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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

Genomic prediction of gene bank wheat landraces

Crossa, J.; Jarquin, D.; Franco, J.; Pérez-Rodríguez, P.; Burgueño, J.; Saint Pierre, C.; Vikram, P.; Sansaloni, C.P.; Petroli, C.D.; Akdemir, D.; Sneller, C.; Reynolds, M.P.; Tattaris, M.; Payne, T.S.; Guzman, C.; Peña-Bautista, R.J.; Wenzl, P.; Sukhwinder Singh (Genetics Society of America, 2016)

This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including ...

Article

Single-step genomic and pedigree genotype x environment interaction models for predicting wheat lines in international environments

Perez-Rodriguez, P.; Crossa, J.; Rutkoski, J.; Singh, R. P.; Legarra, A.; Autrique, E.; De los Campos, G.; Burgueño, J.; Dreisigacker, S. (Crop Science Society of America, 2017)

Genomic prediction models have been commonly used in plant breeding but only in reduced datasets comprising a few hundred genotyped individuals. However, pedigree information for an entire breeding population is frequently ...

Article

Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat

Krause, M.; Gonzalez-Perez, L.; Crossa, J.; Perez-Rodriguez, P.; Montesinos-Lopez, O.A.; Singh, R.P.; Dreisigacker, S.; Poland, J.A.; Rutkoski, J.; Sorrells, M.E.; Gore, M.A.; Mondal, S. (Genetics Society of America, 2019)

Article

Maize responsiveness to Azospirillum brasilense: insights into genetic control, heterosis and genomic prediction

Vidotti, M.S.; Matias, F.I.; Alves, F.C.; Perez-Rodriguez, P.; Alvarado Beltrán, G.; Burgueño, J.; Crossa, J.; Fritsche-Neto, R. (Public Library of Science, 2019)

Article

Empirical comparison of tropical maize hybrids selected through genomic and phenotypic selections

Beyene, Y.; Gowda, M.; Olsen, M.; Robbins, K.; Perez-Rodriguez, P.; Alvarado Beltrán, G.; Dreher, K.A.; Yanxin Gao; Mugo, S.N.; Prasanna, B.M.; Crossa, J. (Frontiers, 2019)

Article

Joint use of genome, pedigree, and their interaction with environment for predicting the performance of wheat lines in new environments

Howard, R.; Gianola, D.; Montesinos-Lopez, O.A.; Juliana, P.; Singh, R.P.; Poland, J.A.; Shrestha, S.; Perez-Rodriguez, P.; Crossa, J.; Jarquin, D. (Genetics Society of America, 2019)

Article

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Genomic prediction in CIMMYT maize and wheat breeding programs

Genomic prediction of gene bank wheat landraces

Single-step genomic and pedigree genotype x environment interaction models for predicting wheat lines in international environments

Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat

Maize responsiveness to Azospirillum brasilense: insights into genetic control, heterosis and genomic prediction

Empirical comparison of tropical maize hybrids selected through genomic and phenotypic selections

Joint use of genome, pedigree, and their interaction with environment for predicting the performance of wheat lines in new environments

Deep kernel and deep learning for genome-based prediction of single traits in multienvironment breeding trials

Deep kernel for genomic and near infrared predictions in multi-environment breeding trials

Bayesian Genomic Prediction with Genotype x Environment Interaction Kernel Models

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Genomic prediction in CIMMYT maize and wheat breeding programs

Genomic prediction of gene bank wheat landraces

Single-step genomic and pedigree genotype x environment interaction models for predicting wheat lines in international environments

Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat

Maize responsiveness to Azospirillum brasilense: insights into genetic control, heterosis and genomic prediction

Empirical comparison of tropical maize hybrids selected through genomic and phenotypic selections

Joint use of genome, pedigree, and their interaction with environment for predicting the performance of wheat lines in new environments

Deep kernel and deep learning for genome-based prediction of single traits in multienvironment breeding trials

Deep kernel for genomic and near infrared predictions in multi-environment breeding trials

Bayesian Genomic Prediction with Genotype x Environment Interaction Kernel Models