Now showing items 1-10 of 17
A reaction norm model for genomic selection using high-dimensional genomic and environmental data
In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in ...
Genomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper‑spectral image data
(BioMed Central, 2017)
Modern agriculture uses hyperspectral cameras that provide hundreds of reflectance data at discrete narrow bands in many environments. These bands often cover the whole visible light spectrum and part of the infrared and ...
Genomic prediction of gene bank wheat landraces
(Genetics Society of America, 2016)
This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including ...
Single-step genomic and pedigree genotype x environment interaction models for predicting wheat lines in international environments
(Crop Science Society of America, 2017)
Genomic prediction models have been commonly used in plant breeding but only in reduced datasets comprising a few hundred genotyped individuals. However, pedigree information for an entire breeding population is frequently ...
Modelación de la interacción genotipo x ambiente en rendimiento de hibridos de maiz blanco en ambientes múltiples
(Sociedad Mexicana de Fitogenética, 2015)
Los programas de fitomejoramiento enfocados a la obtención de genotipos con mayor rendimiento y estables en una amplia gama de condiciones ambientales enfrentan factores ambientales que enmascaran el potencial de los ...
Genomic prediction in CIMMYT maize and wheat breeding programs
(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 ...
Genomic prediction enhanced sparse testing for multi-environment trials
(Genetics Society of America, 2020)
Genomic prediction of genotype x environment interaction kernel regression models
(Crop Science Society of America, 2016)
In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this ...
Bayesian Genomic Prediction with Genotype x Environment Interaction Kernel Models
(Genetics Society of America, 2017)
The phenomenon of genotype · environment (G · E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G · E have been ...
BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models
(Genetics Society of America, 2018)
One of the major issues in plant breeding is the occurrence of genotype × environment (GE) interaction. Several models have been created to understand this phenomenon and explore it. In the genomic era, several models were ...