Dissecting seed composition QTL from wild soybean: fine-mapping, candidate gene identification, and evaluation of introgression effects on agronomic performance.

Abstract

Key message: Seed composition QTL from wild soybean were confirmed and validated in two genetic backgrounds across multiple environments, candidate genes were identified, and agronomic performance of backcross introgression lines was evaluated. Through selection for soybean yield, breeders have inadvertently reduced seed protein content and increased oil due to phenotypic and genetic correlations between these three traits. Therefore, identifying alleles that increase protein without adversely affecting oil and yield is of interest for breeders and the entire soybean value chain. Previously, a G. max × G. soja population was used to map a protein-associated region to ~ 4.6 Mbp on chromosome (Chr) 14. The G. soja allele significantly increased protein 6.5-7.2 g kg^-1, without significantly decreasing oil. Additionally, two oil quantitative trait loci (QTL) were reported on Chrs 8 and 14. In this study, we aimed to confirm the Chr 14 protein QTL, evaluate QTL effects on seed composition and agronomic performance, and further fine-map to identify candidate genes. We validated and fine-mapped the Chr 14 protein QTL to a 0.6 Mbp region in a different genetic background, where the G. soja allele significantly increased protein by 9.3 g kg^-1. Further, we confirmed the Chr 14 oil QTL linked to the protein QTL and the Chr 8 oil QTL. Chr 14 protein QTL effects on agronomic traits were evaluated in a backcross population across eight environments. The QTL significantly increased protein content, without significantly impacting oil, maturity, or plant height. While the QTL impacted yield and lodging, its effect and significance varied within environments. The candidate genes identified for these three validated seed composition QTL, along with additional molecular markers developed, offer valuable resources for improving seed composition in soybean breeding programs.