Investigating trait variability of gene co-expression network architecture in brain by controlling for genomic risk of schizophrenia.

The effect of schizophrenia (SCZ) genetic risk on gene expression in brain remains elusive. A popular approach to this problem has been the application of gene co-expression network algorithms (e.g., WGCNA). To improve reliability with this method it is critical to remove unwanted sources of variance while also preserving biological signals of interest. In this WCGNA study of RNA-Seq data from postmortem prefrontal cortex (78 neurotypical donors, EUR ancestry), we tested the effects of SCZ genetic risk on co-expression networks. Specifically, we implemented a novel design in which gene expression was adjusted by linear regression models to preserve or remove variance explained by biological signal of interest (GWAS genomic scores for SCZ risk-(GS-SCZ), and genomic scores- GS of height (GS-Ht) as a negative control), while removing variance explained by covariates of non-interest. We calculated co-expression networks from adjusted expression (GS-SCZ and GS-Ht preserved or removed), and consensus between them (representative of a "background" network free of genomic scores effects). We then tested the overlap between GS-SCZ preserved modules and background networks reasoning that modules with reduced overlap would be most affected by GS-SCZ biology. Additionally, we tested these modules for convergence of SCZ risk (i.e., enrichment in PGC3 SCZ GWAS priority genes, enrichment in SCZ risk heritability and relevant biological ontologies. Our results highlight key aspects of GS-SCZ effects on brain co-expression networks, specifically: 1) preserving/removing SCZ genetic risk alters the co-expression modules; 2) biological pathways enriched in modules affected by GS-SCZ implicate processes of transcription, translation and metabolism that converge to influence synaptic transmission; 3) priority PGC3 SCZ GWAS genes and SCZ risk heritability are enriched in modules associated with GS-SCZ effects. Overall, our results indicate that gene co-expression networks that selectively integrate information about genetic risk can reveal novel combinations of biological pathways involved in schizophrenia.

Identification and prioritization of gene sets associated with schizophrenia risk by co-expression network analysis in human brain.

Schizophrenia polygenic risk is plausibly manifested by complex transcriptional dysregulation in the brain, involving networks of co-expressed and functionally related genes. The main purpose of this study was to identify and prioritize co-expressed gene sets in a hierarchical manner, based on the strength of the relationships with clinical diagnosis and with polygenic risk for schizophrenia. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to RNA-quality-adjusted DLPFC RNA-Seq data from the LIBD Postmortem Human Brain Repository (90 controls, 74 schizophrenia cases; all Caucasians) to construct co-expression networks and detect "modules" of co-expressed genes. After multiple internal and external validation procedures, modules of selected interest were tested for enrichment in biological ontologies, for association with schizophrenia polygenic risk scores (PRSs) and with diagnosis, and also for enrichment in genes within the significant GWAS loci reported by the Psychiatric Genomic Consortium (PGC2). The association between schizophrenia genetic signals and modules of co-expression converged on one module showing not only a significant association with both diagnosis and PRS but also significant overlap with 36 PGC2 loci genes, deemed the strongest candidates for drug targets. This module contained many genes involved in synaptic signaling and neuroplasticity. Fifty-three PGC2 genes were in modules associated only with diagnosis and 59 in modules unrelated to diagnosis or PRS. Our study highlights complex relationships between gene co-expression networks in the brain and clinical state and polygenic risk for SCZ and provides a strategy for using this information in selecting and prioritizing potentially targetable gene sets for therapeutic drug development.

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

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

Genetics and Brain Transcriptomics of Completed Suicide.

OBJECTIVE: The authors sought to study the transcriptomic and genomic features of completed suicide by parsing the method chosen, to capture molecular correlates of the distinctive frame of mind of individuals who die by suicide, while reducing heterogeneity. METHODS: The authors analyzed gene expression (RNA sequencing) from postmortem dorsolateral prefrontal cortex of patients who died by suicide with violent compared with nonviolent means, nonsuicide patients with the same psychiatric disorders, and a neurotypical group (total N=329). They then examined genomic risk scores (GRSs) for each psychiatric disorder included, and GRSs for cognition (IQ) and for suicide attempt, testing how they predict diagnosis or traits (total N=888). RESULTS: Patients who died by suicide by violent means showed a transcriptomic pattern remarkably divergent from each of the other patient groups but less from the neurotypical group; consistently, their genomic profile of risk was relatively low for their diagnosed illness as well as for suicide attempt, and relatively high for IQ: they were more similar to the neurotypical group than to other patients. Differentially expressed genes (DEGs) associated with patients who died by suicide by violent means pointed to purinergic signaling in microglia, showing similarities to a genome-wide association study of Drosophila aggression. Weighted gene coexpression network analysis revealed that these DEGs were coexpressed in a context of mitochondrial metabolic activation unique to suicide by violent means. CONCLUSIONS: These findings suggest that patients who die by suicide by violent means are in part biologically separable from other patients with the same diagnoses, and their behavioral outcome may be less dependent on genetic risk for conventional psychiatric disorders and be associated with an alteration of purinergic signaling and mitochondrial metabolism.

Analysis of the caudate nucleus transcriptome in individuals with schizophrenia highlights effects of antipsychotics and new risk genes.

Most studies of gene expression in the brains of individuals with schizophrenia have focused on cortical regions, but subcortical nuclei such as the striatum are prominently implicated in the disease, and current antipsychotic drugs target the striatum's dense dopaminergic innervation. Here, we performed a comprehensive analysis of the genetic and transcriptional landscape of schizophrenia in the postmortem caudate nucleus of the striatum of 443 individuals (245 neurotypical individuals, 154 individuals with schizophrenia and 44 individuals with bipolar disorder), 210 from African and 233 from European ancestries. Integrating expression quantitative trait loci analysis, Mendelian randomization with the latest schizophrenia genome-wide association study, transcriptome-wide association study and differential expression analysis, we identified many genes associated with schizophrenia risk, including potentially the dopamine D2 receptor short isoform. We found that antipsychotic medication has an extensive influence on caudate gene expression. We constructed caudate nucleus gene expression networks that highlight interactions involving schizophrenia risk. These analyses provide a resource for the study of schizophrenia and insights into risk mechanisms and potential therapeutic targets.

Selective updating of working memory content modulates meso-cortico-striatal activity.

Accumulating evidence from non-human primates and computational modeling suggests that dopaminergic signals arising from the midbrain (substantia nigra/ventral tegmental area) mediate striatal gating of the prefrontal cortex during the selective updating of working memory. Using event-related functional magnetic resonance imaging, we explored the neural mechanisms underlying the selective updating of information stored in working memory. Participants were scanned during a novel working memory task that parses the neurophysiology underlying working memory maintenance, overwriting, and selective updating. Analyses revealed a functionally coupled network consisting of a midbrain region encompassing the substantia nigra/ventral tegmental area, caudate, and dorsolateral prefrontal cortex that was selectively engaged during working memory updating compared to the overwriting and maintenance of working memory content. Further analysis revealed differential midbrain-dorsolateral prefrontal interactions during selective updating between low-performing and high-performing individuals. These findings highlight the role of this meso-cortico-striatal circuitry during the selective updating of working memory in humans, which complements previous research in behavioral neuroscience and computational modeling.

Imaging genetics of structural brain connectivity and neural integrity markers.

We review studies that have used diffusion imaging (DI) and magnetic resonance spectroscopy (MRS) to investigate genetic associations. A brief description of the measures obtainable with these methods and of some methodological and interpretability limitations is given. The usefulness of DI and MRS in defining intermediate phenotypes and in demonstrating the effects of common genetic variants known to increase risk for psychiatric manifestations on anatomical and metabolic phenotypes is reviewed. The main focus is on schizophrenia where the greatest amount of data has been collected. Moreover, we present an example coming from a different approach, where the genetic alteration is known (the deletion that causes Williams syndrome) and the DI phenotype can shed new light on the function of genes affected by the mutation. We conclude that, although these are still early days of this type of research and many findings remain controversial, both techniques can significantly contribute to the understanding of genetic effects in the brain and the pathophysiology of psychiatric disorders.

Association of a Noncoding RNA Postmortem With Suicide by Violent Means and In Vivo With Aggressive Phenotypes.

BACKGROUND: Previous findings suggest that differences in brain expression of a human-specific long intergenic noncoding RNA (LINC01268; GRCh37/hg19: LOC285758) may be linked to suicide by violent methods. We sought to replicate and extend these findings in a new sample and translate the results to the behavioral level in living healthy subjects. METHODS: We examined RNA sequencing data in human brains to confirm the prior postmortem association of the long intergenic noncoding RNA specifically with suicide by violent means. In addition, we used a genetic variant associated with LINC01268 expression to detect association in healthy subjects with trait aggression and with in vivo prefrontal physiology related to behavioral control. Finally, we performed weighted gene coexpression network analysis and gene ontology analysis to identify biological processes associated with a LINC01268 coexpression network. RESULTS: In the replication sample, prefrontal expression of LINC01268 was again higher in suicides by violent means (n = 65) than in both nonsuicides (n = 78; p = 1.29 × 10-6) and suicides by nonviolent means (n = 46; p = 1.4 × 10-6). In the living cohort, carriers of the minor allele of a single nucleotide polymorphism associated with increased LINC01268 expression in brain scored higher on a lifetime aggression questionnaire and show diminished engagement of prefrontal cortex (Brodmann area 10) when viewing angry faces during functional magnetic resonance imaging. Weighted gene coexpression network analysis highlighted the immune response. CONCLUSIONS: These results suggest that LINC01268 influences emotional regulation, aggressive behavior, and suicide by violent means; the underlying biological dynamics may include modulation of genes potentially engaged in the immune response.

Finding the Elusive Psychiatric "Lesion" With 21st-Century Neuroanatomy: A Note of Caution.

The widespread use of MRI has led to a wealth of structural and functional anatomical findings in patients with diverse psychiatric disorders that may represent insights into pathobiology. However, recent technical reports indicate that data from popular MRI research-particularly structural MRI, resting--state functional MRI, and diffusion tensor imaging--are highly sensitive to common artifacts (e.g., head motion and breathing effects) that may dominate the results. Because these and other important confounders of MRI data (e.g., smoking, body weight, metabolic variations, medical comorbidities, psychoactive drugs, alcohol use, mental state) tend to vary systematically between patient and control groups, the evidence that findings are neurobiologically meaningful is inconclusive and may represent artifacts or epiphenomena of uncertain value. The authors caution that uncritically accepting from study to study findings that may represent fallacies of all sorts carries the risk of misinforming practitioners and patients about biological abnormalities underlying psychiatric illness.

Grey-matter texture abnormalities and reduced hippocampal volume are distinguishing features of schizophrenia.

Neurodevelopmental processes are widely believed to underlie schizophrenia. Analysis of brain texture from conventional magnetic resonance imaging (MRI) can detect disturbance in brain cytoarchitecture. We tested the hypothesis that patients with schizophrenia manifest quantitative differences in brain texture that, alongside discrete volumetric changes, may serve as an endophenotypic biomarker. Texture analysis (TA) of grey matter distribution and voxel-based morphometry (VBM) of regional brain volumes were applied to MRI scans of 27 patients with schizophrenia and 24 controls. Texture parameters (uniformity and entropy) were also used as covariates in VBM analyses to test for correspondence with regional brain volume. Linear discriminant analysis tested if texture and volumetric data predicted diagnostic group membership (schizophrenia or control). We found that uniformity and entropy of grey matter differed significantly between individuals with schizophrenia and controls at the fine spatial scale (filter width below 2mm). Within the schizophrenia group, these texture parameters correlated with volumes of the left hippocampus, right amygdala and cerebellum. The best predictor of diagnostic group membership was the combination of fine texture heterogeneity and left hippocampal size. This study highlights the presence of distributed grey-matter abnormalities in schizophrenia, and their relation to focal structural abnormality of the hippocampus. The conjunction of these features has potential as a neuroimaging endophenotype of schizophrenia.

Effect of schizophrenia risk-associated alleles in SREB2 (GPR85) on functional MRI phenotypes in healthy volunteers.

Genetic variants in GPR85 (SREB2: rs56080411 and rs56039557) have been associated with risk for schizophrenia. Here, we test the hypothesis that these variants impact on brain function in normal subjects, measured with functional magnetic resonance imaging (fMRI) paradigms that target regions with greatest SREB2 expression (hippocampal formation and amygdaloid complex). During a facial emotion recognition paradigm, a significant interaction of rs56080411 genotype by sex was found in the left amygdaloid complex (male risk allele carriers showed less activation than male homozygotes for the non-risk allele, while females showed the opposite pattern). During aversive encoding of an emotional memory paradigm, we found that risk allele carriers for rs56080411 had greater activation in the right inferior frontal gyrus. Trends in the same direction were present for rs56039557 in the right occipital cortex and right fusiform gyrus. During a working memory paradigm, a significant sex-by-genotype interaction was found with male risk allele carriers of rs56080411 having inefficient activation within the left dorsolateral prefrontal cortex (DLPFC), compared with same sex non-risk carriers, while females revealed an opposite pattern, despite similar levels of performance. These data suggest that risk-associated variants in SREB2 are associated with phenotypes similar to those found in patients with schizophrenia in the DLPFC and the amygdala of males, while the pattern is opposite in females. The findings in females and during the emotional memory paradigm are consistent with modulation by SREB2 of brain circuitries implicated in mood regulation and may be relevant to neuropsychiatric conditions other than schizophrenia.

Abnormalities in fronto-striatal connectivity within language networks relate to differences in grey-matter heterogeneity in Asperger syndrome.

Asperger syndrome (AS) is an Autism Spectrum Disorder (ASD) characterised by qualitative impairment in the development of emotional and social skills with relative preservation of general intellectual abilities, including verbal language. People with AS may nevertheless show atypical language, including rate and frequency of speech production. We previously observed that abnormalities in grey matter homogeneity (measured with texture analysis of structural MR images) in AS individuals when compared with controls are also correlated with the volume of caudate nucleus. Here, we tested a prediction that these distributed abnormalities in grey matter compromise the functional integrity of brain networks supporting verbal communication skills. We therefore measured the functional connectivity between caudate nucleus and cortex during a functional neuroimaging study of language generation (verbal fluency), applying psycho-physiological interaction (PPI) methods to test specifically for differences attributable to grey matter heterogeneity in AS participants. Furthermore, we used dynamic causal modelling (DCM) to characterise the causal directionality of these differences in interregional connectivity during word production. Our results revealed a diagnosis-dependent influence of grey matter heterogeneity on the functional connectivity of the caudate nuclei with right insula/inferior frontal gyrus and anterior cingulate, respectively with the left superior frontal gyrus and right precuneus. Moreover, causal modelling of interactions between inferior frontal gyri, caudate and precuneus, revealed a reliance on bottom-up (stimulus-driven) connections in AS participants that contrasted with a dominance of top-down (cognitive control) connections from prefrontal cortex observed in control participants. These results provide detailed support for previously hypothesised central disconnectivity in ASD and specify discrete brain network targets for diagnosis and therapy in ASD.

Sex differences and autism: brain function during verbal fluency and mental rotation.

Autism spectrum conditions (ASC) affect more males than females. This suggests that the neurobiology of autism: 1) may overlap with mechanisms underlying typical sex-differentiation or 2) alternately reflect sex-specificity in how autism is expressed in males and females. Here we used functional magnetic resonance imaging (fMRI) to test these alternate hypotheses. Fifteen men and fourteen women with Asperger syndrome (AS), and sixteen typically developing men and sixteen typically developing women underwent fMRI during performance of mental rotation and verbal fluency tasks. All groups performed the tasks equally well. On the verbal fluency task, despite equivalent task-performance, both males and females with AS showed enhanced activation of left occipitoparietal and inferior prefrontal activity compared to controls. During mental rotation, there was a significant diagnosis-by-sex interaction across occipital, temporal, parietal, middle frontal regions, with greater activation in AS males and typical females compared to AS females and typical males. These findings suggest a complex relationship between autism and sex that is differentially expressed in verbal and visuospatial domains.

Genetic modulation of GABA levels in the anterior cingulate cortex by GAD1 and COMT.

Gamma-aminobutyric acid (GABA)-ergic transmission is critical for normal cortical function and is likely abnormal in a variety of neuropsychiatric disorders. We tested the in vivo effects of variations in two genes implicated in GABA function on GABA concentrations in prefrontal cortex of living subjects: glutamic acid decarboxylase 1 (GAD1), which encodes GAD67, and catechol-o-methyltransferase (COMT), which regulates synaptic dopamine in the cortex. We studied six single nucleotide polymorphisms (SNPs) in GAD1 previously associated with risk for schizophrenia or cognitive dysfunction and the val158met polymorphism in COMT in 116 healthy volunteers using proton magnetic resonance spectroscopy. Two of the GAD1 SNPs (rs1978340 (p=0.005) and rs769390 (p=0.004)) showed effects on GABA levels as did COMT val158met (p=0.04). We then tested three SNPs in GAD1 (rs1978340, rs11542313, and rs769390) for interaction with COMT val158met based on previous clinical results. In this model, rs11542313 and COMT val158met showed significant main effects (p=0.001 and 0.003, respectively) and a trend toward a significant interaction (p=0.05). Interestingly, GAD1 risk alleles for schizophrenia were associated with higher GABA/Cre, and Val-Val homozygotes had high GABA/Cre levels when on a GAD1 risk genotype background (N=6). These results support the importance of genetic variation in GAD1 and COMT in regulating prefrontal cortical GABA function. The directionality of the effects, however, is inconsistent with earlier evidence of decreased GABA activity in schizophrenia.

Biological validation of increased schizophrenia risk with NRG1, ERBB4, and AKT1 epistasis via functional neuroimaging in healthy controls.

CONTEXT: NRG1 is a schizophrenia candidate gene and plays an important role in brain development and neural function. Schizophrenia is a complex disorder, with etiology likely due to epistasis. OBJECTIVE: To examine epistasis between NRG1 and selected N-methyl-d-aspartate-glutamate pathway partners implicated in its effects, including ERBB4, AKT1, DLG4, NOS1, and NOS1AP. DESIGN: Schizophrenia case-control sample analyzed using machine learning algorithms and logistic regression with follow-up using neuroimaging on an independent sample of healthy controls. PARTICIPANTS: A referred sample of schizophrenic patients (n = 296) meeting DSM-IV criteria for schizophrenia spectrum disorder and a volunteer sample of controls for case-control comparison (n = 365) and a separate volunteer sample of controls for neuroimaging (n = 172). MAIN OUTCOME MEASURES: Epistatic association between single-nucleotide polymorphisms (SNPs) and case-control status; epistatic association between SNPs and the blood oxygen level-dependent physiological response during working memory measured by functional magnetic resonance imaging. RESULTS: We observed interaction between NRG1 5' and 3' SNPs rs4560751 and rs3802160 (likelihood ratio test P = .00020) and schizophrenia, which was validated using functional magnetic resonance imaging of working memory in healthy controls; carriers of risk-associated genotypes showed inefficient processing in the dorsolateral prefrontal cortex (P = .015, familywise error corrected). We observed epistasis between NRG1 (rs10503929; Thr286/289/294Met) and its receptor ERBB4 (rs1026882; likelihood ratio test P = .035); a 3-way interaction with these 2 SNPs and AKT1 (rs2494734) was also observed (odds ratio, 27.13; 95% confidence interval, 3.30-223.03; likelihood ratio test P = .042). These same 2- and 3-way interactions were further biologically validated via functional magnetic resonance imaging: healthy individuals carrying risk genotypes for NRG1 and ERBB4, or these 2 together with AKT1, were disproportionately less efficient in dorsolateral prefrontal cortex processing. Lower-level interactions were not observed between NRG1 /ERBB4 and AKT1 in association or neuroimaging, consistent with biological evidence that NRG1 × ERBB4 interaction modulates downstream AKT1 signaling. CONCLUSION: Our data suggest complex epistatic effects implicating an NRG1 molecular pathway in cognitive brain function and the pathogenesis of schizophrenia.