Global epistasis emerges from a generic model of a complex trait
Abstract
Epistasis between mutations makes adaptation contingent on evolutionary history. Yet despite widespread 'microscopic' epistasis between the mutations involved, microbial evolution experiments show consistent patterns of fitness increase between replicate lines. Recent work shows that this consistency is driven in part by global patterns of diminishing-returns and increasing-costs epistasis, which make mutations systematically less beneficial (or more deleterious) on fitter genetic backgrounds. However, the origin of this 'global' epistasis remains unknown. Here we show that diminishing-returns and increasing-costs epistasis emerge generically as a consequence of pervasive microscopic epistasis. Our model predicts a specific quantitative relationship between the magnitude of global epistasis and the stochastic effects of microscopic epistasis, which we confirm by re-analyzing existing data. We further show that the distribution of fitness effects has a universal form when epistasis is widespread, and introduce a novel fitness landscape model to show how phenotypic evolution can be repeatable despite sequence-level stochasticity.
Data availability
The code and data used to generate the figures are available at https://github.com/greddy992/global_epistasis.
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Long-term dynamics of adaptation in asexual populationsDOI: 10.1126/science.1243357.
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Higher-fitness yeast genotypes are less robust to deleterious mutationsDOI: 10.1126/science.aay4199.
Article and author information
Author details
Funding
Simons Foundation (NSF-Simons Center at Harvard #1764269)
- Gautam Reddy
Simons Foundation (376196)
- Michael M Desai
National Science Foundation (PHY-1914916)
- Michael M Desai
National Institutes of Health (R01GM104239)
- Michael M Desai
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2021, Reddy & Desai
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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