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Prediction of multiple-trait and multiple-environment genomic data using recommender systems 

Montesinos-Lopez, O.A.; Montesinos-Lopez, A.; Crossa, J.; Montesinos-López, J.C.; Mota-Sanchez, D.; Estrada-González, F.; Gillberg, J.; Singh, R.G.; Mondal, S.; Juliana, P. (Genetics Society of America, 2018)
In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. ...
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A Bayesian decision theory approach for genomic selection 

Villar-Hernandez, B.d.J.; Perez-Elizalde, S.; Crossa, J.; Perez-Rodriguez, P.; Toledo, F.H.; Burgueño, J. (Genetics Society of America, 2018)
Plant and animal breeders are interested in selecting the best individuals from a candidate set for the next breeding cycle. In this paper, we propose a formal method under the Bayesian decision theory framework to tackle ...
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A bayesian genomic regression model with skew normal random errors 

Pérez-Rodríguez, P.; Acosta-Pech, R.; Perez-Elizalde, S.; Velasco Cruz, C.; Suarez Espinosa, J.; Crossa, J. (Genetics Society of America, 2018)
Genomic selection (GS) has become a tool for selecting candidates in plant and animal breeding programs. In the case of quantitative traits, it is common to assume that the distribution of the response variable can be ...
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BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models 

Granato, I.; Cuevas, J.; Luna-Vazquez, F.J.; Crossa, J.; Montesinos-Lopez, O.A.; Burgueño, J.; Fritsche-Neto, R. (Genetics Society of America, 2018)
One of the major issues in plant breeding is the occurrence of genotype × environment (GE) interaction. Several models have been created to understand this phenomenon and explore it. In the genomic era, several models were ...
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Genomic-enabled prediction Kernel models with random intercepts for multi-environment trials 

Cuevas, J.; Granato, I.; Fritsche-Neto, R.; Montesinos-Lopez, O.A.; Burgueño, J.; Bandeira e Sousa, M.; Crossa, J. (Genetics Society of America, 2018)
In this study, we compared the prediction accuracy of the main genotypic effect model (MM) without G×E interactions, the multi-environment single variance G×E deviation model (MDs), and the multienvironment environment-specific ...

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Author
Crossa, J. (5)Burgueño, J. (3)Montesinos-Lopez, O.A. (3)Cuevas, J. (2)Fritsche-Neto, R. (2)Granato, I. (2)Pérez-Elizalde, S. (2)Pérez-Rodríguez, P. (2)Acosta-Pech, R. (1)E Sousa, M. (1)... View More
Date Issued
2018 (5)
Type
Article (5)
Agrovoc
GENOMICS (4)BAYESIAN THEORY (3)GENOTYPE ENVIRONMENT INTERACTION (3)REGRESSION ANALYSIS (2)SELECTION (2)STATISTICAL METHODS (2)SIMULATION (1)... View More
Keywords
GenPred (5)
Shared Data Resources (5)Genomic Selection (4)Assymetric Distributions (1)Bayesian Decision Theory (1)Bayesian Genomic Linear Regression (1)BGGE (1)BGLR (1)Collaborative Foltering (1)Data Augmentation (1)... View More


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