Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders
Open accessAbstract
In a research environment dominated by reductionist approaches to brain disease mechanisms, gene network analysis provides a complementary framework in which to tackle the complex dysregulations that occur in neuropsychiatric and other neurological disorders. Gene–gene expression correlations are a common source of molecular networks because they can be extracted from high‐dimensional disease data and encapsulate the activity of multiple regulatory systems. However, the analysis of gene coexpression patterns is often treated as a mechanistic black box, in which looming ‘hub genes’ direct cellular networks, and where other features are obscured. By examining the biophysical bases of coexpression and gene regulatory changes that occur in disease, recent studies suggest it is possible to use coexpression networks as a multi‐omic screening procedure to generate novel hypotheses for disease mechanisms. Because technical processing steps can affect the outcome and interpretation of coexpression networks, we examine the assumptions and alternatives to common patterns of coexpression analysis and discuss additional topics such as acceptable datasets for coexpression analysis, the robust identification of modules, disease‐related prioritization of genes and molecular systems and network meta‐analysis. To accelerate coexpression research beyond modules and hubs, we highlight some emerging directions for coexpression network research that are especially relevant to complex brain disease, including the centrality–lethality relationship, integration with machine learning approaches and network pharmacology .
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There is no guarantee that any of the hub genes is causal to a phenotype.
“Nor is there a guarantee that any of the hub genes is causal to a phenotype [78].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [78]
At currently available sample sizes, networks are most reproducible at module level, then hub gene level, and least at precise gene connectivity rankings or module memberships.
“At currently available sample sizes, networks are most reproducible at a module level35,38,39, then at the hub gene level41,223 and, last, at the level of precise gene connectivity rankings or precise module memberships of genes40,49.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [41]
Coexpressed gene sets represent the outcome of transcriptional regulatory mechanisms including transcription factors, chromatin conformation, and latent factors such as cell type proportions.
“Coexpressed gene sets represent the outcome of transcriptional regulatory mechanisms that include transcription factors, chromatin conformation and latent factors that generate correlations, such as the proportion of different cell types present in the sampled tissue10.”
From A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease (2018) · cites this paper as [10]
Network analyses of AD brain tissue demonstrate the most reproducibility at the module level, followed by the hub protein level, and then at the level of precise protein connectivity rankings and module protein membership.
“Network analyses of AD brain tissue demonstrate the most reproducibility at the module level, followed by the hub protein level, and then finally at the level of precise protein connectivity rankings and module protein membership [61, 89–94].”
From Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau (2021) · cites this paper as [92]
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- Authors
- Gaiteri, C., Ding, Y., French, B., Tseng, G. C., & Sibille, E.
- Affiliations
- Allen Institute for Brain Science (US)
- University of Pittsburgh (US)
- Corresponding author
- Sibille, E.
- Venue
- Genes, Brain and Behavior
- Publisher
- Wiley
- Published
- 2013-12-10
- Volume
- 13
- Issue
- 1
- Pages
- 13-24
- ISSN
- 1601-183X (ISSN-L)
- 1601-1848
- PMC
- PMC3896950
- OpenAlex
- W1823837540
- Cited by
- 289
- References
- 105
- Citations by year
- 2026: 9 · 2025: 20 · 2024: 14 · 2023: 14 · 2022: 25 · 2021: 38 · 2020: 21 · 2019: 24 · 2018: 29 · 2017: 32 · 2016: 34 · 2015: 20 · 2014: 9
- FWCI
- 1.97
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genes Brain & Behavior (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute of Mental Health (MH084060, R01MH093723, R01MH077159, K02MH084060)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- gene co-expression network, neuropsychiatric disorders, hub genes, network modules, network meta-analysis, gene prioritization, network pharmacology, machine learning integration
- MeSH terms
- Animals, Humans, Nervous System Diseases, Transcription, Genetic, Gene Regulatory Networks
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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