Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection

Rare variants in pharmacogenes influence clozapine metabolism in individuals with schizophrenia

Clozapine is the only licensed medication for treatment-resistant schizophrenia (TRS). Few predictors for variation in response to clozapine have been identified, but clozapine metabolism is known to influence therapeutic response and adverse side effects. Here, we expand on genome-wide studies of clozapine metabolism, previously focused on common genetic variation, by analysing whole-exome sequencing data from 2062 individuals with schizophrenia taking clozapine in the UK. We investigated whether rare genomic variation in genes and gene sets involved in the clozapine metabolism pathway influences plasma concentrations of clozapine metabolites, assessed through the longitudinal analysis of 6585 pharmacokinetic assays. We observed a statistically significant association between the burden of rare damaging coding variants (MAF ≤ 1 %) in gene sets broadly related to drug pharmacokinetics and lower clozapine (β = -0.054, SE = 0.019, P-value = 0.005) concentrations in plasma. We estimate that the effects in clozapine plasma concentrations of a single damaging allele in this gene set are akin to reducing the clozapine dose by about 35 mg/day. The gene-based analysis identified rare variants in CYP1A2, which encodes the enzyme responsible for converting clozapine to norclozapine, as having the strongest effects of any gene on clozapine metabolism (β = 0.324, SE = 0.124, P = 0.009). Our findings support the hypothesis that rare genetic variants in known drug-metabolising enzymes and transporters can markedly influence clozapine plasma concentrations; these results suggest that pharmacogenomic efforts trying to predict clozapine metabolism and personalise drug therapy could benefit from the inclusion of rare damaging variants in pharmacogenes beyond those already identified and catalogued as PGx star alleles.

A polygenic resilience score moderates the genetic risk for schizophrenia: Replication in 18,090 cases and 28,114 controls from the Psychiatric Genomics Consortium

Identifying heritable factors that moderate the genetic risk for schizophrenia (SCZ) could help clarify why some individuals remain unaffected despite having relatively high genetic liability. Previously, we developed a framework to mine genome-wide association (GWAS) data for common genetic variants that protect high-risk unaffected individuals from SCZ, leading to derivation of the first-ever "polygenic resilience score" for SCZ (resilient controls n = 3786; polygenic risk score-matched SCZ cases n = 18,619). Here, we performed a replication study to verify the moderating effect of our polygenic resilience score on SCZ risk (OR = 1.09, p = 4.03 × 10-5 ) using newly released GWAS data from 23 independent case-control studies collated by the Psychiatric Genomics Consortium (PGC) (resilient controls n = 2821; polygenic risk score-matched SCZ cases n = 5150). Additionally, we sought to optimize our polygenic resilience-scoring formula to improve subsequent modeling of resilience to SCZ and other complex disorders. We found significant replication of the polygenic resilience score, and found that strict pruning of SNPs based on linkage disequilibrium to known risk SNPs and their linked loci optimizes the performance of the polygenic resilience score.

Parkinson's disease and schizophrenia interactomes contain temporally distinct gene clusters underlying comorbid mechanisms and unique disease processes

Genome-wide association studies suggest significant overlaps in Parkinson's disease (PD) and schizophrenia (SZ) risks, but the underlying mechanisms remain elusive. The protein-protein interaction network ('interactome') plays a crucial role in PD and SZ and can incorporate their spatiotemporal specificities. Therefore, to study the linked biology of PD and SZ, we compiled PD- and SZ-associated genes from the DisGeNET database, and constructed their interactomes using BioGRID and HPRD. We examined the interactomes using clustering and enrichment analyses, in conjunction with the transcriptomic data of 26 brain regions spanning foetal stages to adulthood available in the BrainSpan Atlas. PD and SZ interactomes formed four gene clusters with distinct temporal identities (Disease Gene Networks or 'DGNs'1-4). DGN1 had unique SZ interactome genes highly expressed across developmental stages, corresponding to a neurodevelopmental SZ subtype. DGN2, containing unique SZ interactome genes expressed from early infancy to adulthood, correlated with an inflammation-driven SZ subtype and adult SZ risk. DGN3 contained unique PD interactome genes expressed in late infancy, early and late childhood, and adulthood, and involved in mitochondrial pathways. DGN4, containing prenatally-expressed genes common to both the interactomes, involved in stem cell pluripotency and overlapping with the interactome of 22q11 deletion syndrome (comorbid psychosis and Parkinsonism), potentially regulates neurodevelopmental mechanisms in PD-SZ comorbidity. Our findings suggest that disrupted neurodevelopment (regulated by DGN4) could expose risk windows in PD and SZ, later elevating disease risk through inflammation (DGN2). Alternatively, variant clustering in DGNs may produce disease subtypes, e.g., PD-SZ comorbidity with DGN4, and early/late-onset SZ with DGN1/DGN2.

The structure of psychiatric comorbidity without selection and assortative mating

The widespread comorbidity observed across psychiatric disorders may be the result of processes such as assortative mating, gene-environment correlation, or selection into population studies. Between-family analyses of comorbidity are subject to these sources of bias, whereas within-family analyses are not. Because of Mendelian inheritance, alleles are randomly assigned within families, conditional on parental alleles. We exploit this variation to compare the structure of comorbidity across broad psychiatric polygenic scores when calculated either between-family (child polygenic scores) or within-family (child polygenic scores regressed on parental polygenic scores) in over 25,000 genotyped parent-offspring trios from the Norwegian Mother Father and Child Cohort study (MoBa). We fitted a series of factor models to the between- and within-family data, which consisted of a single genetic p-factor and a varying number of uncorrelated subfactors. The best-fitting model was identical for between- and within-family analyses and included three subfactors capturing variants associated with neurodevelopment, psychosis, and constraint, in addition to the genetic p-factor. Partner genetic correlations, indicating assortative mating, were not present for the genetic p-factor, but were substantial for the psychosis (b = 0.081;95% CI [0.038,0.124]) and constraint (b = 0.257;95% CI [0.075,0.439]) subfactors. When average factor levels for MoBa mothers and fathers were compared to a population mean of zero we found evidence of sex-specific participation bias, which has implications for the generalizability of findings from cohort studies. Our results demonstrate the power of the within-family design for better understanding the mechanisms driving psychiatric comorbidity and their consequences on population health.

Coexpression network analysis of the adult brain sheds light on the pathogenic mechanism of DDR1 in schizophrenia and bipolar disorder

DDR1 has been linked to schizophrenia (SCZ) and bipolar disorder (BD) in association studies. DDR1 encodes 58 distinct transcripts, which can be translated into five isoforms (DDR1a-e) and are expressed in the brain. However, the transcripts expressed in each brain cell type, their functions and their involvement in SCZ and BD remain unknown. Here, to infer the processes in which DDR1 transcripts are involved, we used transcriptomic data from the human brain dorsolateral prefrontal cortex of healthy controls (N = 936) and performed weighted gene coexpression network analysis followed by enrichment analyses. Then, to explore the involvement of DDR1 transcripts in SCZ (N = 563) and BD (N = 222), we studied the association of coexpression modules with disease and performed differential expression and transcript significance analyses. Some DDR1 transcripts were distributed across five coexpression modules identified in healthy controls (MHC). MHC1 and MHC2 were enriched in the cell cycle and proliferation of astrocytes and OPCs; MHC3 and MHC4 were enriched in oligodendrocyte differentiation and myelination; and MHC5 was enriched in neurons and synaptic transmission. Most of the DDR1 transcripts associated with SCZ and BD pertained to MHC1 and MHC2. Altogether, our results suggest that DDR1 expression might be altered in SCZ and BD via the proliferation of astrocytes and OPCs, suggesting that these processes are relevant in psychiatric disorders.

Processing speed mediates the relationship between DDR1 and psychosocial functioning in euthymic patients with bipolar disorder presenting psychotic symptoms

The DDR1 locus is associated with the diagnosis of schizophrenia and with processing speed in patients with schizophrenia and first-episode psychosis. Here, we investigated whether DDR1 variants are associated with bipolar disorder (BD) features. First, we performed a case‒control association study comparing DDR1 variants between patients with BD and healthy controls. Second, we performed linear regression analyses to assess the associations of DDR1 variants with neurocognitive domains and psychosocial functioning. Third, we conducted a mediation analysis to explore whether neurocognitive impairment mediated the association between DDR1 variants and psychosocial functioning in patients with BD. Finally, we studied the association between DDR1 variants and white matter microstructure. We did not find any statistically significant associations in the case‒control association study; however, we found that the combined genotypes rs1264323AA-rs2267641AC/CC were associated with worse neurocognitive performance in patients with BD with psychotic symptoms. In addition, the combined genotypes rs1264323AA-rs2267641AC/CC were associated with worse psychosocial functioning through processing speed. We did not find correlations between white matter microstructure abnormalities and the neurocognitive domains associated with the combined genotypes rs1264323AA-rs2267641AC/CC. Overall, the results suggest that DDR1 may be a marker of worse neurocognitive performance and psychosocial functioning in patients with BD, specifically those with psychotic symptoms.

Temporal changes of gene expression in health, schizophrenia, bipolar disorder, and major depressive disorder

The molecular events underlying the development, manifestation, and course of schizophrenia, bipolar disorder, and major depressive disorder span from embryonic life to advanced age. However, little is known about the early dynamics of gene expression in these disorders due to their relatively late manifestation. To address this, we conducted a secondary analysis of post-mortem prefrontal cortex datasets using bioinformatics and machine learning techniques to identify differentially expressed gene modules associated with aging and the diseases, determine their time-perturbation points, and assess enrichment with expression quantitative trait loci (eQTL) genes. Our findings revealed early, mid, and late deregulation of expression of functional gene modules involved in neurodevelopment, plasticity, homeostasis, and immune response. This supports the hypothesis that multiple hits throughout life contribute to disease manifestation rather than a single early-life event. Moreover, the time-perturbed functional gene modules were associated with genetic loci affecting gene expression, highlighting the role of genetic factors in gene expression dynamics and the development of disease phenotypes. Our findings emphasize the importance of investigating time-dependent perturbations in gene expression before the age of onset in elucidating the molecular mechanisms of psychiatric disorders.

Sex-dependent placental methylation quantitative trait loci provide insight into the prenatal origins of childhood onset traits and conditions

Molecular quantitative trait loci (QTLs) allow us to understand the biology captured in genome-wide association studies (GWASs). The placenta regulates fetal development and shows sex differences in DNA methylation. We therefore hypothesized that placental methylation QTL (mQTL) explain variation in genetic risk for childhood onset traits, and that effects differ by sex. We analyzed 411 term placentas from two studies and found 49,252 methylation (CpG) sites with mQTL and 2,489 CpG sites with sex-dependent mQTL. All mQTL were enriched in regions that typically affect gene expression in prenatal tissues. All mQTL were also enriched in GWAS results for growth- and immune-related traits, but male- and female-specific mQTL were more enriched than cross-sex mQTL. mQTL colocalized with trait loci at 777 CpG sites, with 216 (28%) specific to males or females. Overall, mQTL specific to male and female placenta capture otherwise overlooked variation in childhood traits.

Incorporating functional annotation with bilevel continuous shrinkage for polygenic risk prediction

BACKGROUND

Genetic variants can contribute differently to trait heritability by their functional categories, and recent studies have shown that incorporating functional annotation can improve the predictive performance of polygenic risk scores (PRSs). In addition, when only a small proportion of variants are causal variants, PRS methods that employ a Bayesian framework with shrinkage can account for such sparsity. It is possible that the annotation group level effect is also sparse. However, the number of PRS methods that incorporate both annotation information and shrinkage on effect sizes is limited. We propose a PRS method, PRSbils, which utilizes the functional annotation information with a bilevel continuous shrinkage prior to accommodate the varying genetic architectures both on the variant-specific level and on the functional annotation level.

RESULTS

We conducted simulation studies and investigated the predictive performance in settings with different genetic architectures. Results indicated that when there was a relatively large variability of group-wise heritability contribution, the gain in prediction performance from the proposed method was on average 8.0% higher AUC compared to the benchmark method PRS-CS. The proposed method also yielded higher predictive performance compared to PRS-CS in settings with different overlapping patterns of annotation groups and obtained on average 6.4% higher AUC. We applied PRSbils to binary and quantitative traits in three real world data sources (the UK Biobank, the Michigan Genomics Initiative (MGI), and the Korean Genome and Epidemiology Study (KoGES)), and two sources of annotations: ANNOVAR, and pathway information from the Kyoto Encyclopedia of Genes and Genomes (KEGG), and demonstrated that the proposed method holds the potential for improving predictive performance by incorporating functional annotations.

CONCLUSIONS

By utilizing a bilevel shrinkage framework, PRSbils enables the incorporation of both overlapping and non-overlapping annotations into PRS construction to improve the performance of genetic risk prediction. The software is available at https://github.com/styvon/PRSbils .

Identifying risk loci for obsessive-compulsive disorder and shared genetic component with schizophrenia: A large-scale multi-trait association analysis with summary statistics

Due to limited samples, no genetic loci have been identified for obsessive-compulsive disorder (OCD) in genome-wide association studies. Additionally, although co-morbidities between OCD and schizophrenia (SCZ) were observed, their common genetic etiology was not completely known. Here, we conducted a comprehensive investigation regarding the genetic architecture of OCD and the common genetic foundation shared by OCD and SCZ using summary statistics data (2688 cases and 7037 controls for OCD; 53,386 cases and 77,258 controls for SCZ). We discovered significant genetic correlation between OCD and SCZ (r̂g=0.296, P = 2.82 × 10-11). We then performed two multi-trait association analyses to detect OCD-associated loci and colocalization analysis to detect causal variants. Parallel gene-level analyses were also implemented. We identified 323 OCD-relevant variants located within 12 loci, with four loci shared the same causal variants between OCD and SCZ. Further, the gene-level analyses discovered 8 OCD-associated genes. Finally, multiple functional analyses at both SNP and gene levels showed that these genetic association signals had significant enrichments in the regions of left ventricle and anterior cingulate cortex, and suggested an important role of pathways involving regulation of telomere maintenance, histone phosphorylation, and GnRH secretion. Overall, this study identified new genetic loci for OCD and provided substantial evidence supporting common genetic foundation underlying OCD and SCZ. The findings advanced our understanding of genetic architecture and pathophysiology of OCD as well as shedding light on shared genetic etiology of the two disorders.

Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits

Many methods have been developed to leverage expression quantitative trait loci (eQTL) data to nominate candidate genes from genome-wide association studies. These methods, including colocalization, transcriptome-wide association studies (TWAS) and Mendelian randomization-based methods; however, all suffer from a key problem-when assessing the role of a gene in a trait using its eQTLs, nearby variants and genetic components of other genes' expression may be correlated with these eQTLs and have direct effects on the trait, acting as potential confounders. Our extensive simulations showed that existing methods fail to account for these 'genetic confounders', resulting in severe inflation of false positives. Our new method, causal-TWAS (cTWAS), borrows ideas from statistical fine-mapping and allows us to adjust all genetic confounders. cTWAS showed calibrated false discovery rates in simulations, and its application on several common traits discovered new candidate genes. In conclusion, cTWAS provides a robust statistical framework for gene discovery.

Genetic evidence for causal effects of immune dysfunction in psychiatric disorders: where are we?

The question of whether immune dysfunction contributes to risk of psychiatric disorders has long been a subject of interest. To assert this hypothesis a plethora of correlative evidence has been accumulated from the past decades; however, a variety of technical and practical obstacles impeded on a cause-effect interpretation of these data. With the advent of large-scale omics technology and advanced statistical models, particularly Mendelian randomization, new studies testing this old hypothesis are accruing. Here we synthesize these new findings from genomics and genetic causal inference studies on the role of immune dysfunction in major psychiatric disorders and reconcile these new data with pre-omics findings. By reconciling these evidences, we aim to identify key gaps and propose directions for future studies in the field.

Network propagation for GWAS analysis: a practical guide to leveraging molecular networks for disease gene discovery

MOTIVATION

Genome-wide association studies (GWAS) have enabled large-scale analysis of the role of genetic variants in human disease. Despite impressive methodological advances, subsequent clinical interpretation and application remains challenging when GWAS suffer from a lack of statistical power. In recent years, however, the use of information diffusion algorithms with molecular networks has led to fruitful insights on disease genes.

RESULTS

We present an overview of the design choices and pitfalls that prove crucial in the application of network propagation methods to GWAS summary statistics. We highlight general trends from the literature, and present benchmark experiments to expand on these insights selecting as case study three diseases and five molecular networks. We verify that the use of gene-level scores based on GWAS P-values offers advantages over the selection of a set of 'seed' disease genes not weighted by the associated P-values if the GWAS summary statistics are of sufficient quality. Beyond that, the size and the density of the networks prove to be important factors for consideration. Finally, we explore several ensemble methods and show that combining multiple networks may improve the network propagation approach.

Leveraging single-cell ATAC-seq and RNA-seq to identify disease-critical fetal and adult brain cell types

Prioritizing disease-critical cell types by integrating genome-wide association studies (GWAS) with functional data is a fundamental goal. Single-cell chromatin accessibility (scATAC-seq) and gene expression (scRNA-seq) have characterized cell types at high resolution, and studies integrating GWAS with scRNA-seq have shown promise, but studies integrating GWAS with scATAC-seq have been limited. Here, we identify disease-critical fetal and adult brain cell types by integrating GWAS summary statistics from 28 brain-related diseases/traits (average N = 298 K) with 3.2 million scATAC-seq and scRNA-seq profiles from 83 cell types. We identified disease-critical fetal (respectively adult) brain cell types for 22 (respectively 23) of 28 traits using scATAC-seq, and for 8 (respectively 17) of 28 traits using scRNA-seq. Significant scATAC-seq enrichments included fetal photoreceptor cells for major depressive disorder, fetal ganglion cells for BMI, fetal astrocytes for ADHD, and adult VGLUT2 excitatory neurons for schizophrenia. Our findings improve our understanding of brain-related diseases/traits and inform future analyses.

Polygenic risk for schizophrenia converges on alternative polyadenylation as molecular mechanism underlying synaptic impairment

Schizophrenia (SCZ) is a genetically heterogenous psychiatric disorder of highly polygenic nature. Correlative evidence from genetic studies indicate that the aggregated effects of distinct genetic risk factor combinations found in each patient converge onto common molecular mechanisms. To prove this on a functional level, we employed a reductionistic cellular model system for polygenic risk by differentiating induced pluripotent stem cells (iPSCs) from 104 individuals with high polygenic risk load and controls into cortical glutamatergic neurons (iNs). Multi-omics profiling identified widespread differences in alternative polyadenylation (APA) in the 3' untranslated region of many synaptic transcripts between iNs from SCZ patients and healthy donors. On the cellular level, 3'APA was associated with a reduction in synaptic density of iNs. Importantly, differential APA was largely conserved between postmortem human prefrontal cortex from SCZ patients and healthy donors, and strongly enriched for transcripts related to synapse biology. 3'APA was highly correlated with SCZ polygenic risk and affected genes were significantly enriched for SCZ associated common genetic variation. Integrative functional genomic analysis identified the RNA binding protein and SCZ GWAS risk gene PTBP2 as a critical trans-acting factor mediating 3'APA of synaptic genes in SCZ subjects. Functional characterization of PTBP2 in iNs confirmed its key role in 3'APA of synaptic transcripts and regulation of synapse density. Jointly, our findings show that the aggregated effects of polygenic risk converge on 3'APA as one common molecular mechanism that underlies synaptic impairments in SCZ.

The Association of Hippocampal Long-Term Potentiation-Induced Gene Expression with Genetic Risk for Psychosis

Genomic studies focusing on the contribution of common and rare genetic variants of schizophrenia and bipolar disorder support the view that substantial risk is conferred through molecular pathways involved in synaptic plasticity in the neurons of cortical and subcortical brain regions, including the hippocampus. Synaptic long-term potentiation (LTP) is central to associative learning and memory and depends on a pattern of gene expression in response to neuronal stimulation. Genes related to the induction of LTP have been associated with psychiatric genetic risk, but the specific cell types and timepoints responsible for the association are unknown. Using published genomic and transcriptomic datasets, we studied the relationship between temporally defined gene expression in hippocampal pyramidal neurons following LTP and enrichment for common genetic risk for schizophrenia and bipolar disorder, and for copy number variants (CNVs) and de novo coding variants associated with schizophrenia. We observed that upregulated genes in hippocampal pyramidal neurons at 60 and 120 min following LTP induction were enriched for common variant association with schizophrenia and bipolar disorder subtype I. At 60 min, LTP-induced genes were enriched in duplications from patients with schizophrenia, but this association was not specific to pyramidal neurons, perhaps reflecting the combined effects of CNVs in excitatory and inhibitory neuron subtypes. Gene expression following LTP was not related to enrichment for de novo coding variants from schizophrenia cases. Our findings refine our understanding of the role LTP-related gene sets play in conferring risk to conditions causing psychosis and provide a focus for future studies looking to dissect the molecular mechanisms associated with this risk.

The genetic architecture of the human hypothalamus and its involvement in neuropsychiatric behaviours and disorders

Despite its crucial role in the regulation of vital metabolic and neurological functions, the genetic architecture of the hypothalamus remains unknown. Here we conducted multivariate genome-wide association studies (GWAS) using hypothalamic imaging data from 32,956 individuals to uncover the genetic underpinnings of the hypothalamus and its involvement in neuropsychiatric traits. There were 23 significant loci associated with the whole hypothalamus and its subunits, with functional enrichment for genes involved in intracellular trafficking systems and metabolic processes of steroid-related compounds. The hypothalamus exhibited substantial genetic associations with limbic system structures and neuropsychiatric traits including chronotype, risky behaviour, cognition, satiety and sympathetic-parasympathetic activity. The strongest signal in the primary GWAS, the ADAMTS8 locus, was replicated in three independent datasets (N = 1,685-4,321) and was strengthened after meta-analysis. Exome-wide association analyses added evidence to the association for ADAMTS8, and Mendelian randomization showed lower ADAMTS8 expression with larger hypothalamic volumes. The current study advances our understanding of complex structure-function relationships of the hypothalamus and provides insights into the molecular mechanisms that underlie hypothalamic formation.

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.

Genetic effects of sequence-conserved enhancer-like elements on human complex traits

BACKGROUND

The vast majority of findings from human genome-wide association studies (GWAS) map to non-coding sequences, complicating their mechanistic interpretations and clinical translations. Non-coding sequences that are evolutionarily conserved and biochemically active could offer clues to the mechanisms underpinning GWAS discoveries. However, genetic effects of such sequences have not been systematically examined across a wide range of human tissues and traits, hampering progress to fully understand regulatory causes of human complex traits.

RESULTS

Here we develop a simple yet effective strategy to identify functional elements exhibiting high levels of human-mouse sequence conservation and enhancer-like biochemical activity, which scales well to 313 epigenomic datasets across 106 human tissues and cell types. Combined with 468 GWAS of European (EUR) and East Asian (EAS) ancestries, these elements show tissue-specific enrichments of heritability and causal variants for many traits, which are significantly stronger than enrichments based on enhancers without sequence conservation. These elements also help prioritize candidate genes that are functionally relevant to body mass index (BMI) and schizophrenia but were not reported in previous GWAS with large sample sizes.

CONCLUSIONS

Our findings provide a comprehensive assessment of how sequence-conserved enhancer-like elements affect complex traits in diverse tissues and demonstrate a generalizable strategy of integrating evolutionary and biochemical data to elucidate human disease genetics.

Stress, Environment and Early Psychosis

Existing literature provides extended evidence of the close relationship between stress dysregulation, environmental insults, and psychosis onset. Early stress can sensitize genetically vulnerable individuals to future stress, modifying their risk for developing psychotic phenomena. Neurobiological substrate of the aberrant stress response to hypothalamic-pituitary-adrenal axis dysregulation, disrupted inflammation processes, oxidative stress increase, gut dysbiosis, and altered brain signaling, provides mechanistic links between environmental risk factors and the development of psychotic symptoms. Early-life and later-life exposures may act directly, accumulatively, and repeatedly during critical neurodevelopmental time windows. Environmental hazards, such as pre- and perinatal complications, traumatic experiences, psychosocial stressors, and cannabis use might negatively intervene with brain developmental trajectories and disturb the balance of important stress systems, which act together with recent life events to push the individual over the threshold for the manifestation of psychosis. The current review presents the dynamic and complex relationship between stress, environment, and psychosis onset, attempting to provide an insight into potentially modifiable factors, enhancing resilience and possibly influencing individual psychosis liability.

Linking Polygenic Risk of Schizophrenia to Variation in Magnetic Resonance Imaging Brain Measures: A Comprehensive Systematic Review

BACKGROUND AND HYPOTHESIS

Schizophrenia is highly heritable, with a polygenic effect of many genes conferring risk. Evidence on whether cumulative risk also predicts alterations in brain morphology and function is inconsistent. This systematic review examined evidence for schizophrenia polygenic risk score (sczPRS) associations with commonly used magnetic resonance imaging (MRI) measures. We expected consistent evidence to emerge for significant sczPRS associations with variation in structure and function, specifically in frontal, temporal, and insula cortices that are commonly implicated in schizophrenia pathophysiology.

STUDY DESIGN

In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched MEDLINE, Embase, and PsycINFO for peer-reviewed studies published between January 2013 and March 2022. Studies were screened against predetermined criteria and National Institutes of Health (NIH) quality assessment tools.

STUDY RESULTS

In total, 57 studies of T1-weighted structural, diffusion, and functional MRI were included (age range = 9-80 years, Nrange = 64-76 644). We observed moderate, albeit preliminary, evidence for higher sczPRS predicting global reductions in cortical thickness and widespread variation in functional connectivity, and to a lesser extent, region-specific reductions in frontal and temporal volume and thickness. Conversely, sczPRS does not predict whole-brain surface area or gray/white matter volume. Limited evidence emerged for sczPRS associations with diffusion tensor measures of white matter microstructure in a large community sample and smaller cohorts of children and young adults. These findings were broadly consistent across community and clinical populations.

CONCLUSIONS

Our review supports the hypothesis that schizophrenia is a disorder of disrupted within and between-region brain connectivity, and points to specific whole-brain and regional MRI metrics that may provide useful intermediate phenotypes.

Rare genetic brain disorders with overlapping neurological and psychiatric phenotypes

Understanding rare genetic brain disorders with overlapping neurological and psychiatric phenotypes is of increasing importance given the potential for developing disease models that could help to understand more common, polygenic disorders. However, the traditional clinical boundaries between neurology and psychiatry result in frequent segregation of these disorders into distinct silos, limiting cross-specialty understanding that could facilitate clinical and biological advances. In this Review, we highlight multiple genetic brain disorders in which neurological and psychiatric phenotypes are observed, but for which in-depth, cross-spectrum clinical phenotyping is rarely undertaken. We describe the combined phenotypes observed in association with genetic variants linked to epilepsy, dystonia, autism spectrum disorder and schizophrenia. We also consider common underlying mechanisms that centre on synaptic plasticity, including changes to synaptic and neuronal structure, calcium handling and the balance of excitatory and inhibitory neuronal activity. Further investigation is needed to better define and replicate these phenotypes in larger cohorts, which would help to gain greater understanding of the pathophysiological mechanisms and identify common therapeutic targets.

Genetic architectures of cerebral ventricles and their overlap with neuropsychiatric traits

The cerebral ventricles are recognized as windows into brain development and disease, yet their genetic architectures, underlying neural mechanisms and utility in maintaining brain health remain elusive. Here we aggregated genetic and neuroimaging data from 61,974 participants (age range, 9 to 98 years) in five cohorts to elucidate the genetic basis of ventricular morphology and examined their overlap with neuropsychiatric traits. Genome-wide association analysis in a discovery sample of 31,880 individuals identified 62 unique loci and 785 candidate genes associated with ventricular morphology. We replicated over 80% of loci in a well-matched cohort of lateral ventricular volume. Gene set analysis revealed enrichment of ventricular-trait-associated genes in biological processes and disease pathogenesis during both early brain development and degeneration. We explored the age-dependent genetic associations in cohorts of different age groups to investigate the possible roles of ventricular-trait-associated loci in neurodevelopmental and neurodegenerative processes. We describe the genetic overlap between ventricular and neuropsychiatric traits through comprehensive integrative approaches under correlative and causal assumptions. We propose the volume of the inferior lateral ventricles as a heritable endophenotype to predict the risk of Alzheimer's disease, which might be a consequence of prodromal Alzheimer's disease. Our study provides an advance in understanding the genetics of the cerebral ventricles and demonstrates the potential utility of ventricular measurements in tracking brain disorders and maintaining brain health across the lifespan.

Association of clinical parameters and polygenic risk scores for body mass index, schizophrenia, and diabetes with antipsychotic-induced weight gain

Antipsychotic-induced weight gain (AIWG) is a common adverse event in schizophrenia. Genome-wide association studies (GWAS) and polygenic risk scores (PRS) for other diseases or traits are recent approaches to disentangling the genetic architecture of AIWG. 200 patients with schizophrenia treated monotherapeutically with antipsychotics were included in this study. A multiple linear regression analysis with ten-fold crossvalidation was performed to predict the percentage weight change after five weeks of treatment. Independent variables were sex, age, body mass index (BMI) at baseline, medication-associated risk, and PRSs (BMI, schizophrenia, diabetes, and metabolic syndrome). An explorative GWAS analysis was performed on the same subjects and traits. PRSs for BMI (β = 3.78; p = 0.0041), schizophrenia (β = 5.38; p = 0.021) and diabetes type 2 (β = 13.4; p = 0.046) were significantly associated with AIWG. Other significant factors were sex, baseline BMI and medication. Compared to the model without genetic factors, the addition of PRSs for BMI, schizophrenia, and diabetes type 2 increased the goodness of fit by 6.5 %. The GWAS identified the association of three variants (rs10668573, rs10249381 and rs1988834) with AIWG at a genome-wide level of p < 1 · 10-6. Using PRS for schizophrenia, BMI, and diabetes type 2 increased the explained variation of predicted weight gain, compared to a model without PRSs. For more precise results, PRSs derived from other traits (ideally AIWG) should be investigated. Potential risk variants identified in our GWAS need to be further investigated and replicated in independent samples.

Immunomodulatory Effects of Clozapine: More Than Just a Side Effect in Schizophrenia

Recent evidence suggests a possible relationship between the immune system and schizophrenia spectrum disorders (SSDs), as neuroinflammation appears to play a role in major psychiatric conditions. Neuroinflammation is as a broad concept representing a physiological protective response to infection or injury, but in some cases, especially if chronic, it may represent an expression of maladaptive processes, potentially driving to clinical dysfunction and neurodegeneration. Several studies are concurrently highlighting the importance of microglia, the resident immune cells of the central nervous system, in a huge number of neurodegenerative diseases, including multiple sclerosis, Alzheimer's and Parkinson's diseases, as well as SSDs. A more fundamental phenomenon of maladaptive coupling of microglia may contribute to the genesis of dysfunctional brain inflammation involved in SSDs, from the onset of their neurophenomenological evolution. Clozapine and other antipsychotic drugs seem to express a provable immunomodulant effect and a more specific action on microglia, while neuroactive steroids and nonsteroidal anti-inflammatory drugs may reduce some SSDs symptoms in add-on therapy. Given these theoretical premises, this article aims to summarize and interpret the available scientific evidence about psychotropic and anti-inflammatory drugs that could express an immunomodulant activity on microglia.

The role of cell adhesion molecule IgSF9b at the inhibitory synapse and psychiatric disease

Understanding perturbations in synaptic function between health and disease states is crucial to the treatment of neuropsychiatric illness. While genome-wide association studies have identified several genetic loci implicated in synaptic dysfunction in disorders such as autism and schizophrenia, many have not been rigorously characterized. Here, we highlight immunoglobulin superfamily member 9b (IgSF9b), a cell adhesion molecule thought to localize exclusively to inhibitory synapses in the brain. While both pre-clinical and clinical studies suggest its association with psychiatric diseases, our understanding of IgSF9b in synaptic maintenance, neural circuits, and behavioral phenotypes remains rudimentary. Moreover, these functions wield undiscovered influences on neurodevelopment. This review evaluates current literature and publicly available gene expression databases to explore the implications of IgSF9b dysfunction in rodents and humans. Through a focused analysis of one high-risk gene locus, we identify areas requiring further investigation and unearth clues related to broader mechanisms contributing to the synaptic etiology of psychiatric disorders.

The impact of maternal immune activation on GABAergic interneuron development: A systematic review of rodent studies and their translational implications

Mothers exposed to infections during pregnancy disproportionally birth children who develop autism and schizophrenia, disorders associated with altered GABAergic function. The maternal immune activation (MIA) model recapitulates this risk factor, with many studies also reporting disruptions to GABAergic interneuron expression, protein, cellular density and function. However, it is unclear if there are species, sex, age, region, or GABAergic subtype specific vulnerabilities to MIA. Furthermore, to fully comprehend the impact of MIA on the GABAergic system a synthesised account of molecular, cellular, electrophysiological and behavioural findings was required. To this end we conducted a systematic review of GABAergic interneuron changes in the MIA model, focusing on the prefrontal cortex and hippocampus. We reviewed 102 articles that revealed robust changes in a number of GABAergic markers that present as gestationally-specific, region-specific and sometimes sex-specific. Disruptions to GABAergic markers coincided with distinct behavioural phenotypes, including memory, sensorimotor gating, anxiety, and sociability. Findings suggest the MIA model is a valid tool for testing novel therapeutics designed to recover GABAergic function and associated behaviour.

Altered Sphingolipid Hydrolase Activities and Alpha-Synuclein Level in Late-Onset Schizophrenia

Recent data described that patients with lysosomal storage disorders (LSDs) may have clinical schizophrenia (SCZ) features. Disruption of lipid metabolism in SCZ pathogenesis was found. Clinical features of schizophrenia (SCZ) have been demonstrated in patients with several lysosomal storage disorders (LSDs). Taking into account the critical role of lysosomal function for neuronal cells' lysosomal dysfunction could be proposed in SCZ pathogenesis. The current study analyzed lysosomal enzyme activities and the alpha-synuclein level in the blood of patients with late-onset SCZ. In total, 52 SCZ patients with late-onset SCZ, 180 sporadic Parkinson's disease (sPD) patients, and 176 controls were recruited. The enzymatic activity of enzymes associated with mucopolysaccharidosis (alpha-L-Iduronidase (IDUA)), glycogenosis (acid alpha-glucosidase (GAA)) and sphingolipidosis (galactosylceramidase (GALC), glucocerebrosidase (GCase), alpha-galactosidase (GLA), acid sphingomyelinase (ASMase)) and concentration of lysosphingolipids (hexosylsphingosine (HexSph), globotriaosylsphingosine (LysoGb3), and lysosphingomyelin (LysoSM)) were measured using LC-MS/MS. The alpha-synuclein level was estimated in magnetically separated CD45+ blood cells using the enzyme-linked immunosorbent assay (ELISA). Additionally, NGS analysis of 11 LSDs genes was conducted in 21 early-onset SCZ patients and 23 controls using the gene panel PGRNseq-NDD. Decreased ASMase, increased GLA activities, and increased HexSpn, LysoGb3, and LysoSM concentrations along with an accumulation of the alpha-synuclein level were observed in late-onset SCZ patients in comparison to the controls (p < 0.05). Four rare deleterious variants among LSDs genes causing mucopolysaccharidosis type I (IDUA (rs532731688, rs74385837) and type III (HGSNAT (rs766835582)) and sphingolipidosis (metachromatic leukodystrophy (ARSA (rs201251634)) were identified in five patients from the group of early-onset SCZ patients but not in the controls. Our findings supported the role of sphingolipid metabolism in SCZ pathogenesis. Aberrant enzyme activities and compounds of sphingolipids associated with ceramide metabolism may lead to accumulation of alpha-synuclein and may be critical in SCZ pathogenesis.

Genome-scale copy number variant analysis in schizophrenia patients and controls from South India

Copy number variants (CNVs) are among the main genetic factors identified in schizophrenia (SZ) through genome-scale studies conducted mostly in Caucasian populations. However, to date, there have been no genome-scale CNV reports on patients from India. To address this shortcoming, we generated, for the first time, genome-scale CNV data for 168 SZ patients and 168 controls from South India. In total, 63 different CNVs were identified in 56 patients and 46 controls with a significantly higher proportion of medium-sized deletions (100 kb-1 Mb) after multiple testing (FDR = 2.7E-4) in patients. Of these, 13 CNVs were previously reported; however, when searched against GWAS, transcriptome, exome, and DNA methylation studies, another 17 CNVs with candidate genes were identified. Of the total 30 CNVs, 28 were present in 38 patients and 12 in 27 controls, indicating a significantly higher representation in the former (p = 1.87E-5). Only 4q35.1-q35.2 duplications were significant (p = 0.020) and observed in 11 controls and 2 patients. Among the others that are not significant, a few examples of patient-specific and previously reported CNVs include deletions of 11q14.1 (DLG2), 22q11.21, and 14q21.1 (LRFN5). 16p13.3 deletion (RBFOX1), 3p14.2 duplication (CADPS), and 7p11.2 duplication (CCT6A) were some of the novel CNVs containing candidate genes. However, these observations need to be replicated in a larger sample size. In conclusion, this report constitutes an important foundation for future CNV studies in a relatively unexplored population. In addition, the data indicate that there are advantages in using an integrated approach for better identification of candidate CNVs for SZ and other mental health disorders.

A resource of induced pluripotent stem cell (iPSC) lines including clinical, genomic, and cellular data from genetically isolated families with mood and psychotic disorders

Genome-wide (GWAS) and copy number variant (CNV) association studies have reproducibly identified numerous risk alleles associated with bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SCZ), but biological characterization of these alleles lags gene discovery, owing to the inaccessibility of live human brain cells and inadequate animal models for human psychiatric conditions. Human-derived induced pluripotent stem cells (iPSCs) provide a renewable cellular reagent that can be differentiated into living, disease-relevant cells and 3D brain organoids carrying the full complement of genetic variants present in the donor germline. Experimental studies of iPSC-derived cells allow functional characterization of risk alleles, establishment of causal relationships between genes and neurobiology, and screening for novel therapeutics. Here we report the creation and availability of an iPSC resource comprising clinical, genomic, and cellular data obtained from genetically isolated families with BD and related conditions. Results from the first 324 study participants, 61 of whom have validated pluripotent clones, show enrichment of rare single nucleotide variants and CNVs overlapping many known risk genes and pathogenic CNVs. This growing iPSC resource is available to scientists pursuing functional genomic studies of BD and related conditions.

Mapping the landscape of lineage-specific dynamic regulation of gene expression using single-cell transcriptomics and application to genetics of complex disease

Single-cell transcriptome data can provide insights into how genetic variation influences biological processes involved in human biology and disease. However, the identification of gene-level associations in distinct cell types faces several challenges, including the limited reference resource from population scale studies, data sparsity in single-cell RNA sequencing, and the complex cell-state pattern of expression within individual cell types. Here we develop genetic models of cell type specific and cell state adjusted gene expression in mid-brain neurons in the process of specializing from induced pluripotent stem cells. The resulting framework quantifies the dynamics of the genetic regulation of gene expression and estimates its cell type specificity. As an application, we show that the approach detects known and new genes associated with schizophrenia and enables insights into context-dependent disease mechanisms. We provide a genomic resource from a phenome-wide application of our models to more than 1500 phenotypes from the UK Biobank. Using longitudinal genetically determined expression, we implement a predictive causality framework, evaluating the prediction of future values of a target gene expression using prior values of a putative regulatory gene. Collectively, this work demonstrates the insights that can be gained into the molecular underpinnings of diseases by quantifying the genetic control of gene expression at single-cell resolution.

Microglia at the Tripartite Synapse during Postnatal Development: Implications for Autism Spectrum Disorders and Schizophrenia

Synapses are the fundamental structures of neural circuits that control brain functions and behavioral and cognitive processes. Synapses undergo formation, maturation, and elimination mainly during postnatal development via a complex interplay with neighboring astrocytes and microglia that, by shaping neural connectivity, may have a crucial role in the strengthening and weakening of synaptic functions, that is, the functional plasticity of synapses. Indeed, an increasing number of studies have unveiled the roles of microglia and astrocytes in synapse formation, maturation, and elimination as well as in regulating synaptic function. Over the past 15 years, the mechanisms underlying the microglia- and astrocytes-dependent regulation of synaptic plasticity have been thoroughly studied, and researchers have reported that the disruption of these glial cells in early postnatal development may underlie the cause of synaptic dysfunction that leads to neurodevelopmental disorders such as autism spectrum disorder (ASD) and schizophrenia.

Disturbed Oligodendroglial Maturation Causes Cognitive Dysfunction in Schizophrenia: A New Hypothesis

BACKGROUND AND HYPOTHESIS

Cognitive impairment is a hallmark of schizophrenia, but no effective treatment is available to date. The underlying pathophysiology includes disconnectivity between hippocampal and prefrontal brain regions. Supporting evidence comes from diffusion-weighted imaging studies that suggest abnormal organization of frontotemporal white matter pathways in schizophrenia.

STUDY DESIGN

Here, we hypothesize that in schizophrenia, deficient maturation of oligodendrocyte precursor cells (OPCs) into mature oligodendrocytes substantially contributes to abnormal frontotemporal macro- and micro-connectivity and subsequent cognitive deficits.

STUDY RESULTS

Our postmortem studies indicate a reduced oligodendrocyte number in the cornu ammonis 4 (CA4) subregion of the hippocampus, and others have reported the same histopathological finding in the dorsolateral prefrontal cortex. Our series of studies on aerobic exercise training showed a volume increase in the hippocampus, specifically in the CA4 region, and improved cognition in individuals with schizophrenia. The cognitive effects were subsequently confirmed by meta-analyses. Cell-specific schizophrenia polygenic risk scores showed that exercise-induced CA4 volume increase significantly correlates with OPCs. From animal models, it is evident that early life stress and oligodendrocyte-related gene variants lead to schizophrenia-related behavior, cognitive deficits, impaired oligodendrocyte maturation, and reduced myelin thickness.

CONCLUSIONS

Based on these findings, we propose that pro-myelinating drugs (e.g., the histamine blocker clemastine) combined with aerobic exercise training may foster the regeneration of myelin plasticity as a basis for restoring frontotemporal connectivity and cognition in schizophrenia.

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.

The genetic relationships between brain structure and schizophrenia

Genetic risks for schizophrenia are theoretically mediated by genetic effects on brain structure but it has been unclear which genes are associated with both schizophrenia and cortical phenotypes. We accessed genome-wide association studies (GWAS) of schizophrenia (N = 69,369 cases; 236,642 controls), and of three magnetic resonance imaging (MRI) metrics (surface area, cortical thickness, neurite density index) measured at 180 cortical areas (N = 36,843, UK Biobank). Using Hi-C-coupled MAGMA, 61 genes were significantly associated with both schizophrenia and one or more MRI metrics. Whole genome analysis with partial least squares demonstrated significant genetic covariation between schizophrenia and area or thickness of most cortical regions. Genetic similarity between cortical areas was strongly coupled to their phenotypic covariance, and genetic covariation between schizophrenia and brain phenotypes was strongest in the hubs of structural covariance networks. Pleiotropically associated genes were enriched for neurodevelopmental processes and positionally concentrated in chromosomes 3p21, 17q21 and 11p11. Mendelian randomization analysis indicated that genetically determined variation in a posterior cingulate cortical area could be causal for schizophrenia. Parallel analyses of GWAS on bipolar disorder, Alzheimer's disease and height showed that pleiotropic association with MRI metrics was stronger for schizophrenia compared to other disorders.

The impact of common variants on gene expression in the human brain: from RNA to protein to schizophrenia risk

Background

The impact of genetic variants on gene expression has been intensely studied at the transcription level, yielding in valuable insights into the association between genes and the risk of complex disorders, such as schizophrenia (SCZ). However, the downstream impact of these variants and the molecular mechanisms connecting transcription variation to disease risk are not well understood.

Results

We quantitated ribosome occupancy in prefrontal cortex samples of the BrainGVEX cohort. Together with transcriptomics and proteomics data from the same cohort, we performed cis-Quantitative Trait Locus (QTL) mapping and identified 3,253 expression QTLs (eQTLs), 1,344 ribosome occupancy QTLs (rQTLs), and 657 protein QTLs (pQTLs) out of 7,458 genes quantitated in all three omics types from 185 samples. Of the eQTLs identified, only 34% have their effects propagated to the protein level. Further analysis on the effect size of prefrontal cortex eQTLs identified from an independent dataset showed clear post-transcriptional attenuation of eQTL effects. To investigate the biological relevance of the attenuated eQTLs, we identified 70 expression-specific QTLs (esQTLs), 51 ribosome-occupancy-specific QTLs (rsQTLs), and 107 protein-specific QTLs (psQTLs). Five of these omics-specific QTLs showed strong colocalization with SCZ GWAS signals, three of them are esQTLs. The limited number of GWAS colocalization discoveries from omics-specific QTLs and the apparent prevalence of eQTL attenuation prompted us to take a complementary approach to investigate the functional relevance of attenuated eQTLs. Using S-PrediXcan we identified 74 SCZ risk genes, 34% of which were novel, and 67% of these risk genes were replicated in a MR-Egger test. Notably, 52 out of 74 risk genes were identified using eQTL data and 70% of these SCZ-risk-gene-driving eQTLs show little to no evidence of driving corresponding variations at the protein level.

Conclusion

The effect of eQTLs on gene expression in the prefrontal cortex is commonly attenuated post-transcriptionally. Many of the attenuated eQTLs still correlate with SCZ GWAS signal. Further investigation is needed to elucidate a mechanistic link between attenuated eQTLs and SCZ disease risk.

Massively parallel functional dissection of schizophrenia-associated noncoding genetic variants

Schizophrenia (SCZ) is a highly heritable mental disorder with thousands of associated genetic variants located mostly in the noncoding space of the genome. Translating these associations into insights regarding the underlying pathomechanisms has been challenging because the causal variants, their mechanisms of action, and their target genes remain largely unknown. We implemented a massively parallel variant annotation pipeline (MVAP) to perform SCZ variant-to-function mapping at scale in disease-relevant neural cell types. This approach identified 620 functional variants (1.7%) that operate in a highly developmental context and neuronal-activity-dependent manner. Multimodal integration of epigenomic and CRISPRi screening data enabled us to link these functional variants to target genes, biological processes, and ultimately alterations of neuronal physiology. These results provide a multistage prioritization strategy to map functional single-nucleotide polymorphism (SNP)-to-gene-to-endophenotype relations and offer biological insights into the context-dependent molecular processes modulated by SCZ-associated genetic variation.

Cross species review of the physiological role of D-serine in translationally relevant behaviors

Bridging the gap between preclinical models of neurological and psychiatric disorders with their human manifestations is necessary to understand their underlying mechanisms, identify biomarkers, and develop novel therapeutics. Cognitive and social impairments underlie multiple neuropsychiatric and neurological disorders and are often comorbid with sleep disturbances, which can exacerbate poor outcomes. Importantly, many symptoms are conserved between vertebrates and invertebrates, although they may have subtle differences. Therefore, it is essential to determine the molecular mechanisms underlying these behaviors across different species and their translatability to humans. Genome-wide association studies have indicated an association between glutamatergic gene variants and both the risk and frequency of psychiatric disorders such as schizophrenia, bipolar disorder, and autism spectrum disorder. For example, changes in glutamatergic neurotransmission, such as glutamate receptor subtype N-methyl-D-aspartate receptor (NMDAR) hypofunction, have been shown to contribute to the pathophysiology of schizophrenia. Furthermore, in neurological disorders, such as traumatic brain injury and Alzheimer's disease, hyperactivation of NMDARs leads to synaptic damage. In addition to glutamate binding, NMDARs require the binding of a co-agonist D-serine or glycine to the GluN1 subunit to open. D-serine, which is racemized from L-serine by the neuronal enzyme serine racemase (SRR), and both SRR and D-serine are enriched in cortico-limbic brain regions. D-serine is critical for complex behaviors, such as cognition and social behavior, where dysregulation of its synthesis and release has been implicated in many pathological conditions. In this review, we explore the role of D-serine in behaviors that are translationally relevant to multiple psychiatric and neurological disorders in different models across species.

Cross-trait genome-wide association analysis of C-reactive protein level and psychiatric disorders

C-reactive protein (CRP) tends to be elevated in individuals with psychiatric disorders. Recent findings have suggested a protective effect of the genetic liability to elevated CRP on schizophrenia risk and a causative effect on depression despite weak genetic correlations, while causal relationships with bipolar disorder were inconclusive. We investigated the shared genetic underpinnings of psychiatric disorders and variation in CRP levels. Genome-wide association studies for CRP (n = 575,531), bipolar disorder (n = 413,466), depression (n = 480,359), and schizophrenia (n = 130,644) were used in causal mixture models to compare CRP with psychiatric disorders based on polygenicity, discoverability, and genome-wide genetic overlap. The conjunctional false discovery rate method was used to identify specific shared genetic loci. Shared variants were mapped to putative causal genes, which were tested for overrepresentation among gene ontology gene-sets. CRP was six to ten times less polygenic (n = 1400 vs 8600-14,500 variants) and had a discoverability one to two orders of magnitude higher than psychiatric disorders. Most CRP-associated variants were overlapping with psychiatric disorders. We identified 401 genetic loci jointly associated with CRP and psychiatric disorders with mixed effect directions. Gene-set enrichment analyses identified predominantly CNS-related gene sets for CRP and each of depression and schizophrenia, and basic cellular processes for CRP and bipolar disorder. In conclusion, CRP has a markedly different genetic architecture to psychiatric disorders, but the majority of CRP associated variants are also implicated in psychiatric disorders. Shared genetic loci implicated CNS-related processes to a greater extent than immune processes, which may have implications for how we conceptualise causal relationships between CRP and psychiatric disorders.

eQTL colocalization analysis highlights novel susceptibility genes in Autism Spectrum Disorders (ASD)

Autism Spectrum Disorders (ASD) are a group of neurodevelopmental disorders (NDDs) characterized by difficulties in social interaction and communication, repetitive behavior, and restricted interests. ASD has proven to have a strong genetic component. However, defining causal genes is still one of the main challenges in GWAS, since the vast majority (>90%) of detected signals lie within the non-coding genome. Expression quantitative trait locus (eQTL) colocalization analysis determines whether a specific variant is responsible for both a local eQTL and GWAS association and has helped leverage data and rendering gene discovery for a wide array of diseases. Here we further mine the largest ASD GWAS performed to date (18,381 cases and 27,969 controls) altogether with GWAS summary statistics from the main PGC studies (Schizophrenia, MD (Major Depression) and ADHD (Attention Deficit/Hyperactivity Disorder)), by using eQTpLot, a newly developed tool that illustrates the colocalization of GWAS and eQTL signals in a locus, and the enrichment of and correlation between the candidate gene eQTLs and trait-significant variants. This analysis points up 8 genes with a significant eQTL colocalization signal in ASD (CRHR1, KANSL1, MANBA, MAPT, MMP12, NKX2-2, PTPRE and WNT3) and one gene (SRPK2) with a marginally significant colocalization signal (r = 0.69, p < 1 × 10-6), and specifically highlights the potentially causal role of MAPT (r = 0.76, p < 1 × 10-6), NKX2-2 (r = 0.71, p-value = 2.26-02) and PTPRE (r = 0.97, p-value = 2.63-04) when restricting the analysis to brain tissue.

Mediation and longitudinal analysis to interpret the association between clozapine pharmacokinetics, pharmacogenomics, and absolute neutrophil count

Clozapine is effective at reducing symptoms of treatment-resistant schizophrenia, but it can also induce several adverse outcomes including neutropenia and agranulocytosis. We used linear mixed-effect models and structural equation modelling to determine whether pharmacokinetic and genetic variables influence absolute neutrophil count in a longitudinal UK-based sample of clozapine users not currently experiencing neutropenia (N = 811). Increased daily clozapine dose was associated with elevated neutrophil count, amounting to a 133 cells/mm3 rise per standard deviation increase in clozapine dose. One-third of the total effect of clozapine dose was mediated by plasma clozapine and norclozapine levels, which themselves demonstrated opposing, independent associations with absolute neutrophil count. Finally, CYP1A2 pharmacogenomic activity score was associated with absolute neutrophil count, supporting lower neutrophil levels in CYP1A2 poor metabolisers during clozapine use. This information may facilitate identifying at-risk patients and then introducing preventative interventions or individualised pharmacovigilance procedures to help mitigate these adverse haematological reactions.

Human-specific evolutionary markers linked to foetal neurodevelopment modulate brain surface area in schizophrenia

Schizophrenia may represent a trade-off in the evolution of human-specific ontogenetic mechanisms that guide neurodevelopment. Human Accelerated Regions (HARs) are evolutionary markers functioning as neurodevelopmental transcription enhancers that have been associated with brain configuration, neural information processing, and schizophrenia risk. Here, we have investigated the influence of HARs' polygenic load on neuroanatomical measures through a case-control approach (128 patients with schizophrenia and 115 controls). To this end, we have calculated the global schizophrenia Polygenic Risk Score (Global PRSSZ) and that specific to HARs (HARs PRSSZ). We have also estimated the polygenic burden restricted to the HARs linked to transcriptional regulatory elements active in the foetal brain (FB-HARs PRSSZ) and the adult brain (AB-HARs PRSSZ). We have explored the main effects of the PRSs and the PRSs x diagnosis interactions on brain regional cortical thickness (CT) and surface area (SA). The results indicate that a higher FB-HARs PRSSZ is associated with patients' lower SA in the lateral orbitofrontal cortex, the superior temporal cortex, the pars triangularis and the paracentral lobule. While noHARs-derived PRSs show an effect on the risk, our neuroanatomical findings suggest that the human-specific transcriptional regulation during the prenatal period underlies SA variability, highlighting the role of these evolutionary markers in the schizophrenia genomic architecture.

Systematic investigation of allelic regulatory activity of schizophrenia-associated common variants

Genome-wide association studies (GWASs) have successfully identified 145 genomic regions that contribute to schizophrenia risk, but linkage disequilibrium makes it challenging to discern causal variants. We performed a massively parallel reporter assay (MPRA) on 5,173 fine-mapped schizophrenia GWAS variants in primary human neural progenitors and identified 439 variants with allelic regulatory effects (MPRA-positive variants). Transcription factor binding had modest predictive power, while fine-map posterior probability, enhancer overlap, and evolutionary conservation failed to predict MPRA-positive variants. Furthermore, 64% of MPRA-positive variants did not exhibit expressive quantitative trait loci signature, suggesting that MPRA could identify yet unexplored variants with regulatory potentials. To predict the combinatorial effect of MPRA-positive variants on gene regulation, we propose an accessibility-by-contact model that combines MPRA-measured allelic activity with neuronal chromatin architecture.

Shared genetic basis informs the roles of polyunsaturated fatty acids in brain disorders

The neural tissue is rich in polyunsaturated fatty acids (PUFAs), components that are indispensable for the proper functioning of neurons, such as neurotransmission. PUFA nutritional deficiency and imbalance have been linked to a variety of chronic brain disorders, including major depressive disorder (MDD), anxiety, and anorexia. However, the effects of PUFAs on brain disorders remain inconclusive, and the extent of their shared genetic determinants is largely unknown. Here, we used genome-wide association summary statistics to systematically examine the shared genetic basis between six phenotypes of circulating PUFAs (N = 114,999) and 20 brain disorders (N = 9,725-762,917), infer their potential causal relationships, identify colocalized regions, and pinpoint shared genetic variants. Genetic correlation and polygenic overlap analyses revealed a widespread shared genetic basis for 77 trait pairs between six PUFA phenotypes and 16 brain disorders. Two-sample Mendelian randomization analysis indicated potential causal relationships for 16 pairs of PUFAs and brain disorders, including alcohol consumption, bipolar disorder (BIP), and MDD. Colocalization analysis identified 40 shared loci (13 unique) among six PUFAs and ten brain disorders. Twenty-two unique variants were statistically inferred as candidate shared causal variants, including rs1260326 (GCKR), rs174564 (FADS2) and rs4818766 (ADARB1). These findings reveal a widespread shared genetic basis between PUFAs and brain disorders, pinpoint specific shared variants, and provide support for the potential effects of PUFAs on certain brain disorders, especially MDD, BIP, and alcohol consumption.

Genomic Structural Equation Modeling Reveals Latent Phenotypes in the Human Cortex with Distinct Genetic Architecture

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

Clinical utility of polygenic risk scores: a critical 2023 appraisal

Since their first appearance in the context of schizophrenia and bipolar disorder in 2009, polygenic risk scores (PRSs) have been described for a large number of common complex diseases. However, the clinical utility of PRSs in disease risk assessment or therapeutic decision making is likely limited because PRSs usually only account for the heritable component of a trait and ignore the etiological role of environment and lifestyle. We surveyed the current state of PRSs for various diseases, including breast cancer, diabetes, prostate cancer, coronary artery disease, and Parkinson disease, with an extra focus upon the potential improvement of clinical scores by their combination with PRSs. We observed that the diagnostic and prognostic performance of PRSs alone is consistently low, as expected. Moreover, combining a PRS with a clinical score at best led to moderate improvement of the power of either risk marker. Despite the large number of PRSs reported in the scientific literature, prospective studies of their clinical utility, particularly of the PRS-associated improvement of standard screening or therapeutic procedures, are still rare. In conclusion, the benefit to individual patients or the health care system in general of PRS-based extensions of existing diagnostic or treatment regimens is still difficult to judge.

Use of big data and machine learning algorithms to extract possible treatment targets in neurodevelopmental disorders

Neurodevelopmental disorders (NDDs) impact multiple aspects of an individual's functioning, including social interactions, communication, and behaviors. The underlying biological mechanisms of NDDs are not yet fully understood, and pharmacological treatments have been limited in their effectiveness, in part due to the complex nature of these disorders and the heterogeneity of symptoms across individuals. Identifying genetic loci associated with NDDs can help in understanding biological mechanisms and potentially lead to the development of new treatments. However, the polygenic nature of these complex disorders has made identifying new treatment targets from genome-wide association studies (GWAS) challenging. Recent advances in the fields of big data and high-throughput tools have provided radically new insights into the underlying biological mechanism of NDDs. This paper reviews various big data approaches, including classical and more recent techniques like deep learning, which can identify potential treatment targets from GWAS and other omics data, with a particular emphasis on NDDs. We also emphasize the increasing importance of explainable and causal machine learning (ML) methods that can aid in identifying genes, molecular pathways, and more complex biological processes that may be future targets of intervention in these disorders. We conclude that these new developments in genetics and ML hold promise for advancing our understanding of NDDs and identifying novel treatment targets.

Treatment resistance NMDA receptor pathway polygenic score is associated with brain glutamate in schizophrenia

Dysfunction of glutamate neurotransmission has been implicated in the pathophysiology of schizophrenia and may be particularly relevant in severe, treatment-resistant symptoms. The underlying mechanism may involve hypofunction of the NMDA receptor. We investigated whether schizophrenia-related pathway polygenic scores, composed of genetic variants within NMDA receptor encoding genes, are associated with cortical glutamate in schizophrenia. Anterior cingulate cortex (ACC) glutamate was measured in 70 participants across 4 research sites using Proton Magnetic Resonance Spectroscopy (1H-MRS). Two NMDA receptor gene sets were sourced from the Molecular Signatories Database and NMDA receptor pathway polygenic scores were constructed using PRSet. The NMDA receptor pathway polygenic scores were weighted by single nucleotide polymorphism (SNP) associations with treatment-resistant schizophrenia, and associations with ACC glutamate were tested. We then tested whether NMDA receptor pathway polygenic scores with SNPs weighted by associations with non-treatment-resistant schizophrenia were associated with ACC glutamate. A higher NMDA receptor complex pathway polygenic score was significantly associated with lower ACC glutamate (β = -0.25, 95 % CI = -0.49, -0.02, competitive p = 0.03). When SNPs were weighted by associations with non-treatment-resistant schizophrenia, there was no association between the NMDA receptor complex pathway polygenic score and ACC glutamate (β = 0.05, 95 % CI = -0.18, 0.27, competitive p = 0.79). These results provide initial evidence of an association between common genetic variation implicated in NMDA receptor function and ACC glutamate levels in schizophrenia. This association was specific to when the NMDA receptor complex pathway polygenic score was weighted by SNP associations with treatment-resistant schizophrenia.

The Role of IgLON Cell Adhesion Molecules in Neurodegenerative Diseases

In the brain, cell adhesion molecules (CAMs) are critical for neurite outgrowth, axonal fasciculation, neuronal survival and migration, and synapse formation and maintenance. Among CAMs, the IgLON family comprises five members: Opioid Binding Protein/Cell Adhesion Molecule Like (OPCML or OBCAM), Limbic System Associated Membrane Protein (LSAMP), neurotrimin (NTM), Neuronal Growth Regulator 1 (NEGR1), and IgLON5. IgLONs exhibit three N-terminal C2 immunoglobulin domains; several glycosylation sites; and a glycosylphosphatidylinositol anchoring to the membrane. Interactions as homo- or heterodimers in cis and in trans, as well as binding to other molecules, appear critical for their functions. Shedding by metalloproteases generates soluble factors interacting with cellular receptors and activating signal transduction. The aim of this review was to analyse the available data implicating a role for IgLONs in neuropsychiatric disorders. Starting from the identification of a pathological role for antibodies against IgLON5 in an autoimmune neurodegenerative disease with a poorly understood mechanism of action, accumulating evidence links IgLONs to neuropsychiatric disorders, albeit with still undefined mechanisms which will require future thorough investigations.

Kirigami electronics for long-term electrophysiological recording of human neural organoids and assembloids

Organoids and assembloids have emerged as a promising platform to model aspects of nervous system development. Longterm, minimally-invasive recordings in these multi-cellular systems are essential for developing disease models. Current technologies, such as patch-clamp, penetrating microelectrodes, planar electrode arrays and substrate-attached flexible electrodes, do not, however, allow chronic recording of organoids in suspension, which is necessary to preserve their architecture. Inspired by the art of kirigami, we developed flexible electronics that transition from a 2D pattern to a 3D basketlike configuration to accommodate the long-term culture of organoids in suspension. This platform, named kirigami electronics (KiriE), integrates with and enables chronic recording of cortical organoids while preserving morphology, cytoarchitecture, and cell composition. KiriE can be integrated with optogenetic and pharmacological stimulation and model disease. Moreover, KiriE can capture activity in cortico-striatal assembloids. Moving forward, KiriE could reveal disease phenotypes and activity patterns underlying the assembly of the nervous system.