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.

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Description file content

Package
EBPRS
Type
Package
Title
Derive Polygenic Risk Score Based on Emprical Bayes Theory
Version
1.2.2
Author
Shuang Song [aut, cre], Wei Jiang [aut], Lin Hou [aut] and Hongyu Zhao [aut]
Maintainer
Shuang Song
Description
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.
License
GPL-3
Depends
R (>= 3.5.0), ROCR, methods
Encoding
UTF-8
LazyData
true
RoxygenNote
6.1.1
NeedsCompilation
no
Packaged
2019-11-07 15:20:16 UTC; lenovo
Repository
CRAN
Date/Publication
2019-11-07 16:10:02 UTC

install.packages('EBPRS')

1.2.2

8 days ago

Shuang Song

GPL-3

Depends on

R (>= 3.5.0), ROCR, methods

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