signals between schizophrenia and neurodevelopmental disorders. However, functional interpretation of the nominated genes in the relevant brain cell types is often lacking. We executed interaction proteomics for six schizophrenia risk genes that have also been implicated in neurodevelopment in human induced cortical neurons. The resulting protein network is enriched for common variant risk of schizophrenia in Europeans and East Asians, is down-regulated in layer 5/6 cortical neurons of individuals affected by schizophrenia, and can complement fine-mapping and eQTL data to prioritize additional genes in GWAS loci. A sub-network centered on HCN1 is enriched for common variant risk and contains proteins (HCN4 and AKAP11) enriched for rare protein-truncating mutations in individuals with schizophrenia and bipolar disorder. Our findings showcase brain cell-type-specific interactomes as an organizing framework to facilitate interpretation of genetic and transcriptomic data in schizophrenia and its related disorders.

Rare coding variants as risk modifiers of the 22q11.2 deletion implicate postnatal cortical development in syndromic schizophrenia

22q11.2 deletion is one of the strongest known genetic risk factors for schizophrenia. Recent whole-genome sequencing of schizophrenia cases and controls with this deletion provided an unprecedented opportunity to identify risk modifying genetic variants and investigate their contribution to the pathogenesis of schizophrenia in 22q11.2 deletion syndrome. Here, we apply a novel analytic framework that integrates gene network and phenotype data to investigate the aggregate effects of rare coding variants and identified modifier genes in this etiologically homogenous cohort (223 schizophrenia cases and 233 controls of European descent). Our analyses revealed significant additive genetic components of rare nonsynonymous variants in 110 modifier genes (adjusted P = 9.4E-04) that overall accounted for 4.6% of the variance in schizophrenia status in this cohort, of which 4.0% was independent of the common polygenic risk for schizophrenia. The modifier genes affected by rare coding variants were enriched with genes involved in synaptic function and developmental disorders. Spatiotemporal transcriptomic analyses identified an enrichment of coexpression between modifier and 22q11.2 genes in cortical brain regions from late infancy to young adulthood. Corresponding gene coexpression modules are enriched with brain-specific protein-protein interactions of SLC25A1, COMT, and PI4KA in the 22q11.2 deletion region. Overall, our study highlights the contribution of rare coding variants to the SCZ risk. They not only complement common variants in disease genetics but also pinpoint brain regions and developmental stages critical to the etiology of syndromic schizophrenia.

The molecular pathology of schizophrenia: an overview of existing knowledge and new directions for future research

Despite enormous efforts employing various approaches, the molecular pathology in the schizophrenia brain remains elusive. On the other hand, the knowledge of the association between the disease risk and changes in the DNA sequences, in other words, our understanding of the genetic pathology of schizophrenia, has dramatically improved over the past two decades. As the consequence, now we can explain more than 20% of the liability to schizophrenia by considering all analyzable common genetic variants including those with weak or no statistically significant association. Also, a large-scale exome sequencing study identified single genes whose rare mutations substantially increase the risk for schizophrenia, of which six genes (SETD1A, CUL1, XPO7, GRIA3, GRIN2A, and RB1CC1) showed odds ratios larger than ten. Based on these findings together with the preceding discovery of copy number variants (CNVs) with similarly large effect sizes, multiple disease models with high etiological validity have been generated and analyzed. Studies of the brains of these models, as well as transcriptomic and epigenomic analyses of patient postmortem tissues, have provided new insights into the molecular pathology of schizophrenia. In this review, we overview the current knowledge acquired from these studies, their limitations, and directions for future research that may redefine schizophrenia based on biological alterations in the responsible organ rather than operationalized criteria.

Neurotransmission-related gene expression in the frontal pole is altered in subjects with bipolar disorder and schizophrenia

The frontal pole (Brodmann area 10, BA10) is the largest cytoarchitectonic region of the human cortex, performing complex integrative functions. BA10 undergoes intensive adolescent grey matter pruning prior to the age of onset for bipolar disorder (BP) and schizophrenia (SCHIZ), and its dysfunction is likely to underly aspects of their shared symptomology. In this study, we investigated the role of BA10 neurotransmission-related gene expression in BP and SCHIZ. We performed qPCR to measure the expression of 115 neurotransmission-related targets in control, BP, and SCHIZ postmortem samples (n = 72). We chose this method for its high sensitivity to detect low-level expression. We then strengthened our findings by performing a meta-analysis of publicly released BA10 microarray data (n = 101) and identified sources of convergence with our qPCR results. To improve interpretation, we leveraged the unusually large database of clinical metadata accompanying our samples to explore the relationship between BA10 gene expression, therapeutics, substances of abuse, and symptom profiles, and validated these findings with publicly available datasets. Using these convergent sources of evidence, we identified 20 neurotransmission-related genes that were differentially expressed in BP and SCHIZ in BA10. These results included a large diagnosis-related decrease in two important therapeutic targets with low levels of expression, HTR2B and DRD4, as well as other findings related to dopaminergic, GABAergic and astrocytic function. We also observed that therapeutics may produce a differential expression that opposes diagnosis effects. In contrast, substances of abuse showed similar effects on BA10 gene expression as BP and SCHIZ, potentially amplifying diagnosis-related dysregulation.

Mouse mutants in schizophrenia risk genes GRIN2A and AKAP11 show EEG abnormalities in common with schizophrenia patients

Schizophrenia is a heterogeneous psychiatric disorder with a strong genetic basis, whose etiology and pathophysiology remain poorly understood. Exome sequencing studies have uncovered rare, loss-of-function variants that greatly increase risk of schizophrenia [1], including loss-of-function mutations in GRIN2A (aka GluN2A or NR2A, encoding the NMDA receptor subunit 2A) and AKAP11 (A-Kinase Anchoring Protein 11). AKAP11 and GRIN2A mutations are also associated with bipolar disorder [2], and epilepsy and developmental delay/intellectual disability [1, 3, 4], respectively. Accessible in both humans and rodents, electroencephalogram (EEG) recordings offer a window into brain activity and display abnormal features in schizophrenia patients. Does loss of Grin2a or Akap11 in mice also result in EEG abnormalities? We monitored EEG in heterozygous and homozygous knockout Grin2a and Akap11 mutant mice compared with their wild-type littermates, at 3- and 6-months of age, across the sleep/wake cycle and during auditory stimulation protocols. Grin2a and Akap11 mutants exhibited increased resting gamma power, attenuated auditory steady-state responses (ASSR) at gamma frequencies, and reduced responses to unexpected auditory stimuli during mismatch negativity (MMN) tests. Sleep spindle density was reduced in a gene dose-dependent manner in Akap11 mutants, whereas Grin2a mutants showed increased sleep spindle density. The EEG phenotypes of Grin2a and Akap11 mutant mice show a variety of abnormal features that overlap considerably with human schizophrenia patients, reflecting systems-level changes caused by Grin2a and Akap11 deficiency. These neurophysiologic findings further substantiate Grin2a and Akap11 mutants as genetic models of schizophrenia and identify potential biomarkers for stratification of schizophrenia patients.

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.

Schizophrenia risk conferred by rare protein-truncating variants is conserved across diverse human populations

Schizophrenia (SCZ) is a chronic mental illness and among the most debilitating conditions encountered in medical practice. A recent landmark SCZ study of the protein-coding regions of the genome identified a causal role for ten genes and a concentration of rare variant signals in evolutionarily constrained genes1. This recent study-and most other large-scale human genetics studies-was mainly composed of individuals of European (EUR) ancestry, and the generalizability of the findings in non-EUR populations remains unclear. To address this gap, we designed a custom sequencing panel of 161 genes selected based on the current knowledge of SCZ genetics and sequenced a new cohort of 11,580 SCZ cases and 10,555 controls of diverse ancestries. Replicating earlier work, we found that cases carried a significantly higher burden of rare protein-truncating variants (PTVs) among evolutionarily constrained genes (odds ratio = 1.48; P = 5.4 × 10-6). In meta-analyses with existing datasets totaling up to 35,828 cases and 107,877 controls, this excess burden was largely consistent across five ancestral populations. Two genes (SRRM2 and AKAP11) were newly implicated as SCZ risk genes, and one gene (PCLO) was identified as shared by individuals with SCZ and those with autism. Overall, our results lend robust support to the rare allelic spectrum of the genetic architecture of SCZ being conserved across diverse human populations.

The genetic architecture of schizophrenia: review of large-scale genetic studies

Schizophrenia is a complex and often chronic psychiatric disorder with high heritability. Diagnosis of schizophrenia is still made clinically based on psychiatric symptoms; no diagnostic tests or biomarkers are available. Pathophysiology-based diagnostic scheme and treatments are also not available. Elucidation of the pathogenesis is needed for development of pathology-based diagnostics and treatments. In the past few decades, genetic research has made substantial advances in our understanding of the genetic architecture of schizophrenia. Rare copy number variations (CNVs) and rare single-nucleotide variants (SNVs) detected by whole-genome CNV analysis and whole-genome/-exome sequencing analysis have provided the great advances. Common single-nucleotide polymorphisms (SNPs) detected by large-scale genome-wide association studies have also provided important information. Large-scale genetic studies have been revealed that both rare and common genetic variants play crucial roles in this disorder. In this review, we focused on CNVs, SNVs, and SNPs, and discuss the latest research findings on the pathogenesis of schizophrenia based on these genetic variants. Rare variants with large effect sizes can provide mechanistic hypotheses. CRISPR-based genetics approaches and induced pluripotent stem cell technology can facilitate the functional analysis of these variants detected in patients with schizophrenia. Recent advances in long-read sequence technology are expected to detect variants that cannot be detected by short-read sequence technology. Various studies that bring together data from common variant and transcriptomic datasets provide biological insight. These new approaches will provide additional insight into the pathophysiology of schizophrenia and facilitate the development of pathology-based therapeutics.

Toward Best Practices for Imaging Transcriptomics of the Human Brain

Modern brainwide transcriptional atlases provide unprecedented opportunities for investigating the molecular correlates of brain organization, as quantified using noninvasive neuroimaging. However, integrating neuroimaging data with transcriptomic measures is not straightforward, and careful consideration is required to make valid inferences. In this article, we review recent work exploring how various methodological choices affect 3 main phases of imaging transcriptomic analyses, including 1) processing of transcriptional atlas data; 2) relating transcriptional measures to independently derived neuroimaging phenotypes; and 3) evaluating the functional implications of identified associations through gene enrichment analyses. Our aim is to facilitate the development of standardized and reproducible approaches for this rapidly growing field. We identify sources of methodological variability, key choices that can affect findings, and considerations for mitigating false positive and/or spurious results. Finally, we provide an overview of freely available open-source toolboxes implementing current best-practice procedures across all 3 analysis phases.

Rescue of neuropsychiatric phenotypes in a mouse model of 16p11.2 duplication syndrome by genetic correction of an epilepsy network hub

Neuropsychiatric disorders (NPDs) are frequently co-morbid with epilepsy, but the biological basis of shared risk remains poorly understood. The 16p11.2 duplication is a copy number variant that confers risk for diverse NPDs including autism spectrum disorder, schizophrenia, intellectual disability and epilepsy. We used a mouse model of the 16p11.2 duplication (16p11.2dup/+) to uncover molecular and circuit properties associated with this broad phenotypic spectrum, and examined genes within the locus capable of phenotype reversal. Quantitative proteomics revealed alterations to synaptic networks and products of NPD risk genes. We identified an epilepsy-associated subnetwork that was dysregulated in 16p11.2dup/+ mice and altered in brain tissue from individuals with NPDs. Cortical circuits from 16p11.2dup/+ mice exhibited hypersynchronous activity and enhanced network glutamate release, which increased susceptibility to seizures. Using gene co-expression and interactome analysis, we show that PRRT2 is a major hub in the epilepsy subnetwork. Remarkably, correcting Prrt2 copy number rescued aberrant circuit properties, seizure susceptibility and social deficits in 16p11.2dup/+ mice. We show that proteomics and network biology can identify important disease hubs in multigenic disorders, and reveal mechanisms relevant to the complex symptomatology of 16p11.2 duplication carriers.

Common and rare variant associations with latent traits underlying depression, bipolar disorder, and schizophrenia

Genetic studies in psychiatry have primarily focused on the effects of common genetic variants, but few have investigated the role of rare genetic variants, particularly for major depression. In order to explore the role of rare variants in the gap between estimates of single nucleotide polymorphism (SNP) heritability and twin study heritability, we examined the contribution of common and rare genetic variants to latent traits underlying psychiatric disorders using high-quality imputed genotype data from the UK Biobank. Using a pre-registered analysis, we used items from the UK Biobank Mental Health Questionnaire relevant to three psychiatric disorders: major depression (N = 134,463), bipolar disorder (N = 117,376) and schizophrenia (N = 130,013) and identified a general hierarchical factor for each that described participants' responses. We calculated participants' scores on these latent traits and conducted single-variant genetic association testing (MAF > 0.05%), gene-based burden testing and pathway association testing associations with these latent traits. We tested for enrichment of rare variants (MAF 0.05-1%) in genes that had been previously identified by common variant genome-wide association studies, and genes previously associated with Mendelian disorders having relevant symptoms. We found moderate genetic correlations between the latent traits in our study and case-control phenotypes in previous genome-wide association studies, and identified one common genetic variant (rs72657988, minor allele frequency = 8.23%, p = 1.01 × 10-9) associated with the general factor of schizophrenia, but no other single variants, genes or pathways passed significance thresholds in this analysis, and we did not find enrichment in previously identified genes.

Genomic insights into schizophrenia

Schizophrenia is a common, complex, heterogeneous psychiatric syndrome which can have profound impacts on affected individuals and imposes significant burdens on society. Despite intensive research, it has been challenging to understand basic mechanisms and to identify novel therapeutic targets. Given its high heritability and the complexity and inaccessibility of the human brain, much hope has been invested in the application of genomics as a route to better understanding. This work has identified many common and rare risk alleles and laid the foundations for a new generation of mechanistic studies. Genomics has also thrown new light on the relationship between schizophrenia and other psychiatric disorders and revealed its previously unappreciated aetiological relationship with childhood neurodevelopmental disorders, providing further evidence that it has its origins in disturbances of brain development. In addition, genomic findings suggest that the condition reflects fundamental disturbances in neuronal, and particularly synaptic, function that impact broadly on brain function, rather than being a disorder of specific brain regions and circuits. Finally, genomics has provided a plausible solution to the evolutionary paradox of how the condition persists in the face of high heritability and reduced fecundity.

Clinical characteristics indexing genetic differences in schizophrenia: a systematic review

Genome-wide studies are among the best available tools for identifying etiologic processes underlying psychiatric disorders such as schizophrenia. However, it is widely recognized that disorder heterogeneity may limit genetic insights. Identifying phenotypes indexing genetic differences among patients with non-affective psychotic disorder will improve genome-wide studies of these disorders. The present study systematically reviews existing literature to identify phenotypes that index genetic differences among patients with schizophrenia and related disorders. We systematically reviewed family-based studies and genome-wide molecular-genetic studies investigating whether phenotypic variation in patients with non-affective psychotic disorders (according to DSM or equivalent systems) was associated with genome-wide genetic variation (PROSPERO number CRD42019136169). An electronic database search of PubMed, EMBASE, and PsycINFO from inception until 17 May 2019 resulted in 4347 published records. These records included a total of 813 relevant analyses from 264 articles. Two independent raters assessed the quality of all analyses based on methodologic rigor and power. We found moderate to strong evidence for a positive association between genetic/familial risk for non-affective psychosis and four phenotypes: early age of onset, negative/deficit symptoms, chronicity, and functional impairment. Female patients also tended to have more affected relatives. Severity of positive symptoms was not associated with genetic/familial risk for schizophrenia. We suggest that phenotypes with the most evidence for reflecting genetic difference in participating patients should be measured in future large-scale genetic studies of schizophrenia to improve power to discover causal variants and to facilitate discovery of modifying genetic variants.

New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications

Psychiatric genetics has made substantial progress in the last decade, providing new insights into the genetic etiology of psychiatric disorders, and paving the way for precision psychiatry, in which individual genetic profiles may be used to personalize risk assessment and inform clinical decision-making. Long recognized to be heritable, recent evidence shows that psychiatric disorders are influenced by thousands of genetic variants acting together. Most of these variants are commonly occurring, meaning that every individual has a genetic risk to each psychiatric disorder, from low to high. A series of large-scale genetic studies have discovered an increasing number of common and rare genetic variants robustly associated with major psychiatric disorders. The most convincing biological interpretation of the genetic findings implicates altered synaptic function in autism spectrum disorder and schizophrenia. However, the mechanistic understanding is still incomplete. In line with their extensive clinical and epidemiological overlap, psychiatric disorders appear to exist on genetic continua and share a large degree of genetic risk with one another. This provides further support to the notion that current psychiatric diagnoses do not represent distinct pathogenic entities, which may inform ongoing attempts to reconceptualize psychiatric nosology. Psychiatric disorders also share genetic influences with a range of behavioral and somatic traits and diseases, including brain structures, cognitive function, immunological phenotypes and cardiovascular disease, suggesting shared genetic etiology of potential clinical importance. Current polygenic risk score tools, which predict individual genetic susceptibility to illness, do not yet provide clinically actionable information. However, their precision is likely to improve in the coming years, and they may eventually become part of clinical practice, stressing the need to educate clinicians and patients about their potential use and misuse. This review discusses key recent insights from psychiatric genetics and their possible clinical applications, and suggests future directions.

Genetic insights into childhood-onset schizophrenia: The yield of clinical exome sequencing

Childhood-onset schizophrenia (COS) is a rare form of schizophrenia with an onset prior to 13 years of age. Although genetic factors play a role in COS etiology, only a few causal variants have been reported to date. This study presents a diagnostic exome sequencing (ES) in 37 Israeli Jewish families with a proband diagnosed with COS. By implementing a trio/duo ES approach and applying a well-established diagnostic pipeline, we detected clinically significant variants in 7 probands (19 %). These single nucleotide variants and indels were mostly inherited. The implicated genes were ANKRD11, GRIA2, CHD2, CLCN3, CLTC, IGF1R and MICU1. In a secondary analysis that compared COS patients to 4721 healthy controls, we observed that patients had a significant enrichment of rare loss of function (LoF) variants in LoF intolerant genes associated with developmental diseases. Taken together, ES could be considered as a valuable tool in the genetic workup for COS patients.