Sharpening Pathway-Based Analyses using Hierarchical Clustering: Results of a Prostate Cancer Genome-Wide Association Study

Idan Menashe, Dennis Maeder, Stephen Chanock, Philip S. Rosenberg, Nilanjan Chatterjee

Research output: Contribution to journalMeeting Abstract

Abstract

Pathway analyses compliment primary scans of genome-wide association studies (GWAS) to indentify overrepre-sentation of susceptibility loci in predefined gene-sets.We applied two pathway analysis methods: gene-setenrichment analysis (GSEA), and adaptive rank-truncatedproduct (ARTP) to the Cancer Genetic Markers of Suscept-ibility (CGEMS) prostate cancer GWAS (1172 cases and 1157control), using pathways with 10-100 genes from threepublically available resources (BioCarta, KEGG, and PID).An artificial pathway composed of 31 genes associated withprostate cancer was used as a positive control. Pathwayswith FDRo0.2 were considered noteworthy.A high concordance was seen between results of GSEA andARTP (R 5 0.72; P0). Of the 445 pathways tested, only the‘ ‘Action of Nitric Oxide in the Heart’’ pathway from BioCartawas noteworthy using ARTP (p 5 0.0002; FDR 5 0.09). Thepositive control pathway was ranked 2nd using AR TP(p 5 0.0027; FDR 5 0.60), but only 12th using GSEA(p 5 0.0182; FDR 5 0.54). Next, we used hierarchical cluster-ing to merge pathways with similar gene content and reducethe number of gene-sets to 167. Post clustering, one gene-setswasnoteworthybybothGSEAandARTP(p 5 0.0008;FDR 5 0.13, and p 5 0.001; FDR 5 0.18 respectively), andthree other gene-sets wer e noteworthy by ARTP only.These results suggest that gene-set clustering may improvepower of both GSEA and ARTP, and facilitate detection ofpathways underlying prostate cancer predisposition.
Original languageEnglish GB
Pages (from-to)939-939
JournalGenetic Epidemiology
Volume34
Issue number8
DOIs
StatePublished - Dec 2010

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