Code

Software code

Our Mendelian randomization package for R can be obtained from CRAN at https://cran.r-project.org/web/packages/MendelianRandomization/index.html or from GitHub at https://github.com/cran/MendelianRandomization. This package implements a number of methods for Mendelian randomization using summarized data.

A GitHub-editable document containing software code for running Mendelian randomization (no longer maintained / somewhat out of date) is available here.

Guidelines for reading Mendelian randomization investigations

A printable version of the checklist guidelines for MR studies, taken from Burgess et al, Wellcome Open Research 2020, can be downloaded here.

Bayesian model averaging (MR-BMA):  https://github.com/verena-zuber/demo_AMD
Adaptive pleiotropy adjustment:  https://github.com/aj-grant/mrcovreg
Robust multivariable MR:  https://github.com/aj-grant/robust-mvmr
MRClust clustering method:  https://github.com/cnfoley/mrclust
NAvMIX clustering method:  https://github.com/aj-grant/navmix
Multivariable MR with measurement error:  https://github.com/aj-grant/mvmr-measurement-error
Focused MR:  https://github.com/ash-res/focused-MR
Conditional inference:  https://github.com/ash-res/con-cis-MR
Hypothesis prioritization colocalization (HyPrColoc):  https://github.com/jrs95/hyprcoloc
Non-linear Mendelian randomization:  https://github.com/jrs95/nlmr and https://github.com/amymariemason/SUMnlmr
Fine-mapping  https://github.com/vkarhune/finimom
Sample size calculation with a continuous or binary outcome:  https://shiny.cnsgenomics.com/mRnd/
Sample size calculation with a continuous or binary outcome:  http://mrc-bsu.shinyapps.io/power/
Fieller’s theorem for confidence intervals with a single instrumental variable:  http://mrc-bsu.shinyapps.io/fieller/
Likelihood-based method (with heterogeneity test) with summarized data:  http://mrc-bsu.shinyapps.io/summarized/
Bias and type 1 error rates from sample overlap:  https://mrc-bsu.shinyapps.io/overlap/