Observational epidemiologic studies are prone to confounding, measurement error, and reverse causation, undermining robust causal inference. Mendelian randomization (MR) uses genetic variants to proxy modifiable exposures to generate more reliable estimates of the causal effects of these exposures on diseases and their outcomes. MR has seen widespread adoption within cardio-metabolic epidemiology, but also holds much promise for identifying possible interventions for cancer prevention and treatment. However, some methodologic challenges in the implementation of MR are particularly pertinent when applying this method to cancer etiology and prognosis, including reverse causation arising from disease latency and selection bias in studies of cancer progression. These issues must be carefully considered to ensure appropriate design, analysis, and interpretation of such studies. In this review, we provide an overview of the key principles and assumptions of MR, focusing on applications of this method to the study of cancer etiology and prognosis. We summarize recent studies in the cancer literature that have adopted a MR framework to highlight strengths of this approach compared with conventional epidemiological studies. Finally, limitations of MR and recent methodologic developments to address them are discussed, along with the translational opportunities they present to inform public health and clinical interventions in cancer.

Original languageEnglish
Pages (from-to)995-1010
Number of pages16
JournalCancer Epidemiology, Biomarkers and Prevention
Issue number9
Early online date25 Jun 2018
Publication statusPublished - 1 Sept 2018

Structured keywords

  • ICEP


  • Mendelian Randomization
  • Causal Inference
  • Review
  • Genetic Epidemiology


Dive into the research topics of 'Causal inference in cancer epidemiology: What is the role of mendelian randomization?'. Together they form a unique fingerprint.

Cite this