Now showing items 1-10 of 16
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 ...
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 ...
A singular value decomposition Bayesian multiple-trait and multiple-environment genomic model
(Springer Nature, 2019)
Today, breeders perform genomic-assisted breeding to improve more than one trait. However, frequently there are several traits under study at one time, and the implementation of current genomic multiple-trait and ...
Breeding-assisted genomics: applying meta- GWAS for milling and baking quality in CIMMYT wheat breeding program
(Public Library of Science, 2018)
One of the biggest challenges for genetic studies on natural or unstructured populations is the unbalanced datasets where individuals are measured at different times and environments. This problem is also common in crop ...
Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding
(Crop Science Society of America, 2018)
Genomics and phenomics have promised to revolutionize the field of plant breeding. The integration of these two fields has just begun and is being driven through big data by advances in next-generation sequencing and ...
Genomic selection for processing and end-use quality traits in the CIMMYT spring bread wheat breeding program
(Crop Science Society of America, 2016)
Wheat (Triticum aestivum L.) cultivars must possess suitable end-use quality for release and consumer acceptability. However, breeding for quality traits is often considered a secondary target relative to yield largely ...
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 ...
Identification of genomic regions for grain yield and yield stability and their epistatic interactions
(Nature Publishing, 2017)
The task of identifying genomic regions conferring yield stability is challenging in any crop and requires large experimental data sets in conjunction with complex analytical approaches. We report findings of a first attempt ...
Genomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper‑spectral image data
(BioMed Central, 2017)
Modern agriculture uses hyperspectral cameras that provide hundreds of reflectance data at discrete narrow bands in many environments. These bands often cover the whole visible light spectrum and part of the infrared and ...
A Bayesian genomic multi-output regressor stacking model for predicting multi-trait multi-environment plant breeding data
(Genetics Society of America, 2019)
In this paper we propose a Bayesian multi-output regressor stacking (BMORS) model that is a generalization of the multi-trait regressor stacking method. The proposed BMORS model consists of two stages: in the first stage, ...