Research HighlightsConfirmation, Characterization and Deployment of the Perfect Markers and Developing Germplasm for Resistance to Phytophthora Sojae, Pythium Spp. and Fusarium Graminearum in Soybean
By Anne E. Dorrance, The Ohio State University
Seed is expensive for companies to produce and for growers to plant. Ultimately, growers would like to plant once (no replanting due to seedling diseases) and at reduced seeding rates, to maximize their profitability. They would also prefer not to have to worry about resistance to diseases and other pathogens. Reducing the impact of seedling diseases caused by soil-borne pathogens through variety development and selection would go a long way to achieving this goal.
In fact, soybean growers lose up to 90 million bushels of potential crop annually due to seedling pathogens and root rot, which are especially bad during wet springs and when seedlings are planted early. Understanding the causes of these diseases, and enhancing the seedlings’ ability to resist them, would significantly improve early stand development and increase yields, not to mention avoid the necessity to re-plant.
One of the most effective management tools for minimizing losses to disease is the use of genetic resistance — developing and planting genetic varieties that are naturally resistant to these pathogens, thus allowing growers to maintain consistently high yields. However, resistance to these soil-borne pathogens is controlled by several to many genes that are scattered throughout the soybean genome. Today’s soybean varieties are developed through marker-assisted selection. However, with today’s expedited breeding platforms, knowing the region(s) associated with resistance is no longer sufficient. Identifying the specific gene or pathway responsible for a specific characteristic is really essential for developers and growers to ensure they have the right gene(s) for all of these pathogens. Very little was known
about the genetics of resistance or susceptibility to the seedling and root rot diseases at the start of this project.
To make this identification possible, $677,801 was allocated in FY19 as a continuing effort to identify and map resistance to several pathogens, including those caused by Phytophthora sojae and more than eight species of Pythium and Fusarium graminearum. The first goal was to identify the candidate genes and defense pathways that are necessary for high levels of resistance. That information was then used to lead to the development of accurate molecular markers for rapid incorporation of this resistance into U.S. germplasm, and ultimately into commercially available varieties that growers can select for planting.
A second goal was to make germplasm and soybean lines that contain many of these new and novel gene(s) for resistance to more than one of the pathogens across numerous maturity groups. The germplasm was made available to public and private breeders to help expedite incorporation of this resistance into soybeans for U.S. growers. The more widely a variety can be planted, the more likely it is to be accepted by growers.
The third and final aim is to improve the process itself — the identification and analysis of candidate gene discovery and function in soybean. These mapping efforts have identified and proposed numerous genes that may be involved in disease resistance; however, proving they are the actual gene is a long and cumbersome process. As part of this project, new tools for candidate gene analysis through virus-induced gene silencing will be developed.
To date, more than 100 different sources of resistance combined to soil-borne diseases caused by Phytophthora sojae, more than eight species of Pythium, and Fusarium graminearum have been identified. Over the past five years, resistance has been mapped toward the soil-borne pathogens, Phytophthora sojae, six different Pythium spp. and Fusarium graminearum from populations derived from soybean cultivars and plant introductions. Additionally, RNA sequencing has been used to identify thousands of genes responding to pathogen infection. The results from this sequencing is being used to characterize the resistance mechanisms and facilitate rapid marker development and deployment of resistance in high-yielding cultivars. This work is advancing understanding of how resistance works, and could be applied more broadly to develop a general mechanism of disease resistance in soybean.
Results to date have been promising. Several germplasm lines with enhanced resistance were released, and more are in progress as resistance to multiple pathogens is being stacked in a single new germplasm for release. The project is well on its way toward its long-term goal of releasing multiple lines for different types of soybean use, with a diversity of resistance to several soybean pathogens.
In addition, several new tools for candidate gene analysis have been improved, mainly through virus-induced gene silencing. A bean pod mottle virus gene silencing vector and an apple latent spherical virus gene silencing vector have both been successfully tested.
The results from this project will enable developers and growers to choose from a diverse soybean germplasm base with high levels of resistance to a very diverse and constantly changing group of soil-borne pathogens and diseases. It will provide a database for companies to choose the best form of a gene to combat diseases in specific regions of the U.S., and will also provide a base to ensure that resistance levels do not decline as new traits are incorporated into germplasm. Growers will have varieties that will perform better in high disease environments, especially when heavy rains occur shortly after planting.
In addition, soybean researchers will have a diverse set of tools to study the functions of genes in their systems, and develop even better and more specific varieties to meet the challenges of the future.
This project was funded by the soybean checkoff. To find research related to this research highlight or to see other checkoff research projects, please visit the National Soybean Checkoff Research Database.