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Article

Increased prediction accuracy in wheat breeding trials using a marker x environment interaction Genomic Selection model

Lopez-Cruz, M.; Poland, J.; Jannink, J.L.; De los Campos, G.; Crossa, J.; Singh, R.P.; Dreisigacker, S.; Bonnett, D.; Autrique, E. (Genetics Society of America, 2015)

Genomic selection (GS) models use genome-wide genetic information to predict genetic values of candidates of selection. Originally, these models were developed without considering genotype · environment interaction( G·E). ...

Article

A genomic selection index applied to simulated and real data

Ceron Rojas, J.J.; Crossa, J.; Arief, V.N.; Basford, K.E.; Rutkoski, J.; Jarquin, D.; Alvarado Beltrán, G.; Beyene, Y.; Fentaye Kassa Semagn; DeLacy, I.H. (Genetics Society of America, 2015)

A genomic selection index (GSI) is a linear combination of genomic estimated breeding values that uses genomic markers to predict the net genetic merit and select parents from a nonphenotyped testing population. Some authors ...

Article

Genomic-enabled prediction in maize using kernel models with genotype x environment interaction

Bandeira e Sousa, M.; Cuevas, J.; De Oliveira Couto, E.G.; Pérez-Rodríguez, P.; Jarquin, D.; Fritsche-Neto, R.; Burgueño, J.; Crossa, J. (Genetics Society of America, 2017)

Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: ...

Article

Genomic prediction in maize breeding populations with genotyping-by sequencing

Crossa, J.; Beyene, Y.; Semagn, K.; Perez, P.; Hickey, J.M.; Chen Charles; De los Campos, G.; Burgueño, J.; Windhausen, V.S.; Buckler, E.S.; Jannink, J.L.; Lopez Cruz, M.A.; Babu, R. (Genetics Society of America, 2013)

Genotyping-by-sequencing (GBS) technologies have proven capacity for delivering large numbers of marker genotypes with potentially less ascertainment bias than standard single nucleotide polymorphism (SNP) arrays. Therefore, ...

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)

Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical models for ordinal phenotypes to improve the accuracy of the selection of candidate genotypes. For this reason, in this ...

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Increased prediction accuracy in wheat breeding trials using a marker x environment interaction Genomic Selection model

A genomic selection index applied to simulated and real data

Genomic-enabled prediction in maize using kernel models with genotype x environment interaction

Genomic prediction in maize breeding populations with genotyping-by sequencing

A benchmarking between deep learning, support vector machine and bayesian threshold best linear unbiased prediction for predicting ordinal traits in plant breeding

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Increased prediction accuracy in wheat breeding trials using a marker x environment interaction Genomic Selection model

A genomic selection index applied to simulated and real data

Genomic-enabled prediction in maize using kernel models with genotype x environment interaction

Genomic prediction in maize breeding populations with genotyping-by sequencing

A benchmarking between deep learning, support vector machine and bayesian threshold best linear unbiased prediction for predicting ordinal traits in plant breeding