Gene set analysis using variance component tests.

ABSTRACT:

Background

Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses.

Results

We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled u

SUBMITTER: Huang YT 

PROVIDER: S-EPMC3776447 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature