Catechol-O-methyltransferase (COMT) is a key enzyme in the elimination of dopamine in the prefrontal cortex of the human brain. Genetic variation in the COMT gene (MIM 116790) has been associated with altered prefrontal cortex function and higher risk for schizophrenia, but the specific alleles and their functional implications have been controversial. We analyzed the effects of several single-nucleotide polymorphisms (SNPs) within COMT on mRNA expression levels (using reverse-transcriptase polymerase chain reaction analysis), protein levels (using Western blot analysis), and enzyme activity (using catechol methylation) in a large sample (n = 108) of postmortem human prefrontal cortex tissue, which predominantly expresses the -membrane-bound isoform. A common coding SNP, Val158Met (rs4680), significantly affected protein abundance and enzyme activity but not mRNA expression levels, suggesting that differences in protein integrity account for the difference in enzyme activity between alleles. A SNP in intron 1 (rs737865) and a SNP in the 3' flanking region (rs165599)--both of which have been reported to contribute to allelic expression differences and to be associated with schizophrenia as part of a haplotype with Val--had no effect on mRNA expression levels, protein immunoreactivity, or enzyme activity. In lymphocytes from 47 subjects, we confirmed a similar effect on enzyme activity in samples with the Val/Met genotype but no effect in samples with the intron 1 or 3' SNPs. Separate analyses revealed that the subject's sex, as well as the presence of a SNP in the P2 promoter region (rs2097603), had small effects on COMT enzyme activity. Using site-directed mutagenesis of mouse COMT cDNA, followed by in vitro translation, we found that the conversion of Leu at the homologous position into Met or Val progressively and significantly diminished enzyme activity. Thus, although we cannot exclude a more complex genetic basis for functional effects of COMT, Val is a predominant factor that determines higher COMT activity in the prefrontal cortex, which presumably leads to lower synaptic dopamine levels and relatively deleterious prefrontal function.

Dopamine in prefrontal cortex (PFC) modulates core cognitive processes, notably working memory and executive control. Dopamine regulating genes and polymorphisms affecting PFC--including Catechol-O-Methyltransferase (COMT) Val158Met--are crucial to understanding the molecular genetics of cognitive function and dysfunction. A mechanistic account of the COMT Val158Met effect associates the Met allele with increased tonic dopamine transmission underlying maintenance of relevant information, and the Val allele with increased phasic dopamine transmission underlying the flexibility of updating new information. Thus, consistent with some earlier work, we predicted that Val carriers would display poorer performance when the maintenance component was taxed, while Met carriers would be less efficient when rapid updating was required.

Neuregulin1 (NRG1)-ErbB signaling has been implicated in the pathogenesis of cancer and schizophrenia. We have previously reported that NRG1-stimulated migration of B lymphoblasts is PI3K-AKT1dependent and impaired in patients with schizophrenia and significantly linked to the catechol-o-methyltransferase (COMT) Val108/158Met functional polymorphism.

Functional variants in the catechol-O-methyltransferase (COMT) gene have been shown to impact cognitive function, cortical physiology and risk for schizophrenia. A recent study showed that previously reported effects of the functional val158met SNP (rs4680) on brain function are modified by other functional SNPs and haplotypes in the gene, though it was unknown if these effects are also seen in brain structure.

Cognitive impairments are a core feature in patients with schizophrenia. These deficits could serve as effective tools for understanding the genetic architecture of schizophrenia. This study investigated whether genetic variants associated with cognitive impairments aggregate in functional gene networks related to the pathogenesis of schizophrenia. Here, genome-wide association studies (GWAS) of a range of cognitive phenotypes relevant to schizophrenia were performed in 411 healthy subjects. We attempted to replicate the GWAS data using 257 patients with schizophrenia and performed a meta-analysis of the GWAS findings and the replicated results. Because gene networks, rather than a single gene or genetic variant, may be strongly associated with the susceptibility to schizophrenia and cognitive impairments, gene-network analysis for genes in close proximity to the replicated variants was performed. We observed nominal associations between 3054 variants and cognitive phenotypes at a threshold of P < 1.0 × 10(-) (4). Of the 3054 variants, the associations of 191 variants were replicated in the replication samples (P < .05). However, no variants achieved genome-wide significance in a meta-analysis (P > 5.0 × 10(-) (8)). Additionally, 115 of 191 replicated single nucleotide polymorphisms (SNPs) have genes located within 10 kb of the SNPs (60.2%). These variants were moderately associated with cognitive phenotypes that ranged from P = 2.50 × 10(-) (5) to P = 9.40 × 10(-) (8). The genes located within 10 kb from the replicated SNPs were significantly grouped in terms of glutamate receptor activity (false discovery rate (FDR) q = 4.49 × 10(-) (17)) and the immune system related to major histocompatibility complex class I (FDR q = 8.76 × 10(-) (11)) networks. Our findings demonstrate that genetic variants related to cognitive trait impairment in schizophrenia are involved in the N-methyl-d-aspartate glutamate network.

The prediction of future events is fundamental in a large number of critical neurobehavioral contexts including implicit motor learning. This learning process relies on the probabilities with which events occur, and is a dynamic phenomenon. The aim of present study was to investigate the development of anticipatory processes during implicit learning. A decision making task was employed in which the frequency of trial types was manipulated such that one trial type was disproportionately prevalent as compared to the remaining three trial types. A 275 channel whole-head magnetoencephalography (MEG) system was used to investigate the spatiotemporal distribution of event-related desynchronization (ERD) and synchronization (ERS). The results revealed that oscillations within the alpha (10-12 Hz) and beta (14-30 Hz) frequencies were associated with anticipatory processes in distinct networks in the course of learning. During early phases of learning the contralateral motor cortex, the anterior cingulate, the caudate and the inferior frontal gyrus showed ERDs within beta and alpha frequencies, putatively reflecting preparation of next motor response. As the task progressed, alpha ERSs in occipitotemporal regions and putamen likely reflect perceptual anticipation of the forthcoming stimuli.

Episodic memory (EM) declines with age and the rate of decline is variable across individuals. A single nucleotide polymorphism (rs17070145) in the WWC1 gene that encodes the KIBRA protein critical for long-term potentiation and memory consolidation has previously been associated with EM performance, as well as differences in hippocampal engagement during EM tasks using functional magnetic resonance imaging (fMRI). In the current study, we explore the effect of this polymorphism on EM-related activity and cognitive performance across the adult life span using fMRI.

Most of the genetic architecture of schizophrenia (SCZ) has not yet been identified. Here, we apply a novel statistical algorithm called Covariate-Modulated Mixture Modeling (CM3), which incorporates auxiliary information (heterozygosity, total linkage disequilibrium, genomic annotations, pleiotropy) for each single nucleotide polymorphism (SNP) to enable more accurate estimation of replication probabilities, conditional on the observed test statistic ("z-score") of the SNP. We use a multiple logistic regression on z-scores to combine information from auxiliary information to derive a "relative enrichment score" for each SNP. For each stratum of these relative enrichment scores, we obtain nonparametric estimates of posterior expected test statistics and replication probabilities as a function of discovery z-scores, using a resampling-based approach that repeatedly and randomly partitions meta-analysis sub-studies into training and replication samples. We fit a scale mixture of two Gaussians model to each stratum, obtaining parameter estimates that minimize the sum of squared differences of the scale-mixture model with the stratified nonparametric estimates. We apply this approach to the recent genome-wide association study (GWAS) of SCZ (n = 82,315), obtaining a good fit between the model-based and observed effect sizes and replication probabilities. We observed that SNPs with low enrichment scores replicate with a lower probability than SNPs with high enrichment scores even when both they are genome-wide significant (p < 5x10-8). There were 693 and 219 independent loci with model-based replication rates ≥80% and ≥90%, respectively. Compared to analyses not incorporating relative enrichment scores, CM3 increased out-of-sample yield for SNPs that replicate at a given rate. This demonstrates that replication probabilities can be more accurately estimated using prior enrichment information with CM3.

Genetic variation and early adverse environmental events work together to increase risk for schizophrenia. γ-aminobutyric acid (GABA), the major inhibitory neurotransmitter in adult mammalian brain, plays a major role in normal brain development, and has been strongly implicated in the pathobiology of schizophrenia. GABA synthesis is controlled by two glutamic acid decarboxylase (GAD) genes, GAD1 and GAD2, both of which produce a number of alternative transcripts. Genetic variants in the GAD1 gene are associated with increased risk for schizophrenia, and reduced expression of its major transcript in the human dorsolateral prefrontal cortex (DLPFC). No consistent changes in GAD2 expression have been found in brains from patients with schizophrenia. In this work, with the use of RNA sequencing and PCR technologies, we confirmed and tracked the expression of an alternative truncated transcript of GAD2 (ENST00000428517) in human control DLPFC homogenates across lifespan besides the well-known full length transcript of GAD2. In addition, using quantitative RT-PCR, expression of GAD2 full length and truncated transcripts were measured in the DLPFC of patients with schizophrenia, bipolar disorder and major depression. The expression of GAD2 full length transcript is decreased in the DLPFC of schizophrenia and bipolar disorder patients, while GAD2 truncated transcript is increased in bipolar disorder patients but decreased in schizophrenia patients. Moreover, the patients with schizophrenia with completed suicide or positive nicotine exposure showed significantly higher expression of GAD2 full length transcript. Alternative transcripts of GAD2 may be important in the growth and development of GABA-synthesizing neurons as well as abnormal GABA signaling in the DLPFC of patients with schizophrenia and affective disorders.

Brain development and function depend on the precise regulation of gene expression. However, our understanding of the complexity and dynamics of the transcriptome of the human brain is incomplete. Here we report the generation and analysis of exon-level transcriptome and associated genotyping data, representing males and females of different ethnicities, from multiple brain regions and neocortical areas of developing and adult post-mortem human brains. We found that 86 per cent of the genes analysed were expressed, and that 90 per cent of these were differentially regulated at the whole-transcript or exon level across brain regions and/or time. The majority of these spatio-temporal differences were detected before birth, with subsequent increases in the similarity among regional transcriptomes. The transcriptome is organized into distinct co-expression networks, and shows sex-biased gene expression and exon usage. We also profiled trajectories of genes associated with neurobiological categories and diseases, and identified associations between single nucleotide polymorphisms and gene expression. This study provides a comprehensive data set on the human brain transcriptome and insights into the transcriptional foundations of human neurodevelopment.

Imaging genetics provides an enormous amount of functional-structural data on gene effects in living brain, but the sheer quantity of potential phenotypes raises concerns about false discovery. Here, we provide the first empirical results on false positive rates in imaging genetics. We analyzed 720 frequent coding SNPs without significant association with schizophrenia and a subset of 492 of these without association with cognitive function. Effects on brain structure (using voxel-based morphometry, VBM) and brain function, using two archival imaging tasks, the n-back working memory task and an emotional face matching task, were studied in whole brain and regions of interest and corrected for multiple comparisons using standard neuroimaging procedures. Since these variants are unlikely to impact relevant brain function, positives obtained provide an upper empirical estimate of the false positive association rate. In a separate analysis, we randomly permuted genotype labels across subjects, removing any true genotype-phenotype association in the data, to derive a lower empirical estimate. At a set correction level of 0.05, in each region of interest and data set used, the rate of positive findings was well below 5% (0.2-4.1%). There was no relationship between the region of interest and the false positive rate. Permutation results were in the same range as empirically derived rates. The observed low rates of positives provide empirical evidence that the type I error rate is well controlled by current commonly used correction procedures in imaging genetics, at least in the context of the imaging paradigms we have used. In fact, our observations indicate that these statistical thresholds are conservative.

A subset of human neocortical neurons harbors complex karyotypes wherein megabase-scale copy-number variants (CNVs) alter allelic diversity. Divergent levels of neurons with complex karyotypes (CNV neurons) are reported in different individuals, yet genome-wide and familial studies implicitly assume a single brain genome when assessing the genetic risk architecture of neurological disease. We assembled a brain CNV atlas using a robust computational approach applied to a new dataset (>800 neurons from 5 neurotypical individuals) and to published data from 10 additional neurotypical individuals. The atlas reveals that the frequency of neocortical neurons with complex karyotypes varies widely among individuals, but this variability is not readily accounted for by tissue quality or CNV detection approach. Rather, the age of the individual is anti-correlated with CNV neuron frequency. Fewer CNV neurons are observed in aged individuals than in young individuals.

We generated cortical interneurons (cINs) from induced pluripotent stem cells derived from 14 healthy controls and 14 subjects with schizophrenia. Both healthy control cINs and schizophrenia cINs were authentic, fired spontaneously, received functional excitatory inputs from host neurons, and induced GABA-mediated inhibition in host neurons in vivo. However, schizophrenia cINs had dysregulated expression of protocadherin genes, which lie within documented schizophrenia loci. Mice lacking protocadherin-α showed defective arborization and synaptic density of prefrontal cortex cINs and behavioral abnormalities. Schizophrenia cINs similarly showed defects in synaptic density and arborization that were reversed by inhibitors of protein kinase C, a downstream kinase in the protocadherin pathway. These findings reveal an intrinsic abnormality in schizophrenia cINs in the absence of any circuit-driven pathology. They also demonstrate the utility of homogenous and functional populations of a relevant neuronal subtype for probing pathogenesis mechanisms during development.

Antipsychotics are the most widely used medications for the treatment of schizophrenia spectrum disorders. While such drugs generally ameliorate positive symptoms, clinical responses are highly variable in terms of negative symptoms and cognitive impairments. However, predictors of individual responses have been elusive. Here, we report a pharmacogenetic interaction related to a core cognitive dysfunction in patients with schizophrenia. We show that genetic variations reducing dysbindin-1 expression can identify individuals whose executive functions respond better to antipsychotic drugs, both in humans and in mice. Multilevel ex vivo and in vivo analyses in postmortem human brains and genetically modified mice demonstrate that such interaction between antipsychotics and dysbindin-1 is mediated by an imbalance between the short and long isoforms of dopamine D2 receptors, leading to enhanced presynaptic D2 function within the prefrontal cortex. These findings reveal one of the pharmacodynamic mechanisms underlying individual cognitive response to treatment in patients with schizophrenia, suggesting a potential approach for improving the use of antipsychotic drugs.

A recent development in the genetic architecture of schizophrenia suggested that an omnigenic model may underlie the risk for this disorder. The aim of our study was to use polygenic profile scoring to quantitatively assess whether a number of experimentally derived sets would contribute to the disorder above and beyond the omnigenic effect. Using the PGC2 secondary analysis schizophrenia case-control cohort (N = 29,125 cases and 34,836 controls), a robust polygenic signal was observed from gene sets based on TCF4, FMR1, upregulation from MIR137 and downregulation from CHD8. Additional analyses revealed a constant floor effect in the amount of variance explained, consistent with the omnigenic model. Thus, we report that putative core gene sets showed a significant effect above and beyond the floor effect that might be linked with the underlying omnigenic background. In addition, we demonstrate a method to quantify the contribution of specific gene sets within the omnigenic context.

There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in the genetics of complex traits, including psychiatric disorders. However, due to extensive linkage disequilibrium (LD) of the genome, it is challenging to identify causal genetic variations that drive DNAm levels by population-based genetic association studies. This limits the utility of mQTLs for fine-mapping risk loci underlying psychiatric disorders identified by genome-wide association studies (GWAS). Here we present INTERACT, a deep learning model that integrates convolutional neural networks with transformer, to predict effects of genetic variations on DNAm levels at CpG sites in the human brain. We show that INTERACT-derived DNAm regulatory variants are not confounded by LD, are concentrated in regulatory genomic regions in the human brain, and are convergent with mQTL evidence from genetic association analysis. We further demonstrate that predicted DNAm regulatory variants are enriched for heritability of brain-related traits and improve polygenic risk prediction for schizophrenia across diverse ancestry samples. Finally, we applied predicted DNAm regulatory variants for fine-mapping schizophrenia GWAS risk loci to identify potential novel risk genes. Our study shows the power of a deep learning approach to identify functional regulatory variants that may elucidate the genetic basis of complex traits.

Alterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals. To capture individual brain features more accurately than single-session fMRI, we studied an average of three fMRI scans per individual. To identify potential familial risk-related FNC changes, we compared age-related FNC in first-degree relatives of SCZ patients mostly including unaffected siblings (SIB) with neurotypical controls (NC) at the same age stage. Then, we examined how polygenic risk scores for SCZ influenced risk-related FNC patterns. Finally, we investigated the same risk-related FNC patterns in adult SCZ patients (oSCZ) and young individuals with subclinical psychotic symptoms (PSY). Age-sensitive risk-related FNC patterns emerge during adolescence and early adulthood, but not before. Young SIB always followed older NC patterns, with decreased FNC in a cerebellar-occipitoparietal circuit and increased FNC in two prefrontal-sensorimotor circuits when compared to young NC. Two of these FNC alterations were also found in oSCZ, with one exhibiting reversed pattern. All were linked to polygenic risk for SCZ in unrelated individuals (R2 varied from 0.02 to 0.05). Young PSY showed FNC alterations in the same direction as SIB when compared to NC. These results suggest that age-related neurotypical FNC correlates with genetic risk for SCZ and is detectable with MRI in young participants.

Our earlier work has shown that genomic risk for schizophrenia converges with early life complications in affecting risk for the disorder and sex-biased neurodevelopmental trajectories. Here, we identify specific genes and potential mechanisms that, in placenta, may mediate such outcomes. We performed TWAS in healthy term placentae (N = 147) to derive candidate placental causal genes that we confirmed with SMR; to search for placenta and schizophrenia-specific associations, we performed an analogous analysis in fetal brain (N = 166) and additional placenta TWAS for other disorders/traits. The analyses in the whole sample and stratifying by sex ultimately highlight 139 placenta and schizophrenia-specific risk genes, many being sex-biased; the candidate molecular mechanisms converge on the nutrient-sensing capabilities of placenta and trophoblast invasiveness. These genes also implicate the Coronavirus-pathogenesis pathway and showed increased expression in placentae from a small sample of SARS-CoV-2-positive pregnancies. Investigating placental risk genes for schizophrenia and candidate mechanisms may lead to opportunities for prevention that would not be suggested by study of the brain alone.

RNA sequencing offers advantages over other quantification methods for microRNA (miRNA), yet numerous biases make reliable quantification challenging. Previous evaluations of these biases have focused on adapter ligation bias with limited evaluation of reverse transcription bias or amplification bias. Furthermore, evaluations of the quantification of isomiRs (miRNA isoforms) or the influence of starting amount on performance have been very limited. No study had yet evaluated the quantification of isomiRs of altered length or compared the consistency of results derived from multiple moderate starting inputs. We therefore evaluated quantifications of miRNA and isomiRs using four library preparation kits, with various starting amounts, as well as quantifications following removal of duplicate reads using unique molecular identifiers (UMIs) to mitigate reverse transcription and amplification biases.

Schizophrenia and bipolar disorder share genetic liability, and some structural brain abnormalities are common to both conditions. First-degree relatives of patients with schizophrenia (FDRs-SZ) show similar brain abnormalities to patients, albeit with smaller effect sizes. Imaging findings in first-degree relatives of patients with bipolar disorder (FDRs-BD) have been inconsistent in the past, but recent studies report regionally greater volumes compared with control subjects.