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Mostrando ítems 1-10 de 14
Article
Genomic-enabled prediction with classification algorithms
(Springer Nature, 2014)
Pearson’s correlation coefficient (ρ) is the most commonly reported metric of the success of prediction in genomic selection (GS). However, in real breeding ρ may not be very useful for assessing the quality of the regression ...
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Bayesian multitrait kernel methods improve multienvironment genome-based prediction
(Oxford University Press, 2022)
Article
Harnessing translational research in wheat for climate resilience
(Oxford University Press, 2021)
Article
Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data
(John Wiley & Sons Inc., 2022)
Article
Prospects and challenges of applied genomic selection-a new paradigm in breeding for grain yield in bread wheat
(Crop Science Society of America, 2018)
Genomic selection (GS) has been promising for increasing genetic gains in several species. Therefore, we evaluated the potential integration of GS for grain yield (GY) in bread wheat (Triticum aestivum L.) in CIMMYT's elite ...
Article
Hybrid wheat prediction using genomic, pedigree, and environmental covariables interaction models
(Crop Science Society of America, 2019)
In this study, we used genotype × environment interactions (G×E) models for hybrid prediction, where similarity between lines was assessed by pedigree and molecular markers, and similarity between environments was accounted ...
Article
Genomic prediction of genetic values for resistance to wheat rusts
(Crop Science Society of America, 2012)
Durable resistance to the rust diseases of wheat (Triticum aestivum L.) can be achieved by developing lines that have race-nonspecific adult plant resistance conferred by multiple minor slow-rusting genes. Genomic selection ...
Article
Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat
(Springer, 2019)
Genomic selection and high-throughput phenotyping (HTP) are promising tools to accelerate breeding gains for high-yielding and climate-resilient wheat varieties. Hence, our objective was to evaluate them for predicting ...