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Article

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

Crossa, J.; Martini, J.W.R.; Gianola, D.; Perez-Rodriguez, P.; Jarquín, D.; JULIANA P.; Montesinos-Lopez, O.A.; Cuevas, J. (Frontiers, 2019)

Presentation

Importancia da biometria no melhoramento

Toledo, F.H.; Juliana, P.; Crespo-Herrera, L.A.; Crossa, J.; Burgueño, J. (CIMMYT, 2019)

Article

A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data

Montesinos-Lopez, O.A.; Montesinos-Lopez, A.; Crossa, J.; Cuevas, J.; Montesinos-Lopez, J.C.; Salas Gutiérrez, Z.; Lillemo, M.; Juliana, P.; Singh, R.P. (Genetics Society of America, 2019)

In this paper we propose a Bayesian multi-output regressor stacking (BMORS) model that is a generalization of the multi-trait regressor stacking method. The proposed BMORS model consists of two stages: in the first stage, ...

Article

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

Cuevas, J.; Montesinos-Lopez, O.A.; Juliana, P.; Guzman, C.; Perez-Rodriguez, P.; González-Bucio, J.; Burgueño, J.; Montesinos-Lopez, A.; Crossa, J. (Genetics Society of America, 2019)

Kernel methods are flexible and easy to interpret and have been successfully used in genomic-enabled prediction of various plant species. Kernel methods used in genomic prediction comprise the linear genomic best linear ...

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)

Genome-enabled prediction plays an essential role in wheat breeding because it has the potential to increase the rate of genetic gain relative to traditional phenotypic and pedigree-based selection. Since the performance ...

Presentation

From genebank to field-leveraging genomics to identify and bring novel native variation to breeding pools

Romero, A.; Hickey, J.M.; Kilian, A.; Buckler, E.; Marshall, D.; Crossa, J.; Petroli, C.D.; Sansaloni, C.P.; Molnar, T.L.; Pixley, K.V.; Wenzl, P.; Sukhwinder-Singh; Burgueño, J.; Chen, C.; Salinas García, G.; Willcox, M.; Saint Pierre, C. (CIMMYT; SAGARPA; CGIAR, 2016)

Potentially valuable genetic variation, the raw material for crop improvement, remains untapped on genebank shelves, at a time when challenges to crop production are unprecedented. Genebanks should NOT be museums. They ...

Presentation

Implementation of genomic selection in the CIMMYT Global Wheat Program, learnings from the past 10 years

Dreisigacker, S.; Crossa, J.; Perez-Rodriguez, P.; Montesinos-Lopez, O.A.; Rosyara, U.; Juliana, P.; Mondal, S.; Crespo-Herrera, L.A.; Jarquín, D.; Velu, G.; Singh, R.P.; Braun, H.J. (CIMMYT, [2020])

Article

Evaluation and interpretation of interactions

Crossa, Jose; Vargas Hernández, M.; Cossani, C.M.; Alvarado, G.; Burgueño, J.; Mathews, K.L.; Reynolds, M.P. (American Society of Agronomy, 2013)

Understanding the factors that define a given interaction is important in agricultural, agronomic, and plant breeding research, where agronomic treatments or genotypes are evaluated under several environmental conditions ...

Article

Quantitative genetic studies with applications in plant breeding in the omics era

Jiankang Wang; Crossa, J.; Junyi Gai (Elsevier, 2020)

Article

A review of deep learning applications for genomic selection

Montesinos-Lopez, O.A.; Montesinos-Lopez, A.; Perez-Rodriguez, P.; Barrón-López, A.; Martini, J.W.R.; Fajardo-Flores, S.B.; Gaytan-Lugo, L.S.; Santana-Mancilla, P.C.; Crossa, J. (BioMed Central, 2021)

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

Importancia da biometria no melhoramento

A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data

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

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

From genebank to field-leveraging genomics to identify and bring novel native variation to breeding pools

Implementation of genomic selection in the CIMMYT Global Wheat Program, learnings from the past 10 years

Evaluation and interpretation of interactions

Quantitative genetic studies with applications in plant breeding in the omics era

A review of deep learning applications for genomic selection

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

Importancia da biometria no melhoramento

A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data

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

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

From genebank to field-leveraging genomics to identify and bring novel native variation to breeding pools

Implementation of genomic selection in the CIMMYT Global Wheat Program, learnings from the past 10 years

Evaluation and interpretation of interactions

Quantitative genetic studies with applications in plant breeding in the omics era

A review of deep learning applications for genomic selection