Now showing items 1-10 of 24
Utilizing genomics and phenomics in CIMMYT wheat breeding
Increasing genetic gains in wheat through physiological genetics and breeding
In order to meet future wheat demand it is necessary to increase yield potential and develop stress adapted genotypes. To do so, research and breeding is conducted at CIMMYT through the International Wheat Yield Partnership ...
Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat
(Genetics Society of America, 2012)
In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The ...
Regularized selection indices for breeding value prediction using hyper-spectral image data
(Nature Publishing Group, 2020)
Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat
(Genetics Society of America, 2019)
Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras ...
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 ...
Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat
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 ...
The use of unbalanced historical data for genomic selection in an international wheat breeding program
Genomic selection (GS) offers breeders the possibility of using historic data and unbalanced breeding trials to form training populations for predicting the performance of new lines. However, when using datasets that are ...