WFDriftSim: Simulating generations of genetic drift in a Wright–Fisher...

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Simulating generations of genetic drift in a Wright–Fisher (WF) population

Description

WFDriftSim simulates genetic drift of diploid Wright–Fisher populations with a given effective population size through a certain number of generations.

Usage

WFDriftSim(
  Ne,
  n.gen,
  p0 = 0.5,
  n.sim = 1,
  plot.type = "animate",
  print.data = FALSE,
  knitr = FALSE
)

Arguments

Ne	Number giving the effective population size of the population
n.gen	Number of generations to be simulated.
p0	Initial frequency of a given allele. As the simulated organism is diploid, the other alleles frequency will be 1-(p0). Default value is 0.5.
n.sim	Number of simulations to be made. If decimals are inserted, they will be rounded. Default value is 1.
plot.type	Character indicating if simulations should be plotted as colored lines. Each color represents a different population. If plot.type = "animate" (default value) it animates each generation individually. If plot.type = "static" it plots all lines rapidly. If plot.type = "none" nothing is plotted.
print.data	Logical indicating whether all simulation results should be returned as a data.frame. Default value is FALSE.
knitr	Logical indicating if plot is intended to show up in RMarkdown files made by the Knitr R package.

Details

The effective population size (Ne) is strongly connected with the rate of genetic drift (for details, see Waples, 2022).

Value

If plot.type = "static" or "animate", plots the timeseries of all simulations, with each line+color referring to a different simulation. Note that if many simulations (generally more than 20) are simulated, colors might be cycled and different simulation will have the same color. If print.data = TRUE, returns a data.frame with the simulation results.

Author(s)

Matheus Januario, Dan Rabosky, Jennifer Auler

References

Fisher RA (1922) On the dominance ratio. Proc. R. Soc. Edinb 42:321–341

Kimura M (1955) Solution of a process of random genetic drift with a continuous model. PNAS–USA 41(3):144–150

Tran, T. D., Hofrichter, J., & Jost, J. (2013). An introduction to the mathematical structure of the Wright–Fisher model of population genetics. Theory in Biosciences, 132(2), 73-82. [good for the historical review, math can be challenging]

Waples, R. S. (2022). What is Ne, anyway?. Journal of Heredity.

Wright S (1931) Evolution in Mendelian populations. Genetics 16:97–159

Examples

#Default values:
WFDriftSim(Ne = 5, n.gen = 10, knitr = TRUE)

#A population which has already fixed one of the alleles:
WFDriftSim(Ne = 5, n.gen = 10, p0=1, knitr = TRUE)

#Many populations::
WFDriftSim(Ne = 5, n.gen = 10, p0=0.2, n.sim=10, knitr = TRUE)

######## continuing a previous simulation:
n.gen_1stsim <- 10 # number of gens in the 1st sim:
sim1 <- WFDriftSim(Ne = 5, n.gen = n.gen_1stsim, p0=.2, n.sim=10, 
plot.type = "none", print.data = TRUE, knitr = TRUE)
n.gen_2ndsim <-7 # number of gens in the 2nd sim:
# now, note how we assigned p0:
sim2 <- WFDriftSim(Ne = 5, n.gen = n.gen_2ndsim, p0=sim1[,ncol(sim1)], 
plot.type = "static", n.sim=10, print.data = TRUE, knitr = TRUE)

# if we want to merge both simulations, then we have to:
# remove first column of 2nd sim (because it repeats
# the last column of the 1st sim)
sim2 <- sim2[,-1]

# re-name 2nd sim columns:
colnames(sim2) <- paste0("gen", (n.gen_1stsim+1):(n.gen_1stsim+n.gen_2ndsim))

#finally, merging both rounds of simulations:
all_sims <- cbind(sim1, sim2)
head(all_sims)

evolved documentation built on April 3, 2025, 9:23 p.m.