Comparative genomic evidence for the involvement of schizophrenia risk genes in antipsychotic effects

Kim, Y; Giusti‐Rodríguez, Paola; Crowley, James J.; Bryois, Julien; Nonneman, Randal J.; Ryan, Allison; Quackenbush, Corey R.; Iglesias-Ussel, Maria D.; Lee, P H; Sun, Wei; Villena, F P-M de; Sullivan, Patrick F.

doi:10.1038/mp.2017.111

Cited by 28 publications

(20 citation statements)

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“…A popular bioinformatics tool for constructing and studying gene coexpression networks is Weighted Gene Co-Expression Analysis (WGCNA) [6]. This approach has been used to characterize patterns of co-expression in the normal brain [7], in autism spectrum disorders [8], in schizophrenia (human and animal modeling studies) [9][10][11][12] and across mental disorders [13]. Importantly, co-expression network analysis has also been used to understand the partitioning of polygenic SCZ risk in the brain transcriptome.…”

Section: Introductionmentioning

confidence: 99%

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

Radulescu

Jaffe

Straub

et al. 2018

Preprint

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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 the 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; Caucasians) to construct co-expression networks and detect modules of co-expressed genes. After internal and external validation, modules of selected interest were tested for enrichment in biological ontologies, association with schizophrenia polygenic risk scores (PRS), with diagnosis and 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 a significant association with diagnosis, PRS and significant overlap with 36 PGC2 loci genes, deemed as tier 1 (strongest candidates for drug targets). Fifty-three PGC2 loci genes were in modules associated only with diagnosis (tier 2) and 59 in modules unrelated to diagnosis or PRS (tier 3). In conclusion, our study highlights complex relationships between gene co-expression networks in the brain and polygenic risk for SCZ and provides a strategy for using this information in selecting potentially targetable gene sets for therapeutic drug development.All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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Section: Introductionmentioning

confidence: 99%

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

Radulescu

Jaffe

Straub

et al. 2018

Preprint

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“…Since SCZ patients are usually treated with antipsychotics, it can be hypothesized that drugs contributed to the aggregation of genes into modules. In a recent study, Kim and coworkers ( 30 ) identified genes differentially expressed in the striatum and in the whole brain of mice exposed to haloperidol vs. not exposed mice. We computed for all modules the enrichment for the differentially expressed genes and found a single module ( Brown ) enriched (11 genes, 16% of total differentially expressed genes, Bonferroni-corrected p-value = .00447, Data file S4).…”

Section: Resultsmentioning

confidence: 99%

“…Taken together, both findings highlight the potential importance of the genes co-expressed in Darkgreen for the physiology of olanzapine and clozapine, two atypical antipsychotics. Darkgreen included also genes coding for proteins involved in synaptic transmission mediated by serotonin, glutamate and GABA ( HTR1F , GRM5 , GABRB1 , GABRG3 ), or involved in neural excitability ( KCNH1 , KCNA3 , KCNH7 , KCNH5 ), along with CACNA1C , a risk gene for SCZ and bipolar disorder supported by multiple lines of evidence ( 40 – 44 ). The functions of the genes in Darkgreen are consistent with previous pathway analyses of SCZ risk ( 45 ) and enhance the biological plausibility that the coregulation of this module has functional relevance.…”

Section: Discussionmentioning

confidence: 99%

Prefrontal co-expression of schizophrenia risk genes is associated with treatment response in patients

Pergola

Carlo

Jaffe

et al. 2018

Preprint

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Gene co-expression networks are relevant to functional and clinical translation of schizophrenia (SCZ) risk genes. We hypothesized that SCZ risk genes may converge into coexpression pathways which may be associated with gene regulation mechanisms and with response to treatment in patients with SCZ. We identified gene co-expression networks in two prefrontal cortex post-mortem RNA sequencing datasets (total N=688) and replicated them in four more datasets (total N=227). We identified and replicated (all p-values<.001) a single module enriched for SCZ risk loci (13 risk genes in 10 loci). In silico screening of potential regulators of the SCZ risk module via bioinformatic analyses identified two transcription factors and three miRNAs associated with the risk module. To translate post-mortem information into clinical phenotypes, we identified polymorphisms predicting co-expression and combined them to obtain an index approximating module co-expression (Polygenic Co-expression Index: PCI). The PCI-co-expression association was successfully replicated in two independent brain transcriptome datasets (total N=131; all p-values<.05). Finally, we tested the association between the PCI and short-term treatment response in two independent samples of patients with SCZ treated with olanzapine (total N=167). The PCI was associated with treatment response in the positive symptom domain in both clinical cohorts (all p-values<.05).In summary, our findings in a large sample of human post-mortem prefrontal cortex show that coexpression of a set of genes enriched for schizophrenia risk genes is relevant to treatment response. This co-expression pathway may be co-regulated by transcription factors and miRNA associated with it.

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“…Two studies performed using the region of interest method showed that common variants in the MHC gene family had a significant relationship with decreased thalamus and hippocampal volumes in schizophrenia patients (Brucato, Guadalupe, Franke, Fisher, & Francks, 2015;Walters et al, 2013). In addition, the schizophrenia patients in these studies mostly received antipsychotic treatment, and this might have had an impact on illness-related gene expression, protein phosphorylation, and new protein synthesis in addition to further effects on the mediation cerebral development and functional maturation (Kari, Silje, Christine, & Vidar, 2017;Kim, Giusti-Rodriguez, & Crowley, 2018).…”

Section: Introductionmentioning

confidence: 99%

Reduced cortical thickness related to single nucleotide polymorphisms in the major histocompatibility complex region in antipsychotic‐naive schizophrenia

Tao

Xiao

et al. 2019

Brain and Behavior

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Instructions The aim of this study was to explore the relationships between changes in cortical thickness and single nucleotide polymorphisms (SNPs) in the major histocompatibility complex (MHC) region in a group of antipsychotic‐naive schizophrenia (AN‐SCZ) patients. Methods Twenty‐five AN‐SCZ patients and 51 healthy controls (HCs) participated in this study. General linear models were used to identify associations between the average cortical thicknesses of each brain region ( N = 68) and each of the 11 SNPs in the MHC region in the AN‐SCZ patients and HCs. Next, we performed independent‐sample t tests to investigate whether cortical thickness was significantly lower in the AN‐SCZ patients than in HCs in the brain regions that were significantly associated with the SNPs. Finally, we examined the correlations between clinical symptoms and cortical thickness in the above brain areas in the whole AN‐SCZ group using Pearson correlation tests. Results Seven of the 11 SNPs within the MHC region were significantly associated with cortical thickness only in the AN‐SCZ patients; these included rs1635, rs1736913, rs2021722, rs204999, rs2523722, rs3131296, and rs9272105. The AN‐SCZ patients had significantly thinner cortical thicknesses in the above brain regions, especially the prefrontal cortex. Furthermore, the left entorhinal region was negatively correlated with Positive and Negative Symptom Scale (PANSS) activation scores in the AN‐SCZ group ( r = −0.601, p = 0.03). Conclusions This study provides evidence demonstrating the potential effects of MHC risk variants in cortical thickness deficits in AN‐SCZ. These data also support the notion that the immune system plays critical roles in the pathology of schizophrenia, which is mediated via the modulation of the development of cerebral cortical structures.

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Comparative genomic evidence for the involvement of schizophrenia risk genes in antipsychotic effects

Cited by 28 publications

References 50 publications

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

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

Prefrontal co-expression of schizophrenia risk genes is associated with treatment response in patients

Reduced cortical thickness related to single nucleotide polymorphisms in the major histocompatibility complex region in antipsychotic‐naive schizophrenia

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