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Epidemiology, genetic epidemiology and Mendelian randomisation: more need than ever to attend to detail.

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journal contribution
posted on 17.06.2019, 15:28 by Nuala A. Sheehan, Vanessa Didelez
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.

History

Citation

Human Genetics, 2019

Author affiliation

/Organisation/COLLEGE OF LIFE SCIENCES/School of Medicine/Department of Health Sciences

Version

VoR (Version of Record)

Published in

Human Genetics

Publisher

Springer (part of Springer Nature)

eissn

1432-1203

Acceptance date

09/05/2019

Copyright date

2019

Available date

17/06/2019

Publisher version

https://link.springer.com/article/10.1007/s00439-019-02027-3

Language

en

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