lunes, 9 de julio de 2018

Causal inference in cancer epidemiology: what is the role of Mendelian randomization? - PubMed - NCBI

2018 Jun 25. pii: cebp.1177.2017. doi: 10.1158/1055-9965.EPI-17-1177. [Epub ahead of print]

Causal inference in cancer epidemiology: what is the role of Mendelian randomization?

Yarmolinsky J1, Wade KH2, Richmond RC1, Langdon RJ2, Bull CJ3, Tilling KM4, Relton CL1, Lewis SJ4, Davey Smith G1, Martin RM5.

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Abstract

Observational epidemiological 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 methodological challenges in the implementation of MR are particularly pertinent when applying this method to cancer aetiology 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 aetiology and prognosis. We summarize recent studies in the cancer literature that have adopted a MR framework to highlight strengths of this approach compared to conventional epidemiological studies. Lastly, limitations of MR and recent methodological developments to address them are discussed, along with the translational opportunities they present to inform public health and clinical interventions in cancer.