Clusters of co-expressed genes in mammalian genomes are conserved by natural selection
Open accessAbstract
Genes that belong to the same functional pathways are often packaged into operons in prokaryotes. However, aside from examples in nematode genomes, this form of transcriptional regulation appears to be absent in eukaryotes. Nevertheless, a number of recent studies have shown that gene order in eukaryotic genomes is not completely random, and that genes with similar expression patterns tend to be clustered together. What remains unclear is whether co-expressed genes have been gathered together by natural selection to facilitate their regulation, or if the genes are co-expressed simply by virtue of their being close together in the genome. Here, we show that gene expression clusters tend to contain fewer chromosomal breakpoints between human and mouse than expected by chance, which indicates that they are being held together by natural selection. This conclusion applies to clusters defined on the basis of broad (housekeeping) expression, or on the basis of correlated transcription profiles across tissues. Contrary to previous reports, we find that genes with high expression are not clustered to a greater extent than expected by chance and are not conserved during evolution.
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Assuming that co-expressed genes are functionally related, enriched functions can be assigned to poorly annotated genes within the same co-expression module, an approach commonly referred to as guilt by association (GBA).
“Assuming that co-expressed genes are functionally related, enriched functions can be assigned to poorly annotated genes within the same co-expression module, an approach commonly referred to as ‘guilt by association’ (GBA) [121].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [121]
The Introduction discusses whether neighboring co-expressed genes are functionally related or merely co-expressed because of shared regulatory effects. Our copy does not describe assigning enriched functions to poorly annotated genes within a co-expression module or call that approach ‘guilt by association’ (searched: functional annotation, unknown function, and guilt by association). (LLM, conf=0.72)
Experimental — accuracy not yet measuredGBA 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 [121]
The Mapping and Sliding-Window Algorithm sections describe how the paper identifies clusters of co-expressed genes, not how disease genes are predicted. Searches for guilt-by-association approaches and disease-associated genes found no passage addressing the claimed use of modules to identify potential disease genes. (LLM, conf=0.76)
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Gene co-expression analysis for functional classification and gene–disease predictions
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- Authors
- Singer, G. A. C., Lloyd, A. T., Huminiecki, L. B., & Wolfe, K. H.
- Affiliations
- Trinity College Dublin (IE)
- Corresponding author
- Singer, Gregory A. C.
- Venue
- Molecular Biology and Evolution
- Publisher
- Oxford University Press (OUP)
- Published
- 2004-12-01
- Volume
- 22
- Issue
- 3
- Pages
- 767-775
- ISSN
- 0737-4038 (ISSN-L)
- 1537-1719
- OpenAlex
- W1979076392
- Cited by
- 164
- References
- 29
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 3 · 2023: 1 · 2022: 6 · 2021: 8 · 2020: 5 · 2019: 7 · 2018: 3 · 2017: 2 · 2016: 9 · 2015: 3 · 2014: 7 · 2013: 14 · 2012: 8
- FWCI
- 4.80
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Molecular Biology and Evolution (publishedVersion)
- PubMed (publishedVersion)
- Trinity's Access to Research Output (TARA) (Trinity College Dublin) (submittedVersion)
- Funders
- Science Foundation Ireland
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genome Rearrangement Algorithms
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Chromosomal and Genetic Variations
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- gene co-expression, gene clusters, chromosomal breakpoints, natural selection, gene order, transcriptional regulation, tissue-specific gene expression, housekeeping genes, eukaryotic genomes, gene expression evolution
- MeSH terms
- Animals, Gene Expression Regulation, Humans, Models, Genetic, Selection, Genetic, Genome, Human, Evolution, Molecular, Gene Expression Profiling, Databases, Protein, Mice
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