Detecting individual and global horizontal pleiotropy in Mendelian randomization: a job for the humble heterogeneity statistic?

Abstract

Mendelian randomization (MR) is gaining in recognition and popularity as a method for strengthening causal inference in epidemiology by utilizing genetic variants as instrumental variables. Concurrently with the explosion in empirical MR studies there has been the steady production of new approaches for MR-analysis. The recently proposed Global and Individual tests of Direct Effects (GLIDE) approach fits into a family of methods that aim to detect horizontal pleiotropy - at the individual SNP level and at the global level - and to adjust the analysis by removing outlying SNPs. The purpose of this commentary is to explain how existing methods can (and indeed are) being used to detect pleiotropy at the individual and global level, although not explicitly using this terminology. By doing so, we will show that the true comparator for GLIDE is not MR-Egger regression (as the authors of the GLIDE approach claim), but rather the humble heterogeneity statistic.