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dc.contributor.authorPotenciano, Víctores_ES
dc.contributor.authorAbad-Grau, María M.es_ES
dc.contributor.authorAlcina, Antonioes_ES
dc.contributor.authorMatesanz, F.es_ES
dc.date.accessioned2016-04-19T11:18:46Z-
dc.date.available2016-04-19T11:18:46Z-
dc.date.issued2016-01-19-
dc.identifier.citationBMC Medical Genomicses_ES
dc.identifier.issn1755-8794-
dc.identifier.urihttp://hdl.handle.net/10261/131242-
dc.description.abstractBackground: Various approaches are being used to predict individual risk to polygenic diseases from data provided by genome-wide association studies. As there are substantial differences between the diseases investigated, the data sets used and the way they are tested, it is difficult to assess which models are more suitable for this task. Results: We compared different approaches for seven complex diseases provided by the Wellcome Trust Case Control Consortium (WTCCC) under a within-study validation approach. Risk models were inferred using a variety of learning machines and assumptions about the underlying genetic model, including a haplotype-based approach with different haplotype lengths and different thresholds in association levels to choose loci as part of the predictive model. In accordance with previous work, our results generally showed low accuracy considering disease heritability and population prevalence. However, the boosting algorithm returned a predictive area under the ROC curve (AUC) of 0.8805 for Type 1 diabetes (T1D) and 0.8087 for rheumatoid arthritis, both clearly over the AUC obtained by other approaches and over 0.75, which is the minimum required for a disease to be successfully tested on a sample at risk, which means that boosting is a promising approach. Its good performance seems to be related to its robustness to redundant data, as in the case of genome-wide data sets due to linkage disequilibrium. Conclusions: In view of our results, the boosting approach may be suitable for modeling individual predisposition to Type 1 diabetes and rheumatoid arthritis based on genome-wide data and should be considered for more in-depth researches_ES
dc.description.sponsorshipThis work was supported by the Spanish Secretary of Research, Development and Innovation [TIN2010-20900-C04-1]; the Spanish Health Institute Carlos III [PI13/02714]and [PI13/01527] and the Andalusian Research Program under project P08-TIC-03717 with the help of the European Regional Development Fund (ERDF)es_ES
dc.language.isoenges_ES
dc.publisherBioMed Centrales_ES
dc.relation.isversionofPublisher's versiones_ES
dc.rightsopenAccesses_ES
dc.titleA comparison of genomic profiles of complex diseases under different modelses_ES
dc.typeartículoes_ES
dc.identifier.doi10.1186/s12920-015-0157-2-
dc.description.peerreviewedPeer reviewedes_ES
dc.relation.publisherversionhttp://www.ncbi.nlm.nih.gov/pubmed/26782991es_ES
dc.identifier.e-issn1755-8794-
dc.rights.licensehttp://www.biomedcentral.com/about/policieses_ES
dc.relation.csices_ES
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