Using Gene Expression to Improve the Power of Genome-Wide Association Analysis

作者：Ho Yen Yi^*; Baechler Emily C; Ortmann Ward; Behrens Timothy W; Graham Robert R; Bhangale Tushar R; Pan Wei

来源：Human Heredity, 2014, 78(2): 94-103.

DOI：10.1159/000362837

• p value weighting
• Family-wise error rate
• Statistical power
• Integrative genomic analysis
• SLE

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 erythennatosus 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/similar to yho/research.html.

• 出版日期2014