Claim · model-written summary
Genomic best linear unbiased prediction, BayesB, BayesC and reproducing kernel Hilbert spaces models showed no substantial differences in single-environment analysis.
- Taxon
- sesame
Sesamum indicumt0 - Outcome
- differencese1
- Comparator
- genomic best linear unbiased prediction, BayesB, BayesC, and reproducing kernel Hilbert spaces modelse1
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingDirectionValueQualifier
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”
e1“In single-environment analysis, genomic best linear unbiased prediction, BayesB, BayesC, and reproducing kernel Hilbert spaces models showed no substantial differences”
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 Average genomic prediction accuracy of the nine sesame traits ranged from 0.39 to 0.79.
- 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.