Diagnostics for pleiotropy in Mendelian randomization studies: Global and individual tests for direct effects

James Y. Dai, Ulrike Peters, Xiaoyu Wang, Jonathan Kocarnik, Jenny Chang-Claude, Martha L. Slattery, Andrew Chan, Mathieu Lemire, Sonja I. Berndt, Graham Casey, Mingyang Song, Mark A. Jenkins, Hermann Brenner, Aaron P. Thrift, Emily White, Li Hsu

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Diagnosing pleiotropy is critical for assessing the validity of Mendelian randomization (MR) analyses. The popular MR-Egger method evaluates whether there is evidence of bias-generating pleiotropy among a set of candidate genetic instrumental variables. In this article, we propose a statistical method—global and individual tests for direct effects (GLIDE)—for systematically evaluating pleiotropy among the set of genetic variants (e.g., single nucleotide polymorphisms (SNPs)) used for MR. As a global test, simulation experiments suggest that GLIDE is nearly uniformly more powerful than the MR-Egger method. As a sensitivity analysis, GLIDE is capable of detecting outliers in individual variant-level pleiotropy, in order to obtain a refined set of genetic instrumental variables. We used GLIDE to analyze both body mass index and height for associations with colorectal cancer risk in data from the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colon Cancer Family Registry (multiple studies). Among the body mass index–associated SNPs and the height-associated SNPs, several individual variants showed evidence of pleiotropy. Removal of these potentially pleiotropic SNPs resulted in attenuation of respective estimates of the causal effects. In summary, the proposed GLIDE method is useful for sensitivity analyses and improves the validity of MR.

Original languageEnglish
Pages (from-to)2672-2680
Number of pages9
JournalAmerican Journal of Epidemiology
Issue number12
StatePublished - 1 Dec 2018
Externally publishedYes


  • Causal inference
  • Direct effect
  • Instrumental variables
  • Sensitivity analysis


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