Interpreting findings from Mendelian randomization using the MR-Egger method

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 met...

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Veröffentlicht in:European journal of epidemiology Jg. 32; H. 5; S. 377 - 392
Hauptverfasser: Burgess, Stephen, Thompson, Simon G.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer 01.05.2017
Springer Netherlands
Springer Nature B.V
Schlagworte:
Cardiology
Data Interpretation, Statistical
Epidemiology
Estimates
Genetic Pleiotropy
Genetic variance
Genetic Variation
Genetics
Humans
Infectious Diseases
Medicine
Medicine & Public Health
Mendelian Randomization Analysis - methods
METHODS
Models, Biological
Oncology
Pleiotropy
Public Health
Random Allocation
Randomization
Risk Factors
Sensitivity analysis
Instrumental variable
Robust methods
Summarized data
MR-Egger
Mendelian randomization
ISSN:0393-2990, 1573-7284, 1573-7284
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:0393-2990
1573-7284
1573-7284
DOI:10.1007/s10654-017-0255-x