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Article
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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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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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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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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 ...
Article
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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 ...
Article
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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 ...
Article
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Use of genomic selection and breeding simulation in cross prediction for improvement of yield and quality in wheat (Triticum aestivum L.) 

Ji Yao; Dehui Zhao; Xinmin Chen; Yong Zhang; Jiankang Wang (Elsevier, 2018)
In wheat breeding, it is a difficult task to select the most suitable parents for making crosses aimed at the improvement of both grain yield and grain quality. By quantitative genetics theory, the best cross should have ...
Presentation
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Upstream research for accelerated genetic gain 

Olsen, M. (CIMMYT, 2018)

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Genetic ResourcesInstitutionalIntegrated DevelopmentMaizeSocioeconomicsSustainable IntensificationWheat

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Author
Crossa, J. (4)Burgueño, J. (3)Cuevas, Jaime (2)Fritsche-Neto, Roberto (2)Granato, Italo (2)Montesinos-Lopez, Osval Antonio (2)Pérez-Elizalde, Sergio (2)Pérez-Rodríguez, Paulino (2)Acosta-Pech, R. (1)Babu, R. (1)... View More
Date Issued
2018 (8)
Type
Article (7)Presentation (1)
Agrovoc
GENOMICS (4)BAYESIAN THEORY (3)GENOTYPE ENVIRONMENT INTERACTION (2)MARKER-ASSISTED SELECTION (2)REGRESSION ANALYSIS (2)SELECTION (2)ARTIFICIAL SELECTION (1)BREEDING (1)BREEDING VALUE (1)CROSSBREEDING (1)... View More
Keywords
Genomic Selection (8)
GenPred (4)Shared Data Resources (4)Assymetric Distributions (1)Bayesian Decision Theory (1)Bayesian Genomic Linear Regression (1)BGGE (1)BGLR (1)Breeding Simulation (1)Cross Prediction (1)... View More


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