de Leeuw, C.; Savage, J.; Bucur, I.G.; Heskes, T.; Posthuma, D. Understanding the Assumptions Underlying Mendelian Randomization. Preprints2021, 2021010035. https://doi.org/10.20944/preprints202101.0035.v1
APA Style
de Leeuw, C., Savage, J., Bucur, I.G., Heskes, T., & Posthuma, D. (2021). Understanding the Assumptions Underlying Mendelian Randomization. Preprints. https://doi.org/10.20944/preprints202101.0035.v1
Chicago/Turabian Style
de Leeuw, C., Tom Heskes and Danielle Posthuma. 2021 "Understanding the Assumptions Underlying Mendelian Randomization" Preprints. https://doi.org/10.20944/preprints202101.0035.v1
Abstract
With the rapidly increasing availability of large genetic data sets in recent years, Mendelian Randomization (MR) has quickly gained popularity as a novel secondary analysis method. Leveraging genetic variants as instrumental variables, MR can be used to estimate the causal effects of one phenotype on another even when experimental research is not feasible, and therefore has the potential to be highly informative. It is dependent on strong assumptions however, often producing strongly biased results if these are not met. It is therefore imperative that these assumptions are well-understood by researchers aiming to use MR, in order to evaluate their validity in the context of their analyses and data. The aim of this perspective is therefore to further elucidate these assumptions and the role they play in MR, as well as how different kinds of data can be used to further support them.
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
