Robust Mendelian Randomization in the Presence of Residual Population Stratification, Batch Effects, and Horizontal Pleiotropy

• Activity Number: 56 - Causal Inference
• Type: Contributed
• Date/Time: Sunday, August 8, 2021 : 3:30 PM to 5:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Carlos Cinelli* and Nathan LaPierre and Brian Hill and Sriram Sankararaman and Eleazar Eskin
• Companies: UCLA and UCLA and UCLA and UCLA and UCLA
• Keywords: Mendelian Randomization; Robustness Value; Sensitivity Analysis; population stratification; instrumental variables; pleiotropy

Abstract

The validity of Mendelian Randomization (MR) studies is threatened by population stratification, batch effects, and horizontal pleiotropy. In this talk we describe a suite of sensitivity analysis tools for MR that enables investigators to properly quantify the robustness of their findings against these (and other) unobserved validity threats. Specifically, we propose the routine reporting of sensitivity statistics that can be used to readily quantify the robustness of a MR result: (i) the partial R2 of the genetic instrument with the exposure and the outcome traits; and, (ii) the robustness value of both genetic associations. These statistics quantify the minimal strength of violations of the MR assumptions that would be necessary to explain away the MR causal effect estimate. We also provide intuitive displays to visualize the sensitivity of the MR estimate to any degree of violation, and formal methods to bound the worst-case bias caused by violations in terms of multiples of the observed strength of principal components, batch effects, as well as putative pleiotropic pathways. We demonstrate how these tools can aid researchers in distinguishing robust from fragile findings.