000362837_sm_Table.xls (95.5 kB)

Supplementary Material for: Using Gene Expression to Improve the Power of Genome-Wide Association Analysis

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posted on 30.07.2014 by Ho Y.-Y., Baechler E.C., Ortmann W., Behrens T.W., Graham R.R., Bhangale T.R., Pan W.
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.

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