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Article
Genomic prediction enhanced sparse testing for multi-environment trials
(Genetics Society of America, 2020)
Article
Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones
(Nature Publishing Group, 2016)
Genomic and pedigree predictions for grain yield and agronomic traits were carried out using high density molecular data on a set of 803 spring wheat lines that were evaluated in 5 sites characterized by several environmental ...
Article
Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea
(Nature Research; Springer Nature, 2018)
Genomic selection (GS) by selecting lines prior to field phenotyping using genotyping data has the potential to enhance the rate of genetic gains. Genotype × environment (G × E) interaction inclusion in GS models can improve ...
Article
Genomic-enabled prediction accuracies increased by modeling genotype × environment interaction in durum wheat
(Crop Science Society of America, 2018)
Genomic prediction studies incorporating genotype × environment (G×E) interaction effects are limited in durum wheat. We tested the genomic-enabled prediction accuracy (PA) of Genomic Best Linear Unbiased Predictor (GBLUP) ...
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
Genomic prediction with pedigree and genotype X environment interaction in spring wheat grown in South and West Asia, North Africa, and Mexico
(Genetics Society of America, 2017)
Developing genomic selection (GS) models is an important step in applying GS to accelerate the rate of genetic gain in grain yield in plant breeding. In this study, seven genomic prediction models under two cross-validation ...