Bayesian Analysis

Bayesian Polynomial Regression Models to Fit Multiple Genetic Models for Quantitative Traits

Harold Bae, Thomas Perls, Martin Steinberg, and Paola Sebastiani

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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.

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Bayesian Anal., Volume 10, Number 1 (2015), 53-74.

First available in Project Euclid: 28 January 2015

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marginal likelihood GWAS Bayesian model selection parameterization additive dominant recessive co-dominant


Bae, Harold; Perls, Thomas; Steinberg, Martin; Sebastiani, Paola. Bayesian Polynomial Regression Models to Fit Multiple Genetic Models for Quantitative Traits. Bayesian Anal. 10 (2015), no. 1, 53--74. doi:10.1214/14-BA880.

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