A BAYESIAN GRAPHICAL MODEL FOR GENOME-WIDE ASSOCIATION STUDIES (GWAS).

ABSTRACT: The analysis of GWAS data has long been restricted to simple models that cannot fully capture the genetic architecture of complex human diseases. As a shift from standard approaches, we propose here a general statistical framework for multi-SNP analysis of GWAS data based on a Bayesian graphical model. Our goal is to develop a general approach applicable to a wide range of genetic association problems, including GWAS and fine-mapping studies, and, more specifically, be able to: (1) Assess the joint effect of multiple SNPs that can be linked or unlinked and interact or not; (2) Explore the multi-SNP model space efficiently using the Mode Oriented Stochastic Search (MOSS) algorithm and determine the best models. We illustrate our new methodology with an application to the CGEM breast cancer

SUBMITTER: Briollais L 

PROVIDER: S-EPMC8075301 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature