Genomic selection of soybean (Glycine max) for genomic improvement of yield and seed composition in a breeding context

Authors: MILLER, MARK - University Of Georgia; Song, Qijian; LI, ZENGLU - University Of Georgia

Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/1/2023
Publication Date: 9/25/2023
Citation: Miller, M., Song, Q., Li, Z. 2023. Genomic selection of soybean (Glycine max) for genomic improvement of yield and seed composition in a breeding context. The Plant Genome. 16(4). Article e20384. https://doi.org/10.1002/tpg2.20384.
DOI: https://doi.org/10.1002/tpg2.20384

Interpretive Summary: The value of soybean is determined by multiple traits, including yield, protein, oil, and amino acid content. The variation of these traits is typically regulated by a large number of genes with small effects. Therefore, improvement of these traits using marker-assisted selection is ineffective. This is an impediment to breeding efforts. Genomic selection (GS) may be a good option to improve these traits. GS utilizes genetic and phenotypic data from a known set of plant materials to estimate the phenotypic values of future plant materials and has been explored in wheat, maize and other major crops. However, the amount of GS research specific to soybean is limited, with minimal utilization of the technique within public breeding programs. This study evaluated GS under various contexts to determine which factors impacted GS prediction accuracy and which GS models were preferable for yield and seed composition traits. The study identifies the best predictive models that can provide high predictive accuracy for all traits, as well as the preferred assays that allow more efficient use of resources for genotyping large numbers of materials. This study provides useful information for efficient genetic breeding, which will help to speed up the breeding process and increase the rate of genetic gain.

Technical Abstract: Genomic selection has been utilized for genetic improvement in both plant and animal breeding and is a favorable technique for quantitative trait development. Within this study genomic selection was evaluated within a breeding program, using novel validation methods in addition to plant materials and data from a commercial soybean breeding program. A total of 1,501 inbred lines were used to test multiple genomic selection models for multiple traits. Validation included cross validation, inter-environment and empirical validation. The results indicated that the extended genomic BLUP model was the most effective model tested for yield, protein, and oil in cross validation with accuracies of 0.50, 0.68, and 0.64, respectively. Increasing marker number from 1000 to 3000 to 6000 SNP markers lead to statistically significant increases in accuracy. Cross-environment predictions were statistically lower than cross validation with accuracies of 0.24, 0.54, and 0.42 for yield, protein, and oil using the extended genomic BLUP model. Empirical validation, predicting the yield of 510 soybean lines, had a prediction accuracy of 0.34, with the inclusion of a maturity covariate leading to a notable increase in accuracy. Genomic selection identified high-performance lines in inter-environment predictions: 34% of lines within the upper quartile of yield, and 51 and 48% of the highest quartile protein and oil lines, respectively. Statistically similar results occurred comparing rankings in empirical validation and selection for advancements in yield trials. These results indicate genomic selection is a useful tool for selection decisions.