diversity.range: Range of genetic diversity over time

Description

Generates multiple populations stochastically from an identical source, and measures the resulting diversity over time in each.

Usage

diversity.range(m.rate, runtime, equi.pop, iterations = 10, n.points = 100, 
genomelength = 1e+05, bottle.times=0, bottle.size=1, feedback = 1000, 
makeplot = TRUE, area = TRUE, colline = "blue", colarea = rgb(0, 0, 1, 0.4), 
ref.strain = NULL, init.freq = 1, libr=NULL, nuc=NULL, ...)

Arguments

m.rate

Mutation rate (per genome per generation).

runtime

Number of bacterial generations over which to simulate.

equi.pop

Equilibrium effective population size.

iterations

Number of populations to simulate.

n.points

Number of equidistant points to sample diversity during runtime.

genomelength

Genome length.

bottle.times

Vector of population bottleneck times.

bottle.size

Size of population bottleneck.

feedback

Number of generations between each simulation report.

makeplot

Should resulting diversity be plotted?

area

Should 95% central quantile of genetic diversity be shaded? If FALSE, then individual diversity trajectories are plotted.

colline

Colour of lines (if makeplot=TRUE).

colarea

Colour of shaded area (if makeplot=TRUE and area=TRUE).

ref.strain

Reference strain, if required.

init.freq

Initial frequency of strains in starting population (if libr and nuc specified)

libr

Library of initial sequences.

nuc

Nucleotides at polymorphic sites, corresponding to libr.

...

Additional arguments to be passed to plot.

Value

A iterations by n.points matrix with diversity over time for each simulation.

Details

Provides an empirical estimate of the expected genetic diversity (pairwise SNP distance) over time, with associated uncertainty. Initial population can be specified using the libr, nuc and init.freq arguments, otherwise population is grown from a single genotype. Resolution can be improved by increasing n.points, and accuracy by increasing iterations (at the expense of accuracy).

Examples

Run this code

iterations <- 10
K <- diversity.range(m.rate=0.0005, runtime=1000, equi.pop=1000,
iterations=iterations, n.points=100, genomelength=100000, feedback=100, 
makeplot=TRUE, area=TRUE, colline="blue", colarea=rgb(0,0,1,0.4))