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Article
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Genomic prediction in CIMMYT maize and wheat breeding programs 

Crossa, J.; Perez, P.; Hickey, J.; Burgueño, J.; Ornella, L.; Ceron-Rojas, J.; Zhang, X.; Dreisigacker, S.; Babu, R.; Li, Y.; Bonnett, D.; Mathews, K. (Springer Nature, 2014)
Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending ...
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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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Genome-wide association study and QTL mapping reveal genomic loci associated with Fusarium ear rot resistance in tropical maize germplasm 

Jiafa Chen; Shrestha, R.; Ding, J.; Zheng, H.; Chunhua Mu; Wu, J.; Mahuku, G. (Genetics Society of America, 2016)
Fusarium ear rot (FER) incited by Fusarium verticillioides is a major disease of maize that reduces grain quality globally. Host resistance is the most suitable strategy for managing the disease. We report the results of ...
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Genetic architecture is more complex for resistance to Septoria tritici blotch than to Fusarium head blight in Central European winter wheat 

Mirdita, V.; Guozheng Liu; Yusheng Zhao; Miedaner, T.; Longin, F.H.; Gowda, M.; Florian Mette, M.; Reif, J.C. (BioMed Central, 2015)
Fusarium head blight (FHB) and Septoria tritici blotch (STB) severely impair wheat production. With the aim to further elucidate the genetic architecture underlying FHB and STB resistance, we phenotyped 1604 European wheat ...
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Harnessing diversity in wheat to enhance grain yield, climate resilience, disease and insect pest resistance and nutrition through conventional and modern breeding approaches 

Mondal, S.; Rutkoski, J.; Velu, G.; Singh, P.K.; Crespo-Herrera, L.A.; Guzman, C.; Bhavani, S.; Caixia Lan; Xinyao He; Singh, R.P. (Frontiers, 2016)
Current trends in population growth and consumption patterns continue to increase the demand for wheat, a key cereal for global food security. Further, multiple abiotic challenges due to climate change and evolving pathogen ...
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Wheat quality improvement at CIMMYT and the use of genomic selection on it 

Guzman, C.; Peña, R.; Singh, R.G.; Autrique, E.; Dreisigacker, S.; Crossa, J.; Rutkoski, J.; Poland, J.; Battenfield, S. (Elsevier, 2016)
The International Center for Maize and Wheat Improvement (CIMMYT) leads the Global Wheat Program, whose main objective is to increase the productivity of wheat cropping systems to reduce poverty in developing countries. ...
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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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Genomic prediction in maize breeding populations with genotyping-by sequencing 

Crossa, J.; Beyene, Y.; Semagn, K.; Perez, P.; Hickey, J.M.; Chen Charles; Campos, G. de los; 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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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. (5)Babu, R. (3)Burgueño, J. (3)Pérez-Rodríguez, Paulino (3)Dreisigacker, S. (2)Guzman, Carlos (2)Hickey, John (2)Rutkoski, Jessica (2)Singh, R.P. (2)Zhang, Xuecai (2)... View More
Date Issued
2019 (1)2018 (1)2017 (1)2016 (3)2015 (1)2014 (1)2013 (1)
Type
Article (8)
Agrovoc
ARTIFICIAL SELECTION (9)
STATISTICAL METHODS (4)DATA ANALYSIS (3)WHEAT (3)BAYESIAN THEORY (2)DISEASE RESISTANCE (2)FORECASTING (2)FUSARIUM (2)GENOTYPE ENVIRONMENT INTERACTION (2)PLANT BREEDING (2)... View More
Keywords
Genomic Selection (9)
GenPred (4)Shared Data Resources (4)Association Mapping (1)Bayesian LASSO (1)CIMMYT (1)Cisgenesis (1)Deep Learning (1)DH (1)Fusarium Head Blight (1)... View More


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