Genetic architecture of protein and oil content in soybean seed and meal

Soybean is grown primarily for the protein and oil extracted from its seed and its value is influenced by these components. The objective of this study was to map marker-trait associations (MTAs) for the concentration of seed protein, oil, and meal protein using the soybean nested association mapping (SoyNAM) population. The composition traits were evaluated on seed harvested from over 5000 inbred lines of the SoyNAM population grown in 10 field locations across 3 years. Estimated heritabilities were at least 0.85 for all three traits. The genotyping of lines with single nucleotide polymorphism markers resulted in the identification of 107 MTAs for the three traits. When MTAs for the three traits that mapped within 5 cM intervals were binned together, the MTAs were mapped to 64 intervals on 19 of the 20 soybean chromosomes. The majority of the MTA effects were small and of the 107 MTAs, 37 were for protein content, 39 for meal protein, and 31 for oil content. For cases where a protein and oil MTAs mapped to the same interval, most (94%) significant effects were opposite for the two traits, consistent with the negative correlation between these traits. A coexpression analysis identified candidate genes linked to MTAs and 18 candidate genes were identified. The large number of small effect MTAs for the composition traits suggest that genomic prediction would be more effective in improving these traits than marker-assisted selection.

Automated summary

This study aimed to map marker-trait associations (MTAs) for seed protein, oil, and meal protein concentration using the SoyNAM population. The composition traits were evaluated on seed harvested from over 5000 inbred lines in 10 field locations across 3 years. Single nucleotide polymorphism markers were used for genotyping, resulting in the identification of 107 MTAs. Coexpression analysis identified candidate genes linked to MTAs, with 18 candidate genes identified. Genomic prediction is suggested to be more effective in improving these traits than marker-assisted selection due to the large number of small effect MTAs.