PRSice: Polygenic Risk Score software

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ID: 295428
2014
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Abstract
Abstract 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.
Reference Key
openalex_W2110048796 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jack Euesden, Cathryn M. Lewis, Paul F. O’Reilly
Journal BMC Bioinformatics
Year 2014
DOI
10.1093/bioinformatics/btu848
URL
https://academic.oup.com/bioinformatics/article-pdf/31/9/1466/17085880/btu848.pdf
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