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 200, per-site per-gen mutation rate of 0.000025, 100 sites and 1000 generations, but with a chance of 0.01 for a mutation to have a fitness effect of 1.5:
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?