Skip to content

Causal inference in cancer epidemiology: What is the role of mendelian randomization?

Research output: Contribution to journalReview article

Original languageEnglish
Pages (from-to)995-1010
Number of pages16
JournalCancer Epidemiology, Biomarkers and Prevention
Issue number9
Early online date25 Jun 2018
DateSubmitted - 9 Mar 2018
DateAccepted/In press - 5 Jun 2018
DateE-pub ahead of print - 25 Jun 2018
DatePublished (current) - 1 Sep 2018


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.

    Research areas

  • Mendelian Randomization, Causal Inference, Review, Genetic Epidemiology

    Structured keywords

  • ICEP

Download statistics

No data available



  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via AACR at . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 605 KB, PDF document


View research connections

Related faculties, schools or groups