The inclusion of multiple variants in a Mendelian randomization analysis typically leads to increased statistical power (Freeman et al., 2013), but presents new challenges (Glymour et al., 2012). First, if there is substantial overlap in the datasets from which the association estimates with the exposure and with the outcome were obtained, then the resulting analysis suffers from bias and inflated type 1 error rates when the included variants are “weak” (i.e., they do not explain a substantial proportion of variation in the exposure in the dataset under analysis) (Burgess et al., 2011; Pierce and Burgess, 2013). Second, it may not be the case that all included genetic variants are valid IVs.
