Causal Inference in Cancer Epidemiology: What Is the Role of Mendelian Randomization?

Abstract

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. Cancer Epidemiol Biomarkers Prev; 27(9); 995-1010. ©2018 AACR.

Figure 1. Schematic comparison of the structure of a randomized controlled trial (SELECT) and a Mendelian randomization analysis (PRACTICAL)

In SELECT (left), individuals were randomly allocated to the intervention (200 μg daily selenium supplementation, which lead to a 114μg/L increase in blood selenium) or control group (placebo). In PRACTICAL (right), the additive effects of selenium-raising alleles at eleven SNPs, randomly allocated at conception, were scaled to mirror a 114μg/L increase in blood selenium. If an RCT trial is adequately sized, randomization should ensure that intervention and control groups are comparable in all respects (e.g., distribution of potential confounding factors) except for the intervention being tested. In an intention-to-treat analysis, any observed differences in outcomes between intervention and control groups can then be attributed to the trial arm to which they were allocated. Likewise, in a MR analysis, groups defined by genotype should be comparable in all respects (e.g., distribution of both genetic and environmental confounding factors) except for their exposure to a trait of interest. Any observed differences in outcomes between groups defined by genotype can then be attributed to differences in life-long exposure to the trait of interest under study.

Figure 2. Illustration of MR methodology

(A) A genetic variant (G) is used as a proxy for a modifiable exposure (E) to assess the association between E and an outcome of interest (O) without the issues of reverse causation, and confounding (U). MR methodology relies on three main assumptions, in that G must (i) be reliably associated with E; (ii) not be associated with U; and (iii) not be independently associated with O, except through E. This method is exemplified in the context of assessing the association of smoking and lung cancer (B), using the CHRNA5-A3-B4 SNP as a genetic instrument for heaviness of smoking.

Figure 3. Directed acyclic graph for selection bias in prognostic studies

In this example, the square bracket indicates that we are conditioning on pancreatic cancer incidence in a survival study by only studying pancreatic cancer cases, thus inducing an association between smoking (a factor that is otherwise independent of pancreatic cancer survival) and pancreatic cancer survival. This link is broken when conditioning on the factor that influences both cancer incidence and survival (e.g., insulin resistance), which can otherwise be seen as a confounder of the association between smoking and cancer survival. If a factor appears to influence pancreatic cancer survival that is not associated with pancreatic cancer incidence (e.g., treatment for pancreatic cancer), selection bias in such an MR analysis would not be expected.

Figure 4. Two-step Mendelian randomization analysis examining the mediating effect of methylation on the association between smoke exposure and lung cancer

In the first step, a SNP within CHRNA5-A3-B4 is used as an instrument for smoke exposure to assess the causal association between smoking and DNA methylation. In the second step, an independent cis-SNP is used as an instrument for DNA methylation to assess the causal association of DNA methylation with lung cancer risk. The two-step method allows interrogation of the mediation effect of DNA methylation in the association between smoking and lung cancer risk.