Claim · model-written summary
Genomic selection on index values in univariate models gave forward prediction accuracy of r = .21 to .44 for the 2013 validation set.
- Taxon
- Triticum aestivum
Triticum aestivumt0 - Outcome
- forward prediction accuracye6
- Value
- r = .21 to .44e6
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e6“Genomic selection applied to index values in univariate models provided forward prediction accuracy ranging between r = .21 to .44 for the 2013 validation set”
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 Forward prediction accuracy approached zero for the 2014 validation set.
- Method / resource Bivariate genomic selection did not substantially improve prediction accuracy for individual trait values compared with univariate models.
- 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.