Now showing items 1-10 of 15
A review of deep learning applications for genomic selection
(BioMed Central, 2021)
Application of multi-trait Bayesian decision theory for parental genomic selection
(Genetics Society of America, 2021)
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 ...
Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs
(Springer Nature, 2015)
One of the most important applications of genomic selection in maize breeding is to predict and identify the best untested lines from biparental populations, when the training and validation sets are derived from the same ...
An R Package for Bayesian analysis of multi-environment and multi-trait multi-environment data for genome-based prediction
(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 ...
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 ...
Genomic-enabled prediction in maize using kernel models with genotype x environment interaction
(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: ...