Location: Plant Genetics Research
2013 Annual Report
The project is designed to apply next generation sequence technologies to determine gene sequence and expression patterns in developing soybean seeds that lead to variation in seed oil composition and content in major soybean lines. The project shares the same long-term objectives as its parent project, which aims to discover genes and transcript variations important in seed oil quality traits and develop new germplasm with superior seed quality traits. ARS scientists in St. Louis, MO sequenced ribonucleic acid (RNA) accumulating in soybean seeds of nine germplasm varying in oil content and composition. We further developed a bioinformatic algorithm and data mining strategy to conduct extensive bioinformatic analysis of RNA sequencing data. We identified a large collection of seed RNA polymorphisms including variations in sequence and accumulation among the genotypes. We detected a significant amount of RNA polymorphisms in putative acyl lipid biosynthesis genes, and confirmed published reports that the deletion of FAD2-1A gene in mutant line M23 and the presence of a non-synonymous single nucleic polymorphism (SNP) in the coding sequence of FAB2C in FAM94-41 likely caused their oil composition changes. This served as a validation of our bioinformatic methods. In addition, we conducted bioinformatic analysis of the genome sequences and seed transcriptome of Jack generated previously, and compared the efficiency of DNA and RNA sequencing approaches in discovering transcript SNPs at various sequencing depth. We also constructed and sequenced libraries of additional 12 soybean germplasm varying in oil composition and content to further identify the genes and alleles important for oil quality improvement. To select additional lines for transcriptome sequencing, we determined seed lipid profiles from 84 soybean lines including ancestral landraces, milestone cultivars and mutant lines. The identified RNA polymorphisms will be developed into a set of functional markers for breeders to design effective crossing and marker assisted selection strategies for oil quality improvement. The transcriptomes of different genotypes will be further used to improve gene regulatory network inference.