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

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.

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 that the proposed procedures may achieve substantial power gains, while controlling family-wise type I error rates at the nominal level. To demonstrate the implementation of our proposed approach, we apply the weight adjustment procedure to a GWA study on serum interferon-regulated chemokine levels in systemic lupus erythematosus patients. The study results can provide valuable insights for the functional interpretation of GWA signals.

The R source code for implementing the proposed weighting procedure is available at http://www.biostat.umn.edu/∼yho/research.html.