Exercise: effects of positive and negative selection on population dynamics
Here, we’ll keep N and μ fixed and keep θ = 1, but include a small fraction of selectively advantageous mutations.
This exercise can be completed by running the supplied Python script mutation-drift-selection.py. To run the script with default population size of 500, per-site per-gen mutation rate of 0.000025, 100 sites and 1000 generations, but with a chance of 0.005 for a mutation to have a fitness effect of 1.5:
python mutation-drift-selection.py
This will output the file fig_mutation_drift_selection.png that can be examined locally. Again, you might try increasing generations to something greater than 1000 to get a better feel for equilibrium divergence and diversity, say --generations 2500. In this case, it may be easier to not plot the haplotype trajectories, using --summary, like so:
python mutation-drift-selection.py --generations 2500 --summary
(1) Can you recognize “selective sweeps” in these dynamics where a new advantage mutation appears and rapidly increases to fixation?
(2) What do sweeps do to diversity and divergence relative to the neutral scenario?
Alternatively, if you don’t have a working local Python install, you can run the mutation-drift-selection.ipynb notebook or the mutation-drift-selection.py script online with MyBinder: 