Lecture and exercises on simulation in population genetics

You can find the slides here.

Here is a render of the slides as a single HTML page (easier for quick reading).

This lecture has been originally presented as part of the "Summer course in analysis of high throughput data for population genetics 2022". However, the materials in this repository are completely self-contained and will be updated going forward as their own thing.

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This README summarizes steps needed to set up your machine for the lecture and exercises. After you're done installing everything, make sure to run a small testing simulation (code below) to know that everything works as needed.

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Installation instructions

Prerequisites

• Working R installation(at least R 3.6, preferably 4.x)
• RStudio highly recommended
• macOS or Linux (but Windows should work too! see the bottom of this page)

slendr simulation package

Getting slendr to work is critical. The whole lecture is dedicated to this package.

First, run this in your R console:

install.packages("slendr")

Assuming the above runs successfully, you will next need to setup a dedicated Python environment with tools we'll be using for simulation and analysis. To do this, slendr provides a helper function setup_env() that takes care of everything on your behalf.

First, load slendr itself.

library(slendr)

This will very likely write a message that you are:

1. missing SLiM -- this is OK, feel free to ignore this

2. missing a Python environment.

Next, run the following command. This will ask for permission to install an isolated Python mini-environment just for slendr -- this won't affect your own Python setup at all, so don't be afraid to confirm this!

setup_env()

Finally, make sure you get a positive confirmation from the following check:

check_env()

Other R package dependencies

I will use some tidyverse packages for analysis and plotting.

I recommend you install the following packages:

install.packages(c("tidyverse", "MASS"))

Note that tidyverse is a huge collection of packages so the installation might take a bit of time.

Testing the setup

Copy the following script to your R session after you successfully installed your R dependencies as described above.

library(slendr)

o <- population("outgroup", time = 1, N = 100)
b <- population("b", parent = o, time = 500, N = 100)
c <- population("c", parent = b, time = 1000, N = 100)
x1 <- population("x1", parent = c, time = 2000, N = 100)
x2 <- population("x2", parent = c, time = 2000, N = 100)
a <- population("a", parent = b, time = 1500, N = 100)

gf <- gene_flow(from = b, to = x1, start = 2100, end = 2150, rate = 0.1)

model <- compile_model(
  populations = list(a, b, x1, x2, c, o), gene_flow = gf,
  generation_time = 1, simulation_length = 2200
)

ts <- msprime(model, sequence_length = 10e6, recombination_rate = 1e-8)

ts

If this runs without error and you get a small summary table from the ts object, you're all set!

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This work is licensed under a Creative Commons Attribution 4.0 International License.