Claim · model-written summary
A single trait in multiple environments improved accuracy 6.4% compared with multi-trait analysis.
Zea maysNot reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“accuracy of predictions also improved by 6.4% when using a single trait in a multi-environment scheme compared to using multi-trait analysis”
t0“flowering traits in maize”
Title
Freddy Mora-Poblete; Carlos Maldonado; Luma Henrique; et al. Frontiers in Plant Science 2023. doi:10.3389/fpls.2023.1153040
Other claims from this paper (7)
- Method / resource Multi-trait models increased prediction accuracy 14.4% over a single-trait single-environment approach.
- Method / resource Deep learning models outperformed Bayesian models across single and multiple trait and environment schemes.
- Observation GWAS identified 26 candidate genes related to flowering time.
- Observation GWAS identified 31 marker-trait associations.
- Observation The identified associations accounted for 37% of ASI variation.
- Observation The identified associations accounted for 37% of FF variation.
- Observation The identified associations accounted for 22% of MF variation.