Claim · model-written summary
Cross-population prediction accuracy was generally low.
- Taxon
- Cassava
Manihot esculentat0 - Outcome
- Cross-population accuracye2
- Value
- 0.18e2
- Qualifier
- generally lowe2
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Prospects for Genomic Selection in Cassava Breeding”
e2“Cross-population accuracy was generally low (mean = 0.18)”
Title
Prospects for Genomic Selection in Cassava Breeding
Marnin D. Wolfe; Dunia Pino Del Carpio; Olumide Alabi; et al. The Plant Genome 2017. doi:10.3835/plantgenome2017.03.0015
Other claims from this paper (7)
- Method / resource Nonadditive models predicted about 10% better than other models in cross-validation within cassava populations.
- Intervention effect Combining data from another related population increased the prediction of cassava mosaic disease by up to 57% in one Nigerian population.
- Association Prediction accuracy across generations was poorer than within-generation accuracy.
- Observation Accuracy for dry matter content and mosaic disease severity should be sufficient for rapid-cycling genomic selection.
- Intervention effect Increasing the training population size mattered more for accuracy across generations than the choice of prediction model.
- Intervention effect With a genetic algorithm, selecting one-third of progeny for phenotyping gave accuracy equivalent to phenotyping all progeny.
- Recommendation The authors recommend growing training populations while phenotyping only a carefully selected subset of individuals.