The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Tutorial: a guide to performing polygenic risk score analyses
Open accessAbstract
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. In this review, the authors present comprehensive guidelines for performing and evaluating PRS analyses. This is accompanied by an introductory online tutorial that takes users through quality control and visualization steps.
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Cites
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Cited by
Genome-wide association studies
Uffelmann, E., Huang, Q. Q., Munung, N. S., de Vries, J., Okada, Y., Martin, A. R., Martin, H. C., Lappalainen, T., & Posthuma, D. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00056-9 · Cited by 1,849
PRSice-2: Polygenic Risk Score software for biobank-scale data
Choi, S. W., & O'Reilly, P. F. (2019) · GigaScience · doi:10.1093/gigascience/giz082 · Cited by 1,532
Self-citationAcosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365
The complex genetic architecture of Alzheimer's disease: novel insights and future directions
Andrews, S. J., Renton, A. E., Fulton-Howard, B., Podlesny-Drabiniok, A., Marcora, E., & Goate, A. M. (2023) · eBioMedicine · doi:10.1016/j.ebiom.2023.104511 · Cited by 377
Privé, F., Aschard, H., Carmi, S., Folkersen, L., Hoggart, C., O’Reilly, P. F., & Vilhjálmsson, B. J. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.11.008 · Cited by 375
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Metadata
- Authors
- Choi, S. W., Mak, T. S.-H., & O’Reilly, P. F.
- Venue
- Nature Protocols
- Published
- 2020-07-24
- Volume
- 15
- Issue
- 9
- Pages
- 2759-2772
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- OpenAlex
- W3045441625
- Cited by
- 1,946
- References
- 44
- FWCI
- 120.10
- Type
- journal article
- Language
- en
- Open access
- green
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