Replication Data for: Bayesian multi-trait kernel methods improve multi-environment genome based prediction

Replication Data for: Bayesian multi-trait kernel methods improve multi-environment genome based prediction

When multi-trait data are available and the degree of correlation between phenotypic traits is moderate or large, genomic prediction accuracy can increase when models are used that account for correlations between the phenotypic traits. The data files associated with this dataset were used to explore Bayesian multi-trait kernel methods for genomic prediction. Linear, Gaussian, polynomial and sigmoid kernels were studied and compared with the GBLUP multi-trait models. The results of the analysis are reported in the accompanying article.

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Bibliographic Details
Main Authors: Montesinos-López, Osval A., Montesinos-López, José Cricelio, Montesinos-López, Abelardo, Ramírez-Alcaraz, Juan Manuel, Poland, Jesse, Singh, Ravi, Dreisigacker, Susanne, Crespo Herrera, Leonardo Abdiel, Govindan, Velu, Juliana, Philomin, Huerta Espino, Julio, Shrestha, Sandesh, Varshney, Rajeev K., Crossa, Jose
Other Authors: Dreher, Kate
Format: Genotypic data biblioteca
Language:English
Published: CIMMYT Research Data & Software Repository Network 2021
Subjects:Agricultural Sciences, Wheat, Triticum aestivum, Agricultural research, Plant Breeding, Plant height, Heading time, Maturity time, Grain yield,
Online Access:https://hdl.handle.net/11529/10548629
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https://hdl.handle.net/11529/10548629

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