  We present a coherent Bayesian framework for selection of the most likely
model from the five genetic models (genotypic, additive, dominant, co-dominant,
and recessive) commonly used in genetic association studies. The approach uses
a polynomial parameterization of genetic data to simultaneously fit the five
models and save computations. We provide a closed-form expression of the
marginal likelihood for normally distributed data, and evaluate the performance
of the proposed method and existing method through simulated and real
genome-wide data sets.
