Almost exact Mendelian randomization

By combining causal graphs and randomization inference, a formal justification for Mendelian randomization is given in the context of with-family studies.

Mendelian randomization

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 …

Discovering mechanistic heterogeneity using Mendelian randomization

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 …

Mendelian randomization with coarsened exposures

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 …

A latent mixture model for heterogeneous causal mechanisms in Mendelian randomization

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.

Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments.

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 …

ivmodel: An R package for inference and sensitivity analysis of instrumental variables models with one endogenous variable

Vignette of an R package for instrumental variable regression.

Profile-likelihood Bayesian model averaging for two-sample summary data Mendelian randomization in the presence of horizontal pleiotropy

We propose a Bayesian model averaging method to account for the uncertainty about instrument validity in Mendelian randomization. This model is extended to allow for a large fraction of SNPs violating the InSIDE assumption.

Resources for Mendelian randomization

Software and tutorials for Mendelian randomization.

MR Data Challenge 2019

This report entered the 2019 MR Data Challenge and contains reproducible R code for our analysis.