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

Harold Bae; Thomas Perls; Martin Steinberg; Paola Sebastiani

doi:10.1214/14-ba880

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arXiv · 1504.05415

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

Harold Bae·Thomas Perls·Martin Steinberg·Paola Sebastiani

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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. 2015-04-21. Bayesian Polynomial Regression Models to Fit Multiple Genetic Models for Quantitative Traits. https://doi.org/10.1214/14-ba880

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