Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies

Philip C Haycock*, Stephen Burgess, Kaitlin H Wade, Jack Bowden, Caroline L Relton, George Davey Smith

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

142 Citations (Scopus)
391 Downloads (Pure)

Abstract

Mendelian randomization (MR) is an increasingly important tool for appraising causality in observational epidemiology. The technique exploits the principle that genotypes are not generally susceptible to reverse causation bias and confounding, reflecting their fixed nature and Mendel's first and second laws of inheritance. The approach is, however, subject to important limitations and assumptions that, if unaddressed or compounded by poor study design, can lead to erroneous conclusions. Nevertheless, the advent of 2-sample approaches (in which exposure and outcome are measured in separate samples) and the increasing availability of open-access data from large consortia of genomewide association studies and population biobanks mean that the approach is likely to become routine practice in evidence synthesis and causal inference research. In this article we provide an overview of the design, analysis, and interpretation of MR studies, with a special emphasis on assumptions and limitations. We also consider different analytic strategies for strengthening causal inference. Although impossible to prove causality with any single approach, MR is a highly cost-effective strategy for prioritizing intervention targets for disease prevention and for strengthening the evidence base for public health policy.

Original languageEnglish
Pages (from-to)965-978
Number of pages14
JournalAmerican Journal of Clinical Nutrition
Volume103
Issue number4
Early online date9 Mar 2016
DOIs
Publication statusPublished - 1 Apr 2016

Structured keywords

  • ICEP

Keywords

  • Causality
  • Confounding
  • Evidence synthesis
  • Mendelian randomization
  • Observational epidemiology
  • Reverse causation

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