Mendelian Randomization (MR) is widely used to identify potential causal relationships among heritable traits, potentially leading to new disease interventions[1-12]. Genetic variants that are significantly associated with one trait, the ``exposure," are used as genetic instruments to test for a causal effect on a second trait, the ``outcome." If the exposure is causal, then variants affecting the exposure should affect the outcome proportionally. For example, LDL[3, 13] and triglycerides[4] (but not HDL[3]) causally affect coronary artery disease risk. However, pleiotropy presents a challenge for MR, especially when it produces a genetic correlation and when the exposure is highly polygenic[2,11,12,14-16]. Sometimes, this challenge can be addressed using curated genetic variants without pleiotropic effects; this approach is most appropriate for molecular traits (e.g. LDL). For other complex traits, statistical approaches have been used to reduce the likelihood of confounding, such as MR-Egger [7] and bidirectional MR [11,17,18]. However, these approaches have their own limitations.
