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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, 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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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: ...
Article
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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 ...
Article
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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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Yield gains and associated changes in an early yellow bi-parental maize population following genomic selection for Striga resistance and drought tolerance 

Badu-Apraku, B.; Talabi, O.; Fakorede, M.A.B.; Fasanmade, Y.; Gedil, M.; Magorokosho, C.; Asiedu, R. (BioMed Central, 2019)
Background: Maize yield potential is rarely maximized in sub-Saharan Africa (SSA) due to the devastating effects of drought stress and Striga hermonthica parasitism. This study was conducted to determine the gains in grain ...
Presentation
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Upstream research for accelerated genetic gain 

Olsen, M. (CIMMYT, 2018)
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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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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 resources in plant breeding for sustainable agriculture 

Thudi, M.; Palakurthi, R.; Schnable, J.C.; Annapurna Chitikineni; Dreisigacker, S.; Mace, E.; Rakesh Kumar Srivastava; Satyavathi, C.T.; Odeny, D.A.; Vijay Tiwari; Hon-Ming Lam; Yan-Bin Hong; Singh, V.K.; Guowei Li; Yunbi Xu; Xiao-Ping Chen; Kaila, S.; Nguyen, H.T.; Sivasankar, S.; Jackson, S.A.; Close, T.J.; Wan Shubo; Varshney, R.K. (Elsevier, 2021)
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Genetic ResourcesInstitutionalIntegrated DevelopmentMaizeSocioeconomicsSustainable IntensificationWheat

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Author
Crossa, J. (6)Beyene, Y. (4)Burgueño, J. (4)Olsen, Michael (4)Semagn, K. (4)ZHANG, XUECAI (4)Babu, R. (3)Boddupalli, Prasanna (3)Pérez-Rodríguez, Paulino (3)Ao Zhang (2)... View More
Date Issued
2021 (1)2020 (2)2019 (1)2018 (1)2017 (2)2015 (2)2014 (1)2013 (1)
Type
Article (10)Presentation (1)
Agrovoc
MARKER-ASSISTED SELECTION (6)GENOMICS (4)ARTIFICIAL SELECTION (3)DATA ANALYSIS (3)FORECASTING (3)MAIZE (3)STATISTICAL METHODS (3)CHROMOSOME MAPPING (2)GENOTYPE ENVIRONMENT INTERACTION (2)BAYESIAN THEORY (1)... View More
Keywords
Genomic Selection (11)
GenPred (3)Shared Data Resources (3)Best Linear Unbiased Prediction (2)Genomic Prediction (2)Association Mapping (1)Bayesian LASSO (1)CIMMYT (1)Fusarium Ear Rot (1)Gaussian Nonlinear Kernel (1)... View More


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