title: Bayesian Polynomial Regression Models to Fit Multiple Genetic Models for Quantitative Traits.
abstract: 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.
authors.affiliation: Department of Biostatistics, Boston University School of Public Health.
authors.forename: Harold
authors.initials: H
authors.lastname: Bae
authors.affiliation: New England Centenarian Study, Section of Geriatrics, Department of Medicine, Boston University School of Medicine.
authors.forename: Thomas
authors.initials: T
authors.lastname: Perls
authors.affiliation: Center of Excellence in Sickle Cell Disease Boston Medical Center, Pediatrics, Pathology and Laboratory Medicine.
authors.forename: Martin
authors.initials: M
authors.lastname: Steinberg
authors.affiliation: Department of Biostatistics, Boston University School of Public Health.
authors.forename: Paola
authors.initials: P
authors.lastname: Sebastiani
keywords: Bayesian model selection; GWAS; additive; co-dominant; dominant; marginal likelihood; parameterization; recessive