  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 .
