Campus Units
Agronomy
Document Type
Article
Publication Version
Published Version
Publication Date
6-9-2017
Journal or Book Title
PLoS ONE
Volume
12
Issue
6
First Page
e0179797
DOI
10.1371/journal.pone.0179191
Abstract
The objective of this study was to explore the potential of genomic prediction (GP) for soybean resistance against Sclerotinia sclerotiorum (Lib.) de Bary, the causal agent of white mold (WM). A diverse panel of 465 soybean plant introduction accessions was phenotyped for WM resistance in replicated field and greenhouse tests. All plant accessions were previously genotyped using the SoySNP50K BeadChip. The predictive ability of six GP models were compared, and the impact of marker density and training population size on the predictive ability was investigated. Cross-prediction among environments was tested to determine the effectiveness of the prediction models. GP models had similar prediction accuracies for all experiments. Predictive ability did not improve significantly by using more than 5k SNPs, or by increasing the training population size (from 50% to 90% of the total of individuals). The GP model effectively predicted WM resistance across field and greenhouse experiments when each was used as either the training or validation population. The GP model was able to identify WM-resistant accessions in the USDA soybean germplasm collection that had previously been reported and were not included in the study panel. This study demonstrated the applicability of GP to identify useful genetic sources of WM resistance for soybean breeding. Further research will confirm the applicability of the proposed approach to other complex disease resistance traits and in other crops.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright Owner
de Azevedo Peixoto et al.
Copyright Date
2017
Language
en
File Format
application/pdf
Recommended Citation
Azevedo Peixoto, Leonardo de; Moellers, Tara C.; Zhang, Jiaoping; Lorenz, Aaron J.; Bhering, Leonardo L.; Beavis, William D.; and Singh, Asheesh K., "Leveraging genomic prediction to scan germplasm collection for crop improvement" (2017). Agronomy Publications. 339.
https://lib.dr.iastate.edu/agron_pubs/339
Included in
Agricultural Science Commons, Agronomy and Crop Sciences Commons, Genetics and Genomics Commons, Plant Breeding and Genetics Commons
Comments
This article is published as de Azevedo Peixoto L, Moellers TC, Zhang J, Lorenz AJ, Bhering LL, Beavis WD, et al. (2017) Leveraging genomic prediction to scan germplasm collection for crop improvement. PLoS ONE 12(6): e0179191. doi: 10.1371/journal.pone.0179191. Posted with permission.