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Article

Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm

Mageto, E.K.; Crossa, J.; Perez-Rodriguez, P.; Dhliwayo, T.; Palacios-Rojas, N.; Lee, M.; Rui Guo; San Vicente, F.M.; Zhang, X.; Hindu, V. (Genetics Society of America, 2020)

Article

Genomic prediction enhanced sparse testing for multi-environment trials

Jarquín, D.; Howard, R.; Crossa, J.; Beyene, Y.; Gowda, M.; Martini, J.W.R.; Covarrubias, E.; Burgueño, J.; Pacheco Gil, R. A.; Grondona, M.; Wimmer, V.; Prasanna, B.M. (Genetics Society of America, 2020)

Article

Maximum a posteriori Threshold Genomic Prediction model for ordinal traits

Montesinos-López, A.; Gutierrez-Pulido, H.; Montesinos-Lopez, O.A.; Crossa, J. (Genetics Society of America, 2020)

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

Origin specific genomic selection: a simple process to optimize the favorable contribution of parents to progeny

Chin Jian Yang; Sharma, R.; Gorjanc, G.; Hearne, S.; Powell, W.; Mackay, I. (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

Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat

Rutkoski, J.; Poland, J.; Mondal, S.; Autrique, E.; Gonzalez-Perez, L.; Crossa, J.; Reynolds, M.P.; Singh, R.G. (Genetics Society of America, 2016)

Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal ...

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, 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 prediction in maize breeding populations with genotyping-by sequencing

Crossa, J.; Beyene, Y.; Semagn, K.; Perez, P.; Hickey, J.M.; Chen Charles; Campos, G. de los; 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, ...

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Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm

Genomic prediction enhanced sparse testing for multi-environment trials

Maximum a posteriori Threshold Genomic Prediction model for ordinal traits

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

Origin specific genomic selection: a simple process to optimize the favorable contribution of parents to progeny

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

Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat

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 prediction in maize breeding populations with genotyping-by sequencing

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Genomic prediction with genotype by environment interaction analysis for kernel zinc concentration in tropical maize germplasm

Genomic prediction enhanced sparse testing for multi-environment trials

Maximum a posteriori Threshold Genomic Prediction model for ordinal traits

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

Origin specific genomic selection: a simple process to optimize the favorable contribution of parents to progeny

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

Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat

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 prediction in maize breeding populations with genotyping-by sequencing