Abstract
The MendelianRandomization package is a software package written for the R software environment that implements methods for Mendelian randomization based on summarized data. In this manuscript, we describe functions that have been added to the package or updated in recent years. These features can be divided into four categories: robust methods for Mendelian randomization, methods for multivariable Mendelian randomization, functions for data visualization, and the ability to load data into the package seamlessly from the PhenoScanner web-resource. We provide examples of the graphical output produced by the data visualization commands, as well as syntax for obtaining suitable data and performing a Mendelian randomization analysis in a single line of code.
Highlights
Mendelian randomization is an epidemiological technique that uses genetic variants to link risk factors to outcomes[1,2]
Summarized data are genetic associations with risk factors and outcomes taken from regression analyses that have been performed for each genetic variant in turn[5]
Where ldlc and ldlcse are genetic associations with low-density lipoprotein (LDL) cholesterol and their standard errors for 28 genetic variants as previously reported by Waterworth et al.[12], and chdlodds and chdloddsse are genetic associations with coronary heart disease risk for the same variants
Summary
Mendelian randomization is an epidemiological technique that uses genetic variants to link risk factors to outcomes[1,2]. Summarized data are genetic associations with risk factors and outcomes taken from regression analyses that have been performed for each genetic variant in turn[5]. Such data (beta-coefficients and standard errors) are generated in a genome-wide association study, and have been publicly reported for hundreds of thousands of variants by many large studies and consortia[6]. While the basic functionality and initial features of the package have been discussed previously[7], several functions have been added to the package in recent years These features can be divided into four categories: robust methods for Mendelian randomization, methods for multivariable Mendelian randomization, functions for data visualization, and the ability to load data into the package seamlessly from the PhenoScanner web-resource. We do not discuss in detail the properties of the various methods
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