Genomic Selection in Preliminary Yield Trials in a Winter Wheat Breeding Program

Authors

Vikas Belamkar, University of Nebraska-LincolnFollow
Mary J. Guttieri, USDA, Agricultural Research ServiceFollow
Waseem Hussain, University of Nebraska-LincolnFollow
Diego Jarquin, University of Nebraska-LincolnFollow
Ibrahim El-basyoni, Damanhour UniversityFollow
Jesse Poland, Kansas State UniversityFollow
Aaron J. Lorenz, University of MinnesotaFollow
P. Stephen Baenziger, University of Nebraska-LincolnFollow

Date of this Version

2018

Citation

G3 Volume 8 | August 2018

Comments

Copyright © 2018 Belamkar et al.

Open access

doi: https://doi.org/10.1534/g3.118.200415

Abstract

Genomic prediction (GP) is now routinely performed in crop plants to predict unobserved phenotypes. The use of predicted phenotypes to make selections is an active area of research. Here, we evaluate GP for predicting grain yield and compare genomic and phenotypic selection by tracking lines advanced. We examined four independent nurseries of F_3:6 and F_3:7 lines trialed at 6 to 10 locations each year. Yield was analyzed using mixed models that accounted for experimental design and spatial variations. Genotype-by-sequencing provided nearly 27,000 high-quality SNPs. Average genomic predictive ability, estimated for each year by randomly masking lines as missing in steps of 10% from 10 to 90%, and using the remaining lines from the same year as well as lines from other years in a training set, ranged from 0.23 to 0.55. The predictive ability estimated for a new year using the other years ranged from 0.17 to 0.28. Further, we tracked lines advanced based on phenotype from each of the four F_3:6 nurseries. Lines with both above average genomic estimated breeding value (GEBV) and phenotypic value (BLUP) were retained for more years compared to lines with either above average GEBV or BLUP alone. The number of lines selected for advancement was substantially greater when predictions were made with 50% of the lines from the testing year added to the training set. Hence, evaluation of only 50% of the lines yearly seems possible. This study provides insights to assess and integrate genomic selection in breeding programs of autogamous crops.