Genetic Drift Simulator
Simulate how random sampling causes allele frequencies to drift over generations in a finite population, showing fixation and loss events.
Use the Genetic Drift Simulator
Simulation Parameters
Number of diploid individuals (2–2000)
Starting frequency of allele A (0.01 – 0.99)
Simulation length (10 – 500)
Independent simulation lineages (1 – 30)
Quick Presets
Allele Frequency Over Generations
Each line is an independent simulation run. Dashed lines mark fixation (p=1) and loss (p=0).
Set parameters and click Run Simulation.
Simulation Summary
Final Allele Frequencies
Theory Check
Summary
Genetic drift is the random change in allele frequencies from one generation to the next due to chance sampling in a finite diploid population. This simulator implements the Wright-Fisher model: each generation is formed by drawing 2N allele copies at random from the previous generation. You can run multiple independent lineages simultaneously to see how drift outcomes vary, and observe whether the allele reaches fixation (100%) or is lost (0%) before the final generation.
How it works
- Set population size (N), initial allele frequency (p), number of generations, and number of simulation runs.
- Click Run Simulation to start the Wright-Fisher random sampling process.
- Each generation, the new allele count is drawn from a binomial distribution with n = 2N and probability = current frequency.
- Results are plotted as a line chart showing allele frequency over time for each run.
- Fixed (100%) and lost (0%) alleles are highlighted so you can see drift outcomes at a glance.
- Summary statistics show how many runs ended in fixation, loss, or polymorphism.
Use cases
- Demonstrate genetic drift and the Wright-Fisher model in genetics or evolution courses.
- Explore how small population size accelerates drift compared with large populations.
- Visualize how an initially rare allele can be lost or fixed purely by chance.
- Compare drift intensity across different starting allele frequencies.
- Illustrate the founder effect and population bottlenecks in introductory biology.
- Understand why genetic diversity is harder to maintain in small populations.
- Support self-study of neutral theory of molecular evolution.
- Generate quick simulation data for classroom discussions or lab reports.