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Increased prediction accuracy in wheat breeding trials using a marker x environment interaction Genomic Selection model 

Lopez-Cruz, M.; Poland, J.; Jannink, J.L.; De los Campos, G.; Crossa, J.; Singh, R.P.; Dreisigacker, S.; Bonnett, D.; Autrique, E. (Genetics Society of America, 2015)
Genomic selection (GS) models use genome-wide genetic information to predict genetic values of candidates of selection. Originally, these models were developed without considering genotype · environment interaction( G·E). ...
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A genomic selection index applied to simulated and real data 

Ceron Rojas, J.J.; Crossa, J.; Arief, V.N.; Basford, K.E.; Rutkoski, J.; Jarquin, D.; Alvarado Beltrán, G.; Beyene, Y.; Fentaye Kassa Semagn; DeLacy, I.H. (Genetics Society of America, 2015)
A genomic selection index (GSI) is a linear combination of genomic estimated breeding values that uses genomic markers to predict the net genetic merit and select parents from a nonphenotyped testing population. Some authors ...
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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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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: ...
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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 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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Author
Crossa, J. (21)
Montesinos-Lopez, O.A. (12)Montesinos-López, A. (10)Pérez-Rodríguez, P. (7)Burgueño, J. (6)Juliana, P. (5)Montesinos-Lopez, J.C. (5)Singh, R.P. (4)Toledo, F.H. (4)Beyene, Y. (3)... View More
Date Issued
2022 (1)2021 (1)2020 (4)2019 (3)2018 (5)2017 (2)2016 (1)2015 (2)2013 (1)2012 (1)
Type
Article (21)
Agrovoc
BAYESIAN THEORY (10)GENOMICS (10)STATISTICAL METHODS (8)GENOTYPE ENVIRONMENT INTERACTION (7)DATA ANALYSIS (5)CROP FORECASTING (4)FORECASTING (4)MARKER-ASSISTED SELECTION (4)PLANT BREEDING (4)ARTIFICIAL SELECTION (3)... View More
Keywords
GenPred (21)
Shared Data Resources (20)Genomic Selection (15)Genomic Prediction (8)GBLUP (3)Deep Learning (2)Genomic Enabled Prediction Accuracy (2)Multi-Trait Multi-Environment (2)Support Vector Machine (2)Allocation of Nonoverlapping (1)... View More
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Global (1)


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