README.md

In jiazhao97/BWMR: Bayesian Weighted Mendelian Randomization (BWMR)

BWMR

BWMR (Bayesian Weighted Mendelian Randomization), is an efficient statistical method to infer the causality between a risk exposure factor and a trait or disease outcome, based on GWAS summary statistics. 'BWMR' package provides the estimate of causal effect with its standard error and the P-value under the test of causality.

Installation

To install the development version of BWMR, it's easiest to use the 'devtools' package.

# install.packages("devtools")
library(devtools)
install_github("jiazhao97/BWMR")

Usage

The 'BWMR' vignette will provide a good start point for Mendelian randomization analysis using BWMR package. The main function is BWMR. You can find examples by running

library(ggplot2)
library(BWMR)
example(BWMR)

Reference

Jia Zhao, Jingsi Ming, Xianghong Hu, Gang Chen, Jin Liu, Can Yang, Bayesian weighted Mendelian randomization for causal inference based on summary statistics, Bioinformatics, btz749, https://doi.org/10.1093/bioinformatics/btz749

Development

This R package is developed by Jia Zhao.

Updates

The BWMR software was created using R version 3.6. However, with the updates in R base, users might face difficulties while using BWMR on R version 4.x. To address this issue, an updated version of BWMR's source code has been uploaded into the "updates" folder.

To use the updated version, please download this Github repository, and run BWMR with following code (an example):

library(ggplot2)
source("BWMR-master/updates/BWMR_updated.R") #load the updated version of BWMR's source code
load("BWMR-master/data/ExampleData.RData") #load example data

fit.BWMR <- BWMR(gammahat = ExampleData$beta.exposure,
                 Gammahat = ExampleData$beta.outcome,
                 sigmaX = ExampleData$se.exposure,
                 sigmaY = ExampleData$se.outcome) #run BWMR
fit.BWMR$plot1
fit.BWMR$plot2
fit.BWMR$plot3
fit.BWMR$plot4

jiazhao97/BWMR documentation built on July 3, 2023, 7:45 a.m.