Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat

dc.creatorPérez-Rodríguez, P.
dc.creatorGianola, D.
dc.creatorGonzález-Camacho, J.M.
dc.creatorCrossa, J.
dc.creatorManes, Y.
dc.creatorDreisigacker, S.
dc.date2013-06-07T21:13:01Z
dc.date2013-06-07T21:13:01Z
dc.date2012
dc.date.accessioned2023-07-17T19:57:05Z
dc.date.available2023-07-17T19:57:05Z
dc.identifierNo
dc.identifierhttp://hdl.handle.net/10883/2970
dc.identifier10.1534/g3.112.003665
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7508995
dc.descriptionIn genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.
dc.description1595-1605
dc.formatPDF
dc.languageEnglish
dc.publisherGenetics Society of America
dc.publisherhttp://www.g3journal.org/content/2/12/1595.full
dc.rightsCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.
dc.rightsOpen Access
dc.source12
dc.source2
dc.sourceG3: Genes, Genomes, Genetics
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectGenPred
dc.subjectShared Data Resources
dc.subjectWHEAT
dc.subjectBAYESIAN THEORY
dc.subjectMATHEMATICAL MODELS
dc.subjectCROP FORECASTING
dc.titleComparison between linear and non-parametric regression models for genome-enabled prediction in wheat
dc.typeArticle


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