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

TL;DR

This paper introduces a Bayesian polynomial regression framework that efficiently selects among five genetic models in association studies, improving model fitting for quantitative traits using a unified approach.

Contribution

It proposes a novel Bayesian polynomial model that simultaneously fits multiple genetic models with a closed-form marginal likelihood, enhancing computational efficiency and model selection accuracy.

Findings

The method accurately identifies the true genetic model in simulations.

It performs well on real genome-wide data sets.

The approach reduces computational complexity compared to existing methods.

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.