CoChat
Sign Up
Cited by 113Genetic Associations and EpidemiologyAdvanced Causal Inference TechniquesStatistical Methods in Epidemiology

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

Are Mendelian randomization investigations immune from bias due to reverse causation?

Open access
Burgess, Stephen, Swanson, Sonja A, Labrecque, Jeremy A·2021·European Journal of Epidemiology
DOI 10.1007/s10654-021-00726-8PMC PMC8032609
Open this paper in CoChat — free accountOpen PDF

Abstract

Reverse causation occurs when the outcome variable at an earlier timepoint, or a proximal precursor of the outcome (such as pre-clinical disease), has a causal effect on the risk factor. This confounding by pre-clinical or prior outcome can lead to bias when trying to estimate an average causal effect of the risk factor on the outcome. By proposing genetic variants as instrumental variables, Mendelian randomization can make inferences about the effect of shifting the trajectory of a risk factor on a later-measured outcome. As genetic variants are fixed at conception, it has been stated that Mendelian randomization investigations are not affected by reverse causation. While Mendelian randomization analyses have some protection against bias due to reverse causation, the claim of complete immunity is a misconception. We provide three plausible scenarios and accompanying examples of how reverse causation can influence Mendelian randomization estimates: 1) the effect of the genetic variant on the risk factor is mediated (at least in part) by the outcome, 2) the genetic association with the outcome is distorted by a feedback mechanism whereby the risk factor and outcome variable both influence each other over time, and 3) the genetic association with the outcome is distorted by an effect of the outcome in the parental generation on the outcome in the offspring generation. In these scenarios, we show that all causal effects typically estimated in Mendelian randomization analyses will be biased, though hypothesis testing can still be valid in Scenario 2 and 3 for a sharp causal null hypothesis. This underscores the general recommendation to view Mendelian randomization as primarily testing a causal null hypothesis rather than estimating a causal effect.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s10654-021-00726-8

Cites

25 of 25 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Diagrams illustrating relationships between a genetic variant (G), risk factor (X), and an outcome (Y), where the effect of the genetic variant on the risk factor is a) through its effect on the outcome previous to the risk factor (Y0) and b) through a confounder (C) – a common cause of risk factor and outcome. Unmeasured confounding is represented by U. In both diagrams, the effect of interest is the effect of X on Y
Fig. 1 Diagrams illustrating relationships between a genetic variant (G), risk factor (X), and an outcome (Y), where the effect of the genetic variant on the risk factor is a) through its effect on the outcome previous to the risk factor (Y0) and b) through a confounder (C) – a common cause of risk factor and outcome. Unmeasured confounding is represented by U. In both diagrams, the effect of interest is the effect of X on YCC BY · PubMed Central
Fig. 2 Diagrams illustrating time-varying relationships between a genetic variant (G), risk factor (X), an outcome (Y) and unmeasured confounder (U) at time 0 and time 1 (indicated by subscripts) where a the genetic variant has a primary effect on the risk factor, and there are bidirectional effects between the risk factor and outcome and b the genetic variant has a primary effect on the risk factor, but only the reverse causal effect of the outcome on the risk factor is present, meaning that genetic variant is independent of the outcome. The effect of interest is the joint effect of X0 and X1 on Y1
Fig. 2 Diagrams illustrating time-varying relationships between a genetic variant (G), risk factor (X), an outcome (Y) and unmeasured confounder (U) at time 0 and time 1 (indicated by subscripts) where a the genetic variant has a primary effect on the risk factor, and there are bidirectional effects between the risk factor and outcome and b the genetic variant has a primary effect on the risk factor, but only the reverse causal effect of the outcome on the risk factor is present, meaning that genetic variant is independent of the outcome. The effect of interest is the joint effect of X0 and X1 on Y1CC BY · PubMed Central
Fig. 3 A cross-generational diagram of genetic variant (G), risk factor (X), and outcome (Y) in both the parent and child. The potential cross-generational reverse causal effect of parental outcome on offspring confounder or outcome is displayed in grey. If estimating the effect of the risk factor in the child on the outcome in the child, when the risk factor causes the outcome in either or both generations (panel a), Mendelian randomization estimates will typically be non-null, but biased. When the risk factor does not cause the outcome in either generation (panel b), Mendelian randomization estimates will not be biased and will provide a valid test of the sharp causal null hypothesis. Shared causes of the parent’s exposure and outcome, and their effects on the child’s exposure and outcome that are not relevant to the bias under study, are omitted for clarity
Fig. 3 A cross-generational diagram of genetic variant (G), risk factor (X), and outcome (Y) in both the parent and child. The potential cross-generational reverse causal effect of parental outcome on offspring confounder or outcome is displayed in grey. If estimating the effect of the risk factor in the child on the outcome in the child, when the risk factor causes the outcome in either or both generations (panel a), Mendelian randomization estimates will typically be non-null, but biased. When the risk factor does not cause the outcome in either generation (panel b), Mendelian randomization estimates will not be biased and will provide a valid test of the sharp causal null hypothesis. Shared causes of the parent’s exposure and outcome, and their effects on the child’s exposure and outcome that are not relevant to the bias under study, are omitted for clarityCC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 25 references

Research works printed but not linked: 1

  1. Hernán M, Robins J. Causal inference: What If? Boca Raton: Chapman and Hall/CRC Press; 2020.

Cited by

Sorted by citations

Related

See all

Metadata

Authors
Burgess, S., Swanson, S. A., & Labrecque, J. A.
Affiliations
  • University of Cambridge (GB)
  • Erasmus MC (NL)
Corresponding author
Burgess, Stephen
ORCID
Venue
European Journal of Epidemiology
Publisher
Springer Science and Business Media LLC
Published
2021-02-21
Volume
36
Issue
3
Pages
253-257
ISSN
  • 0393-2990 (ISSN-L)
  • 1573-7284
DOI
10.1007/s10654-021-00726-8
PMC
PMC8032609
OpenAlex
W3131213308
Cited by
106
References
25
Citations by year
2026: 11 · 2025: 21 · 2024: 28 · 2023: 27 · 2022: 15 · 2021: 4
FWCI
11.31
Type
journal article
Language
en
Open access
Licence
Locations
  • European Journal of Epidemiology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, cc-by)
  • RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
  • Apollo (University of Cambridge) (submittedVersion)
  • Apollo (University of Cambridge) (submittedVersion, cc-by)
  • Apollo
Funders
  • Wellcome Trust (204623/Z/16/Z)
  • National Institute for Health Research
  • ZonMw (91617066)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords
reverse causality, Mendelian randomization, instrumental variables, genetic variants, causal inference, mediation, feedback mechanisms, parental effects
MeSH terms
Humans, Genetic Variation, Bias, Causality, Mendelian Randomization Analysis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review