Polygenic selection analysis for Multiple Sclerosis (MS)

This repository contains code for the ancestry stratified polygenic selection analyses from Elevated Genetic Risk for Multiple Sclerosis Originated in Steppe Pastoralist Populations.

If you reuse any of this code then please cite the preprint:

Barrie, W.*, Yang, Y.*, Irving-Pease, E.K.*, Attfield, K.E.*, Scorrano, G.*, Jensen, L.T.*, Armen, A.P., Dimopoulos, E.A., Stern, A., Refoyo-Martinez, A., Ramsøe, A., Gaunitz, C., Demeter, F., Jørkov, M.L.S., Møller, S.B., Springborg, B., Klassen, L., Hyldgård, I.M., Wickmann, N., Vinner, L., Korneliussen, T.S., Allentoft, M.E., Sikora, M., Kristiansen, K., Rodriguez, S., Nielsen, R., Iversen, A.K.N., Lawson, D.J., Fugger, L., Willerslev, E., 2022. Genetic risk for Multiple Sclerosis originated in Pastoralist Steppe populations. bioRxiv 2022.09.23.509097. https://doi.org/10.1101/2022.09.23.509097

Installation

Download the code:

git clone git@github.com:ekirving/ms_paper.git && cd ms_paper/

The easiest way to install all the dependencies is with the conda package manager.

conda env create --name ms --file environment.yaml

Then activate the environment:

conda activate ms

Running the code

Assuming you have all the input data, you can reproduce the analyses by running:

snakemake all

In practice, computing the DAG of the rule chain will take a long time, and you will need a lot of computational resources to perform an end-to-end replication (e.g., about 1 week of wall-time on a cluster of three 96-core nodes).

Author

Evan K. Irving-Pease, GLOBE Institute, University of Copenhagen

License

This project is licensed under the MIT License - see the LICENSE file for details