Related Experiment Video
Updated: Nov 5, 2025

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
Published on: August 15, 2019
Caution against examining the role of reverse causality in Mendelian Randomization
Sharon M Lutz1,2, Ann Chen Wu1, John E Hokanson3
1Department of Population Medicine, PRecisiOn Medicine Translational Research (PROMoTeR) Center, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.
Abstract:
Recently, Mendelian Randomization (MR) has gained in popularity as a concept to assess the causal relationship between phenotypes in genetic association studies. An extension of standard MR methodology, the MR Steiger approach, has recently been developed to infer the causal direction between two phenotypes in prospective studies. Through simulation studies, we examined and quantified the ability of the MR Steiger approach to determine the causal direction between two phenotypes (i.e., effect direction). Through simulation studies, our results show that the MR Steiger approach may fail to correctly identify the direction of causality. This is true, especially in the presence of pleiotropy. We also applied the MR Steiger method to the COPDGene study, a case-control study of chronic obstructive pulmonary disease (COPD) in current and former smokers, to examine the role of smoking on lung function. We have created an R package on Github called reverseDirection which runs simulations for user-specified scenarios to examine when the MR Steiger approach can correctly determine the causal direction between two phenotypes in any user specified scenario. In summary, our results emphasize the importance of caution when the MR Steiger approach is used in to infer the direction of causality.
Insights
The MR Steiger approach, used for determining causal direction in genetic studies, may inaccurately identify causality, particularly with pleiotropy. Researchers developed an R package to simulate scenarios and assess the method's reliability.
Area of Science:
- Genetic Epidemiology
- Statistical Genetics
- Causal Inference
Background:
- Mendelian Randomization (MR) is increasingly used to investigate causal relationships between phenotypes in genetic association studies.
- The MR Steiger approach is an extension of MR designed to infer the causal direction between two phenotypes in prospective studies.
Purpose of the Study:
- To examine and quantify the ability of the MR Steiger approach to correctly determine the direction of causality between two phenotypes.
- To assess the impact of pleiotropy on the accuracy of the MR Steiger approach.
- To evaluate the MR Steiger method's application in a real-world dataset (COPDGene study) concerning smoking and lung function.
Main Methods:
- Conducted simulation studies to evaluate the MR Steiger approach's performance in determining causal direction.
- Applied the MR Steiger method to the COPDGene study data to investigate the relationship between smoking and lung function.
- Developed an R package ('reverseDirection') to simulate user-specified scenarios for assessing the MR Steiger approach's reliability.
Main Results:
- Simulation results indicate that the MR Steiger approach may fail to correctly identify the direction of causality, especially when pleiotropy is present.
- Application to the COPDGene study provided insights into the role of smoking in lung function using the MR Steiger method.
Conclusions:
- The MR Steiger approach requires cautious application for inferring causal direction due to potential inaccuracies, particularly in the presence of pleiotropy.
- The 'reverseDirection' R package offers a tool for researchers to test the MR Steiger method's validity under various conditions.
Related Concept Videos
Causality in Epidemiology
Confounding in Epidemiological Studies
Criteria for Causality: Bradford Hill Criteria - II
Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Bias in Epidemiological Studies
Randomized Experiments
Simple randomization
Simple...

