Hierarchical modeling of melanocortin 1 receptor variants with skin cancer risk

Abstract

The human MC1R gene is highly polymorphic among lightly pigmented populations, and several variants in the MC1R gene have been associated with increased risk of both melanoma and nonmelanoma skin cancers. The functional consequences of MC1R gene variants have been studied in vitro and in vivo in postulated causal pathways, such as G-protein-coupled signaling transduction, pigmentation, immune response, inflammatory response, cell proliferation, and extracellular matrix adhesion. In a case-control study nested within the Nurses' Health Study, we utilized hierarchical modeling approaches, incorporating quantitative information from these functional studies, to examine the association between particular MC1R alleles and the risk of skin cancers. Different prior matrices were constructed according to the phenotypic associations in controls, cell surface expression, and enzymatic kinetics. Our results showed the parameter variance estimates of each single nucleotide polymorphism (SNP) were smaller when using a hierarchical modeling approach compared to standard multivariable regression. Estimates of second-level parameters gave information about the relative importance of MC1R effects on different pathways, and odds ratio estimates changed depending on prior models (e.g., the change ranged from -21% to 7% for melanoma risk assessment). In addition, the estimates of prior model hyperparameters in the hierarchical modeling approach allow us to determine the relevance of individual pathways on the risk of each of the skin cancer types. In conclusion, hierarchical modeling provides a useful analytic approach in addition to the widely used conventional models in genetic association studies that can incorporate measures of allelic function.