Using potential variable to study gene–gene and gene–environment interaction effects with genetic model uncertainty

Abstract

One of the critical issues in genetic association studies is to evaluate the risk of a disease associated with gene–gene or gene–environment interactions. The commonly employed procedures are derived by assigning a particular set of scores to genotypes. However, the underlying genetic models of inheritance are rarely known in practice. Misspecifying a genetic model may result in power loss. By using some potential genetic variables to separate the genotype coding and genetic model parameter, we construct a model-embedded score test (MEST). Our test is free of assumption of gene–environment independence and allows for covariates in the model. An effective sequential optimization algorithm is developed. Extensive simulations show the proposed MEST is robust and powerful in most of scenarios. Finally, we apply the proposed method to rheumatoid arthritis data from the Genetic Analysis Workshop 16 to further investigate the potential interaction effects.