10.6084/m9.figshare.5126536.v1 Ho Y.-Y. Ho Y.-Y. Baechler E.C. Baechler E.C. Ortmann W. Ortmann W. Behrens T.W. Behrens T.W. Graham R.R. Graham R.R. Bhangale T.R. Bhangale T.R. Pan W. Pan W. Supplementary Material for: Using Gene Expression to Improve the Power of Genome-Wide Association Analysis Karger Publishers 2014 p value weighting Family-wise error rate Statistical power Integrative genomic analysis SLE 2014-07-30 00:00:00 Dataset https://karger.figshare.com/articles/dataset/Supplementary_Material_for_Using_Gene_Expression_to_Improve_the_Power_of_Genome-Wide_Association_Analysis/5126536 <b><i>Background/Aims:</i></b> 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. <b><i>Results:</i></b> 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. <b><i>Availability:</i></b> The R source code for implementing the proposed weighting procedure is available at http://www.biostat.umn.edu/∼yho/research.html.