Bayesian Genome- and Epigenome-wide Association Studies with Gene Level Dependence

TL;DR

This paper introduces a Bayesian hierarchical model for genome- and epigenome-wide association studies that accounts for gene-level dependence, improving the detection of associations in high-throughput genetic data.

Contribution

It proposes a nonparametric Bayesian approach that models gene-specific association probabilities, enabling better multiplicity adjustment and integration into existing screening methods.

Findings

Applied to DNA methylation data from glioma tumors.

Demonstrated improved detection of gene-level associations.

Software implementation available in R package BayesianScreening.

Abstract

High-throughput genetic and epigenetic data are often screened for associations with an observed phenotype. For example, one may wish to test hundreds of thousands of genetic variants, or DNA methylation sites, for an association with disease status. These genomic variables can naturally be grouped by the gene they encode, among other criteria. However, standard practice in such applications is independent screening with a universal correction for multiplicity. We propose a Bayesian approach in which the prior probability of an association for a given genomic variable depends on its gene, and the gene-specific probabilities are modeled nonparametrically. This hierarchical model allows for appropriate gene and genome-wide multiplicity adjustments, and can be incorporated into a variety of Bayesian association screening methodologies with negligible increase in computational complexity. We describe an application to screening for differences in DNA methylation between lower grade glioma and glioblastoma multiforme tumor samples from The Cancer Genome Atlas. Software is available via the package BayesianScreening for R at https://github.com/lockEF/BayesianScreening .