Using gene expression to improve the power of genome-wide association analysis

Yen Yi Ho, Emily C. Baechler, Ward Ortmann, Timothy W. Behrens, Robert R. Graham, Tushar R. Bhangale, Wei Pan

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)94-103
Number of pages10
JournalHuman heredity
Volume78
Issue number2
DOIs
StatePublished - Aug 2014

Keywords

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

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