EBPRS: Derive Polygenic Risk Score Based on Emprical Bayes Theory

EB-PRS is a novel method that leverages information for effect sizes across all the markers to improve the prediction accuracy. No parameter tuning is needed in the method, and no external information is needed. This R-package provides the calculation of polygenic risk scores from the given training summary statistics and testing data. We can use EB-PRS to extract main information, estimate Empirical Bayes parameters, derive polygenic risk scores for each individual in testing data, and evaluate the PRS according to AUC and predictive r2. See Song et al. (2020) <doi:10.1371/journal.pcbi.1007565> for a detailed presentation of the method.

Version: 2.1.0
Depends: R (≥ 3.5.0), ROCR, methods, BEDMatrix, data.table
Published: 2020-08-26
DOI: 10.32614/CRAN.package.EBPRS
Author: Shuang Song [aut, cre], Wei Jiang [aut], Lin Hou [aut] and Hongyu Zhao [aut]
Maintainer: Shuang Song <song-s19 at mails.tsinghua.edu.cn>
License: GPL-3
NeedsCompilation: no
CRAN checks: EBPRS results


Reference manual: EBPRS.pdf


Package source: EBPRS_2.1.0.tar.gz
Windows binaries: r-devel: EBPRS_2.1.0.zip, r-release: EBPRS_2.1.0.zip, r-oldrel: EBPRS_2.1.0.zip
macOS binaries: r-release (arm64): EBPRS_2.1.0.tgz, r-oldrel (arm64): EBPRS_2.1.0.tgz, r-release (x86_64): EBPRS_2.1.0.tgz, r-oldrel (x86_64): EBPRS_2.1.0.tgz
Old sources: EBPRS archive


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