Genomic Footprints

This repository contains simulation scripts used to explore the behavior of f-statistics under a variety of demographic scenarios. The scripts generate the panels shown in Figures 2 and 3 of our study by running coalescent simulations (via msprime/stdpopsim), computing branch-based f₂/f₃/f₄ statistics, and plotting the results. Figure 4 contains a slim script to simulate assortative mating and an R script for plotting the simulation results. Figures are from Williams & Huber 2025, The genomic footprints of migration: how ancient DNA is revealing our history of mobility.

Directory structure:

├── Fig2A-B/
│ └── src/
│ └── Fig2A-B_msp_f3_(P3;P1,P5)alpha_variable.py
├── Fig2C/
│ └── src/
│ └── Fig2C_msp_f3(P3;P1,P5)_alpha_variable.py
├── Fig2D/
│ └── src/
│ └── Fig2D_f3(p3-p1)(p3-p5).py
├── Fig3B/
│ └── src/
│ └── Fig3B_msp_f4(PO,P2;P1,PX)_alpha_variable.py
├── Fig3C/ │ └── src/
│ └── Fig3C_msp_f4(PO,P2;P1,PX)_splitTime.py ├── Fig4/
│ └── src/
│ └── Fig4_assortative_mating_ancestry_proportion.slim └── Fig4_analyze_assortMating_sims_01.R ├── FigS1/
└── src/
└── SI_Fig1.R

Prerequisites

• R 4.4 or newer
  • The following R packages:
    • install.packages(c("data.table", "cowplot", "viridis", "ggplot2", "tidyr"))
• Python 3.7 or newer
  • The following Python packages (e.g. via pip or conda):
    • bash pip install msprime tskit stdpopsim demesdraw numpy pandas seaborn matplotlib

Usage

1. Clone this repository.
2. Install dependencies as above.
3. For each figure (2A-B, 2C, 2D, 3B, 3C, 4, S1) cd into its directory and run the script in src/.
4. Output figures and data will be saved to the paths defined in each script (modify the outdir variable at the top of a script if needed).

License

This project is licensed under the Creative Commons - Attribution 4.0 International License (CC BY 4.0).