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Mol Psychiatry. 2018 Nov 26. doi: 10.1038/s41380-018-0304-1. [Epub ahead of print]

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

Author information

1
Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA.
2
Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA.
3
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA.
4
Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, USA.
5
Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA.
6
Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA.
7
Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA. drweinberger@libd.org.
8
Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA. drweinberger@libd.org.
9
Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA. drweinberger@libd.org.
10
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA. drweinberger@libd.org.

Abstract

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.

PMID:
30478419
DOI:
10.1038/s41380-018-0304-1

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