A focusing framework for testing bi-directional causal effects in Mendelian randomization

Mendelian randomization (MR) is a powerful method that uses genetic variants as instrumental variables to infer the causal effect of a modifiable exposure on an outcome. We study inference for bi-directional causal relationships and causal directions with possibly pleiotropic genetic variants. We show that assumptions for common MR methods are often impossible or too stringent given the potential bi-directional relationships. We propose a new focusing framework for testing bi-directional causal effects and it can be coupled with many state-of-the-art MR methods. We provide theoretical guarantees for our proposal and demonstrate its performance using several simulated and real datasets.

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Publication Details

Journal
Journal of the Royal Statistical Society Series B (Statistical Methodology)
Published
2024-11-21
DOI
https://doi.org/10.1093/jrsssb/qkae101
Citations
4
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
2.20

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article

A focusing framework for testing bi-directional causal effects in Mendelian randomization

Sai Li, Ting Ye
4 citations
Journal of the Royal Statistical Society Series B (Statistical Methodology)
Genetic Associations and Epidemiology
2.20
article

A focusing framework for testing bi-directional causal effects in Mendelian randomization

Sai Li, Ting Ye
article en
4 citations

Abstract

Mendelian randomization (MR) is a powerful method that uses genetic variants as instrumental variables to infer the causal effect of a modifiable exposure on an outcome. We study inference for bi-directional causal relationships and causal directions with possibly pleiotropic genetic variants. We show that assumptions for common MR methods are often impossible or too stringent given the potential bi-directional relationships. We propose a new focusing framework for testing bi-directional causal effects and it can be coupled with many state-of-the-art MR methods. We provide theoretical guarantees for our proposal and demonstrate its performance using several simulated and real datasets.

Journal of the Royal Statistical Society Series B (Statistical Methodology)Vol. 87(2)
University of Washington (US), Renmin University of China (CN)
National Natural Science Foundation of China, National Institutes of Health, National Institute of General Medical Sciences
Openalex Percentile: Top 11%
Genetic Associations and Epidemiology
2.20
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