PRSice: Polygenic Risk Score software

By Jack Euesden, Cathryn M. Lewis, Paul F. O'Reilly. Bioinform., 2014

1,232 citations recorded by the source corpus.

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.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/fc1b96cb3e0e9702eb1689799cf0e75a0adbf67f

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In Genetics

Citations in the corpus

The MR-Base platform supports systematic causal inference across the human phenome (2018, methodology): “…to support other methods for the investigation of complex trait genetic architecture, such as fine-mapping ( Benner et al., 2016 ), colocalisation ( Zhu et al., 2016 ; Newcombe et al., 2016 ; Giambartolomei et al., 2014 ) and polygenic risk prediction ( Dudbridge, 2013 ; Euesden et al., 2015 ).”
PRSice-2: Polygenic Risk Score software for biobank-scale data (2019, neutral): “However, the calculation of PRS, which involves parameter optimization [13–16], can be a computationally intensive process, especially for large datasets and when multiple analyses are conducted.”
Polygenic scores via penalized regression on summary statistics (2016, methodology): “Generally, in the presence of a validation dataset, we can choose λ by maximizing the correlation of the PGS with the validation phenotype data, just as it has been done in the choice of a p -value cutoﬀ points in standard PGS calculations (Wray et al. , 2014; Euesden et al. , 2015).”
Polygenic prediction via Bayesian regression and continuous shrinkage priors (2018, methodology): “In this study, we use the implementation of the P+T method in the software package PRSice-2 [Euesden et al., 2014] and its default parameter settings.”
Heterogeneity in polygenic scores for common human traits (2017, methodology): “Many researchers use programs such as PLINK (5), PRSice (6), and LDPred (7), designed to make PGS creation relatively simple.”
Genome-wide analysis identifies 12 loci influencing human reproductive behavior (2016, neutral): “To assess the predictive power of our results, we produced polygenic scores for AFB and NEB with sets of SNPs whose nominal P values ranged from P 5 × 10 −8 (using only genome-wide significant SNPs) to 1 (using all SNPs that passed quality control) using PRSice 34 ( Supplementary Note ).”