Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Understanding the Assumptions Underlying Mendelian Randomization

Version 1 : Received: 31 December 2020 / Approved: 4 January 2021 / Online: 4 January 2021 (12:36:41 CET)

How to cite: de Leeuw, C.; Savage, J.; Bucur, I.G.; Heskes, T.; Posthuma, D. Understanding the Assumptions Underlying Mendelian Randomization. Preprints 2021, 2021010035 (doi: 10.20944/preprints202101.0035.v1). de Leeuw, C.; Savage, J.; Bucur, I.G.; Heskes, T.; Posthuma, D. Understanding the Assumptions Underlying Mendelian Randomization. Preprints 2021, 2021010035 (doi: 10.20944/preprints202101.0035.v1).

Abstract

With the rapidly increasing availability of large genetic data sets in recent years, Mendelian Randomization (MR) has quickly gained popularity as a novel secondary analysis method. Leveraging genetic variants as instrumental variables, MR can be used to estimate the causal effects of one phenotype on another even when experimental research is not feasible, and therefore has the potential to be highly informative. It is dependent on strong assumptions however, often producing strongly biased results if these are not met. It is therefore imperative that these assumptions are well-understood by researchers aiming to use MR, in order to evaluate their validity in the context of their analyses and data. The aim of this perspective is therefore to further elucidate these assumptions and the role they play in MR, as well as how different kinds of data can be used to further support them.

Subject Areas

GWAS; genetics; Mendelian Randomization; causal inference

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