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March 2015 Bayesian Polynomial Regression Models to Fit Multiple Genetic Models for Quantitative Traits
Harold Bae, Thomas Perls, Martin Steinberg, Paola Sebastiani
Bayesian Anal. 10(1): 53-74 (March 2015). DOI: 10.1214/14-BA880

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.

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Harold Bae. Thomas Perls. Martin Steinberg. Paola Sebastiani. "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

Information

Published: March 2015
First available in Project Euclid: 28 January 2015

zbMATH: 1335.62038
Digital Object Identifier: 10.1214/14-BA880

Rights: Copyright © 2015 International Society for Bayesian Analysis

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Vol.10 • No. 1 • March 2015
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