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Article
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 ...
Article
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: ...
Article
A Bayesian decision theory approach for genomic selection
(Genetics Society of America, 2018)
Plant and animal breeders are interested in selecting the best individuals from a candidate set for the next breeding cycle. In this paper, we propose a formal method under the Bayesian decision theory framework to tackle ...
Article
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 ...
Article
Maize responsiveness to Azospirillum brasilense: insights into genetic control, heterosis and genomic prediction
(Public Library of Science, 2019)
Article
Deep kernel for genomic and near infrared predictions in multi-environment breeding trials
(Genetics Society of America, 2019)
Kernel methods are flexible and easy to interpret and have been successfully used in genomic-enabled prediction of various plant species. Kernel methods used in genomic prediction comprise the linear genomic best linear ...
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