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GWAS Meets Microarray: Are the Results of Genome-Wide Association Studies and Gene-Expression Profiling Consistent? Prostate Cancer as an Example

Author

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  • Ivan P Gorlov
  • Gary E Gallick
  • Olga Y Gorlova
  • Christopher Amos
  • Christopher J Logothetis

Abstract

Background: Genome-wide association studies (GWASs) and global profiling of gene expression (microarrays) are two major technological breakthroughs that allow hypothesis-free identification of candidate genes associated with tumorigenesis. It is not obvious whether there is a consistency between the candidate genes identified by GWAS (GWAS genes) and those identified by profiling gene expression (microarray genes). Methodology/Principal Findings: We used the Cancer Genetic Markers Susceptibility database to retrieve single nucleotide polymorphisms from candidate genes for prostate cancer. In addition, we conducted a large meta-analysis of gene expression data in normal prostate and prostate tumor tissue. We identified 13,905 genes that were interrogated by both GWASs and microarrays. On the basis of P values from GWASs, we selected 1,649 most significantly associated genes for functional annotation by the Database for Annotation, Visualization and Integrated Discovery. We also conducted functional annotation analysis using same number of the top genes identified in the meta-analysis of the gene expression data. We found that genes involved in cell adhesion were overrepresented among both the GWAS and microarray genes. Conclusions/Significance: We conclude that the results of these analyses suggest that combining GWAS and microarray data would be a more effective approach than analyzing individual datasets and can help to refine the identification of candidate genes and functions associated with tumor development.

Suggested Citation

  • Ivan P Gorlov & Gary E Gallick & Olga Y Gorlova & Christopher Amos & Christopher J Logothetis, 2009. "GWAS Meets Microarray: Are the Results of Genome-Wide Association Studies and Gene-Expression Profiling Consistent? Prostate Cancer as an Example," PLOS ONE, Public Library of Science, vol. 4(8), pages 1-5, August.
  • Handle: RePEc:plo:pone00:0006511
    DOI: 10.1371/journal.pone.0006511
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    Cited by:

    1. Ivan P Gorlov & Christopher J Logothetis & Shenying Fang & Olga Y Gorlova & Christopher Amos, 2012. "Building a Statistical Model for Predicting Cancer Genes," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-6, November.
    2. Cherif Ben Hamda & Raphael Sangeda & Liberata Mwita & Ayton Meintjes & Siana Nkya & Sumir Panji & Nicola Mulder & Lamia Guizani-Tabbane & Alia Benkahla & Julie Makani & Kais Ghedira & H3ABioNet Consor, 2018. "A common molecular signature of patients with sickle cell disease revealed by microarray meta-analysis and a genome-wide association study," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-21, July.

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