Using gene expression to improve the power of genome-wide association analysis.

ABSTRACT:

Background/aims

Genome-wide association (GWA) studies have reported susceptible regions in the human genome for many common diseases and traits; however, these loci only explain a minority of trait heritability. To boost the power of a GWA study, substantial research endeavors have been focused on integrating other available genomic information in the analysis. Advances in high through-put technologies have generated a wealth of genomic data and made combining SNP and gene expression data become feasible.

Results

In this paper, we propose a novel procedure to incorporate gene expression information into GWA analysis. This procedure utilizes weights constructed by gene expression measurements to adjust p values from a GWA analysis. RESULTS from simulation analyses indicate th

SUBMITTER: Ho YY 

PROVIDER: S-EPMC4152945 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature