Mind the gap: Characterizing bias due to population mismatch in two-sample Mendelian randomization

We demonstrate that differences between populations can lead to biased causal effect estimates in Mendelian randomization (MR), a causal inference method based on genetic data. We provide a broad empirical survey of the size of these biases, demonstrating that bias can be substantial even when populations are considered close.