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 F3:6 and F3:7 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 F3:6 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.
Bibliographical noteFunding Information:
The authors are thankful to Greg Dorn and Mitch Montgomery for assistance with conducting and harvesting the field trials in Nebraska; to WestBred, LLC and Bayer CropScience for conducting the preliminary yield trial in Mount Hope, KS and Hutchinson, KS; to Shuangye Wu for technical support of genotyping-by-sequencing; to Dr. Gina Brown-Guedira for building the pseudo-reference genome assembly used for SNP calls; to Amanda Easterly, Nicholas Garst, and Dr. Nonoy Bandillo for valuable suggestions and critical discussions; to all graduate and undergraduate students during 2012-2016 in the Baenziger laboratory for their invaluable support in the field; and to the Holland Computing Center at the University of Nebraska-Lincoln for providing the high-performance computing resources. Partial funding for P.S. Baenziger is from Hatch project NEB-22-328, AFRI/2011-68002-30029, the National Institute of Food and Agriculture as part of the International Wheat Yield Partnership, U.S. Department of Agriculture, under award number 2017-67007-2593, and USDA under Agreement No. 59-0790-4-092 which is a cooperative project with the U.S. Wheat and Barley Scab Initiative, and this project was supported by the National Research Initiative Competitive Grants 2011-68002-30029 and 2017-67007-25939 from the USDA National Institute of Food and Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the USDA. Cooperative investigations of the Nebraska Agric. Res. Div., Univ. of Nebraska, and USDA-ARS. On behalf of all authors, the corresponding author states that there is no conflict of interest
© 2018 Belamkar et al.
- Genomic best linear unbiased prediction
- Genomic prediction
- Genomic selection
- Shared data resources
- Spatial variation
- Triticum aestivum