Journal
AMERICAN JOURNAL OF EPIDEMIOLOGY
Volume 187, Issue 12, Pages 2672-2680Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/aje/kwy177
Keywords
causal inference; direct effect; instrumental variables; sensitivity analysis
Categories
Funding
- National Institutes of Health [R01 CA233588, R01 HL114901, P01 CA53996]
- NATIONAL CANCER INSTITUTE [P01CA053996, R01CA189532, U01CA167551, R00CA215314, R01CA222833] Funding Source: NIH RePORTER
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [R01HL114901] Funding Source: NIH RePORTER
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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 theMR-Eggermethod. 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 bodymass 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 GLIDEmethod is useful for sensitivity analyses and improves the validity of MR.
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