Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects

By Stephen Burgess, S. Thompson. American Journal of Epidemiology, 2015

1,438 citations recorded by the source corpus.

A conventional Mendelian randomization analysis assesses the causal effect of a risk factor on an outcome by using genetic variants that are solely associated with the risk factor of interest as instrumental variables. However, in some cases, such as the case of triglyceride level as a risk factor for cardiovascular disease, it may be difficult to find a relevant genetic variant that is not also associated with related risk factors, such as other lipid fractions. Such a variant is known as pleiotropic. In this paper, we propose an extension of Mendelian randomization that uses multiple genetic variants associated with several measured risk factors to simultaneously estimate the causal effect of each of the risk factors on the outcome. This “multivariable Mendelian randomization” approach is similar to the simultaneous assessment of several treatments in a factorial randomized trial. In this paper, methods for estimating the causal effects are presented and compared using real and simulated data, and the assumptions necessary for a valid multivariable Mendelian randomization analysis are discussed. Subject to these assumptions, we demonstrate that triglyceride-related pathways have a causal effect on the risk of coronary heart disease independent of the effects of low-density lipoprotein cholesterol and high-density lipoprotein cholesterol.

Subfields: Epidemiology, Genetics

View Paper: https://www.semanticscholar.org/paper/68d203dd9dc260f615ce577eb8b1ca888eae5055

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Epidemiology

1. E. Sanderson
2. George Davey Smith
3. O. Yavors’ka
4. Stephen Burgess
5. S. Thompson
6. N. Davies
7. R. Richmond
8. L. Howe

In Genetics

1. E. Sanderson
2. Stephen Burgess
3. George Davey Smith
4. S. Thompson
5. J. Bowden
6. O. Yavors’ka
7. N. Davies
8. Nicholas J. Timpson

Citations in the corpus

• Multivariable Mendelian Randomization and Mediation. (2020, methodology): “This has recently been extended to estimate the independent direct effects of multiple, potentially related, exposures through multivariable MR (MVMR) (Burgess and Thompson 2015; Sanderson et al. 2019).”
• MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data (2017, methodology): “23 The package will also be updated as additional robust methods for performing Mendelian randomization using summarized data are developed.”
• Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors (2015, background): “By considering the genetic associations with each of the risk factors in a single model, the causal effects of each of the risk factors on the outcome can be estimated simultaneously even from published data [36].”
• Mendelian randomisation for mediation analysis: current methods and challenges for implementation (2019, methodology): “Indeed, MVMR was developed as a method to account for pleiotropic variants [24, 25, 46].”