RT Journal Article
SR Electronic
T1 Causal inference in cancer epidemiology: what is the role of Mendelian randomization?
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 223966
DO 10.1101/223966
A1 James Yarmolinsky
A1 Kaitlin H Wade
A1 Rebecca C Richmond
A1 Ryan J Langdon
A1 Caroline J Bull
A1 Kate M Tilling
A1 Caroline L Relton
A1 George Davey Smith
A1 Richard M Martin
YR 2017
UL http://biorxiv.org/content/early/2017/11/28/223966.abstract
AB Observational epidemiological studies are prone to confounding, measurement error, and reverse causation, undermining their ability to generate reliable causal estimates of the effect of risk factors to inform cancer prevention and treatment strategies. Mendelian randomization (MR) is an analytical approach that uses genetic variants to proxy potentially modifiable exposures (e.g. environmental factors, biological traits, and druggable pathways) to permit robust causal inference of the effects of these exposures on diseases and their outcomes. MR has seen widespread adoption within population health research in cardio-metabolic disease, but also holds much promise for identifying possible interventions (e.g., dietary, behavioural, or pharmacological) for cancer prevention and treatment. However, some methodological and conceptual 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.