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

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

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.

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

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?

  • Violations of the third IV condition can be caused by pleiotropy, where genetic variants have effects on multiple phenotypes.

    “Violations of the third IV condition can be caused by pleiotropy, where genetic variants have effects on multiple phenotypes.61,62”

    From Mendelian randomization (2022) · cites this paper as [62]

Self-check

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

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

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. [7]Hernán M Robins J Causal inference: What If? 2020 Boca Raton Chapman and Hall/CRC Press

Cited by

Sorted by citations

Authors
Burgess, S., Swanson, S. A., & Labrecque, J. A.
Affiliations
  • University of Cambridge (GB)
  • Erasmus MC (NL)
Corresponding author
Burgess, Stephen
Venue
European Journal of Epidemiology
Published
2021-02-21
Volume
36
Issue
3
Pages
253-257
ISSN
  • 0393-2990 (ISSN-L)
  • 1573-7284
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
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