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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Unpredictable repeatability in molecular evolution.

Suman G Das1, Joachim Krug1

  • 1Institute for Biological Physics, University of Cologne, D-50937 Cologne, Germany.

Proceedings of the National Academy of Sciences of the United States of America
|September 19, 2022
PubMed
Summary
This summary is machine-generated.

Heavy-tailed mutation fitness effects defy standard evolutionary predictions. Parallel evolution becomes more repeatable and less predictable, driven by a few high-impact mutations rather than the overall distribution.

Keywords:
antibiotic resistancedistribution of fitness effectsparallel evolutionpredictability of evolution

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Area of Science:

  • Evolutionary Biology
  • Genetics
  • Population Genetics

Background:

  • Parallel evolution at the genotypic level is typically linked to the distribution of beneficial fitness effects (DBFE) of mutations.
  • The standard view assumes light-tailed DBFEs, predicting inverse proportionality between parallel evolution probability and mutation number.

Purpose of the Study:

  • To investigate how heavy-tailed DBFEs, observed in recent experiments, alter the predictability of parallel evolution.
  • To challenge the sufficiency of DBFE in determining parallel evolution extent under heavy-tailed conditions.

Main Methods:

  • Theoretical modeling of evolutionary processes.
  • Analysis of empirical data, specifically on antibiotic-resistance evolution.

Main Results:

  • Heavy-tailed DBFEs lead to anomalously slow or independent decay of parallel evolution probability with mutation number, suggesting increased repeatability.
  • Parallel evolution probability becomes non-self-averaging, dominated by a few high-weight mutations, leading to wide variations across systems.
  • DBFE is insufficient to predict parallel evolution extent, reducing predictability.

Conclusions:

  • The nature of the DBFE significantly impacts the predictability and repeatability of parallel genotypic evolution.
  • Understanding heavy-tailed DBFEs is crucial for accurate predictions in evolutionary biology and for applied fields like antibiotic resistance.