Now showing items 21-30 of 45
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: ...
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 ...
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 a large African maize population
(Crop Science Society of America (CSSA), 2017)
Genomic prediction (GP) combines genomewide marker data with phenotypic data in a training population to predict the genomic estimated breeding values of untested individuals in a relevant testing population. Our objective ...
Genomic and pedigree-based prediction for leaf, stem, and stripe rust resistance in wheat
The unceasing plant-pathogen arms race and ephemeral nature of some rust resistance genes have been challenging for wheat (Triticum aestivum L.) breeding programs and farmers. Hence, it is important to devise strategies ...
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 ...
Prediction of multiple-trait and multiple-environment genomic data using recommender systems
(Genetics Society of America, 2018)
In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. ...
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) ...
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 ...
New deep learning genomic-based prediction model for multiple traits with binary, ordinal, and continuous phenotypes
(Genetics Society of America, 2019)
Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not rare in animal and plant breeding programs. However, there is a lack of statistical models that can exploit the correlation between ...