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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.

SUBMITTER: Bae H

PROVIDER: S-EPMC4446790 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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

Bae Harold H Perls Thomas T Steinberg Martin M Sebastiani Paola P

Bayesian analysis 20150301 1

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 m ...[more]

PMID: 26029316

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