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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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Incorporation of parental phenotypic data into multi‐omic models improves prediction of yield‐related traits in hybrid rice 

Yang Xu; Yue Zhao; Xin Wang; Ying Ma; Pengcheng Li; Zefeng Yang; Zhang, X.; Chenwu Xu; Shizhong Xu (Wiley, 2020)
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Genomic-enabled prediction with classification algorithms 

Ornella, L.; Pérez, P.; Tapia, E.; González-Camacho, J.M.; Burgueño, J.; Zhang, X.; Singh, S.; Vicente, F.S.; Bonnett, D.; Dreisigacker, S.; Singh, R.; Long, N.; Crossa, J. (Springer Nature, 2014)
Pearson’s correlation coefficient (ρ) is the most commonly reported metric of the success of prediction in genomic selection (GS). However, in real breeding ρ may not be very useful for assessing the quality of the regression ...
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Genome-wide association study and genomic prediction of Fusarium ear rot resistance in tropical maize germplasm 

Yubo Liu; Guanghui Hu; Ao Zhang; Loladze, A.; Yingxiong Hu; Hui Wang; Jingtao Qu; Zhang, X.; Olsen, M.; San Vicente, F.M.; Crossa, J.; Feng Lin; Prasanna, B.M. (Elsevier, 2020)
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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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Author
Zhang, Xuecai (6)
Crossa, J. (4)Burgueño, J. (3)San Vicente Garcia, F.M. (3)Ao Zhang (2)Babu, R. (2)Bonnett, D. (2)Dreisigacker, S. (2)Olsen, M. (2)Ornella, Leonardo (2)... View More
Date Issued
2020 (2)2018 (1)2017 (1)2014 (2)
Type
Article (6)
Agrovoc
MARKER-ASSISTED SELECTION (4)MAIZE (3)ARTIFICIAL SELECTION (2)STATISTICAL METHODS (2)AGRONOMIC CHARACTERS (1)BAYESIAN THEORY (1)DENSITY (1)DROUGHT (1)ENVIRONMENTAL FACTORS (1)FILED EXPERIMENTATION (1)... View More
Keywords
Genomic Selection (6)
Bayesian LASSO (1)Best Linear Unbiased Prediction (1)CIMMYT (1)DH (1)Fusarium Ear Rot (1)Genome-Wide Association Study (1)Genomic Prediction (1)International Maize and Wheat Improvement Center (1)Marker Assisted Selection (1)... View More


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