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Article

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)

Article

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)

Article

A multivariate Poisson deep learning model for genomic prediction of count data

Montesinos-Lopez, O.A.; Montesinos-Lopez, J.C.; Singh, P.K.; Lozano-Ramirez, N.; Barrón-López, A.; Montesinos-Lopez, A.; Crossa, J. (Genetics Society of America, 2020)

Article

A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction

Montesinos-Lopez, O.A.; Montesinos-López, A.; Crossa, J.; Toledo, F.H.; Montesinos-López, J.C.; Singh, P.K.; Juliana, P.; Salinas Ruiz. J. (Genetics Society of America, 2017)

When a plant scientist wishes to make genomic-enabled predictions of multiple traits measured in multiple individuals in multiple environments, the most common strategy for performing the analysis is to use a single trait ...

Article

The use of unbalanced historical data for genomic selection in an international wheat breeding program

Dawson, J.C.; Endelman, J.B.; Heslot, N.; Crossa, J.; Poland, J.; Dreisigacker, S.; Manes, Y.; Sorrells, M.E.; Jean-Luc Jannink (Elsevier, 2013)

Genomic selection (GS) offers breeders the possibility of using historic data and unbalanced breeding trials to form training populations for predicting the performance of new lines. However, when using datasets that are ...

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

Genome‑wide association and genomic prediction of resistance to maize lethal necrosis disease in tropical maize germplasm

Gowda, M.; Das, B.; Makumbi, D.; Babu, R.; Semagn, K.; Mahuku, G.; Olsen, M.S.; Bright, J.M.; Beyene, Y.; Prasanna, B.M. (Springer, 2015)

The maize lethal necrosis disease (MLND) caused by synergistic interaction of Maize chlorotic mottle virus and Sugarcane mosaic virus, and has emerged as a serious threat to maize production in eastern Africa since 2011. ...

Article

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 ...

Presentation

Upstream research for accelerated genetic gain

Olsen, M. (CIMMYT, 2018)

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)

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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

High-throughput phenotyping enabled genetic dissection of crop lodging in wheat

A multivariate Poisson deep learning model for genomic prediction of count data

A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction

The use of unbalanced historical data for genomic selection in an international wheat breeding program

Genomic prediction in CIMMYT maize and wheat breeding programs

Genome‑wide association and genomic prediction of resistance to maize lethal necrosis disease in tropical maize germplasm

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

Upstream research for accelerated genetic gain

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

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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

High-throughput phenotyping enabled genetic dissection of crop lodging in wheat

A multivariate Poisson deep learning model for genomic prediction of count data

A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction

The use of unbalanced historical data for genomic selection in an international wheat breeding program

Genomic prediction in CIMMYT maize and wheat breeding programs

Genome‑wide association and genomic prediction of resistance to maize lethal necrosis disease in tropical maize germplasm

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

Upstream research for accelerated genetic gain

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