ArticlePloS one2009
GWAS meets microarray: are the results of genome-wide association studies and gene-expression profiling consistent? Prostate cancer as an example.
Article in PloS one, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
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- Muscle transcriptome analysis reveals genes and metabolic pathways related to mineral concentration in Bos indicus.Scientific reports · 2019Article
- Network approach identifies Pacer as an autophagy protein involved in ALS pathogenesis.Molecular neurodegeneration · 2019Article
- Post-GWAS in prostate cancer: from genetic association to biological contribution.Nature reviews. Cancer · 2019Review
- An Integrative Genomics Approach for Associating Genome-Wide Association Studies Information With Localized and Metastatic Prostate Cancer Phenotypes.Biomarker insights · 2017Article
- Putative Prostate Cancer Risk SNP in an Androgen Receptor-Binding Site of the Melanophilin Gene Illustrates Enrichment of Risk SNPs in Androgen Receptor Target Sites.Human mutation · 2016Article
- SEPARATING THE CAUSES AND CONSEQUENCES IN DISEASE TRANSCRIPTOME.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2016Article
- Genomic convergence and network analysis approach to identify candidate genes in Alzheimer's disease.BMC genomics · 2014Article
- Molecular signature of cancer at gene level or pathway level? Case studies of colorectal cancer and prostate cancer microarray data.Computational and mathematical methods in medicine · 2013Article
- Synthesizing genome-wide association studies and expression microarray reveals novel genes that act in the human growth plate to modulate height.Human molecular genetics · 2012Article
- Pathway analysis of genomic data: concepts, methods, and prospects for future development.Trends in genetics : TIG · 2012Review
- Beyond comparing means: the usefulness of analyzing interindividual variation in gene expression for identifying genes associated with cancer development.Journal of bioinformatics and computational biology · 2012Article
- Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets.Genome research · 2012Article
- Multiscale integration of -omic, imaging, and clinical data in biomedical informatics.IEEE reviews in biomedical engineering · 2012Review
- Emerging putative biomarkers: the role of alpha 2 and 6 integrins in susceptibility, treatment, and prognosis.Prostate cancer · 2012Article
- Building a statistical model for predicting cancer genes.PloS one · 2012Article
- Associating GWAS Information with the Notch Signaling Pathway Using Transcription Profiling.Cancer informatics · 2011Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
backgroundGenome-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
findingsWe 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.
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