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Article

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 ...

Article

Enhancing hybrid prediction in pearl millet using genomic and/or multi-environment phenotypic information of inbreds

Jarquín, D.; Howard, R.; Zhikai Liang; Shashi K. Gupta; Schnable, J.C.; Crossa, J. (Frontiers Media, 2020)

Article

Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm

Mageto, E.K.; Crossa, J.; Perez-Rodriguez, P.; Dhliwayo, T.; Palacios-Rojas, N.; Lee, M.; Rui Guo; San Vicente, F.M.; Zhang, X.; Hindu, V. (Genetics Society of America, 2020)

Article

Genomic prediction enhanced sparse testing for multi-environment trials

Jarquín, D.; Howard, R.; Crossa, J.; Beyene, Y.; Gowda, M.; Martini, J.W.R.; Covarrubias, E.; Burgueño, J.; Pacheco Gil, R. A.; Grondona, M.; Wimmer, V.; Prasanna, B.M. (Genetics Society of America, 2020)

Article

Increasing genomic-enabled prediction accuracy by modeling genotype × environment interactions in kansas wheat

Jarquín, D.; Lemes da Silva, C.; Gaynor, C.; Poland, J.A.; Fritz, A.K.; Howard, R.; Battenfield, S.D.; Crossa, J. (CSSA :; Wiley, 2017)

Article

Climate change has increased genotype-environment interactions in wheat breeding

Wei Xiong; Reynolds, M.P.; Crossa, J.; Payne, T.S.; Schulthess, U.; Sonder, K.; Addimando, N.; Singh, R.P.; Ammar, K.; Gerard, B. (Research Square, 2020)

Article

Approximate genome-based kernel models for large data sets including main effects and interactions

Cuevas, J.; Montesinos-Lopez, O.A.; Martini, J.W.R.; Perez-Rodriguez, P.; Lillemo, M.; Crossa, J. (Frontiers, 2020)

Article

A reaction norm model for genomic selection using high-dimensional genomic and environmental data

Jarquín, D.; Crossa, J.; Lacaze, X.; Cheyron, P. Du; Daucourt, J.; Lorgeou, J.; Piraux, F.; Guerreiro, L.; Pérez, P.; Calus, M.; Burgueño, J.; Campos, G.de los (Springer, 2013)

In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in ...

Article

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

Genomic bayesian prediction model for count data with genotype X environment interaction

Montesinos-López, A.; Montesinos-López, O.A.; Burgueño, J.; Eskridge, K.; Falconi, E.E.; He, Xinyao; Singh, P.K.; Cichy, K.; Crossa, J. (Genetics Society of America, 2016)

Genomic tools allow the study of the whole genome and are facilitating the study of genotype-environment combinations and their relationship with phenotype. However, most genomic prediction models developed so far are ...

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Genomic prediction in CIMMYT maize and wheat breeding programs

Enhancing hybrid prediction in pearl millet using genomic and/or multi-environment phenotypic information of inbreds

Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm

Genomic prediction enhanced sparse testing for multi-environment trials

Increasing genomic-enabled prediction accuracy by modeling genotype × environment interactions in kansas wheat

Climate change has increased genotype-environment interactions in wheat breeding

Approximate genome-based kernel models for large data sets including main effects and interactions

A reaction norm model for genomic selection using high-dimensional genomic and environmental data

BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models

Genomic bayesian prediction model for count data with genotype X environment interaction

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Genomic prediction in CIMMYT maize and wheat breeding programs

Enhancing hybrid prediction in pearl millet using genomic and/or multi-environment phenotypic information of inbreds

Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm

Genomic prediction enhanced sparse testing for multi-environment trials

Increasing genomic-enabled prediction accuracy by modeling genotype × environment interactions in kansas wheat

Climate change has increased genotype-environment interactions in wheat breeding

Approximate genome-based kernel models for large data sets including main effects and interactions

A reaction norm model for genomic selection using high-dimensional genomic and environmental data

BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models

Genomic bayesian prediction model for count data with genotype X environment interaction