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Rasooly, Danielle; Peloso, Gina M.

Two‐Sample Multivariable Mendelian Randomization Analysis Using R

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  • Medientyp: E-Artikel
  • Titel: Two‐Sample Multivariable Mendelian Randomization Analysis Using R
  • Beteiligte: Rasooly, Danielle; Peloso, Gina M.
  • Erschienen: Wiley, 2021
  • Erschienen in: Current Protocols
  • Sprache: Englisch
  • DOI: 10.1002/cpz1.335
  • ISSN: 2691-1299
  • Entstehung:
  • Anmerkungen:
  • Beschreibung: <jats:title>Abstract</jats:title><jats:p>Mendelian randomization is a framework that uses measured variation in genes for assessing and estimating the causal effect of an exposure on an outcome. Multivariable Mendelian randomization is an extension that can assess the causal effect of <jats:italic>multiple</jats:italic> exposures on an outcome, and can be advantageous when considering a set (&gt;1) of potentially correlated candidate risk factors in evaluating the causal effect of each on a health outcome, accounting for measured pleiotropy. This can be seen, for example, in determining the causal effects of lipids and cholesterol on type 2 diabetes risk, where the correlated risk factors share genetic predictors. Similar to univariate Mendelian randomization, multivariable Mendelian randomization can be conducted using two‐sample summary‐level data where the gene‐exposure and gene‐outcome associations are derived from separate samples from the same underlying population. Here, we present a protocol for conducting a two‐sample multivariable Mendelian randomization study using the ‘MVMR’ package in R and summary‐level genetic data. We also provide a protocol for searching and obtaining instruments using available data sources in the ‘MRInstruments’ R package. Finally, we provide general guidelines and discuss the utility of performing a multivariable Mendelian randomization analysis for simultaneously assessing causality of multiple exposures. © 2021 Wiley Periodicals LLC.</jats:p><jats:p><jats:bold>Basic Protocol</jats:bold>: Performing a two‐sample multivariable Mendelian randomization analysis using the ‘MVMR’ package in R and summarized genetic data</jats:p><jats:p><jats:bold>Support Protocol 1</jats:bold>: Installing the ‘MVMR’ R package</jats:p><jats:p><jats:bold>Support Protocol 2</jats:bold>: Obtaining instruments from the ‘MRInstruments’ R package</jats:p>