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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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New deep learning genomic-based prediction model for multiple traits with binary, ordinal, and continuous phenotypes 

Montesinos-Lopez, O.A.; Martin-Vallejo, J.; Crossa, J.; Gianola, D.; Hernández Suárez, C.M.; Montesinos-López, A.; Juliana, P.; Singh, R.P. (Genetics Society of America, 2019)
Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not rare in animal and plant breeding programs. However, there is a lack of statistical models that can exploit the correlation between ...
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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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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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A benchmarking between deep learning, support vector machine and bayesian threshold best linear unbiased prediction for predicting ordinal traits in plant breeding 

Montesinos-Lopez, O.A.; Martin-Vallejo, J.; Crossa, J.; Gianola, D.; Hernández Suárez, C.M.; Montesinos-Lopez, A.; Juliana, P.; Singh, R.P. (Genetics Society of America, 2019)
Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical models for ordinal phenotypes to improve the accuracy of the selection of candidate genotypes. For this reason, in this ...
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A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction 

Montesinos-Lopez, O.A.; Montesinos-López, A.; Crossa, J.; Toledo, F.H.; Montesinos-López, J.C.; Singh, P.K.; Juliana, P.; Salinas Ruiz. J. (Genetics Society of America, 2017)
When a plant scientist wishes to make genomic-enabled predictions of multiple traits measured in multiple individuals in multiple environments, the most common strategy for performing the analysis is to use a single trait ...
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Bayesian multitrait kernel methods improve multienvironment genome-based prediction 

Montesinos-Lopez, O.A.; Montesinos-Lopez, J.C.; Montesinos-Lopez, A.; Ramirez-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. (Oxford University Press, 2022)
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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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A genomic bayesian multi-trait and multi-environment model 

Montesinos-Lopez, O.A.; Montesinos-López, A.; Toledo, F.H.; Pérez-Hernández, O.; Eskridge, K.; Rutkoski, J.; Crossa, J. (Genetics Society of America, 2016)
When information on multiple genotypes evaluated in multiple environments is recorded, a multi-environment single trait model for assessing genotype · environment interaction (G · E) is usually employed. Comprehensive ...
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Author
Montesinos-Lopez, O.A. (13)
Crossa, J. (12)Montesinos-López, A. (10)Juliana, P. (5)Montesinos-Lopez, J.C. (5)Burgueño, J. (3)Cuevas, J. (3)Singh, P.K. (3)Singh, R.P. (3)Toledo, F.H. (3)... View More
Date Issued
2022 (1)2021 (1)2020 (2)2019 (3)2018 (3)2017 (2)2016 (1)
Type
Article (13)
Agrovoc
BAYESIAN THEORY (8)GENOMICS (7)GENOTYPE ENVIRONMENT INTERACTION (5)PLANT BREEDING (4)STATISTICAL METHODS (4)MARKER-ASSISTED SELECTION (3)MODELS (3)DATA (2)FORECASTING (2)ARTIFICIAL SELECTION (1)... View More
Keywords
GenPred (13)
Shared Data Resources (12)Genomic Selection (10)Genomic Prediction (7)Deep Learning (2)Multi-Environment (2)Multi-Trait Multi-Environment (2)Support Vector Machine (2)Bayesian Estimation (1)Bayesian Genomic Enabled Prediction (1)... View More


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