MRSamePopTest: introducing a simple falsification test for the two-sample mendelian randomisation ‘same population’ assumption

Benjamin Woolf, Amy Mason, Loukas Zagkos, Hannah Sallis, Marcus R. Munafò, Dipender Gill

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1 Citation (SciVal)

Abstract

Two-sample MR is an increasingly popular method for strengthening causal inference in epidemiological studies. For the effect estimates to be meaningful, variant-exposure and variant-outcome associations must come from comparable populations. A recent systematic review of two-sample MR studies found that, if assessed at all, MR studies evaluated this assumption by checking that the genetic association studies had similar demographics. However, it is unclear if this is sufficient because less easily accessible factors may also be important. Here we propose an easy-to-implement falsification test. Since recent theoretical developments in causal inference suggest that a causal effect estimate can generalise from one study to another if there is exchangeability of effect modifiers, we suggest testing the homogeneity of variant-phenotype associations for a phenotype which has been measured in both genetic association studies as a method of exploring the ‘same-population’ test. This test could be used to facilitate designing MR studies with diverse populations. We developed a simple R package to facilitate the implementation of our proposed test. We hope that this research note will result in increased attention to the same-population assumption, and the development of better sensitivity analyses.

Original languageEnglish
Article number27
JournalBMC Research Notes
Volume17
Issue number1
Early online date17 Jan 2024
DOIs
Publication statusPublished - 31 Dec 2024

Data Availability Statement

We developed the MRSamePopTest R package (available from https://github.com/bar-woolf/MRSamePopTest/wiki) to facilitate the implementation of this falsification test. Please note that the current version assumes that variants are independent of each other. The code used in the applied example and simulation is available form https://doi.org/10.17605/OSF.IO/GYXTJ.

Acknowledgements

This work was carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol - http://www.bris.ac.uk/acrc/.

Funding

BW is funded by an Economic and Social Research Council (ESRC) South West Doctoral Training Partnership (SWDTP) 1 + 3 PhD Studentship Award (ES/P000630/1) and the Wellcome Trust (225790/Z/22/Z). A.M. is funded by the National Institute for Health and Care Research (NIHR) Blood and Transplant Research Unit (BTRU) in Donor Health and Behaviour (NIHR203337). The research was supported by the United Kingdom Research and Innovation Medical Research Council (MC_UU_000011/7 and MC_UU_00002/7). This work was also supported by core funding from the British Heart Foundation (RG/18/13/33946) and NIHR Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312). For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.

Keywords

  • Population homogeneity
  • Sensitivity analysis
  • Two-sample Mendelian randomisation

ASJC Scopus subject areas

  • General Biochemistry,Genetics and Molecular Biology

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