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Genomic prediction enhanced sparse testing for multi-environment trials 

Jarquín, D.; Howard, R.; Crossa, J.; Beyene, Y.; Gowda, M.; Martini, J.W.R.; Covarrubias, E.; Burgueño, J.; Pacheco Gil, R. A.; Grondona, M.; Wimmer, V.; Prasanna, B.M. (Genetics Society of America, 2020)
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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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Genomic-enabled prediction in maize using kernel models with genotype x environment interaction 

Bandeira e Sousa, M.; Cuevas, J.; De Oliveira Couto, E.G.; Pérez-Rodríguez, P.; Jarquin, D.; Fritsche-Neto, R.; Burgueño, J.; Crossa, J. (Genetics Society of America, 2017)
Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: ...
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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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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 ...
Article
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Bayesian Genomic Prediction with Genotype x Environment Interaction Kernel Models 

Cuevas, J.; Montesinos-López, Osval A.; Burgueño, J.; Pérez-Rodríguez, P.; De los Campos, G. (Genetics Society of America, 2017)
The phenomenon of genotype · environment (G · E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G · E have been ...
Article
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Genomic prediction in maize breeding populations with genotyping-by sequencing 

Crossa, J.; Beyene, Y.; Semagn, K.; Perez, P.; Hickey, J.M.; Chen Charles; Campos, G. de los; Burgueño, J.; Windhausen, V.S.; Buckler, E.S.; Jannink, J.L.; Lopez Cruz, M.A.; Babu, R. (Genetics Society of America, 2013)
Genotyping-by-sequencing (GBS) technologies have proven capacity for delivering large numbers of marker genotypes with potentially less ascertainment bias than standard single nucleotide polymorphism (SNP) arrays. Therefore, ...

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Author
Burgueño, J. (7)
Crossa, J. (6)Cuevas, Jaime (4)Pérez-Rodríguez, Paulino (4)Fritsche-Neto, Roberto (3)Montesinos-Lopez, Osval Antonio (3)Beyene, Y. (2)De Los Campos, Gustavo (2)E Sousa, M. (2)Granato, Italo (2)... View More
Date Issued
2020 (1)2018 (3)2017 (2)2013 (1)
Type
Article (7)
Agrovoc
GENOMICS (5)GENOTYPE ENVIRONMENT INTERACTION (5)BAYESIAN THEORY (3)ARTIFICIAL SELECTION (2)DATA ANALYSIS (2)FORECASTING (2)SELECTION (2)STATISTICAL METHODS (2)GENETICS (1)MAIZE (1)... View More
Keywords
GenPred (7)
Shared Data Resources (7)
Genomic Selection (6)Genomic Enabled Prediction Accuracy (2)Allocation of Nonoverlapping (1)Bayesian Decision Theory (1)Bayesian Genomic Linear Regression (1)BGGE (1)BGLR (1)Deviations from Main Genetic Effects (1)... View More


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