Extending the MR-Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy
Journal Title
Statistics in Medicine
ISSN
0277-6715
Publisher
Wiley-Blackwell
Language
English
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Rees, J., Wood, A., & Burgess, S. (2017). Extending the MR-Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy. Statistics in Medicine https://doi.org/10.1002/sim.7492
Abstract
Methods have been developed for Mendelian randomization that can obtain consistent causal estimates while relaxing the instrumental variable assumptions. These include multivariable Mendelian randomization, in which a genetic variant may be associated with multiple risk factors so long as any association with the outcome is via the measured risk factors (measured pleiotropy), and the MR-Egger (Mendelian randomization-Egger) method, in which a genetic variant may be directly associated with the outcome not via the risk factor of interest, so long as the direct effects of the variants on the outcome are uncorrelated with their associations with the risk factor (unmeasured pleiotropy). In this paper, we extend the MR-Egger method to a multivariable setting to correct for both measured and unmeasured pleiotropy. We show, through theoretical arguments and a simulation study, that the multivariable MR-Egger method has advantages over its univariable counterpart in terms of plausibility of the assumption needed for consistent causal estimation, and power to detect a causal effect when this assumption is satisfied. The methods are compared in an applied analysis to investigate the causal effect of high-density lipoprotein cholesterol on coronary heart disease risk. The multivariable MR-Egger method will be useful to analyse high-dimensional data in situations where the risk factors are highly related and it is difficult to find genetic variants specifically associated with the risk factor of interest (multivariable by design), and as a sensitivity analysis when the genetic variants are known to have pleiotropic effects on measured risk factors.
Keywords
invalid instruments, Mendelian randomization, MR-Egger, multivariable, pleiotropy
Sponsorship
Jessica Rees is supported by the British Heart Foundation (grant number FS/14/59/31282). Stephen Burgess is supported by Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 204623/Z/16/Z).
Funder references
Wellcome Trust (204623/Z/16/Z)
MRC (G0700463)
MRC (MR/L003120/1)
British Heart Foundation (RG/08/014/24067)
British Heart Foundation (FS/14/59/31282)
Medical Research Council (MC_UU_00002/7)
Embargo Lift Date
2100-01-01
Identifiers
External DOI: https://doi.org/10.1002/sim.7492
This record's URL: https://www.repository.cam.ac.uk/handle/1810/267864
Rights
Attribution 4.0 International, Attribution 4.0 International, Attribution 4.0 International, Attribution 4.0 International, Attribution 4.0 International
Recommended or similar items
The following licence files are associated with this item: