Enrichment of the Local Synaptic Translatome for Genetic Risk Associated With Schizophrenia and Autism Spectrum Disorder.

BACKGROUND: Genes that encode synaptic proteins or messenger RNA targets of the RNA-binding protein FMRP (fragile X messenger ribonucleoprotein) have been linked to schizophrenia and autism spectrum disorder (ASD) through the enrichment of genetic variants that confer risk for these disorders. FMRP binds many transcripts with synaptic functions and is thought to regulate their local translation, a process that enables rapid and compartmentalized protein synthesis required for development and plasticity. METHODS: We used summary statistics from large-scale genome-wide association studies of schizophrenia (74,776 cases, 101,023 controls) and ASD (18,381 cases, 27,969 controls) to test the hypothesis that the subset of synaptic genes that encode localized transcripts is more strongly associated with each disorder than nonlocalized transcripts. We also postulated that this subset of synaptic genes is responsible for associations attributed to FMRP targets. RESULTS: Schizophrenia associations were enriched in genes encoding localized synaptic transcripts compared to the remaining synaptic genes or to the remaining localized transcripts; this also applied to ASD associations, although only for transcripts observed after stimulation by fear conditioning. The genetic associations with either disorder captured by these gene sets were independent of those derived from FMRP targets. Schizophrenia association was related to FMRP interactions with messenger RNAs in somata, but not in dendrites, while ASD association was related to FMRP binding in either compartment. CONCLUSIONS: Our data suggest that synaptic transcripts capable of local translation are particularly relevant to the pathogenesis of schizophrenia and ASD, but they do not characterize the associations attributed to current sets of FMRP targets.

Genetic Implication of Prenatal GABAergic and Cholinergic Neuron Development in Susceptibility to Schizophrenia.

BACKGROUND: The ganglionic eminences (GE) are fetal-specific structures that give rise to gamma-aminobutyric acid (GABA)- and acetylcholine-releasing neurons of the forebrain. Given the evidence for GABAergic, cholinergic, and neurodevelopmental disturbances in schizophrenia, we tested the potential involvement of GE neuron development in mediating genetic risk for the condition. STUDY DESIGN: We combined data from a recent large-scale genome-wide association study of schizophrenia with single-cell RNA sequencing data from the human GE to test the enrichment of schizophrenia risk variation in genes with high expression specificity for developing GE cell populations. We additionally performed the single nuclei Assay for Transposase-Accessible Chromatin with Sequencing (snATAC-Seq) to map potential regulatory genomic regions operating in individual cell populations of the human GE, using these to test for enrichment of schizophrenia common genetic variant liability and to functionally annotate non-coding variants-associated with the disorder. STUDY RESULTS: Schizophrenia common variant liability was enriched in genes with high expression specificity for developing neuron populations that are predicted to form dopamine D1 and D2 receptor-expressing GABAergic medium spiny neurons of the striatum, cortical somatostatin-positive GABAergic interneurons, calretinin-positive GABAergic neurons, and cholinergic neurons. Consistent with these findings, schizophrenia genetic risk was concentrated in predicted regulatory genomic sequence mapped in developing neuronal populations of the GE. CONCLUSIONS: Our study implicates prenatal development of specific populations of GABAergic and cholinergic neurons in later susceptibility to schizophrenia, and provides a map of predicted regulatory genomic elements operating in cells of the GE.

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.

The prefrontal cortex (PFC) has been strongly implicated in the underlying biology of schizophrenia. We tested whether specific cell populations within the PFC preferentially express genes that increase risk for the disorder. We found that a particular type of PFC neuron prominently expresses genes associated with schizophrenia, suggesting its involvement in the condition.

Common risk alleles for schizophrenia within the major histocompatibility complex predict white matter microstructure.

Recent research has highlighted the role of complement genes in shaping the microstructure of the brain during early development, and in contributing to common allele risk for Schizophrenia. We hypothesised that common risk variants for schizophrenia within complement genes will associate with structural changes in white matter microstructure within tracts innervating the frontal lobe. Results showed that risk alleles within the complement gene set, but also intergenic alleles, significantly predict axonal density in white matter tracts connecting frontal cortex with parietal, temporal and occipital cortices. Specifically, risk alleles within the Major Histocompatibility Complex region in chromosome 6 appeared to drive these associations. No significant associations were found for the orientation dispersion index. These results suggest that changes in axonal packing - but not in axonal coherence - determined by common risk alleles within the MHC genomic region - including variants related to the Complement system - appear as a potential neurobiological mechanism for schizophrenia.

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 (ß = -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 (ß = 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.

Genetic and Phenotypic Features of Schizophrenia in the UK Biobank.

Importance: Large-scale biobanks provide important opportunities for mental health research, but selection biases raise questions regarding the comparability of individuals with those in clinical research settings. Objective: To compare the genetic liability to psychiatric disorders in individuals with schizophrenia in the UK Biobank with individuals in the Psychiatric Genomics Consortium (PGC) and to compare genetic liability and phenotypic features with participants recruited from clinical settings. Design, Setting, and Participants: This cross-sectional study included participants from the population-based UK Biobank and schizophrenia samples recruited from clinical settings (CLOZUK, CardiffCOGS, Cardiff F-Series, and Cardiff Affected Sib-Pairs). Data were collected between January 1993 and July 2021. Data analysis was conducted between July 2021 and June 2023. Main Outcomes and Measures: A genome-wide association study of UK Biobank schizophrenia case-control status was conducted, and the results were compared with those from the PGC via genetic correlations. To test for differences with the clinical samples, polygenic risk scores (PRS) were calculated for schizophrenia, bipolar disorder, depression, and intelligence using PRS-CS. PRS and phenotypic comparisons were conducted using pairwise logistic regressions. The proportions of individuals with copy number variants associated with schizophrenia were compared using Firth logistic regression. Results: The sample of 517 375 participants included 1438 UK Biobank participants with schizophrenia (550 [38.2%] female; mean [SD] age, 54.7 [8.3] years), 499 475 UK Biobank controls (271 884 [54.4%] female; mean [SD] age, 56.5 [8.1] years), and 4 schizophrenia research samples (4758 [28.9%] female; mean [SD] age, 38.2 [21.0] years). Liability to schizophrenia in UK Biobank was highly correlated with the latest genome-wide association study from the PGC (genetic correlation, 0.98; SE, 0.18) and showed the expected patterns of correlations with other psychiatric disorders. The schizophrenia PRS explained 6.8% of the variance in liability for schizophrenia case status in UK Biobank. UK Biobank participants with schizophrenia had significantly lower schizophrenia PRS than 3 of the clinically ascertained samples and significantly lower rates of schizophrenia-associated copy number variants than the CLOZUK sample. UK Biobank participants with schizophrenia had higher educational attainment and employment rates than the clinically ascertained schizophrenia samples, lower rates of smoking, and a later age of onset of psychosis. Conclusions and Relevance: Individuals with schizophrenia in the UK Biobank, and likely other volunteer-based biobanks, represent those less severely affected. Their inclusion in wider studies should enhance the representation of the full spectrum of illness severity.

How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry.

Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements-well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms-to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders.

Identifying genetic differences between bipolar disorder and major depression through multiple GWAS.

Background: Accurate diagnosis of bipolar disorder (BD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A key reason is that the first manic episode is often preceded by a depressive one, making it difficult to distinguish BD from unipolar major depressive disorder (MDD). Aims: Here, we use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores that may aid early differential diagnosis. Methods: Based on individual genotypes from case-control cohorts of BD and MDD shared through the Psychiatric Genomics Consortium, we compile case-case-control cohorts, applying a careful merging and quality control procedure. In a resulting cohort of 51,149 individuals (15,532 BD cases, 12,920 MDD cases and 22,697 controls), we perform a variety of GWAS and polygenic risk scores (PRS) analyses. Results: While our GWAS is not well-powered to identify genome-wide significant loci, we find significant SNP-heritability and demonstrate the ability of the resulting PRS to distinguish BD from MDD, including BD cases with depressive onset. We replicate our PRS findings, but not signals of individual loci in an independent Danish cohort (iPSYCH 2015 case-cohort study, N=25,966). We observe strong genetic correlation between our case-case GWAS and that of case-control BD. Conclusions: We find that MDD and BD, including BD with a depressive onset, are genetically distinct. Further, our findings support the hypothesis that Controls - MDD - BD primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BD and, importantly, BD with depressive onset from MDD.

Genetic implication of prenatal GABAergic and cholinergic neuron development in susceptibility to schizophrenia.

Background: The ganglionic eminences are fetal-specific structures that give rise to gamma-aminobutyric acid (GABA)- and acetylcholine- releasing neurons of the forebrain. Given evidence for GABAergic and cholinergic disturbances in schizophrenia, as well as an early neurodevelopmental component to the disorder, we tested the potential involvement of developing cells of the ganglionic eminences in mediating genetic risk for the condition. Study Design: We combined data from a recent large-scale genome-wide association study of schizophrenia with single cell RNA sequencing data from the human ganglionic eminences to test enrichment of schizophrenia risk variation in genes with high expression specificity for particular developing cell populations within these structures. We additionally performed the single nuclei Assay for Transposase-Accessible Chromatin with Sequencing (snATAC-Seq) to map potential regulatory genomic regions operating in individual cell populations of the human ganglionic eminences, using these to additionally test for enrichment of schizophrenia common genetic variant liability and to functionally annotate non-coding variants associated with the disorder. Study Results: Schizophrenia common variant liability was enriched in genes with high expression specificity for developing neuron populations that are predicted to form dopamine D1 and D2 receptor expressing GABAergic medium spiny neurons of the striatum, cortical somatostatin-positive GABAergic interneurons, calretinin-positive GABAergic neurons and cholinergic neurons. Consistent with these findings, schizophrenia genetic risk was also concentrated in predicted regulatory genomic sequence mapped in developing neuronal populations of the ganglionic eminences. Conclusions: Our study provides evidence for a role of prenatal GABAergic and cholinergic neuron development in later susceptibility to schizophrenia.

Assessing the validity of a self-reported clinical diagnosis of schizophrenia.

Background: Diagnoses in psychiatric research can be derived from various sources. This study assesses the validity of a self-reported clinical diagnosis of schizophrenia. Methods: The study included 3,029 clinically ascertained participants with schizophrenia or psychotic disorders diagnosed by self-report and/or research interview and 1,453 UK Biobank participants with self-report and/or medical record diagnosis of schizophrenia or schizoaffective disorder depressed-type (SA-D). We assessed positive predictive values (PPV) of self-reported clinical diagnoses against research interview and medical record diagnoses. We compared polygenic risk scores (PRS) and phenotypes across diagnostic groups, and compared the variance explained by schizophrenia PRS to samples in the Psychiatric Genomics Consortium (PGC). Results: In the clinically ascertained sample, the PPV of self-reported schizophrenia to a research diagnosis of schizophrenia was 0.70, which increased to 0.81 when benchmarked against schizophrenia or SA-D. In UK Biobank, the PPV of self-reported schizophrenia to a medical record diagnosis was 0.74. Compared to self-report participants, those with a research diagnosis were younger and more likely to have a high school qualification (clinically ascertained sample) and those with a medical record diagnosis were less likely to be employed or have a high school qualification (UK Biobank). Schizophrenia PRS did not differ between participants that had a diagnosis from self-report, research diagnosis or medical record diagnosis. Polygenic liability r2, for all diagnosis definitions, fell within the distribution of PGC schizophrenia cohorts. Conclusions: Self-report measures of schizophrenia are justified in research to maximise sample size and representativeness, although within sample validation of diagnoses is recommended.