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Montesinos‐López, O. A., Montesinos-López, J. C., Montesinos‐López, A., Ramírez-Alcaraz, J. M., Poland, J. A., Singh, R. P., Dreisigacker, S., Crespo Herrera, L. A., Mondal, S., Velu, G., Juliana, P., Huerta-Espino, J., Shrestha, S., Varshney, R. K., & Crossa, J. (2021). Bayesian multitrait kernel methods improve multienvironment genome-based prediction. G3: Genes, Genomes, Genetics, 12(2), jkab406. https://doi.org/10.1093/g3journal/jkab406
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Abstract
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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.
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Journal
G3: Genes, Genomes, Genetics
Journal volume
12
Journal issue
2
Article number
jkab406
Place of Publication
Bethesda, MD (USA)
Publisher
Oxford University Press
Related Datasets
CGIAR Initiatives
Initiative
Accelerated Breeding
Breeding Resources
Breeding Resources
Impact Area
Nutrition, health & food security
Poverty reduction, livelihoods & jobs
Poverty reduction, livelihoods & jobs
Action Area
Genetic Innovation
Donor or Funder
Bill & Melinda Gates Foundation (BMGF)
United States Agency for International Development (USAID)
CGIAR Research Program on Wheat
Foundation for Research Levy on Agricultural Products (FFL)
Agricultural Agreement Research Fund
CGIAR Research Program on Maize
United States Agency for International Development (USAID)
CGIAR Research Program on Wheat
Foundation for Research Levy on Agricultural Products (FFL)
Agricultural Agreement Research Fund
CGIAR Research Program on Maize