Coexpression network analysis of neural tissue reveals perturbations in developmental processes in schizophrenia
Open accessAbstract
We performed integrated gene coexpression network analysis on two large microarray-based brain gene expression data sets generated from the prefrontal cortex obtained post-mortem from 101 subjects, 47 subjects with schizophrenia and 54 normal control subjects, ranging in age from 19 to 81 years. Twenty-eight modules of coexpressed genes with functional interpretations were detected in both normal subjects and those with schizophrenia. Significant overlap of “case” and “control” module composition was observed, indicating that extensive differences in underlying molecular connectivity are not likely driving pathology in schizophrenia. Modules of coexpressed genes were characterized according to disease association, cell type specificity, and the effects of aging. We find that genes with altered expression in schizophrenia clustered into distinct coexpression networks and that these were associated primarily with neurons. We further identified a robust effect of age on gene expression modules that differentiates normal subjects from those with schizophrenia. In particular, we report that normal age-related decreases in genes related to central nervous system developmental processes, including neurite outgrowth, neuronal differentiation, and dopamine-related cellular signaling, do not occur in subjects with schizophrenia during the aging process. Extrapolating these findings to earlier stages of development supports the concept that schizophrenia pathogenesis begins early in life and is associated with a failure of normal decreases in developmental-related gene expression. These findings provide a novel mechanism for the “developmental” hypothesis of schizophrenia on a molecular level.
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GBA approaches are widely used to identify new potential disease genes if a substantial proportion of the genes within a module are associated with a particular disease.
“GBA approaches are also widely used to identify new potential disease genes if a substantial proportion of the genes within a module are associated with a particular disease [26, 121–126] (Figure 1).”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [123]
The Discussion describes using module membership for guilt-by-association to infer the functions of new genes, and the disease-association section compares modules with previously identified schizophrenia-associated genes. It does not establish that this approach is widely used to identify new potential disease genes when a substantial proportion of a module’s genes are disease-associated. (LLM, conf=0.82)
Experimental — accuracy not yet measuredOne of the first studies to put schizophrenia transcriptomics into a genome-wide co-expression network used mutual information and WGCNA.
“One of the first studies to put schizophrenia transcriptomics into a genome-wide co-expression network used mutual information and WGCNA107.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [107]
That study showed that in schizophrenia, as in ASD, the overall transcriptomic structure in control brains is intact, but a neural differentiation module associated with schizophrenia does not follow the normal trajectory of downregulation with age.
“This study showed that, as in ASD, the overall transcriptomic structure that is observed in control brains is intact but that a neural differentiation module that is associated with schizophrenia does not follow the normal trajectory of downregulation with age.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [107]
Gene co-expression was organized into modules of distinct cellular and functional categories, consistent with prior brain transcriptome studies.
“Consistent with prior studies of the brain transcriptome15,47–50, we find gene co-expression to be organized into modules of distinct cellular and functional categories (Supplementary data file 7).”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [48]
Gene co-expression is organized into modules of distinct cellular and functional categories.
“Consistent with prior studies of the brain transcriptome15,47–50, we find gene co-expression to be organized into modules of distinct cellular and functional categories (Supplementary data file 7).”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [48]
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References 47 · Cited by 134
- Authors
- Torkamani, A., Dean, B., Schork, N. J., & Thomas, E. A.
- Affiliations
- Mental Health Research Institute (AU)
- Scripps Research Institute (US)
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2010-03-02
- Volume
- 20
- Issue
- 4
- Pages
- 403-412
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC2847743
- OpenAlex
- W2171927996
- Cited by
- 134
- References
- 47
- Citations by year
- 2026: 2 · 2025: 1 · 2023: 3 · 2022: 8 · 2021: 9 · 2020: 5 · 2019: 5 · 2018: 10 · 2017: 5 · 2016: 15 · 2015: 12 · 2014: 13 · 2013: 8 · 2012: 20
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Institutes of Health (MH069696)
- National Health and Medical Research Council (400016)
- National Institute of Mental Health (R01MH069696, MH069696)
- National Center for Research Resources (UL1RR025774, U54RR02504-01)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Keywords
- schizophrenia, gene co-expression network, prefrontal cortex, postmortem brain, aging, neuronal differentiation, neurite outgrowth, dopamine signaling, gene modules, neural development
- MeSH terms
- Adult, Aged, Aged, 80 and over, Aging, Female, Humans, Male, Middle Aged, Nervous System, Schizophrenia, Case-Control Studies, Prefrontal Cortex, Gene Expression Regulation, Developmental, Genetic Predisposition to Disease, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Gene Regulatory Networks, Young Adult
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