Random Effects Model for Multiple Pathway Analysis with Applications to Type II Diabetes Microarray Data.

Journal Article

Close to three percent of the world's population suffer from diabetes. Despite the range of treatment options available for diabetes patients, not all patients benefit from them. Investigating how different pathways correlate with phenotype of interest may help unravel novel drug targets and discover a possible cure. Many pathway-based methods have been developed to incorporate biological knowledge into the study of microarray data. Most of these methods can only analyze individual pathways but cannot deal with two or more pathways in a model based framework. This represents a serious limitation because, like genes, individual pathways do not work in isolation, and joint modeling may enable researchers to uncover patterns not seen in individual pathway-based analysis. In this paper, we propose a random effects model to analyze two or more pathways. We also derive score test statistics for significance of pathway effects. We apply our method to a microarray study of Type II diabetes. Our method may eludicate how pathways crosstalk with each other and facilitate the investigation of pathway crosstalks. Further hypothesis on the biological mechanisms underlying the disease and traits of interest may be generated and tested based on this method.

Full Text

Duke Authors

Cited Authors

  • Pang, H; Kim, I; Zhao, H

Published Date

  • October 2015

Published In

Volume / Issue

  • 7 / 2

Start / End Page

  • 167 - 186

PubMed ID

  • 26640601

Electronic International Standard Serial Number (EISSN)

  • 1867-1772

International Standard Serial Number (ISSN)

  • 1867-1764

Digital Object Identifier (DOI)

  • 10.1007/s12561-014-9109-1


  • eng