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dc.contributor.authorPotenciano, Victores_ES
dc.contributor.authorAbad Grau, María Del Mar es_ES
dc.contributor.authorAlcina Madueño, Antonioes_ES
dc.contributor.authorMatesanz, Fuencislaes_ES
dc.date.accessioned2018-03-13T09:38:07Z
dc.date.available2018-03-13T09:38:07Z
dc.date.issued2016
dc.identifier.citationPotenciano, V.; et al. A comparison of genomic profiles of complex diseases under different models. BMC MEDICAL GENOMICS, 9: 3 (2016). [http://hdl.handle.net/10481/49963]es_ES
dc.identifier.issn1755-8794
dc.identifier.urihttp://hdl.handle.net/10481/49963
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 research.en_EN
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). The authors are very grateful to the reviewers, as they believe that their comments have helped to substantially improve the quality of the paper.en_EN
dc.language.isoenges_ES
dc.publisherBiomed Centrales_ES
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 3.0 Licensees_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es_ES
dc.titleA comparison of genomic profiles of complex diseases under different modelsen_EN
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1186/s12920-015-0157-2


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