|MILLER, MARK - University Of Georgia
|LI, ZENGLU - University Of Georgia
Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/17/2023
Publication Date: 5/10/2023
Citation: Miller, M., Song, Q., Fallen, B.D., Li, Z. 2023. Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean (Glycine Max). Crop Science. 14. Article e1171135. https://doi.org/10.3389/fpls.2023.1171135.
Interpretive Summary: Parental selection and crossing to combine traits from desirable parents are initial, essential steps in the breeding pipeline. Most often these crossing decisions are made based on pedigrees, parental genotypes, or specific, desirable traits. Traditional population development can take half a decade or longer before agronomically important traits, such as yield, can be evaluated. A poor cross combination will therefore use up resources for multiple years prior to evaluation. Furthermore, a breeder can only perform a small subset of all cross combinations possible. A method for predicting the success of a cross is required. We developed selection models based on genetic markers for soybean seed yield and evaluated their prediction accuracy for yield. The study resulted in the identification of the best prediction models under different scenarios. The predictive models can be utilized by soybean as well as other crop breeding programs at the earliest step of the breeding cycle and will improve rates of genetic gain and reduce the number of breeding cycles.
Technical Abstract: Improving yield is a primary soybean breeding goal, as yield is the main determinant of soybean’s profitability. Within the breeding process, selection of cross combinations is one of most important elements. Cross prediction will assist soybean breeders in identifying the best cross combinations among parental genotypes prior to crossing, increasing genetic gain and breeding efficiency. In this study optimal cross selection methods were created and applied in soybean and validated using historical data from the University of Georgia, Athens, Georgia, soybean breeding program, under multiple training set compositions and marker densities utilizing multiple genomic selection models for marker evaluation. Plant materials consisted of 702 advanced breeding lines evaluated in multiple environments and genotyped using SoySNP6k BeadChips. An additional marker set, the SoySNP3k marker set, was tested in this study as well. Optimal cross selection methods were used to predict the yield of 42 previously made crosses and compared to the performance of the cross’s offspring in replicated field trials. The best prediction accuracy was obtained when using Extended Genomic BLUP with the SoySNP6k marker set, consisting of 3,762 polymorphic markers, with an accuracy of 0.56 with a training set maximally related to the crosses predicted and 0.4 in a training set with minimized relatedness to predicted crosses. Prediction accuracy was most significantly impacted by training set relatedness to the predicted crosses, marker density, and the genomic model used to predict marker effects. The usefulness criterion selected had an impact on prediction accuracy within training sets with low relatedness to the crosses predicted. Optimal cross prediction provides a useful method that assists plant breeders in selecting crosses in soybean breeding.