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

Abstract

Mendelian randomization (MR) is a statistical method for estimating causal effects using genetic variants as instrumental variables. In two-sample MR (2SMR), different study samples are used to estimate genetic associations with the exposure and outcome. For valid inference, these studies must include individuals from the same population. Using studies from different populations may bias the MR estimate due to differences in variant-exposure associations resulting from differences in linkage disequilibrium or genetic effects on the exposure trait. We show that violation of the same-population assumption leads to bias in the causal estimate toward zero on average and does not increase the rate of false positives when using the most common MR study design. We verify this result in a broad survey of MR estimates, comparing estimates made with matching and mismatching populations across 546 trait pairs measured in 2-7 ancestries. We find that most population-mismatched estimates are attenuated toward zero compared to their corresponding population-matched estimates and that increasing genetic distance between study populations is associated with greater shrinkage. We observe bias even when mismatched populations have the same continental ancestry. However, we also find that, in some cases, using a larger exposure study with mismatching ancestry can improve power by dramatically increasing precision. These results show that even intra-continental population mismatch can bias MR estimates but also suggest that there is potential to improve the power of MR in understudied populations by properly leveraging larger, mismatching study populations.

Keywords: Mendelian randomization; population mismatch; same-population assumption; two-sample Mendelian randomization.