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ARS Home » Midwest Area » Columbia, Missouri » Plant Genetics Research » Research » Publications at this Location » Publication #351729

Research Project: Gene Discovery and Designing Soybeans for Food, Feed, and Industrial Applications

Location: Plant Genetics Research

Title: Context-specific genomic selection strategies outperform phenotypic selection for soybean quantitative traits in the progeny row stage

Author
item Smallwood, Christopher - University Of Tennessee
item Saxton, Arnold - University Of Tennessee
item Gillman, Jason
item Bhandari, Hem - University Of Tennessee
item Wadl, Phillip
item Fallen, Benjamin - Clemson University
item Hyten, David - University Of Nebraska
item Song, Qijian
item Pantalone, Vincent - University Of Tennessee

Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/16/2018
Publication Date: 1/4/2019
Citation: Smallwood, C., Saxton, A., Gillman, J.D., Bhandari, H., Wadl, P.A., Fallen, B., Hyten, D., Song, Q., Pantalone, V. 2019. Context-specific genomic selection strategies outperform phenotypic selection for soybean quantitative traits in the progeny row stage. Crop Science. 59(1):54-67. https://doi.org/10.2135/cropsci2018.03.0197.
DOI: https://doi.org/10.2135/cropsci2018.03.0197

Interpretive Summary: Soybean is a major high-yielding worldwide crop. Although farmers are rewarded based on seed weight (bushels), the primary economic value is from the high-quality protein and oil in the seeds. Breeding efforts over the last half century have substantially improved seed yield per unit area, but have emphasized selection by evaluating past field performance, which is time and resource intensive. Genomic selection is a complementary means to achieve this goal potentially faster and with less resources, by predicting the genetic value of a breeding line using statistical models developed using previous year’s data. A population was developed to bridge the gap between two important cultivars developed for two distinct soybean growing geographic regions in the United States (northern vs. southern cultivars), and this provided an ideal means to evaluate several different genomic selection breeding strategies. The aim of this study was not just improve seed yield potential, but also targeted for several key seed compositional traits. These efforts will inform and guide ongoing efforts to improve soybean seed yield while also boosting seed value. Overall, molecular breeding strategies outperformed phenotypic selection for the soybean quantitative traits evaluated here.

Technical Abstract: Evaluating different breeding selection methods for relative utility is necessary in order to choose those which maximize efficiency. Soybean [Glycine max (L.) Merrill] seed yield and fatty acids, protein, and oil contest are all commercially important traits that display quantitative inheritance. In addition to phenotypic selection (PS), the molecular breeding methods chosen for this study were BayesB, G-BLUP, and Epistacy, with a soybean population consisting of 860 F5- derived recombinant inbred lines (RILs), genotyped with 11,633 polymorphic SNPs. In order to simulate progeny rows, each RIL was grown in a single plot in 2010 in Knoxville, TN and phenotype was recorded. A subset of 276 RILs was then grown in multi-location, replicated field trials in 2013 to evaluate the relative utility of each selection method. Notably, the preferred method for each trait was a molecular selection strategy. Epistacy was the best method for yield, and BayesB and/or G-BLUP were the preferred methods for each of the other traits. Yield was the only trait for which the predictions had a large change when the number of SNPs and the number of RILs were randomly reduced for the G-BLUP model, with the best predictions occurring when RILs not grown in 2013 were removed. These findings provide important information on how soybean breeders can maximize selections from the progeny row stage for yield and fatty acids, protein, and oil content by using appropriate molecular breeding strategies.