Repository files navigation

pgsc_calc is a bioinformatics best-practice analysis pipeline for calculatingpolygenic [risk] scores on samples with imputed genotypes using existing scoringfiles from thePolygenic Score (PGS) Catalogand/or user-defined PGS/PRS.

The workflow performs the following steps:

• Downloading scoring files using the PGS Catalog API in a specified genome build (GRCh37 and GRCh38).
• Reading custom scoring files (and performing a liftover if genotyping data is in a different build).
• Automatically combines and creates scoring files for efficient parallel computation of multiple PGS
  • Matching variants in the scoring files against variants in the target dataset (in plink bfile/pfile or VCF format)
• Calculates PGS for all samples (linear sum of weights and dosages)
• Creates a summary report to visualize score distributions and pipeline metadata (variant matching QC)

And optionally:

• Genetic Ancestry: calculate similarity of target samples to populations in areference dataset (1000 Genomes (1000G)), using principal components analysis (PCA)
• PGS Normalization: Using reference population data and/or PCA projections to reportindividual-level PGS predictions (e.g. percentiles, z-scores) that account for genetic ancestry

See documentation for a list of plannedfeatures under development.

pgsc_calc uses applications and libraries internally developed at the PGS Catalog, which can do helpful things like:

• Query the PGS Catalog to bulk download scoring files in a specific genome build
• Match variants from scoring files to target variants
• Adjust calculated PGS in the context of genetic ancestry

If you want to write Python code to work with PGS,check out thepygscatalog repository to learn more.

If you want a simpler way of working with PGS, ignore this section and continue below to learn more aboutpgsc_calc.

1. InstallNextflow(>=23.10.0)

2. InstallDocker orSingularity (v3.8.3 minimum)(please only useConda as a last resort)

3. Download the pipeline and test it on a minimal dataset with a single command:

   nextflow run pgscatalog/pgsc_calc -profile test,<docker/singularity/conda>

4. Start running your own analysis!

   nextflow run pgscatalog/pgsc_calc -profile <docker/singularity/conda> --input samplesheet.csv --pgs_id PGS001229

Seegettingstarted for moredetails.

Full documentation is available on Read the Docs

pgscatalog/pgsc_calc is developed as part of the PGS Catalog project, acollaboration between the University of Cambridge’s Department of Public Healthand Primary Care (Michael Inouye, Samuel Lambert) and the EuropeanBioinformatics Institute (Helen Parkinson, Laura Harris).

The pipeline seeks to provide a standardized workflow for PGS calculation andancestry inference implemented in nextflow derived from an existing set oftools/scripts developed by Inouye lab (Rodrigo Canovas, Scott Ritchie, JingqinWu) and PGS Catalog teams (Samuel Lambert, Laurent Gil).

The adaptation of the codebase, nextflow implementation, and PGS Catalog featuresare written by Benjamin Wingfield, Samuel Lambert, Laurent Gil with additional inputfrom Aoife McMahon (EBI). Development of new features, testing, and code reviewis ongoing including Inouye lab members (Rodrigo Canovas, Scott Ritchie) and others. Ifyou use the tool we ask you to cite our paper describing software and updated PGS Catalog resource:

• Lambert, Wingfieldet al. (2024) Enhancing the Polygenic Score Catalog with tools for scorecalculation and ancestry normalization. Nature Genetics.doi:10.1038/s41588-024-01937-x.

This pipeline is distrubuted under anApache License amd uses code andinfrastructure developed and maintained by thenf-core community(Ewelset al. Nature Biotech (2020) doi:10.1038/s41587-020-0439-x),reused here under theMIT license.

Additional references of open-source tools and data used in this pipeline are described inCITATIONS.md.

This work has received funding from EMBL-EBI core funds, the Baker Institute,the University of Cambridge, Health Data Research UK (HDRUK), and the EuropeanUnion’s Horizon 2020 research and innovation programme under grant agreement No101016775 INTERVENE.