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Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding 

Crain, J. L.; Mondal, S.; Rutkoski, J.; Singh, R.P.; Poland, J. (Crop Science Society of America, 2018)
Genomics and phenomics have promised to revolutionize the field of plant breeding. The integration of these two fields has just begun and is being driven through big data by advances in next-generation sequencing and ...
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Effect of trait heritability, training population size and marker density on genomic prediction accuracy estimation in 22 bi-parental tropical maize populations 

Ao Zhang; Hongwu Wang; Beyene, Y.; Semagn, K.; Yubo Liu; Shiliang Cao; Zhenhai Cui; Yanye Ruan; Burgueño, J.; San Vicente, F.M.; Olsen, M.; Prasanna, B.M.; Crossa, J.; Haiqiu Yu; Xuecai Zhang (Frontiers, 2017)
Genomic selection is being used increasingly in plant breeding to accelerate genetic gain per unit time. One of the most important applications of genomic selection in maize breeding is to predict and select the best ...
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Genomic selection outperforms marker assisted selection for grain yield and physiological traits in a maize doubled haploid population across water treatments 

Cerrudo, D.; Shiliang Cao; Yibing Yuan; Martinez, C.; Suarez, E.A.; Babu, R.; Xuecai Zhang; Trachsel, S. (Frontiers, 2018)
To increase genetic gain for tolerance to drought, we aimed to identify environmentally stable QTL in per se and testcross combination under well-watered (WW) and drought stressed (DS) conditions and evaluate the possible ...
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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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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 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 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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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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Author
Crossa, J. (42)Montesinos-Lopez, O.A. (16)Pérez-Rodríguez, P. (15)Singh, R.P. (14)Dreisigacker, S. (13)Poland, J. (12)Burgueño, J. (11)Zhang, Xuecai (10)Montesinos-López, A. (9)Olsen, M. (8)... View More
Date Issued
2022 (14)2021 (25)2020 (8)2019 (4)2018 (7)2017 (4)2016 (5)2015 (4)2014 (3)2013 (3)
Type
Article (78)
Agrovoc
MARKER-ASSISTED SELECTION (54)GENOMICS (22)WHEAT (21)PLANT BREEDING (16)MAIZE (14)BAYESIAN THEORY (11)BREEDING (10)GENOTYPE ENVIRONMENT INTERACTION (10)STATISTICAL METHODS (10)ARTIFICIAL SELECTION (8)... View More
Keywords
Genomic Selection (78)
GenPred (17)Shared Data Resources (16)Genomic Prediction (14)Genotyping by Sequencing (8)Genome-Wide Association Study (7)Wheat Breeding (7)Deep Learning (5)GBLUP (4)Prediction Accuracy (4)... View More
CGIAR Initiative
Accelerated Breeding (1)
CGIAR Impact Area
Climate adaptation & mitigation (1)Poverty reduction, livelihoods & jobs (1)
CGIAR Action Area
Genetic Innovation (1)
CGIAR Donor or Funder
Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AG2MW) (1)Agricultural Agreement Research Fund (JA) in Norway (1)Bill & Melinda Gates Foundation (1)CIMMYT CRP (maize and wheat) (1)Foundation for Research Levy on Agricultural Products (FFL) (1)... View More


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