%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