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The role of estimation in Mendelian randomization: should Mendelian randomization investigations provide estimates?
Benjamin Woolf1,2,3, Stephen Burgess3,4
1MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Abstract:
Mendelian randomization (MR) makes causal claims by treating genetic variation in an analogous way to randomization in a clinical trial. MR investigations can be viewed as analogous to a randomized encouragement design, in that genetic variants do not determine the precise level of an exposure, but increase liability to it. As such, an MR estimate typically does not represent an achievable or well-defined causal effect in terms of the exposure, as it reflects the impact of a life-long shift in the trajectory of the exposure, which likely differs between individuals. We advocate for MR investigations to be performed to assess evidence for a causal hypothesis, rather than to estimate a well-defined causal quantity. MR estimates are useful to combine evidence across genetic variants, to assess the validity of variants as instruments, to provide confidence intervals, and to compare estimates across outcomes. However, numerical estimates from MR should not be over-interpreted. The value of an MR investigation is not to quantify the magnitude of effect for a well-defined intervention in the exposure. Instead, it provides a distinct source of evidence to increase or decrease confidence in a causal hypothesis, which can be triangulated with evidence from other sources.
Insights
Mendelian randomization (MR) uses genetic variation to infer causality, but estimates reflect lifelong exposure changes, not specific interventions. MR strengthens causal hypotheses rather than quantifying precise intervention effects.
Area of Science:
- Epidemiology
- Genetics
- Biostatistics
Background:
- Mendelian randomization (MR) employs genetic variants as instrumental variables to investigate causal relationships.
- MR is conceptually similar to a randomized encouragement design, where genetic predisposition influences exposure liability.
Purpose of the Study:
- To clarify the interpretation of Mendelian randomization (MR) estimates.
- To advocate for MR's role in assessing causal hypotheses rather than quantifying specific causal effects.
Main Methods:
- The study reviews the principles of Mendelian randomization (MR) and its analogy to randomized encouragement designs.
- It discusses the interpretation of MR estimates in the context of lifelong exposure trajectories.
Main Results:
- MR estimates reflect the impact of a lifelong shift in exposure, not a precisely defined intervention effect.
- MR estimates are valuable for synthesizing evidence, assessing instrument validity, and comparing results across outcomes.
Conclusions:
- Mendelian randomization (MR) provides valuable evidence for causal inference but should not be over-interpreted for quantifying specific causal effects.
- The primary value of MR lies in strengthening or weakening confidence in a causal hypothesis, complementing other evidence sources.
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