Mendelian randomization accounting for complex correlated horizontal pleiotropy while elucidating shared genetic etiology

Mendelian randomization (MR) harnesses genetic variants as instrumental variables (IVs) to study the causal effect of exposure on outcome using summary statistics from genome-wide association studies. Classic MR assumptions are violated when IVs are associated with unmeasured confounders, i.e., when correlated horizontal pleiotropy (CHP) arises. Such confounders could be a shared gene or inter-connected pathways underlying exposure and outcome. We propose MR-CUE (MR with Correlated horizontal pleiotropy Unraveling shared Etiology and confounding), for estimating causal effect while identifying IVs with CHP and accounting for estimation uncertainty. For those IVs, we map their cis-associated genes and enriched pathways to inform shared genetic etiology underlying exposure and outcome. We apply MR-CUE to study the effects of interleukin 6 on multiple traits/diseases and identify several S100 genes involved in shared genetic etiology. We assess the effects of multiple exposures on type 2 diabetes across European and East Asian populations. Mendelian randomization uses genetic variation to study the causal effect of exposure on outcome, but results can be biased by confounders, such as horizontal pleiotropy. Here, the authors present MR-CUE, a method to determine causal effects by accounting for correlated and uncorrelated horizontal pleiotropic effects.

Automated summary

Mendelian randomization uses genetic variation to study the causal effect of exposure on outcome, but results can be biased by confounders, such as horizontal pleiotropy. Here, the authors present MR-CUE, a method to determine causal effects by accounting for correlated and uncorrelated horizontal pleiotropic effects.