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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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Initiating maize pre-breeding programs using genomic selection to harness polygenic variation from landrace populations 

Gorjanc, G.; Jenko, J.; Hearne, S.; Hickey, J.M. (BioMed Central, 2016)
Background: The limited genetic diversity of elite maize germplasms raises concerns about the potential to breed for new challenges. Initiatives have been formed over the years to identify and utilize useful diversity from ...
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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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Increased prediction accuracy in wheat breeding trials using a marker x environment interaction Genomic Selection model 

Lopez-Cruz, M.; Poland, J.; Jannink, J.L.; De los Campos, G.; Crossa, J.; Singh, R.P.; Dreisigacker, S.; Bonnett, D.; Autrique, E. (Genetics Society of America, 2015)
Genomic selection (GS) models use genome-wide genetic information to predict genetic values of candidates of selection. Originally, these models were developed without considering genotype · environment interaction( G·E). ...
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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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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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High-throughput phenotyping enabled genetic dissection of crop lodging in wheat 

Singh, D.; Xu Wang; Kumar, U.; Liangliang Gao; Muhammad Noor; Imtiaz, M.; Singh, R.P.; Poland, J.A. (Frontiers, 2019)
Novel high-throughput phenotyping (HTP) approaches are needed to advance the understanding of genotype-to-phenotype and accelerate plant breeding. The first generation of HTP has examined simple spectral reflectance traits ...
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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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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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Author
Crossa, J. (25)Montesinos-Lopez, Osval Antonio (11)Singh, R.P. (10)Burgueño, J. (9)Pérez-Rodríguez, Paulino (9)Poland, Jesse (8)Dreisigacker, S. (7)Montesinos-López, A. (6)Rutkoski, Jessica (6)ZHANG, XUECAI (6)... View More
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2021 (4)2020 (9)2019 (4)2018 (7)2017 (4)2016 (5)2015 (4)2014 (3)2013 (2)
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Article (42)
Agrovoc
MARKER-ASSISTED SELECTION (19)GENOMICS (16)BAYESIAN THEORY (11)WHEAT (11)PLANT BREEDING (10)STATISTICAL METHODS (10)ARTIFICIAL SELECTION (8)GENOTYPE ENVIRONMENT INTERACTION (7)MAIZE (7)DATA ANALYSIS (5)... View More
Keywords
Genomic Selection (42)
GenPred (17)Shared Data Resources (16)Genomic Prediction (9)Wheat Breeding (4)Deep Learning (3)GBLUP (3)Support Vector Machine (3)Association Mapping (2)Best Linear Unbiased Prediction (2)... View More


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