Location: Plant Genetics Research
2019 Annual Report
Objectives
Objective 1: Develop and make available new approaches to evaluate gene functions in gene networks and verify these tools by examining previously identified gene networks in soybean.
Objective 2: Discover, characterize, and make available genes for industry-relevant protein and oil traits from new and existing genetic populations created through various methods, such as fast neutrons, conventional crossing, reverse genetics (TILLING), or mining exotic diversity contained in the USDA National Plant Germplasm System.
Approach
We will apply a genome-wide reverse engineering approach to reconstruct a gene regulatory network in soybean using in-house generated and public available transcriptome sequencing data. An eQTL mapping analysis will conducted with seed transcriptome sequencing and genome sequencing data of the wild and cultivated soybean genotypes to identify the trans-acting eQTL and reveal the relationship of candidate regulatory genes/alleles and their associated genes. The reconstructed gene regulatory network, regulatory relationships generated from eQTL analysis and the co-expression gene network that we previously modeled will be compared to evaluate each regulatory relationship (edge) to generate a consensus soybean seed gene regulatory network. A set of CRISPR/Cas9 genome editing vectors for a regulatory gene (hub) will be constructed to alter its regulatory function in “transgenic” soybean for validation of its regulatory functions in the network.
In addition, a set of big data analysis methodologies and data mining strategies will be developed to integrate the large amount of publically available and in-house generated QTL mapping data, transcriptome and genome sequencing data, soybean seed gene regulatory networks predicted above and seed storage reserve related metabolic pathways to identify putative genes/alleles that cause the variation in oil and/or protein content in soybean. We will sequence transcriptomes of soybean seeds containing different alleles of a putative gene to determine their transcriptome response to the allelic variation for validating its regulatory function and providing an insight into its underlying mode of action in regulating oil and/or protein production in seeds.
Progress Report
Finished downloading, quality controlling and analyzing genome re-sequencing data for 1556 diverse soybean accessions, and transcriptome sequencing data from 3035 soybean samples, which was generated by the ARS laboratory and collaborators, or available from the public databases. Discovered, validated, characterized and functionally annotated a total of 29.5 million single nucleotide polymorphisms. Grew and screened putative soybean transgenic plants, and molecularly and genetically characterized the positive transgenic soybean plants to determine the functions of a gene candidate controlling seed protein and oil. Developed a functional marker for a high protein quantitative trait locus allele for molecular breeding.
Accomplishments
1. Developing a large resource of single nucleotide variants for soybean genetic improvement. With the advent of next-generation sequencing technologies, soybean researchers have generated more than 15 terabytes of genome sequencing data from 1,519 diverse wild and cultivated accessions in the past-decade. ARS researchers in St. Louis, Missouri, demonstrated that the 1,519 diverse soybean accessions represent almost the entire genetic diversity of over 20,000 wild and cultivated soybean accessions in the U.S. soybean collection. They analyzed the entire set of DNA sequencing data, and further identified, validated and annotated a total of 29.5 million single nucleotide variants. The large set of the single nucleotide variants could serve as an important resource for U.S. soybean research and product development and plays an important role in translating U.S. soybean genomic research into soybean improvement.
2. Development of a highly effective in-silico genotyping approach for soybean research. With the advent of next-generation sequencing technologies, soybean researchers have re-sequenced genomes of over 1,500 diverse soybean accessions and generated a huge amount of the genome sequencing data. ARS researchers in St. Louis, Missouri, have developed a bioinformatic algorithm to examine the nucleotide sequences of a given gene or a deoxyribonucleic acid region such as a quantitative trait locus interval in those diverse sequenced soybean accessions. This allows soybean researchers to genotype known quantitative trait locus gene alleles, discover new quantitative trait locus alleles, and identify soybean accessions containing novel trait alleles that can be used as new genetic materials for soybean breeding. The in-silico approach increases soybean genotyping efficiency by orders of magnitude in comparison with traditional wet-lab approaches and should greatly enhance translating soybean genome research into soybean improvement.
Review Publications
Bartels, A., Han, Q., Nair, P., Stacey, L., Gaynier, H., Mosley, M., Huang, Q., Pearson, J., Hsieh, T., An, Y., Xiao, W. 2018. Dynamic DNA methylation in plant growth and development. International Journal of Molecular Sciences. 19(7):2144. https://doi.org/10.3390/ijms19072144.
Diers, B.W., Specht, J., Rainey, K., Cregan, P., Song, Q., Ramasubramanian, V., Graef, G., Nelson, R., Schapaugh, W., Wang, E., Shannon, G., McHale, L., Kantartzi, S., Xavier, A., Mian, R.M., Stupar, R., Michno, J., An, Y., Goettel, W., Ward, R., Fox, C., Lipka, A.E., Hyten, D., Cary, T., Beavis, W.D. 2018. Genetic architecture of soybean yield and agronomic traits. G3, Genes/Genomes/Genetics. 8(10):3367-3375. https://doi.org/10.1534/g3.118.200332.
