Journal article
Bayesian multitrait kernel methods improve multienvironment genome-based prediction
G3 Genes|Genomes|Genetics, Vol.12(2)
2021
Abstract
When multitrait data are available, the preferred models are those that are able to account for correlations between phenotypic traits because when the degree of correlation is moderate or large, this increases the genomic prediction accuracy. For this reason, in this article, we explore Bayesian multitrait kernel methods for genomic prediction and we illustrate the power of these models with three-real datasets. The kernels under study were the linear, Gaussian, polynomial, and sigmoid kernels; they were compared with the conventional Ridge regression and GBLUP multitrait models. The results show that, in general, the Gaussian kernel method outperformed conventional Bayesian Ridge and GBLUP multitrait linear models by 2.2–17.45% (datasets 1–3) in terms of prediction performance based on the mean square error of prediction. This improvement in terms of prediction performance of the Bayesian multitrait kernel method can be attributed to the fact that the proposed model is able to capture nonlinear patterns more efficiently than linear multitrait models. However, not all kernels perform well in the datasets used for evaluation, which is why more than one kernel should be evaluated to be able to choose the best kernel.
Details
- Title
- Bayesian multitrait kernel methods improve multienvironment genome-based prediction
- Authors/Creators
- O.A. Montesinos-López (Author/Creator) - Universidad de ColimaJ.C. Montesinos-López (Author/Creator) - Centro de Investigación en MatemáticasA. Montesinos-López (Author/Creator) - Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Blvd. Marcelino García Barragán #1421, Calzada Olímpica, C.P. 44430, Guadalajara, Jalisco, México.J.M. Ramírez-Alcaraz (Author/Creator) - Universidad de ColimaJ. Poland (Author/Creator) - Kansas State UniversityR. Singh (Author/Creator) - Centro Internacional de Mejoramiento de Maíz Y TrigoS. Dreisigacker (Author/Creator) - Centro Internacional de Mejoramiento de Maíz Y TrigoL. Crespo (Author/Creator) - Centro Internacional de Mejoramiento de Maíz Y TrigoS. Mondal (Author/Creator) - Centro Internacional de Mejoramiento de Maíz Y TrigoV. Govidan (Author/Creator) - Centro Internacional de Mejoramiento de Maíz Y TrigoP. Juliana (Author/Creator) - Centro Internacional de Mejoramiento de Maíz Y TrigoJ.H. Espino (Author/Creator) - Instituto Nacional de Investigaciones Forestales Agrícolas y PecuariasS. Shrestha (Author/Creator) - Kansas State UniversityR.K. Varshney (Author/Creator) - International Crops Research Institute for the Semi-Arid TropicsJ. Crossa (Author/Creator) - Centro Internacional de Mejoramiento de Maíz Y TrigoA. Lipka (Author/Creator)
- Publication Details
- G3 Genes|Genomes|Genetics, Vol.12(2)
- Publisher
- Oxford University Press
- Identifiers
- 991005544957907891
- Copyright
- © 2021 The Authors.
- Murdoch Affiliation
- Centre for Crop and Food Innovation; Food Futures Institute; State Agricultural Biotechnology Centre
- Language
- English
- Resource Type
- Journal article
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