Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants

By Stephen Burgess, J. Bowden, T. Fall, E. Ingelsson, S. Thompson. Epidemiology, 2016

1,628 citations recorded by the source corpus.

Mendelian randomization investigations are becoming more powerful and simpler to perform, due to the increasing size and coverage of genome-wide association studies and the increasing availability of summarized data on genetic associations with risk factors and disease outcomes. However, when using multiple genetic variants from different gene regions in a Mendelian randomization analysis, it is highly implausible that all the genetic variants satisfy the instrumental variable assumptions. This means that a simple instrumental variable analysis alone should not be relied on to give a causal conclusion. In this article, we discuss a range of sensitivity analyses that will either support or question the validity of causal inference from a Mendelian randomization analysis with multiple genetic variants. We focus on sensitivity analyses of greatest practical relevance for ensuring robust causal inferences, and those that can be undertaken using summarized data. Aside from cases in which the justification of the instrumental variable assumptions is supported by strong biological understanding, a Mendelian randomization analysis in which no assessment of the robustness of the findings to violations of the instrumental variable assumptions has been made should be viewed as speculative and incomplete. In particular, Mendelian randomization investigations with large numbers of genetic variants without such sensitivity analyses should be treated with skepticism.

Subfields: Genetics, Rheumatology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1f68657460c8e3f25aa8d179081cb40aaaabb9a9

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In Genetics

1. O. Yavors’ka
2. Can Yang
3. Jin Liu
4. Jingsi Ming
5. Xianghong Hu
6. Jia Zhao
7. Gang Chen
8. Stephen Burgess

In Rheumatology

1. Stephen Burgess
2. Can Yang

In Statistics and Probability

1. Stephen Burgess
2. Can Yang
3. Jin Liu
4. George Davey Smith
5. J. Bowden
6. Gibran Hemani
7. S. Thompson

Citations in the corpus

• The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, neutral): “…modeling (Koo et al., 2020a and 2020b), hydrology (Shin et al., 2013; Haghne-gahdar and Razavi, 2017), water security (Puy et al., 2020c), nuclear safety (Saltelli and Tarantola, 2002; Iooss and Marrel, 2019) and epidemiology (Burgess et al., 2017; VanderWeele and Ding, 2017), to name a few.”
• Bayesian weighted Mendelian randomization for causal inference based on summary statistics (2018, background): “Accordingly, the causal effect can be inferred under the linear framework (Burgess et al., 2017): X = N∑ j=1 γjGj + ηXU + X , Y = βX + ηYU + Y , where ηX , ηY are the effects of confounder on the exposure and the outcome, X and Y are independent error terms, ( X , Y ) |= (G1, G2, ..., GN , U) and X…”
• MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data (2017, methodology): “13 Additionally, several variations on these methods have been proposed, such as the use of robust regression instead of standard linear regression in the IVW or MR-Egger methods, or the penalization of weights from genetic variants with heterogeneous causal estimates.”