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Horizontal pleiotropy occurs when the variant has an effect on disease outside of its effect on the exposure in Mendelian randomization (MR). Violation of the 'no horizontal pleiotropy' assumption can cause severe bias in MR. We developed the Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test to identify horizontal pleiotropic outliers in multi-instrument summary-level MR testing. We showed using simulations that the MR-PRESSO test is best suited when horizontal pleiotropy occurs in <50% of instruments. Next we applied the MR-PRESSO test, along with several other MR tests, to complex traits and diseases and found that horizontal pleiotropy (i) was detectable in over 48% of significant causal relationships in MR; (ii) introduced distortions in the causal estimates in MR that ranged on average from -131% to 201%; (iii) induced false-positive causal relationships in up to 10% of relationships; and (iv) could be corrected in some but not all instances.
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Epidemiological studies have established correlations between numerous exposures and complex diseases1. Drawing causal inferences from such studies can be challenging due to reverse causation and confounding and/or other biases2.
Mendelian randomization (MR) is a commonly used human genetics approach that can be used to infer causality of an exposure for a complex disease outcome3,4. MR presents a number of advantages over observational epidemiology, including the ability to control for non-heritable environmental confounders in such analyses and the use of genetic instruments to evaluate the impact of an exposure without necessitating the measurement of that exposure in the outcome group. MR uses genetic variants as instrumental variables (IVs) that are robustly associated with the exposure of interest and tests whether the effects of the variants on the exposure result in proportional effects on the outcome.
In response to the advent of the genome-wide association (GWA) study and subsequent identification of thousands of trait-associated loci, multiple MR methods that leverage GWA summary statistics have been developed. These multi-instrument MR methods aggregate estimates from multiple IVs, testing for a causal relationship between a given exposure and outcome in a linear regression framework in which the variants' effects on the outcome are regressed on the same variants' effects on the exposure5,6.
A fundamental assumption of MR is the 'no horizontal pleiotropy' assumption (also called...