Location: Crop Germplasm Research
2024 Annual Report
Objectives
Objective 1: Integrate genomic selection technology into traditional methods of sorghum genetic improvement to accelerate the rate of genetic gain for grain sorghum hybrids.
Objective 2: Utilize modern biological tools to conduct fundamental molecular research into end-use quality traits including the black pericarp phenotype in sorghum.
Approach
This project aims to integrate recent advances in genomic selection technology augmented with phenomic and enviromic resources. The ultimate goal is to accelerate the rate of genetic gain in sorghum hybrids and to utilize modern biological tools to conduct fundamental molecular research into end-use quality traits including the black pericarp phenotype in sorghum. The challenge facing crop geneticists and breeders is how to develop strategies that combine the traditional tools available to crop genetics with new genomic- and phenomic-assisted approaches that leverage the extensive 'omics' resources available to crop improvement programs. Utilizing elite sorghum inbreds from U.S. public breeding programs along with novel genetically diverse germplasm developed by the previous project plan, this project aims to emulate the modern genomics-based approach of commercial maize hybrid improvement programs to transform existing sorghum breeding strategies in both public and private sector in the U.S. In addition to the need for an "omics-assisted" approach to hybrid sorghum breeding, there is a need to enhance value-added traits that improve end-use quality of sorghum grain. Black pericarp sorghum, and the associated accumulation of the high-value phytochemicals 3-deoxyanthocyanidins, represents one such value-added trait with enhanced nutritional quality desired by processors and consumers. This project aims to elucidate the genes, gene networks, and novel genomic features (e.g., structural variation, epigenetic control) controlling the black pericarp trait to eliminate knowledge gaps that limit our capacity to improve this valued trait. The products of this research will include an assessment of appropriate genomic- and phenomic-assisted methods to integrate into breeding programs, more effective hybrid breeding programs through the introduction of genomic and phenomic selection models into sorghum breeding pipelines (Obj. 1), and critical knowledge of cellular control of black pericarp phenotype required to devise effective biotechnology- or breeding-based approaches to improve the expression of this trait. (Obj. 2)
Progress Report
Work under this project during FY 2024 in collaboration with university partners resulted in significant progress in sorghum germplasm evaluation and development, which included assembling genomic and environmental datasets to generate prediction models for sorghum hybrid performance. The discoveries made by this collaborative team will facilitate ongoing efforts focused on exploiting novel technologies to develop improved sorghum hybrids for effective utilization by farmers in all temperate production areas. Specific accomplishments under Objective 1 included the development of genomic prediction models, augmented with enviromics data, of sorghum hybrid performance utilizing elite sorghum inbreds along with diverse mapping populations. Achieving this goal resulted in the creation of highly predictive performance equations for use in sorghum breeding programs. Significant progress under Objective 2 was made towards utilizing genomic, metabolomic, and genetic linkage map resources to understand the genes and environmental factors controlling the black pericarp trait in sorghum. These efforts resulted in the identification of genes and gene networks controlling the black pericarp trait which is critical to improving this trait and for eventual transfer of this high value trait to other cereal species.
Accomplishments
1. Genomic prediction models for sorghum hybrid performance. The yield potential in grain sorghum hybrids has increased at a slower rate than other cereal crops including its close relative maize (corn). While there are many reasons for this lag, increasing hybrid performance through mathematical modeling of genetic and environmental factors that control grain yield is a commonly hypothesized mechanism to boost the rate of gain. To address this issue, ARS researchers at College Station, Texas, working with university collaborators, developed equations that utilize genetic and environmental data to predict the performance of hybrids from specific parental lines grown in a series of field locations. This accomplishment provides necessary knowledge to breeders in their work to exploit genetic diversity and environmental data in improving grain yield of hybrid cereal crops, including sorghum, to improve the productivity and profitability of farmers in the U.S. and worldwide.
2. Sorghum’s high value black grain trait. The black grain trait in sorghum has notable value in the specialty food and nutraceutical industries. The black coloration of the grain is associated with a group of rare compounds valued for use as natural food colorants, food preservatives, antioxidant food additives, and as compounds cytotoxic to cancer cells. ARS researchers at College Station, Texas, and at Cold Spring Harbor, New York, working with university collaborators, found that ultraviolet (UV) light increases reactive oxygen species in the grain which leads to the accumulation of these health-associated compounds in the seed. Based on the unique upregulation of a suite of genes in black seed, UV light likely produces a stress in black sorghum grain tissue which triggers the seed to turn black providing a sunscreen to the harmful effects of UV light. This study is critical for breeders and sorghum growers to pick the best environment for growing the blackest sorghum with the highest level of the health-associated compounds in the black seed.
Review Publications
Patil, N., Hoffmann, L., Perumal, R., Hayes, C.M., Emendack, Y., Boyles, R., Dalberg, J., Klein, R.R., Klein, P., Rooney, W. 2024. Registration of sorghum [sorghum bicolor (l.) moench] backcross-nested association mapping (BC-NAM) populations in BTx623 and RTx436 backgrounds . Journal of Plant Registrations. 18(1):204-219. https://doi.org/10.1002/plr2.20286.
Crozier, D., Leon, F., Fonseca, J.M., Klein, P.E., Klein, R.R., Rooney, W.L. 2023. Inbred phenotypic data and non-additive effects can enhance genomic prediction models for hybrid grain sorghum. Crop Science. Article e20927. https://doi.org/10.1002/csc2.20927.
Zhang, S., Wang Jie, He, W., Kan, S., Liao, X., Jordan, D., Mace, E., Tao, Y., Cruickshank, A., Klein, R.R., Yuan, D., Tembrock, L., Wu, Z. 2023. Variation in mitogenome structural conformation in wild and cultivated lineages of sorghum corresponds with domestication history and plastome evolution. BMC Plant Biology. Article e12870-023-04104-2. https://doi.org/10.1186/s12870-023-04104-2.
Winans, N.D., Klein, R.R., Fonseca, J.M., Klein, P.E., Rooney, W.L. 2023. Evaluating introgression sorghum germplasm selected at the population level while exploring genomic resources as a screening method. Plants. Article e12030444. https://doi.org/10.3390/plants12030444.
Kent, M.A., Fonseca, J.M., Klein, P.E., Klein, R.R., Hayes, C.M., Rooney, W.L. 2023. Use of genomic prediction to screen sorghum B-lines in hybrid test crosses. The Plant Genome. Article e20369. https://doi.org/10.1002/tpg2.20369.
Oliverira Fonseca, J.M., Perumal, R., Klein, P.E., Klein, R.R., Rooney, W.L. 2022. Mega-environment analysis to assess adaptability, stability, and genomic predictions in grain sorghum hybrids. Euphytica. 218. Article 128. https://doi.org/10.1007/s10681-022-03075-z.