A Bayesian Partitioning Model for the Detection of Multilocus Effects in Case-Control Studies.

ABSTRACT:

Background

Genome-wide association studies (GWASs) have identified hundreds of genetic variants associated with complex diseases, but these variants appear to explain very little of the disease heritability. The typical single-locus association analysis in a GWAS fails to detect variants with small effect sizes and to capture higher-order interaction among these variants. Multilocus association analysis provides a powerful alternative by jointly modeling the variants within a gene or a pathway and by reducing the burden of multiple hypothesis testing in a GWAS.

Methods

Here, we propose a powerful and flexible dimension reduction approach to model multilocus association. We use a Bayesian partitioning model which clusters SNPs according to their direction of association, mode

SUBMITTER: Ray D 

PROVIDER: S-EPMC4499013 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature