pygscatalog

pygscatalog provides a set of Python CLI applications and developer libraries for working with polygenic scores (PGS), including integration with the PGS Catalog.

These applications and libraries are used internally by the PGS Catalog Calculator, which is an automated workflow for calculating PGS, including adjustment of scores in the context of genetic ancestry similarity.

If you’re interested in PGS but aren’t sure where to begin, the calculator is the best place.

If you’re working with PGS data and want to do some kinds of bespoke analysis not supported by the calculator, these tools might be helpful.

Contents:

• How-to guides
  • How to download scoring files from the PGS Catalog
  • How to combine scoring files from the PGS Catalog
  • How to match scoring file variants against target genomes
  • How to aggregate PGS split across multiple files
  • How to adjust PGS in the context of genetic ancestry
• API Reference
  • calc
  • core
  • match

Credits

pygscatalog (aka pgscatalog_utils) is developed as part of the PGS Catalog project, a collaboration between the University of Cambridge’s Department of Public Health and Primary Care (Michael Inouye, Samuel Lambert) and the European Bioinformatics Institute (Helen Parkinson, Laura Harris).

This package contains code libraries and apps for working with PGS Catalog data and calculating PGS within the PGS Catalog Calculator (pgsc_calc) workflow, and is based on an earlier codebase (pgscatalog_utils) with contributions and input from members of the PGS Catalog team (Samuel Lambert, Benjamin Wingfield, Aoife McMahon Laurent Gil) and Inouye lab (Rodrigo Canovas, Scott Ritchie, Jingqin Wu).

A manuscript describing this package and pgsc_calc pipeline is in preparation. In the meantime if you use the tool we ask you to cite the repo and the paper describing the PGS Catalog resource:

• PGS Catalog Calculator in preparation. 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

All of our code is open source and permissively licensed with Apache 2.

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.