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

Author: Pérez-Rodríguez, P.
Author: Gianola, D.
Author: Gonzalez-Camacho, J.M.
Author: Crossa, J.
Author: Manes, Y.
Author: Dreisigacker, S.
Year: 2012
ISSN: No
URI: http://hdl.handle.net/10883/2970
Abstract: In 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.
Format: PDF
Language: English
Publisher: Genetics Society of America
Copyright: CIMMYT 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.
Type: Article
Region: Global
Pages: 1595-1605
Issue: 12
Volume: 2
DOI: 10.1534/g3.112.003665
Keywords: GenPred
Keywords: Shared Data Resources
Publisher URI: http://www.g3journal.org/content/2/12/1595.full
Agrovoc: WHEAT
Agrovoc: BAYESIAN THEORY
Agrovoc: MATHEMATICAL MODELS
Agrovoc: CROP FORECASTING
Journal: G3: Genes, Genomes, Genetics


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  • Wheat
    Wheat - breeding, phytopathology, physiology, quality, biotech

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