Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy

Abstract

Mendelian Randomization (MR) exploits genetic variants as instrumental variables to estimate the causal effect of an "exposure" trait on an "outcome" trait from observational data. However, the validity of such studies is threatened by population stratification, batch effects, and horizontal pleiotropy. Although a variety of methods have been proposed to partially mitigate those problems, residual biases may still remain, leading to highly statistically significant false positives in large genetic databases. Here, 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, by showing that the MR estimate of the causal effect of body mass index (BMI) on diastolic blood pressure is relatively robust, whereas the MR estimate of the causal effect of BMI on Townsend deprivation index is relatively fragile.

Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

“… 74 MR also is dependent on the identification of a strong genetic IV (i.e., a genetically predicted phenotype) and assumes an absence of pleiotropy (i.e., the effect of G on Y is not exclusively through X ). The development of methods to evaluate these assumptions is an active area of research 75 , 76 and alternative methods, including mediation analysis, have been proposed when assumptions are violated 77 or when biological mechanisms do not conform to assumptions. 78 If IV assumptions are satisfied, MR can inform on the presence and direction of the association between X and Y .…”