PGS Catalog utilities

This repository is a collection of useful tools for working with data from the PGS Catalog. This is mostly used internally by the PGS Catalog calculator, but other users might find some of these tools helpful.

Overview

• download_scorefiles: Download scoring files by PGS ID (accession) in genome builds GRCh37 or GRCh38
• combine_scorefile: Combine multiple scoring files into a single scoring file in 'long' format
• match_variants: Match target variants (bim or pvar files) against the output of combine_scorefile to produce scoring files for plink 2

Installation

$ pip install pgscatalog-utils

Quickstart

$ download_scorefiles -i PGS000922 PGS001229 -o . -b GRCh37
$ combine_scorefiles -s PGS*.txt.gz -o combined.txt 
$ match_variants -s combined.txt -t <example.pvar> --min_overlap 0.75 --outdir .

More details are available using the --help parameter.

Install from source

Requirements:

• python 3.10
• poetry

$ git clone https://github.com/PGScatalog/pgscatalog_utils.git
$ cd pgscatalog_utils
$ poetry install
$ poetry build
$ pip install --user dist/*.whl 

Credits

The pgscatalog_utils package is developed as part of the Polygenic Score (PGS) Catalog (www.PGSCatalog.org) project, a collaboration between the University of Cambridge’s Department of Public Health and Primary Care (Michael Inouye, Samuel Lambert, Laurent Gil) and the European Bioinformatics Institute (Helen Parkinson, Aoife McMahon, Ben Wingfield, Laura Harris).

A manuscript describing the tool and larger PGS Catalog Calculator pipeline (PGSCatalog/pgsc_calc) is in preparation. In the meantime if you use these tools we ask you to cite the repo(s) and the paper describing the PGS Catalog resource:

• PGS Catalog utilities (in development). PGS Catalog Team. https://github.com/PGScatalog/pgscatalog_utils

• PGS Catalog Calculator (in development). PGS Catalog Team. https://github.com/PGScatalog/pgsc_calc

• Lambert et al. (2021) The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nature Genetics. 53:420–425 doi:10.1038/s41588-021-00783-5.

This work has received funding from EMBL-EBI core funds, the Baker Institute, the University of Cambridge, Health Data Research UK (HDRUK), and the European Union's Horizon 2020 research and innovation programme under grant agreement No 101016775 INTERVENE.