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PRSice: Polygenic Risk Score software
Open accessAbstract
Summary: A polygenic risk score (PRS) is a sum of trait-associated alleles across many genetic loci, typically weighted by effect sizes estimated from a genome-wide association study. The application of PRS has grown in recent years as their utility for detecting shared genetic aetiology among traits has become appreciated; PRS can also be used to establish the presence of a genetic signal in underpowered studies, to infer the genetic architecture of a trait, for screening in clinical trials, and can act as a biomarker for a phenotype. Here we present the first dedicated PRS software, PRSice (‘precise'), for calculating, applying, evaluating and plotting the results of PRS. PRSice can calculate PRS at a large number of thresholds (“high resolution”) to provide the best-fit PRS, as well as provide results calculated at broad P-value thresholds, can thin Single Nucleotide Polymorphisms (SNPs) according to linkage disequilibrium and P-value or use all SNPs, handles genotyped and imputed data, can calculate and incorporate ancestry-informative variables, and can apply PRS across multiple traits in a single run. We exemplify the use of PRSice via application to data on schizophrenia, major depressive disorder and smoking, illustrate the importance of identifying the best-fit PRS and estimate a P-value significance threshold for high-resolution PRS studies. Availability and implementation: PRSice is written in R, including wrappers for bash data management scripts and PLINK-1.9 to minimize computational time. PRSice runs as a command-line program with a variety of user-options, and is freely available for download from http://PRSice.info Contact: jack.euesden@kcl.ac.uk or paul.oreilly@kcl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
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Euesden, J., Gowrisankar, S., Qu, A. X., St. Jean, P., Hughes, A. R., & Pulford, D. J. (2020) · Genes · doi:10.3390/genes11050501 · Cited by 9
Self-citationEuesden, J., Ali, M., Robins, C., Surendran, P., Gormley, P., (ADNI), F. T. A. D. N. I., Pulford, D., & Cruchaga, C. (2025) · PLOS ONE · doi:10.1371/journal.pone.0310977 · Cited by 3
Self-citationGenetic drivers of progression in Alzheimer’s disease are distinct from disease risk
Cohen, C. E., Fernandez, S., Yaman, U., Ehyaei, A. R., Kodosaki, E., Askarova, A., Porter, T., O’Brien, E., Study, A. I. B. A. L., Initiative, A. D. N., et al. (2026) · Alzheimer's Research & Therapy · doi:10.1186/s13195-026-02036-1 · Cited by 1
Metadata
- Authors
- Euesden, J., Lewis, C. M., & O’Reilly, P. F.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-12-29
- Volume
- 31
- Issue
- 9
- Pages
- 1466-1468
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2110048796
- Cited by
- 1,191
- References
- 5
- FWCI
- 23.18
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
