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Article

Genomic prediction models for count data

Montesinos-López, O.A.; Montesinos-López, A.; Pérez-Rodríguez, P.; Eskridge, K.; Xinyao He; Juliana, P.; Singh, P.; Crossa, J. (Springer Verlag; American Statistical Association; International Biometrics Society, 2015)

Whole genome prediction models are useful tools for breeders when selecting candidate individuals early in life for rapid genetic gains. However, most prediction models developed so far assume that the response variable ...

Article

An R Package for Bayesian analysis of multi-environment and multi-trait multi-environment data for genome-based prediction

Montesinos-Lopez, O.A.; Montesinos-Lopez, A.; Luna-Vazquez, F.J.; Toledo, F.H.; Perez-Rodriguez, P.; Lillemo, M.; Crossa, J. (Genetics Society of America, 2019)

Evidence that genomic selection (GS) is a technology that is revolutionizing plant breeding continues to grow. However, it is very well documented that its success strongly depends on statistical models, which are used by ...

Article

New deep learning genomic-based prediction model for multiple traits with binary, ordinal, and continuous phenotypes

Montesinos-Lopez, O.A.; Martin-Vallejo, J.; Crossa, J.; Gianola, D.; Hernández Suárez, C.M.; Montesinos-López, A.; Juliana, P.; Singh, R.P. (Genetics Society of America, 2019)

Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not rare in animal and plant breeding programs. However, there is a lack of statistical models that can exploit the correlation between ...

Article

A new deep learning calibration method enhances genome-based prediction of continuous crop traits

Montesinos-Lopez, O.A.; Montesinos-Lopez, A.; Mosqueda-Gonzalez, B.A.; Bentley, A.R.; Lillemo, M.; Varshney, R.K.; Crossa, J. (Frontiers, 2021)

Article

Prediction of near-term climate change impacts on UK wheat quality and the potential for adaptation through plant breeding

Fradgley, N.; Bacon, J.; Bentley, A.R.; Costa-Neto, G.; Cottrell, A.; Crossa, J.; Cuevas, J.; Kerton, M.; Pope, E.; Swarbreck, S.M.; Gardner, K.A. (Wiley, 2022)

Article

Genetic dissection of quantitative resistance to common rust (Puccinia sorghi) in tropical maize (Zea mays L.) by combined genome-wide association study, linkage mapping, and genomic prediction

Jiaojiao Ren; Zhimin Li; Penghao Wu; Ao Zhang; Yubo Liu; Guanghui Hu; Shiliang Cao; Jingtao Qu; Dhliwayo, T.; Hongjian Zheng; Olsen, M.; Prasanna, B.M.; San Vicente, F.M.; Zhang, X. (Frontiers, 2021)

Article

A zero altered Poisson random forest model for genomic-enabled prediction

Montesinos-Lopez, O.A.; Montesinos-López, A.; Mosqueda-Gonzalez, B.A.; Montesinos-Lopez, J.C.; Crossa, J.; Lozano-Ramirez, N.; Singh, P.K.; Valladares-Anguiano, F.A. (Genetics Society of America, 2021)

Article

Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials

Montesinos-Lopez, A.; Runcie, D.E.; Ibba, M.I.; Perez-Rodriguez, P.; Montesinos-Lopez, O.A.; Crespo Herrera, L.A.; Bentley, A.R.; Crossa, J. (Genetics Society of America, 2021)

Article

Bayesian multitrait kernel methods improve multienvironment genome-based prediction

Montesinos-Lopez, O.A.; Montesinos-Lopez, J.C.; Montesinos-Lopez, A.; Ramirez-Alcaraz, J.M.; Poland, J.A.; Singh, R.P.; Dreisigacker, S.; Crespo Herrera, L.A.; Mondal, S.; Velu, G.; Juliana, P.; Huerta-Espino, J.; Shrestha, S.; Varshney, R.K.; Crossa, J. (Oxford University Press, 2022)

Article

Identification of genomic regions associated with agronomic and disease resistance traits in a large set of multiple DH populations

Sadessa, K.; Beyene, Y.; Ifie, B.E.; Mahabaleswara, S.L.; Olsen, M.; Ogugo, V.; Dagne Wegary Gissa; Tongoona, P.; Danquah, E.; Offei, S.K.; Prasanna, B.M.; Gowda, M. (MDPI, 2022)

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Genomic prediction models for count data

An R Package for Bayesian analysis of multi-environment and multi-trait multi-environment data for genome-based prediction

New deep learning genomic-based prediction model for multiple traits with binary, ordinal, and continuous phenotypes

A new deep learning calibration method enhances genome-based prediction of continuous crop traits

Prediction of near-term climate change impacts on UK wheat quality and the potential for adaptation through plant breeding

Genetic dissection of quantitative resistance to common rust (Puccinia sorghi) in tropical maize (Zea mays L.) by combined genome-wide association study, linkage mapping, and genomic prediction

A zero altered Poisson random forest model for genomic-enabled prediction

Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials

Bayesian multitrait kernel methods improve multienvironment genome-based prediction

Identification of genomic regions associated with agronomic and disease resistance traits in a large set of multiple DH populations

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Genomic prediction models for count data

An R Package for Bayesian analysis of multi-environment and multi-trait multi-environment data for genome-based prediction

New deep learning genomic-based prediction model for multiple traits with binary, ordinal, and continuous phenotypes

A new deep learning calibration method enhances genome-based prediction of continuous crop traits

Prediction of near-term climate change impacts on UK wheat quality and the potential for adaptation through plant breeding

Genetic dissection of quantitative resistance to common rust (Puccinia sorghi) in tropical maize (Zea mays L.) by combined genome-wide association study, linkage mapping, and genomic prediction

A zero altered Poisson random forest model for genomic-enabled prediction

Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials

Bayesian multitrait kernel methods improve multienvironment genome-based prediction

Identification of genomic regions associated with agronomic and disease resistance traits in a large set of multiple DH populations