A polygenic score (PGS) or polygenic risk score (PRS) is an estimate of an individual’s genetic liability to a trait or disease, calculated according to their genotype profile and relevant genome-wide association study (GWAS) data. While present PRSs typically explain only a small fraction of trait variance, their correlation with the single largest contributor to phenotypic variation—genetic liability—has led to the routine application of PRSs across biomedical research. Among a range of applications, PRSs are exploited to assess shared etiology between phenotypes, to evaluate the clinical utility of genetic data for complex disease and as part of experimental studies in which, for example, experiments are performed that compare outcomes (e.g., gene expression and cellular response to treatment) between individuals with low and high PRS values. As GWAS sample sizes increase and PRSs become more powerful, PRSs are set to play a key role in research and stratified medicine. However, despite the importance and growing application of PRSs, there are limited guidelines for performing PRS analyses, which can lead to inconsistency between studies and misinterpretation of results. Here, we provide detailed guidelines for performing and interpreting PRS analyses. We outline standard quality control steps, discuss different methods for the calculation of PRSs, provide an introductory online tutorial, highlight common misconceptions relating to PRS results, offer recommendations for best practice and discuss future challenges.
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We thank the participants in the UK Biobank and the scientists involved in the construction of this resource. We thank Jonathan Coleman and Kylie Glanville for help in management of the UK Biobank resource at King’s College London, and we thank Jack Euesden, Carla Giner-Delgado, Clive Hoggart, Hei Man Wu, Tom Bond, Gerome Breen, Cathryn Lewis, Cecile Janssens and Pak Sham for helpful discussions. This research has been conducted using the UK Biobank Resource under application 18177 (P.F.O.). P.F.O. receives funding from the UK Medical Research Council (MR/N015746/1). S.W.C. is funded by the UK Medical Research Council (MR/N015746/1). This report represents independent research partially funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.
The authors declare no competing interests.
Peer review information Nature Protocols thanks Dorret Boomsma, Brandon Johnson and Anubha Mahajan for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Key references using this protocol
PRS tutorial: https://choishingwan.github.io/PRS-Tutorial/
GWAS Tutorial: https://github.com/MareesAT/GWA_tutorial
PRSice software: https://www.prsice.info
LDpred software: https://github.com/bvilhjal/ldpred
Lassosum software: https://github.com/tshmak/lassosum/blob/master/README.md
The R project for statistical computing: https://www.r-project.org
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Choi, S.W., Mak, T.S. & O’Reilly, P.F. Tutorial: a guide to performing polygenic risk score analyses. Nat Protoc (2020). https://doi.org/10.1038/s41596-020-0353-1