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Introduction

Gibran Hemani

introduction.Rmd

Background

Two sample Mendelian randomisation (2SMR) is a method to estimate the causal effect of an exposure on an outcome using only summary statistics from genome wide association studies (GWAS). Though conceptually straightforward, there are a number of steps that are required to perform the analysis properly, and they can be cumbersome. The TwoSampleMR package aims to make this easy by combining three important components

  • data management and harmonisation
  • the statistical routines to estimate the causal effects
  • connection to a large repository of the actual GWAS summary statistics needed to perform the analyses.

The general principles (G. Davey Smith and Ebrahim 2003; George Davey Smith and Hemani 2014), and statistical methods (Pierce and Burgess 2013; Bowden, Davey Smith, and Burgess 2015) can be found elsewhere, here we will just outline how to use the R package.

This package uses the ieugwasr package to connect to the database of thousands of complete GWAS summary data.

Installation

To install directly from the GitHub repository do the following:

library(remotes)
install_github("MRCIEU/TwoSampleMR")

If you don’t have the remotes package install it from CRAN using install.packages("remotes").

Overview

The workflow for performing MR is as follows:

  1. Select instruments for the exposure (perform LD clumping if necessary)
  2. Extract the instruments from the IEU GWAS database for the outcomes of interest
  3. Harmonise the effect sizes for the instruments on the exposures and the outcomes to be each for the same reference allele
  4. Perform MR analysis, sensitivity analyses, create plots, compile reports

A diagrammatic overview is shown here:

A diagrammatic overview of performing a two-sample Mendelian randomization analysis

A basic analysis, e.g. the causal effect of body mass index on coronary heart disease, looks like this:

library(TwoSampleMR)

# List available GWASs
ao <- available_outcomes()

# Get instruments
exposure_dat <- extract_instruments("ieu-a-2")

# Get effects of instruments on outcome
outcome_dat <- extract_outcome_data(snps=exposure_dat$SNP, outcomes = "ieu-a-7")

# Harmonise the exposure and outcome data
dat <- harmonise_data(exposure_dat, outcome_dat)

# Perform MR
res <- mr(dat)

Each step is documented on other pages in the documentation.

Authentication

The authentication method has changed recently due to the GoogleAuthR method changing. The main differences are that:

  1. By default you will not be asked to authenticate and will only have access to public data
  2. If you do need to authenticate in order to access private datasets there is no longer a single file called mrbase.oauth, rather, there is a directory called ieugwasr_oauth.

Detailed information is given here: https://mrcieu.github.io/ieugwasr/articles/guide.html#authentication

References

Bowden, Jack, George Davey Smith, and Stephen Burgess. 2015. “Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression.” International Journal of Epidemiology 44 (2): 512–25. https://doi.org/10.1093/ije/dyv080.
Davey Smith, G., and S. Ebrahim. 2003. “’Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?” International Journal of Epidemiology 32 (1): 1–22. https://doi.org/10.1093/ije/dyg070.
Davey Smith, George, and Gibran Hemani. 2014. “Mendelian randomization: genetic anchors for causal inference in epidemiological studies.” Human Molecular Genetics 23 (R1): R89–98. https://doi.org/10.1093/hmg/ddu328.
Pierce, Brandon L, and Stephen Burgess. 2013. “Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators.” American Journal of Epidemiology 178 (7): 1177–84. https://doi.org/10.1093/aje/kwt084.