PRSice (pronounced 'precise') is a software package for calculating, applying, evaluating and plotting the results of polygenic risk scores (PRS). PRSice can run at high-resolution to provide the best-fit PRS as well as provide results calculated at broad P-value thresholds, illustrating results corresponding to either, can thin SNPs according to linkage disequilibrium and P-value ("clumping"), and can be applied across multiple traits in a single run.
Based on a permutation study we estimate a significance threshold of P = 0.001 for high-resolution PRS analyses - the work on this is included in our Bioinformatics paper on PRSice.
PRSice is a software package written in R and C++. PRSice runs as a command-line program with a variety of user-options and is freely available for download below, compatible for Unix/Linux/Mac OS.
Please refer to our WIKI for more update instructions
GCC version 4.8.1 or higher (for c++11) R version 3.2.3 or higher (for plotting)
You can directly download the binary files for Linux here. If you want to install PRSice, all you have to do is (The binary file will located in PRSice)
git clone https://github.com/choishingwan/PRSice.git cd PRSice git checkout beta_testing g++ --std=c++11 -I inc/ -isystem lib/ -lz -DNDEBUG -O2 -pthread src/*.cpp -o PRSice
Or if you have CMake version 3.1 or higher, you can do (The binary file will located in PRSice/bin)
git clone https://github.com/choishingwan/PRSice.git cd PRSice git checkout beta_testing mkdir build cd build cmake ../ make
You can compile a static version using the following command
git clone https://github.com/choishingwan/PRSice.git cd PRSice git checkout beta_testing make
If you PRSice in any published work, please cite both the software (as an electronic resource/URL):
Package: PRSice [version]
Authors: Shing Wan Choi, Jack Euesden, Cathryn Lewis & Paul O'Reilly
and the manuscript(s):
Jack Euesden Cathryn M. Lewis Paul F. O’Reilly (2015) PRSice: Polygenic Risk Score software . Bioinformatics 31 (9): 1466-1468
Note to Self
PLINK PRS range is inclusive. e.g. 0 - 0.5 includes also SNPs with p-value of 0 and 0.5