Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype× environment interaction on prediction accuracy in chickpea
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) …
has the potential to enhance the rate of genetic gains. Genotype× environment (G× E) …
Genome-enabled prediction models for yield related traits in chickpea
M Roorkiwal, A Rathore, RR Das, MK Singh… - Frontiers in plant …, 2016 - frontiersin.org
Genomic selection (GS) unlike marker-assisted backcrossing (MABC) predicts breeding
values of lines using genome-wide marker profiling and allows selection of lines prior to field …
values of lines using genome-wide marker profiling and allows selection of lines prior to field …
Genomic prediction in pea: effect of marker density and training population size and composition on prediction accuracy
N Tayeh, A Klein, MC Le Paslier, F Jacquin… - Frontiers in Plant …, 2015 - frontiersin.org
Pea is an important food and feed crop and a valuable component of low-input farming
systems. Improving resistance to biotic and abiotic stresses is a major breeding target to …
systems. Improving resistance to biotic and abiotic stresses is a major breeding target to …
Use of multiple traits genomic prediction, genotype by environment interactions and spatial effect to improve prediction accuracy in yield data
Genomic selection has been extensively implemented in plant breeding schemes. Genomic
selection incorporates dense genome-wide markers to predict the breeding values for …
selection incorporates dense genome-wide markers to predict the breeding values for …
Increased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data
In crop breeding, the interest of predicting the performance of candidate cultivars in the field
has increased due to recent advances in molecular breeding technologies. However, the …
has increased due to recent advances in molecular breeding technologies. However, the …
Modeling genotype× environment interaction for genomic selection with unbalanced data from a wheat breeding program
Genomic selection (GS) has successfully been used in plant breeding to improve selection
efficiency and reduce breeding time and cost. However, there is not a clear strategy on how …
efficiency and reduce breeding time and cost. However, there is not a clear strategy on how …
Genome‐enabled prediction for sparse testing in multi‐environmental wheat trials
Sparse testing in genome‐enabled prediction in plant breeding can be emulated throughout
different line allocations where some lines are observed in all environments (overlap) and …
different line allocations where some lines are observed in all environments (overlap) and …
Enhancing hybrid prediction in pearl millet using genomic and/or multi-environment phenotypic information of inbreds
Genomic selection (GS) is an emerging methodology that helps select superior lines among
experimental cultivars in plant breeding programs. It offers the opportunity to increase the …
experimental cultivars in plant breeding programs. It offers the opportunity to increase the …
Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs
LF Merrick, AH Carter - The Plant Genome, 2021 - Wiley Online Library
Traits with a complex unknown genetic architecture are common in breeding programs.
However, they pose a challenge for selection due to a combination of complex …
However, they pose a challenge for selection due to a combination of complex …
Genomic prediction of agronomic traits in wheat using different models and cross-validation designs
Key message Genomic predictions across environments and within populations resulted in
moderate to high accuracies but across-population genomic prediction should not be …
moderate to high accuracies but across-population genomic prediction should not be …