Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/44461
Title: Epidemiology, genetic epidemiology and Mendelian randomisation: more need than ever to attend to detail.
Authors: Sheehan, Nuala A.
Didelez, Vanessa
First Published: 27-May-2019
Publisher: Springer (part of Springer Nature)
Citation: Human Genetics, 2019
Abstract: In the current era, with increasing availability of results from genetic association studies, finding genetic instruments for inferring causality in observational epidemiology has become apparently simple. Mendelian randomisation (MR) analyses are hence growing in popularity and, in particular, methods that can incorporate multiple instruments are being rapidly developed for these applications. Such analyses have enormous potential, but they all rely on strong, different, and inherently untestable assumptions. These have to be clearly stated and carefully justified for every application in order to avoid conclusions that cannot be replicated. In this article, we review the instrumental variable assumptions and discuss the popular linear additive structural model. We advocate the use of tests for the null hypothesis of 'no causal effect' and calculation of the bounds for a causal effect, whenever possible, as these do not rely on parametric modelling assumptions. We clarify the difference between a randomised trial and an MR study and we comment on the importance of validating instruments, especially when considering them for joint use in an analysis. We urge researchers to stand by their convictions, if satisfied that the relevant assumptions hold, and to interpret their results causally since that is the only reason for performing an MR analysis in the first place.
DOI Link: 10.1007/s00439-019-02027-3
eISSN: 1432-1203
Links: https://link.springer.com/article/10.1007%2Fs00439-019-02027-3
http://hdl.handle.net/2381/44461
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © the authors, 2019. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Appears in Collections:Published Articles, Dept. of Health Sciences

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