In the field of pQTL (protein quantitative trait loci) analysis, meta-analysis plays a crucial role in synthesizing findings from multiple studies investigating the genetic regulation of protein levels.
This pipeline harmonises GWAS data from two different studies and runs METAL on them to identify common genetic variants that consistently affect protein levels.
- Singularity
For detailed requirements, refer to environment.yml and Makefile.
To begin using the meta_pipeline, follow these steps:
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Clone the repository with submodules:
git clone --recurse-submodules https://github.com/ht-diva/meta_pipeline.git cd meta_pipeline -
Customize the following files to match your environment:
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Submit the job to the SLURM HPC using the following command:
sbatch submit.sbatch
The pipeline generates output to the path specified by the workspace_path variable in the config.yaml file. By default, this path is set to ./results. The final results are then copied to the destination path defined by the dest_path variable in the config.yaml file, with the default path being ./destination.
The pipeline utilizes 2 path_list files to access the path to the study summary statistics, which can be found in the data directory.
Initially, the pipeline attempts to match each protein label from the first study with its corresponding label from the second study. It generates a mismatch_table.csv file in the workspace. Only matching datasets are processed with METAL.
Any files listed in the path_list but not found in the filesystem will be recorded in the missing_files.csv file.
