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The effects of genetic drift in experimental evolution
Jane M Heffernan1, Lindi M Wahl
1Department of Applied Mathematics, University of Western Ontario, Western Road, London, Ont, Canada N6A 5B7. jheffer2@uwo.ca
Theoretical Population Biology
|November 13, 2002
Summary
Genetic drift significantly reduces beneficial mutation fixation probability by over 25% in experimental evolution populations with periodic bottlenecks. This study models stochasticity, offering a more accurate fixation probability calculation.
Area of Science:
- Evolutionary Biology
- Population Genetics
- Experimental Evolution
Background:
- Experimental evolution commonly involves periodic population bottlenecks and growth phases.
- The fate of beneficial mutations is influenced by bottleneck effects and genetic drift.
- Previous models often neglect the impact of genetic drift during growth phases.
Purpose of the Study:
- To investigate the impact of genetic drift on the fixation probability of beneficial mutations in populations with periodic bottlenecks.
- To develop an analytical model incorporating stochasticity during population growth.
- To provide a more accurate estimation of fixation probability relevant to experimental evolution.
Main Methods:
- Developed an analytical solution for fixation probability incorporating a Poisson distribution for offspring number to model stochasticity.
- Employed computer simulations to validate the analytical results.
- Considered both weak and strong selection coefficients.
Main Results:
- Genetic drift significantly reduces the fixation rate of favorable mutants.
- Fixation probability is decreased by over 25% under realistic experimental conditions.
- The developed method accurately predicts fixation probability across a range of selection strengths.
Conclusions:
- Incorporating genetic drift is crucial for accurately modeling mutation dynamics in experimental evolution.
- The findings necessitate re-evaluation of experimental evolution outcomes where drift was previously ignored.
- This work offers an improved theoretical framework for predicting evolutionary trajectories in bottlenecked populations.
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