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Article
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An R Package for Bayesian analysis of multi-environment and multi-trait multi-environment data for genome-based prediction 

Montesinos-Lopez, O.A.; Montesinos-Lopez, A.; Luna-Vazquez, F.J.; Toledo, F.H.; Perez-Rodriguez, P.; Lillemo, M.; Crossa, J. (Genetics Society of America, 2019)
Evidence that genomic selection (GS) is a technology that is revolutionizing plant breeding continues to grow. However, it is very well documented that its success strongly depends on statistical models, which are used by ...
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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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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: ...
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 ...
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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; De los Campos, G.; 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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Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat 

Pérez-Rodríguez, P.; Gianola, D.; Gonzalez-Camacho, J.M.; Crossa, J.; Manes, Y.; Dreisigacker, S. (Genetics Society of America, 2012)
In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The ...
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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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Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm 

Mageto, E.K.; Crossa, J.; Perez-Rodriguez, P.; Dhliwayo, T.; Palacios-Rojas, N.; Lee, M.; Rui Guo; San Vicente, F.M.; Zhang, X.; Hindu, V. (Genetics Society of America, 2020)

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Author
Pérez-Rodríguez, P. (8)
Crossa, J. (7)Burgueño, J. (4)Cuevas, J. (2)De Los Campos, G. (2)Montesinos-Lopez, O.A. (2)Pérez-Elizalde, S. (2)Toledo, F.H. (2)Acosta-Pech, R. (1)Babu, R. (1)... View More
Date Issued
2020 (1)2019 (1)2018 (2)2017 (2)2013 (1)2012 (1)
Type
Article (8)
Agrovoc
BAYESIAN THEORY (5)GENOMICS (3)GENOTYPE ENVIRONMENT INTERACTION (3)STATISTICAL METHODS (3)ARTIFICIAL SELECTION (2)DATA ANALYSIS (2)FORECASTING (2)GENETICS (2)BREEDING (1)CROP FORECASTING (1)... View More
Keywords
GenPred (8)
Shared Data Resources (8)Genomic Selection (5)GBLUP (2)Genomic Prediction (2)Multi-Environment (2)Assymetric Distributions (1)Bayesian Decision Theory (1)Data Augmentation (1)Gaussian Kernel (1)... View More
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Global (1)


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