Claim · model-written summary
Prediction accuracy remained unaffected after accounting for population structure.
- Taxon
- wheat
Triticum aestivumt0 - Outcome
- GPAe0
- Direction
- remained unaffectede0
- Comparator
- population structuree0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValueQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The GPA also remained unaffected after accounting for the population structure”
t0“wheat”
Title
Prabina Kumar Meher; Ajit Gupta; Sachin Rustgi; et al. The Plant Genome 2023. doi:10.1002/tpg2.20332
Other claims from this paper (7)
- Method / resource BayesRR outperformed seven other genomic prediction models.
- Method / resource BayesLASSO was the least efficient model.
- Association Genomic prediction accuracy increased with training set size.
- Association Genomic prediction accuracy increased with marker density.
- Method / resource Genomic prediction accuracy differed among the three micronutrient traits.
- Method / resource Prediction accuracy from BLUE was higher than accuracy for the two environments.
- Recommendation The authors recommend using only the best model to estimate GEBVs before genomic selection for grain micronutrient improvement.