| Title: | Performs the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test. |
|---|---|
| Description: | MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier) is a framework that allows for the evaluation of pleiotropy in multi-instrument Mendelian Randomization utilizing genome-wide summary association statistics. |
| Authors: | Marie Verbanck |
| Maintainer: | Marie Verbanck <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0 |
| Built: | 2024-09-17 02:25:00 UTC |
| Source: | https://github.com/rondolab/MR-PRESSO |
MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier) is a unified framework that allows for the evaluation of pleiotropy in a standard MR model. The method extends on previous approaches that utilize the general model of multi-instrument MR on summary statistics. MR-PRESSO has three components, including: 1) detection of pleiotropy (MR-PRESSO global test); 2) correction of pleiotropy via outlier removal (MR-PRESSO outlier test); and 3) testing of significant distortion in the causal estimate before and after MR-PRESSO correction (MR-PRESSO distortion test).
The DESCRIPTION file:
| Package: | MRPRESSO |
| Type: | Package |
| Title: | Performs the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test. |
| Version: | 1.0 |
| Date: | 2017-06-29 |
| Author: | Marie Verbanck |
| Maintainer: | Marie Verbanck <[email protected]> |
| Description: | MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier) is a framework that allows for the evaluation of pleiotropy in multi-instrument Mendelian Randomization utilizing genome-wide summary association statistics. |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Packaged: | 2017-29-01 12:14:11 UTC; verbam01 |
| Repository: | https://mrcieu.r-universe.dev |
| RemoteUrl: | https://github.com/rondolab/MR-PRESSO |
| RemoteRef: | HEAD |
| RemoteSha: | 3e3c92d7eda6dce0d1d66077373ec0f7ff4f7e87 |
Index of help topics:
MRPRESSO-package Performs the Mendelian Randomization Pleiotropy
RESidual Sum and Outlier (MR-PRESSO) test.
SummaryStats Simulated toy dataset
mr_presso a function to perform the Mendelian
Randomization Pleiotropy RESidual Sum and
Outlier (MR-PRESSO) test
Marie Verbanck
Maintainer: Marie Verbanck <[email protected]>
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
data(SummaryStats) 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)data(SummaryStats) 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)
MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier) is a unified framework that allows for the evaluation of pleiotropy in a standard MR model. The method extends on previous approaches that utilize the general model of multi-instrument MR on summary statistics. MR-PRESSO has three components, including: 1) detection of pleiotropy (MR-PRESSO global test); 2) correction of pleiotropy via outlier removal (MR-PRESSO outlier test); and 3) testing of significant distortion in the causal estimate before and after MR-PRESSO correction (MR-PRESSO distortion test).
mr_presso(BetaOutcome, BetaExposure, SdOutcome, SdExposure, data, OUTLIERtest = FALSE, DISTORTIONtest = FALSE, SignifThreshold = 0.05, NbDistribution = 1000, seed = NULL)mr_presso(BetaOutcome, BetaExposure, SdOutcome, SdExposure, data, OUTLIERtest = FALSE, DISTORTIONtest = FALSE, SignifThreshold = 0.05, NbDistribution = 1000, seed = NULL)
BetaOutcome |
character, name of the outcome variable |
BetaExposure |
vector of characters, name(s) of the exposure(s) |
SdOutcome |
character, name of the standard deviation of the outcome variable |
SdExposure |
vector of characters, name(s) of the standard deviation of the exposure(s) |
data |
dataframe of summary statistics containing the outcome and exposure variables |
OUTLIERtest |
boolean, if TRUE the MR-PRESSO outlier test will be performed, default is FALSE |
DISTORTIONtest |
boolean, if TRUE the MR-PRESSO distortion test on the causal estimate will be performed, default is FALSE |
SignifThreshold |
float, significance threshold to use between 0 and 1, default is 0.05 |
NbDistribution |
integer, number of elements to simulate to form the null distribution to compute empirical P-values. This is directly impacting the precision of the empirical P-values, it is 1/NbDistribution for the global test and nrow(data)/NbDistribution |
seed |
a single value, interpreted as an integer to use in set.seed() |
Main MR results |
Results of the MR analysis providing the causal estimate, sd and P-value of the raw and outlier-corrected MR analysis |
Global Test |
Results of the MR-PRESSO global test. List of the observed residual sum of squares 'RSSobs' and empirical P-value 'Pvalue' |
Outlier Test |
Results of the MR-PRESSO outlier test. Table of observed residual sum of squares 'RSSobs' and 'Pvalue' per SNV |
Distortion Test |
Results of the MR-PRESSO distortion test. List of the 'Outliers Indices' identified as outliers and excluded to calculate the 'Distortion Coefficient' (in percent) and its 'Pvalue'. |
Marie Verbanck
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
data(SummaryStats) 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)data(SummaryStats) 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)
'SummaryStats' is a simulated toy dataset of summary statistics for Y the outcome and E1 and E2 two exposures. It is composed of 45 single ncleotide variants (SNVs) associated with E1 and 5 additional variants associated with E1 and E2 which are therefore submitted to pleiotropy. Test the function mr_presso() on this toy dataset.
data("SummaryStats")data("SummaryStats")
A data frame with 50 observations on the following 9 variables.
E1_effecta numeric vector
E1_sea numeric vector
E1_pvala numeric vector
E2_effecta numeric vector
E2_sea numeric vector
E2_pvala numeric vector
Y_effecta numeric vector
Y_sea numeric vector
Y_pvala numeric vector
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
data(SummaryStats) 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)data(SummaryStats) 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)