Table 2.
Strategies for combining different MR methods in different contexts
| Strategy | Description | Limitations |
|---|---|---|
| A. Single-instrument MR, for a single hypothesis or hypothesis-free scan | ||
Genetic colocalization
|
Use genetic colocalization to eliminate possibility distinct causal variants (25,30,31); if instruments are available for the outcome then test the reverse causal effect (110); if not use MR Steiger (43); use genetic mediation-based analysis (40,111) to try to separate horizontal and vertical pleiotropy | Statistical power may be low, and MR methods cannot separate horizontal from vertical pleiotropy. Genetic mediation-based methods are susceptible to measurement error and confounding, and require individual level data. MR-RAPS requires instrument selection, SNP-exposure effect estimation and SNP-outcome effect estimation from independent samples |
| B. Single hypothesis analysis with multiple instruments | ||
IVW random effects or MR-RAPS
|
Begin with simplest model and then test for heterogeneity; if heterogeneity is present then perform sensitivity analyses | Power of heterogeneity test is low; this is not a principled way to decide the reliability of the result; use of negative control samples requires individual level data and availability of an appropriate GxE or GxG interaction |
| Rucker framework | Use Q and Q’ heterogeneity statistics to navigate between 4 different models of horizontal pleiotropy | Restricted to specific models of horizontal pleiotropy, and statistical power drops substantially when pleiotropic model increases in complexity |
| Bayesian model averaging | Average across 3 different models of horizontal pleiotropy | As above; difficult to make decision if the posterior distribution is multi-modal |
| C. Hypothesis-free analysis of exposure with multiple instruments | ||
| IVW random effects or MR-RAPS Follow up using section B | Use single method to identify putative associations, then follow up with a strategy from section B | Highest power but likely also highest false discovery rate; MR-RAPS requires that exposure and outcome has no sample overlap which can be difficult to prove |
| Weighted mode estimate | Use single method for all tests, simulations suggest highest performance in terms of high power and low FDR for a single method. Follow up with a strategy from section B | Bandwidth parameter cannot be estimated |
| MR-MoE | Use machine learning approach to select the estimate for each test. Follow up with a strategy from section B | Potentially slower to run, does not give information regarding why a particular method was chosen |