What is a Polygenic Score?

A polygenic score (PGS) aggregates the effects of many genetic variants into a single number which predicts genetic predisposition for a phenotype. PGS are typically composed of hundreds-to-millions of genetic variants (usually SNPs) which are combined using a weighted sum of allele dosages multiplied by their corresponding effect sizes, as estimated from a relevant genome-wide association study (GWAS).

PGS nomenclature is heterogeneous: they can also be referred to as genetic scores or genomic scores, and as polygenic risk scores (PRS) or genomic risk scores (GRS) if they predict a discrete phenotype, such as a disease.

The PGS Catalog Project

The PGS Catalog is an open database of published polygenic scores (PGS). Each PGS in the Catalog is consistently annotated with relevant metadata; including scoring files (variants, effect alleles/weights), annotations of how the PGS was developed and applied, and evaluations of their predictive performance. See the PGS Catalog Data Description page for a complete description of the metadata captured for PGS, Samples, Performance Metrics, Traits, and Publications.

Citation

The PGS Catalog development is led by Samuel Lambert under the supervision of Michael Inouye (University of Cambridge & Baker Institute) in collaboration with Health Data Research - UK (Laurent Gil) and the EBI Samples, Phenotypes and Ontologies team / NHGRI-EBI GWAS Catalog (Helen Parkinson, Aoife McMahon, Laura Harris).

The Catalog is under active development, and we continue to add new features and curate new data. If you use the Catalog or Calculator in your research we ask that you cite our below flagship publications:

Individual PGS obtained from the database should also be cited appropriately, and used in accordance with any licensing restrictions set by the authors (see our Terms of Use for more information).

PGS Catalog Inclusion Criteria

For a publication's data to be included in the PGS Catalog it must contain one of the following:

• A newly developed PGS. This includes the following information about the score and its predictive ability (evaluated on samples not used to develop the score):
  • Variant information necessary to apply the PGS to new samples (variant rsID and/or genomic position, weights/effect sizes, effect allele, genome build).
  • Information about how the PGS was developed (computational method, variant selection, relevant parameters).
  • Descriptions of the samples used to develop the score (e.g. discovery of the variant associations [GWAS samples, which can usually be extracted directly from the GWAS Catalog using GCST IDs], as well as other samples used to develop/train the PGS) and external evaluation.
  • Establishment of the PGS' analytic validity, and a description of its predictive performance (e.g. effect sizes [beta, OR, HR, etc.], classification accuracy, proportion of the variance explained (R²), and any covariates evaluated in the PGS prediction).
• An evaluation of a previously developed PGS. This would include the evaluation of PGS already present in the catalog (or eligible for inclusion), on samples not used for PGS development. The requirements for description would be the same as for the evaluation of a new PGS.

A complete description of the data captured for each PGS and publication can be found here.

In the pilot PGS Catalog (presented at ASHG 2019) we focused on curating PGS developed after 2010, and included well-studied scores for the following traits: coronary artery disease (CAD), diabetes (types 1 and 2), obesity / body mass index (BMI), breast cancer, prostate cancer and Alzheimer’s disease. The catalog however is not limited to these traits, and since then we have focused on broadening the coverage of traits and scores. Researchers are invited to submit their PGS and evaluations to us by e-mail for curation and inclusion in the PGS Catalog - we plan to provide a streamlined interface to submit these data in the future.

Data Submission

If you have a PGS or publication that meets the Catalog's eligibility requirements we invite you to submit your data by e-mail ( pgs-info@ebi.ac.uk). To ensure a speedy curation and inclusion into the catalog it would be helpful if you provide the following information about your study:

• Source Publication. PubMed identifier and/or publication doi.
• The Polygenic Score(s). If you've developed a new PGS we'll need the variant-level information required to calculate the score on new samples (see our Scoring Files documentation for more description). If an existing PGS was used it would be helpful if you provided it's PGS ID, or links to the original publication.
• A completed PGS Catalog Curation Template - Optional.
  1. Download the current PGS Catalog Curation Template .xlsx on GoogleDocs - this template forms the basis of our curation pipeline and future PGS deposition framework.
  2. Fill out the downloaded PGS Catalog curation template with your study metadata. We provide a set of PGS Catalog Curation Guidelines .docx with detailed instructions, and examples of how to record your data in the PGS Catalog template.

PGS Catalog Software/Tools

All the code developed in PGS Catalog is publicly available on GitHub [PGSCatalog]. Here are some of the tools that can be useful for the community:

• pgsc_calc: a reproducible workflow to calculate both PGS Catalog and custom polygenic scores. The workflow automates PGS downloads from the Catalog, reading custom scoring files, variant matching between scoring files and target genotyping samplesets, and the parallel calculation of multiple PGS. See the full documentation here.
  → Features in development:
  • Genetic Ancestry: calculate similarity of target samples to populations in a reference dataset (e.g. 1000 Genomes (1000G), Human Genome Diversity Project (HGDP)) using principal components analysis (PCA).
  • PGS Normalization: using reference population data and/or PCA projections to report individual-level PGS predictions (e.g. percentiles, z-scores) that account for genetic ancestry.
• pgscatalog_utils: python package providing a collection of useful tools for working with data from the PGS Catalog, such as scoring files download, combining multiple scoring files or matching target variants against scoring files. More information on PyPi.

Features Under Development

• Including more PGS & developing a deposition interface.
  We are actively curating more PGS for inclusion in the Catalog, responding to submissions from authors, and are committed to increasing the diversity of traits. We are also developing an interface so that authors can easily deposit PGS or PGS evaluations into the PGS Catalog using a standard template.

Feedback & Contact Information

To submit a PGS to the catalog, provide feedback, or ask questions please contact the PGS Catalog team at pgs-info@ebi.ac.uk.

Acknowledgements

We wish to acknowledge the help of the following people & teams for their support of the PGS Catalog:

PGS Catalog Team: Sam Lambert¹, Laurent Gil², Benjamin Wingfield³, Florent Yvon⁴, Joel Gibson^1,4, Aoife McMahon^1,5, Santhi Ramachandran⁵, Elizabeth Lewis⁵, Laura Harris⁵, Helen Parkinson⁶, Richard Houghton², Prof. John Danesh², Michael Inouye⁴

Previous Contributors: Emily Tinsley³, Shirin Saverimuttu³, Jackie MacArthur³, Simon Jupp³, James Hayhurst³, Trish Whetzel³, Michael Chapman², Jonathan Marten⁴, Petar Scepanovic⁴, Gad Abraham⁴

• 1: PGS Catalog Data Curators
• 2: Health Data Research UK, Cambridge
• 3: European Bioinformatics Institute
• 4: Inouye Lab
• 5: NHGRI-EBI GWAS Catalog Team
• 6: EMBL-EBI Samples Phenotypes and Ontologies Team

The PGS Catalog is delivered by collaboration between the EMBL-EBI and University of Cambridge and funded by NHGRI (1U24HG012542-01), Health Data Research UK and the Baker Heart & Diabetes Institute.