By combining causal graphs and randomization inference, a formal justification for Mendelian randomization is given in the context of with-family studies.
By combining causal graphs and randomization inference, a formal justification for Mendelian randomization is given in the context of with-family studies.
Mendelian randomization (MR) is a term that applies to the use of genetic variation to address causal questions about how modifiable exposures influence different outcomes. The principles of MR are based on Mendel’s laws of inheritance and …
Mendelian Randomization (MR) is a popular method in epidemiology and genetics that uses genetic variation as instrumental variables for causal inference. Existing MR methods usually assume most genetic variants are valid instrumental variables that …
A key assumption in Mendelian randomisation is that the relationship between the genetic instruments and the outcome is fully mediated by the exposure, known as the exclusion restriction assumption. However, in epidemiological studies, the exposure …
There is a general lack of awareness that MR can be used to discover multiple biological mechanisms, partly due to the wide usage of the broad terminology 'effect heterogeneity' to refer to several different phenomena. This article introduces the concept of mechanistic heterogeneity and proposes a latent mixture model to make inference about the causal mechanisms.
We greatly improve the applicability of MR-RAPS. The new GRAPPLE framework can handle multiple exposures and overlapping exposure and outcomes GWAS, and is able to detect multiple pleiotropic pathways. A large-scale experiment was done to understand …