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Bayesian multitrait kernel methods improve multienvironment genome-based prediction

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Title Bayesian multitrait kernel methods improve multienvironment genome-based prediction
 
Creator Montesinos-Lopez, Osval Antonio
Montesinos-Lopez, José Cricelio
Montesinos-Lopez, Abelardo
Ramirez-Alcaraz, Juan Manuel
Poland, Jesse A.
Singh, Ravi P.
Dreisigacker, Susanne
Crespo-Herrera, Leonardo A.
Mondal, Suchismita
Velu, Govindan
Juliana, Philomin
Huerta-Espino, Julio
Shrestha, Sandesh
Varshney, Rajeev K.
Crossa, Jose
 
Subject plant breeding
genomics
forecasting
Bayesian theory
 
Description 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.
 
Date 2022-02-12
2022-12-28T14:59:21Z
2022-12-28T14:59:21Z
 
Type Journal Article
 
Identifier Montesinos-López, O. A., Montesinos-López, J. C., Montesinos-López, A., Ramírez-Alcaraz, J. M., Poland, J., Singh, R., Dreisigacker, S., Crespo, L., Mondal, S., Govindan, V., Juliana, P., Espino, J. H., Shrestha, S., Varshney, R. K., & Crossa, J. (2022). Bayesian multitrait kernel methods improve multienvironment genome-based prediction. G3 Genes|Genomes|Genetics, 12(2), jkab406. https://hdl.handle.net/10883/21989
2160-1836
https://hdl.handle.net/10568/126371
https://hdl.handle.net/10883/21989
https://doi.org/10.1093/g3journal/jkab406
 
Language en
 
Rights CC-BY-4.0
Open Access
 
Format application/pdf
 
Publisher Oxford University Press
 
Source G3: Genes | Genomes | Genetics