Location: Soybean Genomics & Improvement Laboratory
2019 Annual Report
Objectives
Objective 1: Discover QTL and genes controlling biotic and abiotic stress tolerance, and agronomic and quality traits in soybean and common bean and develop new DNA markers that define haplotype variation across new and previously identified genomic regions. [NP301, C1, PS1A; C3, PS3B]
The aim of objective 1 is to develop community resources for efficient identification of genes/QTL impacting a range of traits and to facilitate marker assisted selection of alleles in soybean and common bean in collaboration with breeders. These include highly polymorphic markers, core germplasm collection and genotypic datasets of new exotic elite germplasm introduced to USDA Soybean Germplasm Collection.
Objective 2: Evaluate diverse soybean populations developed from hybridization with wild soybean to discover unique QTL controlling seed protein and oil content, develop molecular markers, and make these available to breeders for improving soybean quality. [NP301, C1, PS1A; C3, PS3B]
As many wild soybean germplasm may has different alleles controlling high protein and oil content than cultivated soybean, here we will explore wild soybean for the improvement of U.S. soybean seed protein and oil content with the markers developed from Objective 1 and genomic tools previously developed in our laboratory.
Objective 3: Characterize genetic diversity of the Soybean Rhizobium Germplasm Collection using whole genome sequencing, evaluate nitrogen fixation efficiency of the core strains, and use the information to identify rhizobium genes associated with host-specific nodulation and nitrogen fixation in specific soybean genotype/rhizobium symbioses. [NP301, C1, PS1A; C3, PS3B]
Genetic diversity of the rhizobia will be evaluated using genomic information and their influence on the nitrogen fixation efficiency in soybean will be analyzed. The research will result in the identification of efficient strains and genes for enhanced nitrogen fixation in soybean, resulting in better utilization of the diversity of rhizobium strains and soybean ancestors to improve biological nitrogen fixation in commercial soybean cultivars.
Approach
Objective 1: Solexa short genomic DNA sequences from 16 diverse genotypes of different common bean market classes will be aligned to the common bean whole genome sequence (WGS) for SSR marker discovery. After filtering, primer sets will be designed to amplify the SSRs. A subset of 100 primer pairs will be randomly selected for testing polymorphism using genomic DNA from the 16 diverse common bean genotypes.
A total of 12 pairs of diverse genotypes from different market classes of the Andean Diverse Panel of common bean will be sequenced. Called SNPs will be filtered based on a number of factors for beadchip assay. SNPs that are polymorphic within multi- market classes will be added to the Illumina Infinium BARCBean6K_3 BeadChip pool or used for KASP markers to fine map gene/QTL in targeted genomic regions.
Based on the SNP data of the >18,000 cultivated soybean accessions assayed with SoySNP50K BeadChip, core sets of soybean accessions for each soybean maturity group will be created. The software Core Hunter 3 will be used to select the core collection with high allelic richness.
Objective 2: a nested association mapping panel consisting of 150-300 F6 lines from each of 10 crosses of NC-Raleigh x wild soybean from the wild soybean core collection will be developed. The parents and the RILs will be grown in the field at two locations in two years. DNA isolated from the RILs and parents will be genotyped with Illumina BARCSoySNP6K BeadChips. Protein content and oil content of the parents and lines will be measured using a DA 7250 NIR Analyzer. The dataset will be used to identify QTL, genes and haplotypes controlling high seed protein and oil content in wild soybean that will be used for improving cultivated soybean and to predict accuracy of genomic selection.
Objective 3: Genomic DNA of 760 soybean Bradyrhizobium strains will be isolated and sequenced at using NextSeq500 Sequencer. The resulting sequence will be aligned to the WGS of the B. japonicum strain USDA110 for variant discovery. Redundant or highly similar strains with 99.9% similarity among the soybean rhizobia will be identified. Within each cluster with 99.9% similarity, an accession from each cluster will be evaluated for nitrogen fixation efficiency using 8 ancestral cultivars which contribute more than 70% of the genetic diversity to the Southern and Northern American elite cultivars. Plant will be measured for chlorophyll content and biomass with or without inoculation of the stains, and scored for plant vegetative growth based on the growth of the plant inoculated with USDA110, a recommended soybean strain. The test in eight ancestors will be carried out in a greenhouse with replications.
Progress Report
Progress was made to characterize new elite soybean germplasm introduced to the USDA-ARS Soybean Germplasm Collection. The Soybean Genomics and Improvement Laboratory at Beltsville, Maryland, previously genotyped over 20,000 soybean accessions in the Collection with SoySNP50K Beadchips containing >50,000 single nucleotide polymorphisms (SNPs). The SoySNP50K dataset is publicly available at Soybase and has been widely used to mine genes or alleles controlling seed quality, resistance to abiotic and biotic stresses, and other agronomic traits. Since the genotyping project was completed, new elite accessions were introduced from other countries. In collaboration with the collaborators at the USDA Soybean Germplasm Collection, DNA from the leaf tissue of 480 elite soybean germplasm accessions from Korea and Vietnam were isolated using the Qiagen DNeasy Plant Mini Kit. The DNA was analyzed with the SoySNP50K BeadChips. SNP genotyping for all the 480 accessions was conducted on the Illumina platform by following the Infinium HD Assay Ultra Protocol. The SNP alleles were called using the GenomeStudio Genotyping Module v1.8.4. The resulting genotypic dataset will be merged with our previous SoySNP50K genotypic dataset. The similarity and dissimilarity of the new accessions to the accessions in the Collection will be determined. We will also compare the genetic relationship of these accessions with the 562 elite cultivars in the USDA-ARS Soybean Germplasm Collection. Those subpopulations that are not represented in current elite cultivars will be provided as a pool of untapped genetic variability that can be exploited for genetic advance for abiotic and biotic stress resistance, seed quality traits, and productivity.
Progress was also made to advance populations from the crosses between cultivated soybean and wild soybean to discover unique quantitative trait loci (QTL) controlling seed protein and oil content in wild soybean, to develop molecular markers, and to make these available to breeders for improving soybean quality. Using the single seed descent method, a total of 18 G. max x G. soja families were advanced to the F6 generation by growing the lines in the field and greenhouse during 2017 and 2018. From these, a total of 1,005 F6 lines from 10 families (NC-Raleigh × PI 549032, NC-Raleigh × PI378684B, NC-Raleigh × PI378690, NC-Raleigh × PI378696B, NC-Raleigh × PI407020, NC-Raleigh × PI407228, NC-Raleigh × PI424007, NC-Raleigh × PI424045, NC-Raleigh × PI424083A, and NC-Raleigh × PI562551) with the highest number of recombinant inbred lines (RILs) and with a common G. max NC-Raleigh parent were chosen to grow at Beltsville, Maryland, and Raleigh, North Carolina, in 2018. Field experiments of 1,005 lines were conducted with a randomized block design of two replications at Beltsville and a complete randomized design of one replication at Raleigh. The DNA from 1,005 RILs, their parents and controls was extracted and genotyped with the BARCSoySNP6K containing 6,000 SNPs. The seeds from >3,000 plots harvested at two locations were measured for quality traits including protein, oil content, amino acids and fatty acids using DA 7250 near-infrared analyzer.
Progress was also made in the characterization of genetic diversity of the USDA Rhizobium Germplasm Collection using whole genome sequencing. About 450 soybean Bradyrhizobium strains have been grown and isolated from cultured cells, and the pellet for each strain is ready for DNA extraction. Genomic DNA of these isolates will be sequenced. We previously planned to extract DNA for approximately 200 isolates each year in three consecutive years; however, we realized that it is more efficient to obtain pellets for all samples at once and then extract DNA of all the samples at once at a later time.
Progress was made in the discovery of genes or QTL controlling biotic and abiotic stress resistance and agronomic and quality traits in soybean and common bean. The BeadChips and molecular markers developed by USDA-ARS scientists at Beltsville, Maryland, were used to analyze soybean and common bean genetic populations created by collaborators across the U.S. and other countries. The analyses resulted in mapping genomic regions or QTL controlling a number of traits including resistance to sudden death syndrome and cyst nematode, resistance to drought, seed compositions and seed size in collaboration with researchers in Iowa State University, Michigan State University, University of North Carolina at Charlotte, University of Nebraska, University of Georgia, University of Missouri, and Huazhong Agricultural University and Hebei Academy of Agricultural Science in China. For common bean, we identified QTL or genes controlling Fusarium oxysporum resistance, anthracnose resistance, pod and seed size in collaboration with researchers at the University of Maringá, EMBRAPA, Federal University of Lavras in Brazil, SERIDA in Spain, and the University of Nova de Lisboa in Portugal.
Accomplishments
1. Slow canopy wilting in soybeans during drought. Drought causes soybean leaf canopies to wilt, but some soybean varieties differ in the time of onset and the severity of canopy wilting. Previous studies identified two exotic soybeans that exhibited reduced and delayed canopy wilting phenotypes, but the genetic mechanisms underlying canopy wilting were unclear. Researchers at University of Missouri, University of North Carolina, Kansas State University, and USDA-ARS, Beltsville, Maryland, deciphered the physiological and genetic mechanisms responsible for improving yield under limited water availability in two soybeans and validated major genetic factors protecting soybean yield under drought in the field. The research resulted in a better understanding of the water-conservation slow canopy wilting mechanism and provides genetic resources for improving drought tolerance in soybeans through various approaches such as gene cloning, editing, or gene transferring by natural breeding.
2. Soybean relatives with new genes. U.S. soybean has perennial relatives that originate from Australia and other locations with warm and drought-prone environments. These species are a source of genes that no longer exist in U.S. soybean. Scientists at USDA-ARS, Beltsville, Maryland, defined the genetic variability in six perennial Glycine species. They found that one of the perennial species, Glycine canescens, had a high level of genetic diversity, and in this species, three varieties from the dry and warm region of western Australia were genetically distinct from the other eight accessions from central and eastern Australia. These three perennial species could be used as donors of useful genes to cultivated soybean to help improve heat resistance, pest resistance, and drought tolerance.
3. A new screen for soybean sudden death syndrome. Sudden death syndrome (SDS), caused by soil-borne fungus Fusarium virguliforme, is one of the most important diseases of soybean. To monitor this disease, farmers look for soybean root damage and signs of Fusarium colonization, but this is a slow and inefficient process. Now, scientists at Michigan State University and USDA-ARS, Beltsville, Maryland, have discovered that leaf damage traits are better indicators to evaluate SDS resistance to predict yield loss. Furthermore, the scientists have identified DNA and genes that control SDS disease resistance. The information from this study will help farmers evaluate their crops for SDS disease incidence and aid soybean breeders in private and public sectors who want to develop new soybean varieties with improved SDS resistance.
4. Improving soybean yield. Increasing seed yield is one of the major objectives of soybean breeders. Although soybean yield has been improved in the past decades, the genes that control yield and related agronomic traits have not been fully identified. Scientists from the University of Illinois, University of Nebraska, Kansas State University, Michigan State University, University of Missouri, Ohio State University, Southern Illinois University, University of Minnesota, Iowa State University, Dow Agroscience, and USDA-ARS in Beltsville, Maryland, developed a soybean population with 5,600 progeny, the parents of which had high-yield and drought tolerance traits. The progeny were grown in eight Midwestern states over three years. Analysis of yield and other traits and analysis of the DNA determined the genome positions of genes for yield, maturity, plant height, plant lodging, and seed mass. A potential candidate gene underlying both maturity and yield was identified. Other potentially important genes from the parents were identified as well. The research provided new genetic resources to further improve soybean yield and other agronomic traits.
Review Publications
Stewart-Brown, B.B., Song, Q., Vaughn, J., Li, Z. 2019. Genomic selection for yield and seed composition traits within an applied soybean breeding program . G3, Genes/Genomes/Genetics. https://doi.org/10.1534/g3.118.200917.
Chen, L., Ding, X., Zhang, H., He, T., Li, Y., Wang, T., Li, X., Jin, L., Song, Q., Yang, S., Gai, J. 2018. Comparative analysis of circular RNAs between soybean cytoplasmic male-sterile line NJCMS1A and its maintainer NJCMS1B by high-throughput sequencing. BMC Genomics. 9(1):663.
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.
Yan, L., Di, R., Wu, C., Hou, W., Zhao, Q., Liu, B., Shi, X., Yang, C., Song, Q., Zhang, M. 2019. Haplotype analysis of a major and stable QTL underlying soybean (Glycine max) seed oil content reveals footprint of artificial selection. Molecular Breeding. 39:57. https://doi.org/10.1007/s11032-019-0951-1.
Ye, H., Song, L., Schapaugh, W.T., Ali, L., Riar, M.K., Raymond, R.N., Li, Y., Vuong, T., Valliyodan, B., Neto, A.P., Klepadlo, M., Song, Q., Shannon, G.J., Pengyin, C., Nguyen, H.T. 2019. Physiological and QTL analysis of slow canopy wilting trait for improvement of drought tolerance in soybean. G3, Genes/Genomes/Genetics. https://doi.org/10.1093/jxb/erz150.
Hwang, E., Schroeder, S.G., Fickus, E.W., Quigley, C.V., Cregan, P., Song, Q. 2019. Genetic diversity and phylogenetic relationships of annual and perennial Glycine species. G3, Genes/Genomes/Genetics. https://doi.org/10.1534/g3.119.400220.
Smallwood, C., Saxton, A., Gillman, J.D., Bhandari, H., Wadl, P.A., Fallen, B., Hyten, D., Song, Q., Pantalone, V. 2019. Context-specific genomic selection strategies outperform phenotypic selection for soybean quantitative traits in the progeny row stage. Crop Science. 59(1):54-67. https://doi.org/10.2135/cropsci2018.03.0197.
