Claim · model-written summary
GBLUP, Bayes-B and random forest models gave similar predictive ability for dry matter yield across harvest-year combinations and both marker platforms.
- Taxon
- oat
Avena sativat0 - Outcome
- predictive ability for dry matter yield (DMY)e0
- Direction
- similare0
- Comparator
- Bayes-Be0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValueQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The genomic best linear unbiased prediction (GBLUP), Bayes-B, and random forest models gave similar predictive ability for dry matter yield (DMY)”
t0“biomass yield in oat”
Title
Samuel A. Adewale; Md Ali Babar; Diego Jarquin; et al. The Plant Genome 2026. doi:10.1002/tpg2.70179
Other claims from this paper (4)
- Method / resource The 3K array and GBS-based SNPs performed similarly for training population optimization, forward prediction, and univariate and multivariate genomic prediction of forage yield in oat.
- Method / resource Multivariate models with secondary traits gave more than twofold higher predictive abilities than univariate models.
- Method / resource The overlap between top 25% observed and predicted genotypes was higher for multivariate GBLUP models (30.10%-66.99%) than univariate models (27.18%-51.46%).
- Recommendation Multivariate genomic selection models with the robust, reproducible 3K array SNP markers have great potential for improving genetic gains of dry matter yield in oat breeding.