4.8 Article

Global epistasis emerges from a generic model of a complex trait

Journal

ELIFE
Volume 10, Issue -, Pages -

Publisher

ELIFE SCIENCES PUBLICATIONS LTD
DOI: 10.7554/eLife.64740

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Funding

  1. Simons Foundation NSF-Simons Center at Harvard [1764269]
  2. Simons Foundation [376196]
  3. National Science Foundation [PHY-1914916]
  4. National Institutes of Health [R01GM104239]

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Recent research shows that consistent patterns of fitness increase in microbial evolution experiments are mainly driven by diminishing-returns and increasing-costs epistasis. Although the origin of this global epistasis remains unknown, it is found to emerge as a consequence of widespread microscopic epistasis. The specific quantitative relationship between the magnitude of global epistasis and the stochastic effects of microscopic epistasis predicts a universal form of fitness effects distribution when epistasis is prevalent.
Epistasis between mutations can make 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 reanalyzing existing data. We further show that the distribution of fitness effects takes on 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.

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