A graphical model approach for inferring large-scale networks integrating gene expression and genetic polymorphism.

ABSTRACT:

Background

Graphical models (e.g., Bayesian networks) have been used frequently to describe complex interaction patterns and dependent structures among genes and other phenotypes. Estimation of such networks has been a challenging problem when the genes considered greatly outnumber the samples, and the situation is exacerbated when one wishes to consider the impact of polymorphisms (SNPs) in genes.

Results

Here we describe a multistep approach to infer a gene-SNP network from gene expression and genotyped SNP data. Our approach is based on 1) construction of a graphical Gaussian model (GGM) based on small sample estimation of partial correlation and false-discovery rate multiple testing; 2) extraction of a subnetwork of genes directly linked to a target candidate gene of int

SUBMITTER: Chu JH 

PROVIDER: S-EPMC2694152 | biostudies-literature | 2009 May

REPOSITORIES: biostudies-literature