Bayesian Analysis

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

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

Full-text: Open access

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.

Article information

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

Dates
First available in Project Euclid: 28 January 2015

Permanent link to this document
https://projecteuclid.org/euclid.ba/1422468423

Digital Object Identifier
doi:10.1214/14-BA880

Zentralblatt MATH identifier
1335.62038

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

Citation

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. https://projecteuclid.org/euclid.ba/1422468423


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