%0 Generic
%A Y.-Y., Ho
%A E.C., Baechler
%A W., Ortmann
%A T.W., Behrens
%A R.R., Graham
%A T.R., Bhangale
%A W., Pan
%D 2014
%T Supplementary Material for: Using Gene Expression to Improve the Power of Genome-Wide Association Analysis
%U https://karger.figshare.com/articles/dataset/Supplementary_Material_for_Using_Gene_Expression_to_Improve_the_Power_of_Genome-Wide_Association_Analysis/5126536
%R 10.6084/m9.figshare.5126536.v1
%2 https://karger.figshare.com/ndownloader/files/8713924
%K p value weighting
%K Family-wise error rate
%K Statistical power
%K Integrative genomic analysis
%K SLE
%X 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 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. Availability: The R source code for implementing the proposed weighting procedure is available at http://www.biostat.umn.edu/∼yho/research.html.
%I Karger Publishers