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Empirical fitness landscapes and the predictability of evolution

Nature Reviews Genetics volume 15, pages 480–490 (2014)Cite this article

Subjects

Key Points

  • A fitness landscape relates the genotype of an organism to its reproductive capacity and therefore has a central role in evolutionary biology.

  • Introduced in the 1930s, the fitness landscape concept has long been used primarily as a metaphor. This has recently changed, as new experimental tools allow the systematic construction and analysis of combinations of predefined sets of genetic mutations.

  • The topography of the fitness landscape is determined by how different mutations interact in their effect on fitness. A particular type of epistasis known as sign epistasis causes the fitness landscape to be rugged, possibly with multiple peaks.

  • A survey of experimental studies shows that most empirical fitness landscapes are rugged, but the amount of ruggedness varies systematically depending on the way the mutations that form the landscape have been chosen.

  • On rugged fitness landscapes, the accessibility of mutational pathways towards higher fitness is reduced, which makes the evolutionary process more constrained and hence predictable. In addition, predictability depends on population size in ways that can be explored using mathematical modelling.

  • A key challenge for the future is to extend current fitness landscape studies to genome-wide scales and to develop models that are informed by the interactions of biomolecules.

Abstract

The genotype–fitness map (that is, the fitness landscape) is a key determinant of evolution, yet it has mostly been used as a superficial metaphor because we know little about its structure. This is now changing, as real fitness landscapes are being analysed by constructing genotypes with all possible combinations of small sets of mutations observed in phylogenies or in evolution experiments. In turn, these first glimpses of empirical fitness landscapes inspire theoretical analyses of the predictability of evolution. Here, we review these recent empirical and theoretical developments, identify methodological issues and organizing principles, and discuss possibilities to develop more realistic fitness landscape models.

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Figure 1: Development of the fitness landscape concept.
Figure 2: Approaches for the empirical study of fitness landscapes.
Figure 3: Trends in the ruggedness of empirical fitness landscapes.
Figure 4: Evolutionary predictability is affected by population size.

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Acknowledgements

The authors thank I. Szendro for assistance with figure 3. They thank D. Weinreich and G. Achaz for sharing subsequently published manuscripts during the preparation phase of this Review, and four anonymous reviewers for constructive comments. This work was supported by Deutsche Forschungsgemeinschaft within SFB 680 “Molecular Basis of Evolutionary Innovation” and SPP 1590 “Probabilistic Structures in Evolution”.

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Authors and Affiliations

  1. Laboratory of Genetics, Wageningen University, Droevendaalsesteeg 1, Wageningen, 6708PB, The Netherlands

    J. Arjan G.M. de Visser

  2. Institute for Theoretical Physics, University of Cologne, Zülpicher Str. 77, Köln, 50937, Germany

    Joachim Krug

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Glossary

Fitness

A measure of reproductive success of an organism that determines the change of the corresponding genotypic frequency in the population by natural selection.

Epistasis

Any kind of genetic interaction that leads to a dependence of mutational effects on the genetic background.

Ruggedness

A measure of the complexity of fitness landscapes due to multidimensional epistasis. However, it is often used in a more restricted way to reflect the presence of multiple peaks.

Magnitude epistasis

Epistatic interactions that affect the magnitude but not the sign of mutational effects on fitness.

Hamming distance

The distance between two genotypes measured by the number of mutations in which they differ.

Unidimensional epistasis

A description of epistasis based on the curvature of the relationship between average fitness and the number of mutations.

Multidimensional epistasis

Epistatic interaction that reflects the high-dimensional nature of genotypic space.

Sign epistasis

Epistatic interaction that affects the sign of mutational effects on fitness, such that a given mutation can be deleterious or beneficial depending on genetic background.

Strong-selection–weak-mutation

(SSWM). A regime of population dynamics in which beneficial mutations are sufficiently rare to arise and fix independently, while selection is strong enough to prevent the fixation of deleterious mutations.

Direct paths

Shortest mutational pathways between genotypes, along which the distance to the _target genotype decreases by one in each step. There are d! direct paths between two genotypes at Hamming distance d.

Adaptive walks

Trajectories of monomorphic populations moving through genotypic space in single mutational steps, each of which increases fitness.

'Greedy' adaptation

An adaptive walk in which the available mutation of largest effect is fixed in each step.

Stochastic tunnelling

A mechanism for the crossing of fitness 'valleys', in which the escape genotype arises by mutation from a small valley population. This mechanism is different from that proposed by Wright for crossing valleys through the fixation of deleterious mutations, which happens only under weak selection.

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de Visser, J., Krug, J. Empirical fitness landscapes and the predictability of evolution. Nat Rev Genet 15, 480–490 (2014). https://doi.org/10.1038/nrg3744

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