Interpreting findings from Mendelian randomization using the MR-Egger method
By Stephen Burgess, S. Thompson. European Journal of Epidemiology, 2017
3,971 citations recorded by the source corpus.
Mendelian randomization-Egger (MR-Egger) is an analysis method for Mendelian randomization using summarized genetic data. MR-Egger consists of three parts: (1) a test for directional pleiotropy, (2) a test for a causal effect, and (3) an estimate of the causal effect. While conventional analysis methods for Mendelian randomization assume that all genetic variants satisfy the instrumental variable assumptions, the MR-Egger method is able to assess whether genetic variants have pleiotropic effects on the outcome that differ on average from zero (directional pleiotropy), as well as to provide a consistent estimate of the causal effect, under a weaker assumption—the InSIDE (INstrument Strength Independent of Direct Effect) assumption. In this paper, we provide a critical assessment of the MR-Egger method with regard to its implementation and interpretation. While the MR-Egger method is a worthwhile sensitivity analysis for detecting violations of the instrumental variable assumptions, there are several reasons why causal estimates from the MR-Egger method may be biased and have inflated Type 1 error rates in practice, including violations of the InSIDE assumption and the influence of outlying variants. The issues raised in this paper have potentially serious consequences for causal inferences from the MR-Egger approach. We give examples of scenarios in which the estimates from conventional Mendelian randomization methods and MR-Egger differ, and discuss how to interpret findings in such cases.
Subfields: Genetics, Plant Science
View Paper: https://www.semanticscholar.org/paper/f6a6cd0340de586e37035a8c9d7a459e757a8b42
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Genetics
- J. Bowden
- George Davey Smith
- Stephen Burgess
- Cosetta Minelli
- Nuala Sheehan
- F. Del Greco M
- Deborah Lawlor
- J. Thompson
In Plant Science
Citations in the corpus
- Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption (2018, methodology): “This highlights the fact that MR-Egger regression is far less efficient than an IVW analysis (as discussed at length in [3, 9, 27] and elsewhere) and requires instruments that are both strong as well as having variable magnitudes of effect on exposure in order to function effectively.”