Bayesian genome- and epigenome-wide association studies with gene level dependence.

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 B

SUBMITTER: Lock EF 

PROVIDER: S-EPMC5507757 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature