ArticleeLife2017
Inferring genetic interactions from comparative fitness data.
Article in eLife, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 43 citations in OpenAlex.
- Distinguishing direct interactions from global epistasis using rank statistics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Resolving discrepancies between chimeric and multiplicative measures of higher-order epistasis.Nature communications · 2025Article
- The importance of epistasis in the evolution of viral pathogens.Virus evolution · 2025Review
- Synthetic lethality and the minimal genome size problem.mSphere · 2024Article
- Master regulators of biological systems in higher dimensions.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Exploring the expanse between theoretical questions and experimental approaches in the modern study of evolvability.Journal of experimental zoology. Part B, Molecular and developmental evolution · 2023Article
- A fast lasso-based method for inferring higher-order interactions.PLoS computational biology · 2022Article
- Functional network motifs defined through integration of protein-protein and genetic interactions.PeerJ · 2022Article
- Cost-Effective Mapping of Genetic Interactions in Mammalian Cells.Frontiers in genetics · 2021Article
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- Inference of clonal selection in cancer populations using single-cell sequencing data.Bioinformatics (Oxford, England) · 2019Article
- Proteostasis Environment Shapes Higher-Order Epistasis Operating on Antibiotic Resistance.Genetics · 2019Article
- Unraveling the causes of adaptive benefits of synonymous mutations in TEM-1 β-lactamase.Heredity · 2018Article
- Article
- The geometry of partial fitness orders and an efficient method for detecting genetic interactions.Journal of mathematical biology · 2018Article
- The Influence of Higher-Order Epistasis on Biological Fitness Landscape Topography.Journal of statistical physics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Darwinian fitness is a central concept in evolutionary biology. In practice, however, it is hardly possible to measure fitness for all genotypes in a natural population. Here, we present quantitative tools to make inferences about epistatic gene interactions when the fitness landscape is only incompletely determined due to imprecise measurements or missing observations. We demonstrate that genetic interactions can often be inferred from fitness rank orders, where all genotypes are ordered according to fitness, and even from partial fitness orders. We provide a complete characterization of rank orders that imply higher order epistasis. Our theory applies to all common types of gene interactions and facilitates comprehensive investigations of diverse genetic interactions. We analyzed various genetic systems comprising HIV-1, the malaria-causing parasite
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.