📄 evolutionary biology

The evolution of genetic drift over 50,000 generations

By analyzing 50,000 generations of the E. coli Long-Term Evolution Experiment, this study demonstrates that the variance in reproductive success driving genetic drift evolves markedly and divergently between replicate populations, thereby altering the probability of beneficial mutations establishing and directly influencing adaptive trajectories.

Original authors: Ascensao, J. A., Yu, Q., Hallatschek, O.

Published 2026-01-27

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CC BY 4.0

Original authors: Ascensao, J. A., Yu, Q., Hallatschek, O.

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ⚕️ This is an AI-generated explanation of a preprint that has not been peer-reviewed. It is not medical advice. Do not make health decisions based on this content. Read full disclaimer

Imagine a massive, 50,000-year-long race where bacteria are the runners. In this race, the most important rule isn't just about who is the fastest or strongest; it's about luck.

In biology, this "luck" is called genetic drift. It's the random chance that determines which bacteria get to have babies and which ones don't, regardless of whether they are good at surviving. Think of it like a lottery: sometimes the winning ticket is just a random draw, not the best ticket in the pile.

The scientists in this study wanted to know: Does the amount of "luck" involved in this lottery change over time?

The Two Key Numbers

To understand their findings, we need to look at two numbers:

The Crowd Size: How many bacteria are actually in the jar at any given time.
The Family Size Variance: How uneven the family sizes are. Do all bacteria have exactly 2 babies? Or does one lucky bacterium have 100 babies while its neighbor has none?

Usually, scientists look at a combined number (called effective population size) that mixes these two together. It's like looking at the total weight of a truck without knowing if it's carrying one heavy rock or a thousand light pebbles. This paper decided to separate the rock from the pebbles to see what was really happening.

The Experiment

The researchers looked at E. coli bacteria that had been evolving in a lab for 50,000 generations. They tracked two different groups of bacteria (let's call them Team A and Team B) that started from the same ancestor.

They used a clever trick: they gave the bacteria "barcodes" (like unique ID tags) so they could watch how the numbers of each specific family changed over time, while also counting the total number of bacteria.

The Big Discovery

After 50,000 generations, they found something surprising: The rules of the lottery changed, and they changed differently for the two teams.

Team A (Ara+2): This team stayed pretty close to the "standard" expectation. The randomness of who had babies stayed relatively low and predictable.
Team B (Ara-2): This team became much more chaotic. The "variance" in family sizes grew huge. Some bacteria became super-parents with massive families, while others died out with no offspring. This meant genetic drift became 1.5 to 5 times stronger in this group.

Crucially, this wasn't because the total number of bacteria changed. It was because the way they reproduced became more uneven and unpredictable. The "luck factor" evolved.

Why Does This Matter?

The paper explains that this change in luck has a direct impact on how new, helpful traits (beneficial mutations) can take hold.

Think of a beneficial mutation as a golden ticket.

In Team A, where the lottery is fair and steady, a golden ticket has a decent chance of being picked.
In Team B, where the lottery is chaotic and dominated by a few super-lucky winners, a golden ticket is much more likely to get lost in the shuffle.

The math shows that a helpful new trait is roughly twice as likely to succeed and spread in Team A compared to Team B.

The Bottom Line

The main takeaway is simple: Evolution isn't just about changing the bacteria; it's about changing the environment of chance itself.

Over 50,000 generations, the bacteria in one group evolved to live in a world where "luck" played a much bigger role in their survival than in the other group. This shift in the rules of chance directly determined how easily they could adapt to new challenges. The "strength of drift" is not a fixed constant of nature; it is a trait that can evolve, just like speed or strength.

Technical Summary: The Evolution of Genetic Drift Over 50,000 Generations

Problem Statement
Genetic drift, defined as random variation in reproductive success, is a fundamental force shaping genetic diversity and evolutionary trajectories. The magnitude of this drift is governed by the variance in descendant number ( $\sigma^2$ ), a parameter that directly influences key evolutionary outcomes, such as the establishment probability of beneficial mutations (which scales inversely with $\sigma^2$ ). Despite its theoretical importance, it has remained unclear whether $\sigma^2$ itself evolves over long timescales. This uncertainty arises because standard observations of allele-frequency fluctuations depend on the effective population size ( $N_e$ ), a composite parameter that conflates the census population size ( $N$ ) with the variance in descendant number ( $\sigma^2$ ). Consequently, changes in drift strength cannot be easily distinguished from changes in population size using traditional metrics alone.

Methodology
To disentangle these components, the authors utilized data from the E. coli Long-Term Evolution Experiment (LTEE), specifically analyzing two replicate populations (Ara-2 and Ara+2) over 50,000 generations. The study employed a model-based Bayesian inference framework combined with two distinct data streams:

Joint Tracking: Simultaneous monitoring of frequency fluctuations in neutrally barcoded lineages and census population sizes across growth cycles.
Temporal Sampling: Analysis of 33 clones spanning from the ancestral strain through 50,000 generations.

This approach allowed the researchers to separate the contributions of census size ( $N$ ) and variance in reproductive success ( $\sigma^2$ ) to the effective population size ( $N_e$ ), thereby isolating the evolution of drift strength itself.

Key Contributions and Results
The study provides empirical evidence that the strength of genetic drift is not a static parameter but evolves markedly and divergently over long evolutionary timescales. The primary findings include:

Decoupling of $N$ and $\sigma^2$ : Both census population size and the variance in descendant number ( $\sigma^2$ ) changed substantially over time. However, the majority of variation in the effective population size ( $N_e$ ) was driven by shifts in $\sigma^2$ rather than changes in census size.
Divergent Evolutionary Trajectories: After approximately 2,000 generations, the two replicate populations diverged sharply in their drift dynamics:
- Ara+2: The variance in descendant number remained close to a "bottleneck-only" null expectation, suggesting minimal evolved stochasticity during growth.
- Ara-2: This population exhibited 1.5 to 5 times stronger drift compared to Ara+2. This increase is consistent with an evolved increase in stochasticity during the growth phase.
Impact on Adaptation: Because the establishment probability of a beneficial mutation scales as $1/\sigma^2$ , the observed divergence has direct functional consequences. A beneficial mutation of a given effect size is calculated to be roughly twice as likely to establish in the Ara+2 population compared to the Ara-2 population.

Significance
The paper demonstrates that the key parameter governing genetic drift ( $\sigma^2$ ) is itself a trait subject to evolution. By showing that drift strength can evolve divergently even in replicate populations under similar conditions, the study establishes that the "noise" of evolution is dynamic. This evolution of drift has direct, quantifiable consequences for the probability of adaptation, altering the likelihood that beneficial mutations will successfully establish in a population.