Related Experiment Video
Updated: Aug 5, 2026
04:52
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
Published on: February 3, 2023
The evolution of genetic drift over 50,000 generations
Joao A Ascensao1, QinQin Yu2, Oskar Hallatschek2,3,4
1Department of Bioengineering, University of California Berkeley, Berkeley, CA 94720, USA.
Genetics
|August 3, 2026
Summary
Genetic drift, driven by random variation in reproductive success, evolves over time. In the E. coli Long-Term Evolution Experiment, variance in descendant number diverged between populations, impacting adaptation rates.
Area of Science:
- Evolutionary biology
- Population genetics
- Microbial evolution
Background:
- Genetic drift, arising from random variation in reproductive success, significantly influences genetic diversity and evolutionary paths.
- The strength of genetic drift is determined by the variance in descendant number (σ2d), affecting crucial evolutionary outcomes like beneficial mutation establishment.
- The effective population size (Ne = N/σ2d) traditionally blends census size (N) and σ2d, obscuring whether σ2d itself evolves.
Purpose of the Study:
- To investigate whether the variance in descendant number (σ2d), a key determinant of genetic drift, evolves over long timescales.
- To disentangle the contributions of census population size and σ2d to changes in effective population size (Ne).
- To analyze the divergent evolution of genetic drift in replicate populations of the E. coli Long-Term Evolution Experiment (LTEE).
Main Methods:
- Utilized model-based Bayesian inference combined with tracking of neutral lineage frequency fluctuations.
- Jointly monitored census population sizes across growth cycles in the E. coli LTEE.
- Analyzed 33 clones from ancestor to 50,000 generations in two replicate populations (Ara-2 and Ara+2).
Main Results:
- The strength of genetic drift evolved markedly and divergently between the two replicate LTEE populations.
- Both census size and σ2d varied substantially over time, with σ2d being the primary driver of Ne fluctuations.
- After ~2,000 generations, σ2d diverged: Ara+2 remained near null expectation, while Ara-2 showed 1.5-5× stronger drift, suggesting evolved increased stochasticity.
Conclusions:
- Mutations can impact both the mean and variance of descendant number distributions.
- The interplay between mutational effects on fitness and σ2d significantly modulates adaptation rates.
- The parameter governing genetic drift (σ2d) can evolve, directly influencing the pace and trajectory of adaptation in evolving populations.
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