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Whole Genome Prediction of Bladder Cancer Risk With the Bayesian LASSO

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dc.contributor Universitat de Vic. Escola Politècnica Superior
dc.contributor Universitat de Vic. Grup de Recerca en Bioinformàtica i Estadística Mèdica
dc.contributor.author López de Maturana, Evangelina
dc.contributor.author Chanock, Stephen
dc.contributor.author Picornell, A.C.
dc.contributor.author Rothman, Nathaniel
dc.contributor.author Herranz, J.
dc.contributor.author Calle, M. Luz
dc.contributor.author García-Closas, Montserrat
dc.contributor.author Marenne, Gaëlle
dc.contributor.author Brand, A.
dc.contributor.author Tardón, Adonina
dc.contributor.author Carrato, Alfredo
dc.contributor.author Silverman, Debra T.
dc.contributor.author Kogevinas, Manolis
dc.contributor.author Gianola, D.
dc.contributor.author Real, Francisco X.
dc.contributor.author Malats i Riera, Núria
dc.date.accessioned 2014-07-10T09:32:28Z
dc.date.available 2014-07-10T09:32:28Z
dc.date.created 2014
dc.date.issued 2014
dc.identifier.citation De Maturana, E. L., Chanok, S. J., Picornell, A. C., Rothman, N., Herranz, J., Calle Rosingana, M. L., et al. (2014). Whole genome prediction of bladder cancer risk with the bayesian LASSO. Genetic Epidemiology, 38(5), 467-476. ca_ES
dc.identifier.issn 10982272
dc.identifier.uri http://hdl.handle.net/10854/3226
dc.description.abstract To build a predictive model for urothelial carcinoma of the bladder (UCB) risk combining both genomic and nongenomic data, 1,127 cases and 1,090 controls from the Spanish Bladder Cancer/EPICURO study were genotyped using the HumanHap 1M SNP array. After quality control filters, genotypes from 475,290 variants were available. Nongenomic information comprised age, gender, region, and smoking status. Three Bayesian threshold models were implemented including: (1) only genomic information, (2) only nongenomic data, and (3) both sources of information. The three models were applied to the whole population, to only nonsmokers, to male smokers, and to extreme phenotypes to potentiate the UCB genetic component. The area under the ROC curve allowed evaluating the predictive ability of each model in a 10-fold cross-validation scenario. Smoking status showed the highest predictive ability of UCB risk (AUCtest = 0.62). On the other hand, the AUC of all genetic variants was poorer (0.53). When the extreme phenotype approach was applied, the predictive ability of the genomic model improved 15%. This study represents a first attempt to build a predictive model for UCB risk combining both genomic and nongenomic data and applying state-of-the-art statistical approaches. However, the lack of genetic relatedness among individuals, the complexity of UCB etiology, as well as a relatively small statistical power, may explain the low predictive ability for UCB risk. The study confirms the difficulty of predicting complex diseases using genetic data, and suggests the limited translational potential of findings from this type of data into public health interventions. © 2014 WILEY PERIODICALS, INC. en
dc.description.sponsorship The work was partially supported by the Fondo de Investigacion Sanitaria, Instituto de Salud Carlos III (G03/174, 00/0745, PI051436, PI061614, PI09-02102, G03/174, and Sara Borrell fellowship to E. L. M.), Spain; Fundacio la Marato de TV3 (#050830); Red Tematica de Investigacion Cooperativa en Cancer (RTICC, (RTICC, #C03/009, #RD06/0020, and #RD12/0036/0050), Instituto de Salud Carlos III (ISCIII), Spanish Ministry of Economy and Competitiveness & European Regional Development Fund (ERDF) "Una manera de hacer Europa"); Asociacion Espanola Contra el Cancer (AECC); EU-FP7-201663-UROMOL; and NIH-RO1-CA089715 and CA34627; and by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, USA.
dc.format application/pdf
dc.format.extent 10 p. ca_ES
dc.language.iso eng ca_ES
dc.publisher Wiley ca_ES
dc.rights Tots els drets reservats ca_ES
dc.rights (c) Wiley [The definitive version is available at www3.interscience.wiley.com]
dc.subject.other Càncer ca_ES
dc.subject.other Genomes ca_ES
dc.title Whole Genome Prediction of Bladder Cancer Risk With the Bayesian LASSO en
dc.type info:eu-repo/semantics/article ca_ES
dc.identifier.doi https://doi.org/10.1002/gepi.21809
dc.relation.publisherversion http://onlinelibrary.wiley.com/doi/10.1002/gepi.21809/abstract;jsessionid=C9F70EAE5654F4ED48FF554BC54F181E.f03t04
dc.relation.publisherversion
dc.rights.accesRights info:eu-repo/semantics/closedAccess ca_ES
dc.type.version info:eu-repo/publishedVersion ca_ES
dc.indexacio Indexat a WOS/JCR
dc.indexacio Indexat a SCOPUS ca_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/201663

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