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.
• Two-sample Mendelian randomisation (2SMR) can be used to estimate the lifetime effect of a modifiable exposure on an outcome of interest. • 2SMR point estimates are not interpretable if the exposure and outcome GWASs do not come from homogeneous populations, so called ‘same population’ assumption. However, this assumption is often not validated in applied studies. • We propose and validate a novel sensitivity analysis for this assumption, which checks if SNP effects for the same trait are homogeneous across the two populations.
Keywords: Population homogeneity; Sensitivity analysis; Two-sample Mendelian randomisation.
© 2024. The Author(s).