ArticleGenetics2017
Detecting High-Order Epistasis in Nonlinear Genotype-Phenotype Maps.
Article in Genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers.
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Who cites it
90 citing papers in PubMed.
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Mount Fuji's stubby peak: the genotypic density of additive landscapes near maximal fitness.bioRxiv : the preprint server for biology · 2026Article
- On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing.Journal of mathematical biology · 2026Article
- Biophysical trade-offs in antibody evolution are resolved by conformation-mediated epistasis.bioRxiv : the preprint server for biology · 2026Article
- Pleiotropic mutational effects on function and stability constrain the antigenic evolution of influenza haemagglutinin.Nature ecology & evolution · 2026Article
- Functional bottlenecks can emerge from non-epistatic underlying traits.PLoS computational biology · 2026Article
- Free energy perturbations in enzyme kinetic models reveal cryptic epistasis.PLoS computational biology · 2026Article
- Evolutionary remodeling of the FXR binding pocket drove functional divergence and ligand discrimination.Molecular biology and evolution · 2026Article
- Interactions with polygenic background impact quantitative traits in the UK Biobank.medRxiv : the preprint server for health sciences · 2025Article
- Epistatic drift in protein evolution.Current opinion in genetics & development · 2025Review
- Pleiotropic mutational effects on function and stability constrain the antigenic evolution of influenza hemagglutinin.bioRxiv : the preprint server for biology · 2025Article
- Distinguishing direct interactions from global epistasis using rank statistics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Learning sequence-function relationships with scalable, interpretable Gaussian processes.bioRxiv : the preprint server for biology · 2025Article
- Importance of higher-order epistasis in protein sequence-function relationships.bioRxiv : the preprint server for biology · 2025Article
- ACOCMPMI: An Ant Colony Optimization Algorithm Based on Composite Multiscale Part Mutual Information for Detecting Epistatic Interactions.Human mutation · 2025Article
- Gauge fixing for sequence-function relationships.PLoS computational biology · 2025Article
- MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data.Genome biology · 2024Article
- The emergence of nonlinear evolutionary trade-offs and the maintenance of genetic polymorphisms.Biology letters · 2024Article
- Evolutionary insights into the stereoselectivity of imine reductases based on ancestral sequence reconstruction.Nature communications · 2024Article
30 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-order epistasis has been observed in many genotype-phenotype maps. These multi-way interactions between mutations may be useful for dissecting complex traits and could have profound implications for evolution. Alternatively, they could be a statistical artifact. High-order epistasis models assume the effects of mutations should add, when they could in fact multiply or combine in some other nonlinear way. A mismatch in the "scale" of the epistasis model and the scale of the underlying map would lead to spurious epistasis. In this article, we develop an approach to estimate the nonlinear scales of arbitrary genotype-phenotype maps. We can then linearize these maps and extract high-order epistasis. We investigated seven experimental genotype-phenotype maps for which high-order epistasis had been reported previously. We find that five of the seven maps exhibited nonlinear scales. Interestingly, even after accounting for nonlinearity, we found statistically significant high-order epistasis in all seven maps. The contributions of high-order epistasis to the total variation ranged from 2.2 to 31.0%, with an average across maps of 12.7%. Our results provide strong evidence for extensive high-order epistasis, even after nonlinear scale is taken into account. Further, we describe a simple method to estimate and account for nonlinearity in genotype-phenotype maps.
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