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Application of multi-SNP approaches Bayesian LASSO and AUC-RF to detect main effects of inflammatory-gene variants associated with bladder cancer risk

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dc.contributor.author López De Maturana, Evangelina
dc.contributor.author Ye, Yuanqing
dc.contributor.author Calle, M. Luz
dc.contributor.author Rothman, Nathaniel
dc.contributor.author Urrea, Víctor
dc.contributor.author Kogevinas, Manolis
dc.contributor.author Petrus, Sandra
dc.contributor.author Chanock, Stephen J.
dc.contributor.author Tardón, Adonina
dc.contributor.author García Closas, Montserrat
dc.contributor.author González Neira, Anna
dc.contributor.author Vellalta, Gemma
dc.contributor.author Carrato, Alfredo
dc.contributor.author Navarro i Cuartiellas, Arcadi, 1969-
dc.contributor.author Lorente-Galdós, Belén, 1981-
dc.contributor.author Silverman, Debra T.
dc.contributor.author Real, Francisco X.
dc.contributor.author Wu, Xifeng
dc.contributor.author Malats i Riera, Núria
dc.date.accessioned 2015-05-21T10:57:53Z
dc.date.available 2015-05-21T10:57:53Z
dc.date.issued 2013
dc.identifier.citation De Maturana EL, Ye Y, Calle ML, Rothman N, Urrea V, Kogevinas M et al. Application of multi-SNP approaches Bayesian LASSO and AUC-RF to detect main effects of inflammatory-gene variants associated with bladder cancer risk. PLoS ONE. 2013;8(12):e83745. DOI: 10.1371/journal.pone.0083745
dc.identifier.issn 1932-6203
dc.identifier.uri http://hdl.handle.net/10230/23607
dc.description.abstract The relationship between inflammation and cancer is well established in several tumor types, including bladder cancer. We performed an association study between 886 inflammatory-gene variants and bladder cancer risk in 1,047 cases and 988 controls from the Spanish Bladder Cancer (SBC)/EPICURO Study. A preliminary exploration with the widely used univariate logistic regression approach did not identify any significant SNP after correcting for multiple testing. We further applied two more comprehensive methods to capture the complexity of bladder cancer genetic susceptibility: Bayesian Threshold LASSO (BTL), a regularized regression method, and AUC-Random Forest, a machine-learning algorithm. Both approaches explore the joint effect of markers. BTL analysis identified a signature of 37 SNPs in 34 genes showing an association with bladder cancer. AUC-RF detected an optimal predictive subset of 56 SNPs. 13 SNPs were identified by both methods in the total population. Using resources from the Texas Bladder Cancer study we were able to replicate 30% of the SNPs assessed. The associations between inflammatory SNPs and bladder cancer were reexamined among non-smokers to eliminate the effect of tobacco, one of the strongest and most prevalent environmental risk factor for this tumor. A 9 SNP-signature was detected by BTL. Here we report, for the first time, a set of SNP in inflammatory genes jointly associated with bladder cancer risk. These results highlight the importance of the complex structure of genetic susceptibility associated with cancer risk.
dc.description.sponsorship The work was partially supported by the Fondo de Investigación Sanitaria, Instituto de Salud Carlos III (G03/174, 00/0745, PI051436, PI061614, PI09-02102, G03/174 and Sara Borrell fellowship to ELM) and Ministry of Science and Innovation (MTM2008-06747-C02-02 and FPU fellowship award to VU), Spain; AGAUR-Generalitat de Catalunya (Grant 2009SGR-581); Fundació la Marató de TV3; Red Temática de Investigación Cooperativa en Cáncer (RTICC); Asociación Española Contra el Cáncer (AECC); EU-FP7-201663; and RO1- CA089715 and CA34627; the Spanish National Institute for Bioinformatics (www.inab.org); and by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, USA. MD Anderson support for this project included U01 CA 127615 (XW); R01 CA 74880 (XW); P50 CA 91846 (XW, CPD); Betty B. Marcus Chair fund in Cancer Prevention (XW); UT Research Trust fund (XW) and R01 CA131335 (JG)
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Public Library of Science (PLoS)
dc.relation.ispartof PLoS ONE. 2013;8(12):e83745
dc.rights © 2013 de Maturana et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
dc.subject.other Bufeta -- Càncer
dc.subject.other Tabac -- Efectes fisiològics
dc.title Application of multi-SNP approaches Bayesian LASSO and AUC-RF to detect main effects of inflammatory-gene variants associated with bladder cancer risk
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1371/journal.pone.0083745
dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/MTM2008-06747
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/201663
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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