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Performs the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) method.
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FullResults Full MR-PRESSO results Mar 1, 2018
R Case with all SNPs outliers Feb 5, 2019
build Version 1 of MR-PRESSO package Jun 29, 2017
data Version 1 of MR-PRESSO package Jun 29, 2017
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README.md

README.md

MR-PRESSO

MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier) is a method that allows for the evaluation of horizontal pleiotropy in multi-instrument Mendelian Randomization utilizing genome-wide summary association statistics.

MR-PRESSO has three components, including:

  1. detection of horizontal pleiotropy (MR-PRESSO global test)
  2. correction of horizontal pleiotropy via outlier removal (MR-PRESSO outlier test)
  3. testing of significant distortion in the causal estimates before and after outlier removal (MR-PRESSO distortion test).

Reference

Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Marie Verbanck, Chia-Yen Chen, Benjamin Neale, Ron Do. Nature Genetics 2018. DOI: 10.1038/s41588-018-0099-7. https://www.nature.com/articles/s41588-018-0099-7

1. Install and load MR-PRESSO

To install the latest development builds directly from GitHub, run this instead:

if (!require("devtools")) { install.packages("devtools") } else {}
devtools::install_github("rondolab/MR-PRESSO")

Load MR-PRESSO

library(MRPRESSO)

2. Example

# Load a simulated toy dataset
data(SummaryStats)

# Run MR-PRESSO global method
mr_presso(BetaOutcome = "Y_effect", BetaExposure = "E1_effect", SdOutcome = "Y_se", SdExposure = "E1_se", OUTLIERtest = TRUE, DISTORTIONtest = TRUE, data = SummaryStats, NbDistribution = 1000,  SignifThreshold = 0.05)

# Run MR-PRESSO on a multi-variable MR (MMR) model specifying several exposures
mr_presso(BetaOutcome = "Y_effect", BetaExposure = c("E1_effect", "E2_effect"), SdOutcome = "Y_se", SdExposure = c("E1_se", "E2_se"), OUTLIERtest = TRUE, DISTORTIONtest = TRUE, data = SummaryStats, NbDistribution = 1000,  SignifThreshold = 0.05)
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