Method for Evaluating Multiple Mediators: Mediating Effects of Smoking and COPD on the Association between the CHRNA5-A3 Variant and Lung Cancer Risk

Abstract

A mediation model explores the direct and indirect effects between an independent variable and a dependent variable by including other variables (or mediators). Mediation analysis has recently been used to dissect the direct and indirect effects of genetic variants on complex diseases using case-control studies. However, bias could arise in the estimations of the genetic variant-mediator association because the presence or absence of the mediator in the study samples is not sampled following the principles of case-control study design. In this case, the mediation analysis using data from case-control studies might lead to biased estimates of coefficients and indirect effects. In this article, we investigated a multiple-mediation model involving a three-path mediating effect through two mediators using case-control study data. We propose an approach to correct bias in coefficients and provide accurate estimates of the specific indirect effects. Our approach can also be used when the original case-control study is frequency matched on one of the mediators. We employed bootstrapping to assess the significance of indirect effects. We conducted simulation studies to investigate the performance of the proposed approach, and showed that it provides more accurate estimates of the indirect effects as well as the percent mediated than standard regressions. We then applied this approach to study the mediating effects of both smoking and chronic obstructive pulmonary disease (COPD) on the association between the CHRNA5-A3 gene locus and lung cancer risk using data from a lung cancer case-control study. The results showed that the genetic variant influences lung cancer risk indirectly through all three different pathways. The percent of genetic association mediated was 18.3% through smoking alone, 30.2% through COPD alone, and 20.6% through the path including both smoking and COPD, and the total genetic variant-lung cancer association explained by the two mediators was 69.1%.

A multiple mediator analysis approach to quantify the effects of the ADH1B and ALDH2 genes on hepatocellular carcinoma risk

“…Existing mediation methods are able to quantify the single-mediation effects on a dichotomous outcome (Imai et al, 2010;Valeri & Vanderweele, 2013;Vanderweele & Vansteelandt, 2010), but may require a rare outcome in order to estimate the effects (Valeri & Vanderweele, 2013;Vanderweele & Vansteelandt, 2010). When multiple mediators are considered, the existing mediation methods may be computationally intensive or may require additional sensitivity parameters to estimate the relationship between the two mediators (Daniel et al, 2015;Wang et al, 2012). The proposed method differs from other methods in that it can estimate the probability of a dichotomous outcome, and therefore, any effect involving a contrast of the probability under two exposure status, such as the risk difference or risk ratio, without the rare outcome assumption and additional assumptions regarding the relationship between the mediators.…”

“…This is different strategy from available multi-mediator methods that rely on various weighting schemes that may not be able to easily incorporate exposure-mediator interactions in this context (VanderWeele & Vansteelandt, 2014) or Monte-Carlo simulations to estimate the combined distribution of the outcome and mediators (Daniel et al, 2015; Preacher & Hayes, 2008; VanderWeele & Vansteelandt, 2014; Vanderweele et al, 2014; Vansteelandt & Daniel, 2017). Wang et al (2012) proposed a structural estimating equation-based approach to investigate all possible paths, which is subject to the aforementioned non-identifiability issue with the two mediators (J. Wang et al, 2012).…”

“…This is different strategy from available multimediator methods that rely on various weighting schemes that may not be able to easily incorporate exposure-mediator interactions in this context or Monte Carlo simulations to estimate the combined distribution of the outcome and mediators (Daniel et al, 2015;Preacher & Hayes, 2008;Vansteelandt & Daniel, 2017). Wang et al (2012) proposed a structural estimating equation-based approach to investigate all possible paths, which is subject to the aforementioned nonidentifiability issue with the two mediators (Wang et al, 2012). Herein we describe a new method incorporating the counterfactual framework that will allow for the identification of the three path-specific effects of the intermediary factors contributing to HCC risk: (1) the direct effect of these genetic SNPs, (2) the mediation effect through ALT levels but not explained by alcohol consumption, and (3) the mediation effect through alcohol consumption and possibly through ALT.…”

Quantifying and Reducing Uncertainty about Causality in Improving Public Health and Safety

“…For example, path analysis methods showing the dependency relations among variables in SEMs have been used for many decades to show how some variables influence others when all relations are assumed to be linear. Figure 7 presents an example involving several variables that are estimated to significantly predict lung cancer risk: the presence of a particular single nucleotide polymorphism (SNP) (the CHRNA5-A3 gene cluster, a genetic variant which is associated with increased risk of lung cancer), smoking, and presence of chronic obstructive pulmonary disease (COPD) [120]. The path coefficient on an arrow indicates by how much (specifically, by how many standard deviations) the expected value of the variable into which it points would change if the variable at the arrow's tail were increased by one standard deviation, holding all other variables fixed and assuming that all relations are well approximated by linear structural equation regression models, i.e., that changing the variable at the arrow's tail will cause a proportional change in the variable at its head.…”

Genetic Influences on Smoking and Clinical Disease. Understanding Behavioral and Biological Pathways with Mediation Analysis

“…VanderWeele and colleagues found no evidence that this CHRNA5/A3/B4 region was associated with lung cancer through cigarettes per day (12), whereas others found that this CHRNA5/ A3/B4 region indirectly acts on lung cancer risk mediated through cigarettes per day (13). Wang and colleagues found that the genetic influences on lung cancer risk were mediated through three distinct pathways: current smoking status, COPD, and both smoking and COPD (14).…”