Simulate neutral genetic drift in finite diploid populations. Adjust population size, starting allele frequency, generation count, replicate populations, and bottleneck events. The simulator shows allele fixation, allele loss, polymorphism, and heterozygosity change in real time.
Start with a preset or build your own neutral drift scenario. Results update as soon as an input changes.
Load a preset, then adjust population size, starting allele frequency, and generation count.
Genetic drift samples 2N allele copies in each diploid generation.
A bottleneck temporarily reduces N and increases sampling error in that generation.
Live simulation result
In this run, 50.0% of replicate populations match the leading outcome after 50 generations.
Mean final frequency
0.282
average p after drift
Each line represents one replicate population sampled across generations.
Allele A fixed
16.7%
p reached 1.00
Allele A lost
50.0%
p reached 0.00
Still polymorphic
33.3%
0 < p < 1
Genetic drift reduces expected heterozygosity because allele copies share more recent ancestors in small populations.
Start H
0.500
Expected H after 50 generations
0.182
Mean simulated H
0.130
Genetic drift changes allele frequency through chance sampling. It does not require natural selection, fitness differences, mutation, or migration. Sewall Wright developed stochastic population-genetic models that helped explain why allele frequencies can wander in finite populations.
A diploid population with N individuals carries 2N allele copies at an autosomal locus. Each generation samples those copies into offspring. Random sampling can increase allele A, decrease it, fix it, or remove it.
OpenStax describes genetic drift as a force that affects small populations more strongly than large populations. Its population genetics chapter also connects drift with bottleneck and founder effects. Read the OpenStax population genetics section.
This field sets the diploid population size. The simulator samples 2N allele copies each generation, so smaller N creates stronger drift.
This slider sets p₀ for allele A. Rare alleles start with fewer copies, so random loss becomes more common.
Each line represents one replicate population. Lines reaching the top show fixation, while bottom lines show loss.
This option reduces N during one generation. It models a temporary crash that magnifies sampling error.
These cards summarize how many replicate populations fixed allele A, lost allele A, or stayed polymorphic.
This panel reports expected and simulated heterozygosity. Lower values show reduced genetic variation.
Choose p₀ for allele A, such as 0.50 for equal A and a frequencies or 0.10 for a rare allele.
Enter N, the number of diploid individuals. The simulator samples 2N allele copies each generation.
Increase generations to watch long-term drift, and increase replicate populations to compare many random paths.
Switch on the bottleneck setting to reduce N for one generation and see how random sampling intensifies.
Use the result cards to compare allele fixation, allele loss, remaining polymorphism, and heterozygosity decay.
A population with 25 diploid individuals carries 50 allele copies. If p₀ = 0.50, the starting pool has about 25 A copies and 25 a copies. After 50 generations, several replicate populations may reach fixation or loss.
The expected starting heterozygosity equals 2 × 0.50 × 0.50 = 0.50. Drift reduces that value as replicate populations move toward p = 0 or p = 1.
A population with 250 diploid individuals carries 500 allele copies. Random sampling still changes p, but each generation samples a much larger allele pool. Most trajectories stay nearer 0.50 over the same time span.
This comparison shows why conservation genetics focuses on effective population size. Smaller breeding populations lose allelic variation faster, even when every allele behaves neutrally.
A bottleneck effect occurs when a population passes through a temporary size crash. The surviving individuals carry only part of the original allele pool. That random subset can reshape allele frequencies quickly.
A founder effect begins when a small group starts a new population. The new population may carry allele frequencies that differ from the source population. Both effects amplify drift because few allele copies define the next gene pool.
Nature Education explains that genetic drift changes allele frequencies unpredictably, while larger population size reduces sampling error. Review genetic drift and effective population size.
Genetic drift helps explain why isolated populations lose variation. Conservation biologists track this process because reduced heterozygosity can limit future adaptive potential. Drift can also make rare alleles common by chance.
In laboratory evolution, replicate populations often diverge even under identical conditions. Drift gives each replicate its own random history. That variation can complicate interpretation when population size stays small.
Drift also connects directly to Hardy-Weinberg equilibrium. Hardy-Weinberg assumes an infinitely large population or negligible sampling error. Finite populations violate that assumption every generation.
This simulator models neutral allele sampling at one autosomal locus. It does not include selection, dominance, migration, mutation, assortative mating, or overlapping generations. Those forces can change real populations at the same time.
The bottleneck setting reduces population size for one generation only. Real bottlenecks may last many generations, change survival, or interact with inbreeding. Use this page as a teaching model, not a full demographic forecast.
Use these tools to compare drift with Hardy-Weinberg expectations and mutation-driven allele input.
Compare genotype frequencies against equilibrium expectations before adding drift as an evolutionary force.
Open CalculatorEstimate mutation rates that introduce new alleles into populations affected by drift.
Open Calculator