|Title||Bayesian Polynomial Regression Models to Fit Multiple Genetic Models for Quantitative Traits|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Bae, H, Perls, T, Steinberg, M, Sebastiani, P|
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 model 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 stimulated and real genome-wide data sets.