Claim · model-written summary
Average genomic prediction accuracy of the nine sesame traits ranged from 0.39 to 0.79.
- Taxon
- sesame
Sesamum indicumt0 - Outcome
- average prediction accuracye0
- Value
- 0.39 to 0.79e0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Multi-environment analysis enhances genomic prediction accuracy of agronomic traits in sesame”
e0“The average prediction accuracy of the nine traits across these models ranged from 0.39 to 0.79 for both growing seasons”
Title
Multi-environment analysis enhances genomic prediction accuracy of agronomic traits in sesame
Idan Sabag; Ye Bi; Zvi Peleg; et al. Frontiers in Genetics 2023. doi:10.3389/fgene.2023.1108416
Other claims from this paper (4)
- Method / resource Genomic best linear unbiased prediction, BayesB, BayesC and reproducing kernel Hilbert spaces models showed no substantial differences in single-environment analysis.
- Method / resource The marker-by-environment interaction model improved prediction accuracies for all traits by 15%–58% compared with the single-environment model.
- Method / resource Single-environment analysis produced moderate-to-high genomic prediction accuracy for agronomic traits in sesame.
- Recommendation Genomic prediction with multi-environment trial data could improve efforts to breed sesame cultivars adapted to the semi-arid Mediterranean climate.