PopG: Single-Locus, Two-Allele Population Genetics Simulator

PopG is a classic population genetics app that explores how alleles shift in frequency under the influence of mutation, migration (gene flow), genetic drift, and selection in one or multiple populations. This guide provides an overview of key features and how to use them.

Screenshot 1. The main PopG interface (placeholder).

1. Getting Started

Upon launching PopG, you’ll see controls for specifying:

• Population Size (N)
• Number of Populations (demes) for multi-deme simulations
• Mutation Rates (e.g., A → a, a → A)
• Migration Rates among populations
• Other parameters such as initial allele frequency, selection coefficients, etc.

Screenshot 2. Example of the parameter entry screen (placeholder).

After setting these values, click the appropriate button (e.g., “Run” or “Start Simulation”) to begin. The program will step through generations, updating the allele frequencies in each population.

2. Core Concepts Illustrated

2.1. Mutation

Specify mutation rates μ_A→a and μ_a→A to see how new alleles appear over time. Low mutation rates typically keep allele frequencies near their current equilibrium, but higher rates can rapidly introduce new variants.

2.2. Migration (Gene Flow)

Multiple populations can exchange alleles at each generation. A higher migration rate means the populations become more homogeneous, whereas very low migration may allow them to diverge.

2.3. Genetic Drift

If population size is small, random sampling can cause drift in allele frequencies. Large populations exhibit less fluctuation due to sampling variance.

2.4. Selection

If an allele confers a fitness advantage, it can rise in frequency over time. PopG may allow specifying relative fitness values (e.g., W_AA, W_Aa, W_aa) to see how selection operates.

3. Viewing Results

As the simulation runs, PopG plots the allele A frequency in each population over time. In addition, a dotted or secondary line may show the “ideal” expected response from theoretical equations (e.g., a deterministic model) for comparison with the actual (stochastic) outcomes.

Screenshot 3. Example of allele frequency plots over generations (placeholder).

Note how randomness (drift) can cause real trajectories to deviate from the ideal curve, especially in small populations.

4. Advanced Settings & Output

Some versions of PopG may include advanced controls for mutation biases, user-defined fitness functions, or variable migration matrices. Look for a link or button labeled “Advanced” or “Show Options” to reveal them.

Screenshot 4. PopG advanced or optional settings (placeholder).

You can typically export data (e.g., generation-by-generation allele frequencies) to CSV or a text file, so students can perform further analysis.

5. Classroom & Self-Study Ideas

• Compare Small vs. Large Populations: Run the same scenario with N=50 vs. N=1,000 to see how drift amplitude changes.
• Mutation-Selection Balance: Observe how a deleterious allele remains at a low equilibrium frequency if it’s continuously re-introduced by mutation.
• Multi-Population Dynamics: Demonstrate how gene flow can homogenize distinct populations, or how isolation fosters differentiation.
• Climate Change Angle: With advanced controls, you might represent a changing environment that alters relative fitness of certain genotypes, simulating real-world adaptation to warmer or more variable climates.

6. Suggested Enhancements

• Climate Variable: Link “fitness” or “selection pressure” to a temperature parameter that shifts each generation.
• Predator-Prey or Host-Pathogen Extension: Add a second species and track how interactions shape allele frequencies (more advanced approach).
• Data Visualization Options: Let users toggle between line graphs, bar charts, or numeric tables of frequencies.

7. Troubleshooting & Tips

• If you see flat lines for all populations, double-check your mutation and migration rates (if everything is zero, the system may remain at the initial state).
• Ensure your JavaScript is enabled and that any required libraries (Plotly, D3, etc.) are correctly loaded.
• Experiment with the seed or random number generator (if provided) to see how different runs might produce varied outcomes.

Screenshot 5. Example of user error or warning messages (placeholder).

8. More Resources

• Evo-Ed – Additional evolutionary biology teaching modules.
• Avida-ED – Digital evolution experiments for the classroom.
• Panda’s Thumb – Blog with evolutionary biology discussions.

9. Contact & Acknowledgments

PopG was originally developed by <Prof. Joe Felsenstein> and was ported to Javascript by <Wesley R. Elsberry>. For more information, bug reports, or feature requests, email <welseber at gmail> or visit evo-edu.org.

Disclaimer

This page was produced primarily by prompting the OpenAI GPT o1 model. Please inform the site administrator if you find errors or inaccuracies, or wish to suggest features and additional content.

Known issues

Screenshots

Or the lack thereof. A work in progress