GEiPRS - A Fast and Powerful Machine Learning Method for Polygenic Risk Score Prediction by Leveraging Genotype-Environment Interactions
License: GPL-2
- Le Huang#, Wujuan Zhong#, Song Zhai, and Judong Shen. GEiPRS: A Fast and Powerful Machine Learning Method for Polygenic Risk Score Prediction by Leveraging Genotype-Environment Interactions.
GEiPRS can be installed by running the following scripts in R. Notice that the installation of pgenlibr requires zstd(>=1.4.4). It can be built from source or simply available from conda, pip or brew.
library(devtools)
devtools::install_github("junyangq/glmnetPlus")
devtools::install_github("chrchang/plink-ng", subdir="/2.0/cindex")
devtools::install_github("chrchang/plink-ng", subdir="/2.0/pgenlibr")
devtools::install_github("dajmcdon/sparsegl")
# install GEiPRS
devtools::install_github("linnabrown/geiprs")We assume the users already have PLINK 2.0. Otherwise it can be installed from https://www.cog-genomics.org/plink/2.0/.
Tutorial of using R package GEiPRS is available at vignettes/vignette.pdf and vignettes/vignette.html.
This package builds upon the snpnet package developed by Junyang Qian, Trevor Hastie, Yosuke Tanigawa, Ruilin Li, Manuel A Rivas, and Christopher Chang. We thank them for their contributions.