Claim · model-written summary
Bivariate genomic selection did not substantially improve prediction accuracy for individual trait values compared with univariate models.
- Taxon
- Triticum aestivum
Triticum aestivumt0 - Outcome
- Prediction accuracy for individual trait valuese1
- Direction
- not substantially improvede1
- Comparator
- univariate modelse2
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValueQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e1“Prediction accuracy for individual trait values was not substantially improved in the bivariate model”
e2“applied to index values in univariate models”
t0“hard winter wheat (Triticum aestivum L.)”
Title
Latshaw, Susan Patricia; Haley, Scott; Poland, Jesse; et al. Digital Collections of Colorado (Colorado State University) 2021. doi:10.25675/3.02669
Other claims from this paper (9)
- Observation Nitrogen use efficiency ranged from 39.9 kg kg-1 for 'RonL' to 46.7 kg kg-1 for 'Byrd' among 20 breeding lines and varieties.
- Association Variation in NUE depended on N uptake efficiency (NUpE) under N sufficiency and on N utilization efficiency (NUtE) under limiting N.
- Observation The cultivar 'Brawl CL Plus' had 6.7 g kg-1 higher GPRO than the average for all 20 genotypes.
- Association Selection based on high values for a particular index gave a characteristic emphasis on GY or GPRO.
- Association Each simultaneous selection strategy focused to differing extents on the primary traits, as shown by correlation of index values with GY or GPRO.
- Method / resource Genomic selection on index values in univariate models gave forward prediction accuracy of r = .21 to .44 for the 2013 validation set.
- Method / resource Forward prediction accuracy approached zero for the 2014 validation set.
- Association Protein-yield indices from bivariate genomic estimated breeding values showed relationships to GY and GPRO similar to those of the univariate-model indices.
- Recommendation The authors recommend selection strategies with sufficient predictive ability as effective tools for simultaneous selection for GY and GPRO.