Carcinogen metabolism, cigarette smoking, and breast cancer risk: a Bayes model averaging approach.

Background: Standard logistic regression with or without stepwise selection has the disadvantage of not incorporating model uncertainty and the dependency of estimates on the underlying model into the final inference. We explore the use of a Bayes Model Averaging approach as an alternative to analyze the influence of genetic variants, environmental effects and their interactions on disease.

Methods: Logistic regression with and without stepwise selection and Bayes Model Averaging were applied to a population-based case-control study exploring the association of genetic variants in tobacco smoke-related carcinogen pathways with breast cancer.

Results: Both regression and Bayes Model Averaging highlighted a significant effect of NAT1*10 on breast cancer, while regression analysis also suggested a significant effect for packyears and for the interaction of packyears and NAT2.

Conclusions: Bayes Model Averaging allows incorporation of model uncertainty, helps reduce dimensionality and avoids the problem of multiple comparisons. It can be used to incorporate biological information, such as pathway data, into the analysis. As with all Bayesian analysis methods, careful consideration must be given to prior specification.