Mendelian randomization and pleiotropy analysis

Abstract

Background: Mendelian randomization (MR) analysis has become popular in inferring and estimating the causality of an exposure on an outcome due to the success of genome wide association studies. Many statistical approaches have been developed and each of these methods require specific assumptions.

Results: In this article, we review the pros and cons of these methods. We use an example of high-density lipoprotein cholesterol on coronary artery disease to illuminate the challenges in Mendelian randomization investigation.

Conclusion: The current available MR approaches allow us to study causality among risk factors and outcomes. However, novel approaches are desirable for overcoming multiple source confounding of risk factors and an outcome in MR analysis.

Author summary

Mendelian randomization analysis is a popular approach to studying the causality of exposures on an outcome, and it shares similarities with randomized controlled trials. Since MR is based on observational data, it requires assumptions that are difficult to validate. We review the current developed MR approaches and the challenges in performing MR analysis and interpreting the results.

Graphical abstract

Keywords

Mendelian randomization / causality / summary statistics / confounding / instrumental variable

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ACKNOWLEDGEMENTS

This work was supported by grants HG003054 and HG011052 (to X.Z.) from the National Human Genome Research Institute (NHGRI), USA.

COMPLIANCE WITH ETHICS GUIDELINES

The author Xiaofeng Zhu declare that he has no conflict of interests.ƒThis article is a review article and does not contain any studies with human or animal subjects performed by the author.

RIGHTS & PERMISSIONS

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