Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants

Abstract

Mendelian randomization investigations are becoming more powerful and simpler to perform, due to the increasing size and coverage of genome-wide association studies and the increasing availability of summarized data on genetic associations with risk factors and disease outcomes. However, when using multiple genetic variants from different gene regions in a Mendelian randomization analysis, it is highly implausible that all the genetic variants satisfy the instrumental variable assumptions. This means that a simple instrumental variable analysis alone should not be relied on to give a causal conclusion. In this article, we discuss a range of sensitivity analyses that will either support or question the validity of causal inference from a Mendelian randomization analysis with multiple genetic variants. We focus on sensitivity analyses of greatest practical relevance for ensuring robust causal inferences, and those that can be undertaken using summarized data. Aside from cases in which the justification of the instrumental variable assumptions is supported by strong biological understanding, a Mendelian randomization analysis in which no assessment of the robustness of the findings to violations of the instrumental variable assumptions has been made should be viewed as speculative and incomplete. In particular, Mendelian randomization investigations with large numbers of genetic variants without such sensitivity analyses should be treated with skepticism.

FIGURE 1.

Diagram of instrumental variable assumptions for Mendelian randomization. The three assumptions (i, ii, iii) are illustrated by the presence of an arrow, indicating the effect of one variable on the other (assumption i), or by a dashed line with a cross, indicating that there is no direct effect of one variable on the other (assumptions ii and iii).

FIGURE 2.

Associations (estimates in standard deviation units and 95% confidence intervals) of four genetic variants in the CRP gene region with a range of covariates per C-reactive protein increasing allele. Adapted from CRP CHD Genetics Collaboration.

FIGURE 3.

Diagram to illustrate the difference between pleiotropy (left, the association of the genetic variant with the covariate is independent of the risk factor) and mediation (right, the association of the genetic variant with the covariate is mediated entirely via the risk factor).

FIGURE 4.

Scatter plots of genetic associations with the outcome against genetic associations with the risk factor (lines represent 95% confidence intervals) for Mendelian randomization analysis of CRP on coronary artery disease risk using genetic variants in the CRP gene region (left) and genetic variants throughout the genome (right) that have been demonstrated as associated with C-reactive protein at a genome-wide level of significance.

FIGURE 5.

Funnel plot of instrument precision against instrumental variable estimates for each genetic variant separately for Mendelian randomization analysis of C-reactive protein on coronary artery disease risk using genetic variants throughout the genome that have been demonstrated as associated with C-reactive protein at a genome-wide level of significance. Horizontal lines represent 95% confidence intervals for the instrumental variable estimates. Solid vertical line is at the null; dashed vertical line is the (fixed-effect) inverse-variance weighted estimate.

FIGURE 6.

Estimates (ordered by magnitude) of causal effect of CRP on CAD risk from inverse-variance weighted method using 17 genome-wide significant genetic variants omitting variants systematically two at a time.