EBPRSpackage: Description of the package

In shuangsong0110/EBPRS: Derive Polygenic Risk Score Based on Emprical Bayes Theory

Description

Description of the package. This is the 2.0.2 version.

Usage

EBPRSpackage()

Details

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 test data. We can use EB-PRS to extract main information, estimate Empirical Bayes parameters, derive polygenic risk scores for each individual in test data, and evaluate the PRS according to AUC and predictive r2.

Package:	EBPRS
Type:	Package
Date:	2019-12
Version:	2.0.0

The package contains two main functions for users, EBPRS, and validate.

1. EBPRS. This function integrate three parts: (1) merge the train and test (if have) data, (2) estimate effectsize (3) generate polygenic risk scores (if test data provided.)

There is a strict requirement for the format of imput, which is detailedly illustrated in details in EBPRS. The training summary statistics are necessary. The test data can either be included in the input or not. If test data are provided. The function will first merge the data, as well as generate scores for each person in the result. Here we mention that the we recommend users first use package plink2R from github to read plink files into R, and the data transfered by read_plink from plink2R can be directly used as our input. A merge of training set and testing set will also be made.

plink2R can be installed using this command:

options(unzip = "internal")

devtools::install_github("gabraham/plink2R/plink2R")

2. validate. We use this to validate the performance of the PRS.

3. data("traindat") for the example training dataset.

A complete pipeline can be:

result <- EBPRS(train=traindat, test=plinkfile, N1, N0)

validate(result$S, truey)

or

result <- EBPRS(train=traindat, N1, N0)

Author(s)

Shuang Song, Wei Jiang, Lin Hou and Hongyu Zhao

References

Song S, Jiang W, Hou L, Zhao H (2020) Leveraging effect size distributions to improve polygenic risk scores derived from summary statistics of genome-wide association studies. PLoS Comput Biol 16(2): e1007565. https://doi.org/10.1371/journal.pcbi.1007565

See Also

EBPRS, validate,

https://github.com/gabraham/plink2R

shuangsong0110/EBPRS documentation built on Feb. 17, 2020, 6:14 p.m.