Bayesian GWAS with Structured and Non-Local Priors.

ABSTRACT:

Motivation

The flexibility of a Bayesian framework is promising for GWAS, but current approaches can benefit from more informative prior models. We introduce a novel Bayesian approach to GWAS, called Structured and Non-Local Priors (SNLPs) GWAS, that improves over existing methods in two important ways. First, we describe a model that allows for a marker's gene-parent membership and other characteristics to influence its probability of association with an outcome. Second, we describe a non-local alternative model for differential minor allele rates at each marker, in which the null and alternative hypotheses have no common support.

Results

We employ a non-parametric model that allows for clustering of the genes in tandem with a regression model for marker-level covariates, a

SUBMITTER: Kaplan A 

PROVIDER: S-EPMC6956774 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature