Setup

1. Get the code

Use git to clone the repository:

git clone https://github.com/MRCIEU/Lifecourse-GWAS.git

2. Configure the analysis

Setup your directory locations. Copy the config-template.env to a new file called config.env, then edit it to have the paths to genotype / phenotype data locations etc as required

cp config-template.env config.env

We recommend using data paths that are outside of the cloned code repository. You will see that you need the following working data directories, ideally on fast disk that can be accessed by HPC nodes.

phenotype_input_dir="/EDIT/THIS/PATH"
genotype_input_dir="/EDIT/THIS/PATH"
phenotype_processed_dir="/EDIT/THIS/PATH"
genotype_processed_dir="/EDIT/THIS/PATH"

Don't commit config.env as this will be visible to everyone otherwise.

Note that we will never request you to transfer any data from the raw individual-level data directories listed above. All the results from the pipeline will be stored in

results_dir="/EDIT/THIS/PATH"

We will only store non-disclosive summary data in here that is safe to transfer to our servers for checking and subsequent meta-analysis etc.

3. Installing R packages

In R (ideally version 4.3.2) run the following:

install.packages("renv")
renv::restore()

This will automatically install all the correctly versioned R packages required to run the pipeline.