“Guilt by Association” Is the Exception Rather Than the Rule in Gene Networks
Open accessAbstract
Gene networks are commonly interpreted as encoding functional information in their connections. An extensively validated principle called guilt by association states that genes which are associated or interacting are more likely to share function. Guilt by association provides the central top-down principle for analyzing gene networks in functional terms or assessing their quality in encoding functional information. In this work, we show that functional information within gene networks is typically concentrated in only a very few interactions whose properties cannot be reliably related to the rest of the network. In effect, the apparent encoding of function within networks has been largely driven by outliers whose behaviour cannot even be generalized to individual genes, let alone to the network at large. While experimentalist-driven analysis of interactions may use prior expert knowledge to focus on the small fraction of critically important data, large-scale computational analyses have typically assumed that high-performance cross-validation in a network is due to a generalizable encoding of function. Because we find that gene function is not systemically encoded in networks, but dependent on specific and critical interactions, we conclude it is necessary to focus on the details of how networks encode function and what information computational analyses use to extract functional meaning. We explore a number of consequences of this and find that network structure itself provides clues as to which connections are critical and that systemic properties, such as scale-free-like behaviour, do not map onto the functional connectivity within networks.
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The fraction of genes in a module that relate to its main biological function is often <20%.
“In reality, the fraction of genes in a module that relate to its main biological function is often <20% [127]”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [127]
The Results sections on yeast and mouse networks discuss how functional information is concentrated in a few edges, not the proportion of genes in a module related to its main function. Searches for module composition and the <20% figure found no passage reporting that claim; page numbers were unavailable in our copy. (LLM, conf=0.74)
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- Authors
- Gillis, J., & Pavlidis, P.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Pavlidis, Paul
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-03-29
- Volume
- 8
- Issue
- 3
- Pages
- e1002444
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC3315453
- OpenAlex
- W2146545950
- Cited by
- 214
- References
- 68
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 6 · 2023: 12 · 2022: 12 · 2021: 21 · 2020: 19 · 2019: 20 · 2018: 16 · 2017: 12 · 2016: 16 · 2015: 10 · 2014: 13 · 2013: 25 · 2012: 10
- FWCI
- 7.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, public-domain)
- Europe PMC (PubMed Central) (publishedVersion, public-domain)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Michael Smith Health Research BC
- National Institutes of Health
- Canadian Institutes of Health Research
- National Institute of General Medical Sciences (GM076990, R01GM076990)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- guilt by association, gene networks, genetic interactions, network topology, scale-free networks, critical interaction, functional connectivity
- MeSH terms
- Algorithms, Animals, Computer Simulation, Humans, Models, Biological, Signal Transduction, Proteome, Protein Interaction Mapping
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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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