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Global epistasis on fitness landscapes

Authors: Juan Diaz-Colunga; Abigail Skwara; Karna Gowda; Ramon Diaz-Uriarte; Mikhail Tikhonov; Djordje Bajic; Alvaro Sanchez;

Global epistasis on fitness landscapes

Abstract

Epistatic interactions between mutations add substantial complexity to adaptive landscapes and are often thought of as detrimental to our ability to predict evolution. Yet, patterns of global epistasis, in which the fitness effect of a mutation is well-predicted by the fitness of its genetic background, may actually be of help in our efforts to reconstruct fitness landscapes and infer adaptive trajectories. Microscopic interactions between mutations, or inherent nonlinearities in the fitness landscape, may cause global epistasis patterns to emerge. In this brief review, we provide a succinct overview of recent work about global epistasis, with an emphasis on building intuition about why it is often observed. To this end, we reconcile simple geometric reasoning with recent mathematical analyses, using these to explain why different mutations in an empirical landscape may exhibit different global epistasis patterns—ranging from diminishing to increasing returns. Finally, we highlight open questions and research directions. This article is part of the theme issue ‘Interdisciplinary approaches to predicting evolutionary biology’.

Countries
Spain, Spain, United States
Keywords

Global epistasis, 570, Models, Genetic, Medicina, global epistasis, fitness landscapes, Populations and Evolution (q-bio.PE), Epistasis, Genetic, idiosyncratic epistasis, Articles, 530, Evolution, Molecular, FOS: Biological sciences, Mutation, Idiosyncratic epistasis, Fitness landscapes, Genetic Fitness, Quantitative Biology - Populations and Evolution

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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