Practical MR session using R

This repository contains material of the Mendelian Randomization (MR) session within the Online Summer School 2021: Genetic Epidemiology of Kidney Function and Chronic Kidney Disease.

The session will cover practical aspects regarding the implementation of Mendelian Randomization (MR) analyses. The course is focused on two-sample MR applications using summary-level data from genome-wide association studies (GWAS). References on methodology and software for one-sample MR methods will be also provided.

To download all the session material, click on Code -> Download ZIP. Then save the .zip file locally and extract all the material of the course.

The main folders are organized as follows:

• data folder contains the raw GWAS summary-level data that are used in the case study. The files are in .txt format.

• scripts folder contains the R script that is used for the case study during the session

• slides folder contains all the files that are used to create the slides for the presentation

• vignettes folder contains all the files that are used to create the tutorial document.

The tutorial document contains a summary of the case study used to show MR analysis workflow. It can be found here.

A PDF version of the presentation can be found here. Please use Google Chrome browser for a correct visualization of the PDF file.

All the other folders and files should not be moved or deleted for reproducing the material of the session.

Note: the material will be continuously updated until the day of the session, i.e. July 14th, 2021. After the session, updates will be performed based on requests or suggestions from the users. If you need to make a request, please open an issue here.

Program

1. Data preparation:

• Collecting summary-level genome-wide association studies (GWAS) data from online repositories
• Data harmonization with TwosampleMR R package

2. Running MR analysis:

• Estimating causal effects using MendelianRandomization and TwoSampleMR R packages
• Displaying and interpreting results

3. Sensitivity analyses:

• Evaluating if MR assumptions are supported by the data
• Estimating causal effects with robust MR methods

Prerequisites

The session will be taught in R (see here for more information). Previous knowledge of R programming can be helpful although not mandatory.

Software setup

• Please download and install the latest R version from here

• For Windows users, please download and install the Rtools version compatible with the latest R version installed on your machine from here. Please use the following tutorial (see Putting Rtools on the PATH) to properly set up Rtools.

• Although it is not mandatory, it is recommended to download and install the latest RStudio version from here (free version) since RStudio will be used during the session

• Please download and install the latest version of the following R packages from CRAN:

install.packages(
  c(
    "remotes",
    "devtools",
    "MendelianRandomization",
    "ggplot2"
  ),
  dependencies = TRUE
)

• Please download and install the latest version of TwoSampleMR from Github repository:

remotes::install_github("MRCIEU/TwoSampleMR")

Software introduction

Introductory material on R, Rstudio and R packages to implement one-sample and two-sample MR is provided below. We suggest the students to check the material before the beginning of the session to start practicing with R programming and the packages that will be used throughout the session

Setting-up R

• See Chapter 1 of ModernDive for beginner-friendly guide to install R and Rstudio

• See BasicBasics 1 and 2 for a gentle introduction to basic commands in R

Two-sample MR packages

• Introduction to the TwoSampleMR package

• Introduction to the MendelianRandomization package

One-sample MR packages

• ivreg function in AER R package or tsls function in sem R package to implement two-stage least squares (2SLS) method

• ivtools R package to implement several one-sample MR methods

Contributions

Name	Affiliation
Daniele Bottigliengo	EURAC Research, Institute for Biomedicine
Adrienne Tin	University of Mississippi Medical Center
Alexander Teumer	Institute for Community Medicine, University Medicine Greifswald

Reading list

Before starting the session, the reading of the following papers is suggested:

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.

Burgess, Stephen, George Davey Smith, Neil M. Davies, Frank Dudbridge, Dipender Gill, M. Maria Glymour, Fernando P. Hartwig, et al. 2019. “Guidelines for Performing Mendelian Randomization Investigations.” Wellcome Open Research 4: 186. https://doi.org/10.12688/wellcomeopenres.15555.2.

Burgess, Stephen, Dylan S. Small, and Simon G. Thompson. 2017. “A Review of Instrumental Variable Estimators for Mendelian Randomization.” Statistical Methods in Medical Research 26 (5): 2333–55. https://doi.org/10.1177/0962280215597579.

Davey Smith, George, and Shah 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.

Davies, Neil M., Michael V. Holmes, and George Davey Smith. 2018. “Reading Mendelian Randomisation Studies: A Guide, Glossary, and Checklist for Clinicians.” BMJ (Clinical Research Ed.) 362 (July): k601. https://doi.org/10.1136/bmj.k601.

Hartwig, Fernando Pires, Neil Martin Davies, Gibran Hemani, and George Davey Smith. 2016. “Two-Sample Mendelian Randomization: Avoiding the Downsides of a Powerful, Widely Applicable but Potentially Fallible Technique.” International Journal of Epidemiology 45 (6): 1717–26. https://doi.org/10.1093/ije/dyx028.

“Mendelian Randomization Dictionary.” n.d. https://mr-dictionary.mrcieu.ac.uk/.

Slob, Eric A. W., and Stephen Burgess. 2020. “A Comparison of Robust Mendelian Randomization Methods Using Summary Data.” Genetic Epidemiology 44 (4): 313–29. https://doi.org/https://doi.org/10.1002/gepi.22295.