More powerful genetic association testing via a new statistical framework for integrative genomics

Sihai D. Zhao, T. Tony Cai, Hongzhe Li

Research output: Contribution to journalArticlepeer-review

Abstract

Summary: Integrative genomics offers a promising approach to more powerful genetic association studies. The hope is that combining outcome and genotype data with other types of genomic information can lead to more powerful SNP detection. We present a new association test based on a statistical model that explicitly assumes that genetic variations affect the outcome through perturbing gene expression levels. It is shown analytically that the proposed approach can have more power to detect SNPs that are associated with the outcome through transcriptional regulation, compared to tests using the outcome and genotype data alone, and simulations show that our method is relatively robust to misspecification. We also provide a strategy for applying our approach to high-dimensional genomic data. We use this strategy to identify a potentially new association between a SNP and a yeast cell's response to the natural product tomatidine, which standard association analysis did not detect.

Original languageEnglish (US)
Pages (from-to)881-890
Number of pages10
JournalBiometrics
Volume70
Issue number4
DOIs
StatePublished - Dec 1 2014

Keywords

  • Genetic association testing
  • Genome-wide association studies
  • Integrative genomics
  • Mediation analysis
  • Missing heritability

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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