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.
"Bayesian Polynomial Regression Models to Fit Multiple Genetic Models for Quantitative Traits." Bayesian Anal. 10 (1) 53 - 74, March 2015. https://doi.org/10.1214/14-BA880