MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data

Abstract

MendelianRandomization is a software package for the R open-source software environment that performs Mendelian randomization analyses using summarized data. The core functionality is to implement the inverse-variance weighted, MR-Egger and weighted median methods for multiple genetic variants. Several options are available to the user, such as the use of robust regression, fixed- or random-effects models and the penalization of weights for genetic variants with heterogeneous causal estimates. Extensions to these methods, such as allowing for variants to be correlated, can be chosen if appropriate. Graphical commands allow summarized data to be displayed in an interactive graph, or the plotting of causal estimates from multiple methods, for comparison. Although the main method of data entry is directly by the user, there is also an option for allowing summarized data to be incorporated from the PhenoScanner database of genotype-phenotype associations. We hope to develop this feature in future versions of the package. The R software environment is available for download from [https://www.r-project.org/]. The MendelianRandomization package can be downloaded from the Comprehensive R Archive Network (CRAN) within R, or directly from [https://cran.r-project.org/web/packages/MendelianRandomization/]. Both R and the MendelianRandomization package are released under GNU General Public Licenses (GPL-2|GPL-3).

Keywords: Mendelian randomization; causal inference; data parasite; instrumental variable; summarized data; two-sample.

Figure 1

A. Code and output from implementation of various Mendelian randomization analysis methods using mr_allmethods() function for analysis of causal effect of high-density lipoprotein-cholesterol (HDL-c) on coronary heart disease (CHD) risk. B. Output from mr_plot() function applied to mr_allmethods() object. Static graph illustrating genetic associations with CHD risk (log odds ratios) against genetic associations with HDL-c (in standard deviation units). Lines represent causal estimates from the different methods. STD, standard.

Figure 2

A. Code and output from implementation of MR-Egger method using mr_egger() function for analysis of causal effect of high-density lipoprotein-cholesterol (HDL-c) on coronary heart disease (CHD) risk. B. Output from mr_plot() function applied to mr_input() object. Screenshot of interactive graph illustrating genetic associations with CHD risk (log odds ratios) against genetic associations with HDL-c (in standard deviation units) with error bars representing 95% confidence intervals for the associations. The variants are all orientated to the HDL-c-increasing allele. The line represents the MR-Egger causal estimate. One of the genetic variants is highlighted by mousing over. The infobox gives the name of the variant (snp_16), and its associations with the exposure and with the outcome. STD, standard.