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Genome-enabled prediction of genetic values using radial basis function neural networks

Author: Gonzalez-Camacho, J.M.
Author: Campos, G. de los
Author: Perez, P.
Author: Gianola, D.
Author: Cairns, J.E.
Author: Mahuku, G.
Author: Babu, R.
Author: Crossa, J.
Year: 2012
ISSN: 0040-5752
URI: http://hdl.handle.net/10883/1889
Abstract: The availability of high density panels of molecular markers has prompted the adoption of genomic selection (GS) methods in animal and plant breeding. In GS, parametric, semi-parametric and non-parametric regressions models are used for predicting quantitative traits. This article shows how to use neural networks with radial basis functions (RBFs) for prediction with dense molecular markers. We illustrate the use of the linear Bayesian LASSO regression model and of two non-linear regression models, reproducing kernel Hilbert spaces (RKHS) regression and radial basis function neural networks (RBFNN) on simulated data and real maize lines genotyped with 55,000 markers and evaluated for several trait?environment combinations. The empirical results of this study indicated that the three models showed similar overall prediction accuracy, with a slight and consistent superiority of RKHS and RBFNN over the additive Bayesian LASSO model. Results from the simulated data indicate that RKHS and RBFNN models captured epistatic effects; however, adding non-signal (redundant) predictors (interaction between markers) can adversely affect the predictive accuracy of the non-linear regression models.
Format: PDF
Language: English
Publisher: Springer Verlag
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Type: Article
Region: Global
Pages: 759-771
Issue: 4
Volume: 125
DOI: 10.1007/s00122-012-1868-9
Publisher URI: https://link.springer.com/article/10.1007%2Fs00122-012-1868-9
Agrovoc: MARKER-ASSISTED SELECTION
Agrovoc: GENETIC MARKERS
Agrovoc: NEURAL NETWORKS
Agrovoc: BAYESIAN THEORY
Agrovoc: SIMULATION MODELS
Journal: Theoretical and Applied Genetics


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This item appears in the following Collection(s)

  • Genetic Resources
    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP
  • Maize
    Maize breeding, phytopathology, entomology, physiology, quality, and biotech

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