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Insights into Pancreatic Cancer Etiology from Pathway Analysis of Genome-Wide Association Study Data

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  • Peng Wei
  • Hongwei Tang
  • Donghui Li

Abstract

Background: Pancreatic cancer is the fourth leading cause of cancer death in the U.S. and the etiology of this highly lethal disease has not been well defined. To identify genetic susceptibility factors for pancreatic cancer, we conducted pathway analysis of genome-wide association study (GWAS) data in 3,141 pancreatic cancer patients and 3,367 controls with European ancestry. Methods: Using the gene set ridge regression in association studies (GRASS) method, we analyzed 197 pathways identified from the Kyoto Encyclopedia of Genes and Genomes database. We used the logistic kernel machine (LKM) test to identify major contributing genes to each pathway. We conducted functional enrichment analysis of the most significant genes (P

Suggested Citation

  • Peng Wei & Hongwei Tang & Donghui Li, 2012. "Insights into Pancreatic Cancer Etiology from Pathway Analysis of Genome-Wide Association Study Data," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-10, October.
    • Handle: RePEc:plo:pone00:0046887
    DOI: 10.1371/journal.pone.0046887
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    1. Gordon Fehringer & Geoffrey Liu & Laurent Briollais & Paul Brennan & Christopher I Amos & Margaret R Spitz & Heike Bickeböller & H Erich Wichmann & Angela Risch & Rayjean J Hung, 2012. "Comparison of Pathway Analysis Approaches Using Lung Cancer GWAS Data Sets," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-8, February.
    2. Eric E. Schadt, 2009. "Molecular networks as sensors and drivers of common human diseases," Nature, Nature, vol. 461(7261), pages 218-223, September.
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