Genetic variation and marker-trait association affect the genomic selection prediction accuracy of soybean protein and oil content

IntroductionGenomic selection (GS) is a potential breeding approach for soybean improvement. MethodsIn this study, GS was performed on soybean protein and oil content using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) based on 1,007 soybean accessions. The SoySNP50K SNP dataset of the accessions was obtained from the USDA-ARS, Beltsville, MD lab, and the protein and oil content of the accessions were obtained from GRIN. ResultsOur results showed that the prediction accuracy of oil content was higher than that of protein content. When the training population size was 100, the prediction accuracies for protein content and oil content were 0.60 and 0.79, respectively. The prediction accuracy increased with the size of the training population. Training populations with similar phenotype or with close genetic relationships to the prediction population exhibited better prediction accuracy. A greatest prediction accuracy for both protein and oil content was observed when approximately 3,000 markers with -log(10)(P) greater than 1 were included. DiscussionThis information will help improve GS efficiency and facilitate the application of GS.

Automated summary

In this study, genomic selection (GS) was used to predict protein and oil content in soybean. The prediction accuracy of oil content was found to be higher than that of protein content. The accuracy of prediction increased with the size of the training population and was improved when the training population had similar phenotype or close genetic relationships with the prediction population. The highest prediction accuracy for both protein and oil content was achieved when approximately 3,000 markers with -log(10)(P) greater than 1 were included.