Bayesian Model Comparison in Genetic Association Analysis: Linear Mixed Modeling and SNP Set Testing

TL;DR

This paper introduces Bayesian model comparison techniques for genetic association analysis, connecting Bayesian linear regression with mixed models and SNP set testing, offering analytic Bayes factors and demonstrating their advantages through simulations and real data.

Contribution

The paper develops analytic approximate Bayes factors for Bayesian linear models in genetic studies, linking them with frequentist tests and showcasing their flexibility and advantages.

Findings

Derived analytic Bayes factors closely relate to classical test statistics.

Bayesian model averaging improves inference in genetic association tests.

Methods outperform traditional approaches in simulated and real data examples.

Abstract

We consider the problems of hypothesis testing and model comparison under a flexible Bayesian linear regression model whose formulation is closely connected with the linear mixed effect model and the parametric models for SNP set analysis in genetic association studies. We derive a class of analytic approximate Bayes factors and illustrate their connections with a variety of frequentist test statistics, including the Wald statistic and the variance component score statistic. Taking advantage of Bayesian model averaging and hierarchical modeling, we demonstrate some distinct advantages and flexibilities in the approaches utilizing the derived Bayes factors in the context of genetic association studies. We demonstrate our proposed methods using real or simulated numerical examples in applications of single SNP association testing, multi-locus fine-mapping and SNP set association testing.