[Paper Review] Epistasis and Adaptation on Fitness Landscapes
A comprehensive review of how epistasis shapes fitness landscapes, contrasting probabilistic models and Fisher’s Geometric Model, and summarizing empirical evidence from laboratory and wild populations.
Epistasis occurs when the effect of a mutation depends on its carrier's genetic background. Despite increasing evidence that epistasis for fitness is common, its role during evolution is contentious. Fitness landscapes, mappings of genotype or phenotype to fitness, capture the full extent and complexity of epistasis. Fitness landscape theory has shown how epistasis affects the course and the outcome of evolution. Moreover, by measuring the competitive fitness of sets of tens to thousands of connected genotypes, empirical fitness landscapes have shown that epistasis is frequent and depends on the fitness measure, the choice of mutations for the landscape, and the environment in which it was measured. Here, I review fitness landscape theory and experiments and their implications for the role of epistasis in adaptation. I discuss theoretical expectations in the light of empirical fitness landscapes and highlight open challenges and future directions towards integrating theory and data, and incorporating ecological factors.
Motivation & Objective
- Summarize fitness landscape theory and how epistasis arises under different modeling frameworks (probabilistic RMF vs. Fisher’s Geometric Model).
- Survey empirical fitness landscapes from engineered lab experiments and natural populations to assess prevalence and types of epistasis.
- Discuss how experimental design, fitness measures, and mutation sampling influence observed landscape shapes.
- Examine environment-dependent epistasis and its implications for evolution and ecological history footprints in genomes.
- Highlight open challenges and future directions for integrating ecological factors into fitness landscape research.
Proposed method
- Describe two main modeling approaches: Rough-Mount-Fuji probabilistic landscapes with tunable epistasis and Fisher’s Geometric Model with non-linear phenotype–fitness mapping.
- Explain how epistasis can arise from genotype–phenotype interactions and from non-linear phenotype–fitness mappings.
- Discuss implications of recombination, ploidy, and population dynamics for evolution on rugged landscapes.
- Review empirical studies using engineered mutation subsets and deep mutational scanning to map fitness landscapes.
- Synthesize evidence for environment-dependent epistasis and its potential ecological footprints.
Experimental results
Research questions
- RQ1What is the prevalence and nature of epistasis for fitness across theoretical models and empirical landscapes?
- RQ2How do modeling assumptions (genotype–fitness vs. genotype–phenotype–fitness, and environment) shape predictions about adaptive trajectories?
- RQ3How do experimental design choices (level of organization, fitness measure, mutation sampling) influence observed landscape topology?
- RQ4To what extent does environment-dependent epistasis influence adaptation and ecological history signals in genomes?
- RQ5What are the open challenges in integrating ecological factors into fitness landscape theory and data?
Key findings
- Epistasis for fitness constrains adaptation and creates rugged landscapes with multiple local peaks in certain models.
- Fisher’s Geometric Model shows epistasis can arise from non-linear phenotype–fitness mapping even when genotype effects are additive.
- Empirical landscapes reveal pervasive epistasis, with design choices and fitness measures shaping observed ruggedness and trajectory accessibility.
- Diminishing-returns epistasis (negative epistasis as background fitness increases) is commonly observed across studies.
- Environment-dependent epistasis can alter adaptive dynamics and leave footprints of ecological history in genomes.
- Recombination, dominance, and population dynamics influence how epistasis shapes evolution on fitness landscapes.
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This review was created by AI and reviewed by human editors.