Shadows of complexity: what biological networks reveal about epistasis and pleiotropy
Open accessAbstract
Pleiotropy, in which one mutation causes multiple phenotypes, has traditionally been seen as a deviation from the conventional observation in which one gene affects one phenotype. Epistasis, or gene–gene interaction, has also been treated as an exception to the Mendelian one gene–one phenotype paradigm. This simplified perspective belies the pervasive complexity of biology and hinders progress toward a deeper understanding of biological systems. We assert that epistasis and pleiotropy are not isolated occurrences, but ubiquitous and inherent properties of biomolecular networks. These phenomena should not be treated as exceptions, but rather as fundamental components of genetic analyses. A systems level understanding of epistasis and pleiotropy is, therefore, critical to furthering our understanding of human genetics and its contribution to common human disease. Finally, graph theory offers an intuitive and powerful set of tools with which to study the network bases of these important genetic phenomena.
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Joint modeling of patient phenotypic features is essential for identifying genetic mutations with pleiotropic effects.
“This joint modeling of patient phenotypic features is essential for identifying genetic mutations with pleiotropic effects74–76.”
From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [74]
The Pleiotropy section describes mutations that produce multiple phenotypes, and Biological Networks says their effects become apparent when the genetic network is analyzed in totality. The paper does not say that jointly modeling patients’ phenotypic features is essential for identifying those mutations; its argument concerns analysis of molecular networks, not patient-feature modeling. (LLM, conf=0.79)
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Jordan, D. M., Verbanck, M., & Do, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1844-7 · Cited by 73 · see citing works
Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases
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- Authors
- Tyler, A. L., Asselbergs, F. W., Williams, S. M., & Moore, J. H.
- Affiliations
- Dartmouth College (US)
- University Medical Center Groningen (NL)
- Vanderbilt University (US)
- Corresponding author
- Moore, Jason H.
- Venue
- BioEssays
- Publisher
- Wiley
- Published
- 2009-02
- Volume
- 31
- Issue
- 2
- Pages
- 220-227
- ISSN
- 0265-9247 (ISSN-L)
- 1521-1878
- PMC
- PMC3159922
- OpenAlex
- W1964525882
- Cited by
- 185
- References
- 72
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 6 · 2023: 17 · 2022: 8 · 2021: 5 · 2020: 8 · 2019: 7 · 2018: 6 · 2017: 10 · 2016: 10 · 2015: 14 · 2014: 16 · 2013: 13 · 2012: 10
- FWCI
- 1.27
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- BioEssays (publishedVersion)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Funders
- National Institute of Allergy and Infectious Diseases (R01AI059694)
- U.S. National Library of Medicine (R01LM009012, R01LM010098)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Keywords
- epistasis, pleiotropy, gene-gene interaction, biological networks, biomolecular networks, Mendelian inheritance, systems genetics, graph theory, human disease genetics
- MeSH terms
- Epistasis, Genetic, Humans, Phenotype, Gene Regulatory Networks
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