A bayesian integrative genomic model for pathway analysis of complex traits

Brooke L. Fridley, Steven Lund, Gregory D. Jenkins, Liewei Wang

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


With new technologies, multiple types of genomic data are commonly collected on a single set of samples. However, standard analysis methods concentrate on a single data type at a time and ignore the relationships between genes, proteins, and biochemical reactions that give rise to complex phenotypes. In this paper, we propose a novel integrative model to incorporate multiple types of genomic data into an association analysis with a complex phenotype. The method combines path analysis and stochastic search variable selection into a Bayesian hierarchical model that simultaneously identifies both direct and indirect genomic effects on the phenotype. Results from a simulation study and application of the Bayesian model to a pharmacogenomic study of the drug gemcitabine demonstrate greater sensitivity to detect genomic effects in some simulation scenarios, when compared to the standard single data type analysis. Further research is required to extend and modify this integrative modeling framework to increase computational efficiency to best capitalize on the wealth of information available across multiple genomic data types.

Original languageEnglish (US)
Pages (from-to)352-359
Number of pages8
JournalGenetic epidemiology
Issue number4
StatePublished - May 2012


  • Cell lines
  • Genetic association
  • MRNA expression
  • Markov chain Monte Carlo (MCMC)
  • Single nucleotide polymorphism (SNPs)
  • Stochastic search variable selection

ASJC Scopus subject areas

  • Epidemiology
  • Genetics(clinical)


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