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Functional genomics reveal gene regulatory mechanisms underlying schizophrenia risk.

ABSTRACT: Genome-wide association studies (GWASs) have identified over 180 independent schizophrenia risk loci. Nevertheless, how the risk variants in the reported loci confer schizophrenia susceptibility remains largely unknown. Here we systematically investigate the gene regulatory mechanisms underpinning schizophrenia risk through integrating data from functional genomics (including 30 ChIP-Seq experiments) and position weight matrix (PWM). We identify 132 risk single nucleotide polymorphisms (SNPs) that disrupt transcription factor binding and we find that 97 of the 132 TF binding-disrupting SNPs are associated with gene expression in human brain tissues. We validate the regulatory effect of some TF binding-disrupting SNPs with reporter gene assays (9 SNPs) and allele-specific expression analysis (10 SNPs). Our study reveals gene regulatory mechanisms affected by schizophrenia risk SNPs (including widespread disruption of POLR2A and CTCF binding) and identifies target genes for mechanistic studies and drug development. Our results can be accessed and visualized at SZDB database ( http://www.szdb.org/ ).

SUBMITTER: Huo Y

PROVIDER: S-EPMC6368563 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Functional genomics reveal gene regulatory mechanisms underlying schizophrenia risk.

Huo Yongxia Y Li Shiwu S Liu Jiewei J Li Xiaoyan X Luo Xiong-Jian XJ

Nature communications 20190208 1

Genome-wide association studies (GWASs) have identified over 180 independent schizophrenia risk loci. Nevertheless, how the risk variants in the reported loci confer schizophrenia susceptibility remains largely unknown. Here we systematically investigate the gene regulatory mechanisms underpinning schizophrenia risk through integrating data from functional genomics (including 30 ChIP-Seq experiments) and position weight matrix (PWM). We identify 132 risk single nucleotide polymorphisms (SNPs) th ...[more]

PMID: 30737407

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Project description:We have used a translational convergent functional genomics (CFG) approach to identify and prioritize genes involved in schizophrenia, by gene-level integration of genome-wide association study data with other genetic and gene expression studies in humans and animal models. Using this polyevidence scoring and pathway analyses, we identify top genes (DISC1, TCF4, MBP, MOBP, NCAM1, NRCAM, NDUFV2, RAB18, as well as ADCYAP1, BDNF, CNR1, COMT, DRD2, DTNBP1, GAD1, GRIA1, GRIN2B, HTR2A, NRG1, RELN, SNAP-25, TNIK), brain development, myelination, cell adhesion, glutamate receptor signaling, G-protein-coupled receptor signaling and cAMP-mediated signaling as key to pathophysiology and as targets for therapeutic intervention. Overall, the data are consistent with a model of disrupted connectivity in schizophrenia, resulting from the effects of neurodevelopmental environmental stress on a background of genetic vulnerability. In addition, we show how the top candidate genes identified by CFG can be used to generate a genetic risk prediction score (GRPS) to aid schizophrenia diagnostics, with predictive ability in independent cohorts. The GRPS also differentiates classic age of onset schizophrenia from early onset and late-onset disease. We also show, in three independent cohorts, two European American and one African American, increasing overlap, reproducibility and consistency of findings from single-nucleotide polymorphisms to genes, then genes prioritized by CFG, and ultimately at the level of biological pathways and mechanisms. Finally, we compared our top candidate genes for schizophrenia from this analysis with top candidate genes for bipolar disorder and anxiety disorders from previous CFG analyses conducted by us, as well as findings from the fields of autism and Alzheimer. Overall, our work maps the genomic and biological landscape for schizophrenia, providing leads towards a better understanding of illness, diagnostics and therapeutics. It also reveals the significant genetic overlap with other major psychiatric disorder domains, suggesting the need for improved nosology.

Project description:Recent analysis of single-cell transcriptomic data has revealed a surprising organization of the transcriptional variability pervasive across individual neurons. In response to distinct combinations of synaptic input-type, a new organization of neuronal subtypes emerged based on transcriptional states that were aligned along a gradient of correlated gene expression. Individual neurons traverse across these transcriptional states in response to cellular inputs. However, the regulatory network interactions driving these changes remain unclear. Here we present a novel fuzzy logic-based approach to infer quantitative gene regulatory network models from highly variable single-cell gene expression data. Our approach involves developing an a priori regulatory network that is then trained against in vivo single-cell gene expression data in order to identify causal gene interactions and corresponding quantitative model parameters. Simulations of the inferred gene regulatory network response to experimentally observed stimuli levels mirrored the pattern and quantitative range of gene expression across individual neurons remarkably well. In addition, the network identification results revealed that distinct regulatory interactions, coupled with differences in the regulatory network stimuli, drive the variable gene expression patterns observed across the neuronal subtypes. We also identified a key difference between the neuronal subtype-specific networks with respect to negative feedback regulation, with the catecholaminergic subtype network lacking such interactions. Furthermore, by varying regulatory network stimuli over a wide range, we identified several cases in which divergent neuronal subtypes could be driven towards similar transcriptional states by distinct stimuli operating on subtype-specific regulatory networks. Based on these results, we conclude that heterogenous single-cell gene expression profiles should be interpreted through a regulatory network modeling perspective in order to separate the contributions of network interactions from those of cellular inputs.

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Functional genomics reveal gene regulatory mechanisms underlying schizophrenia risk.

Publications

Functional genomics reveal gene regulatory mechanisms underlying schizophrenia risk.

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