The central goals of this collaborative, multi-state, and multi-disciplinary project are to explore, apply, and optimize the RenSeq technology for accelerated identification of candidate R genes conferring resistances to various soybean pathogens prevalent in the Mid-west region, and for accelerated development of disease-resistant soybean cultivars by precise R gene selection. Five specific objectives will help us achieve these goals.

Objective 1. Development of a high-quality RenSeq platform for the soybean research community.
Objective 2. Sequencing and assembly of NBS-LRR gene clusters in major soybean lines carrying resistance to prevalent soybean pathogens in the Midwest region.
Objective 3. Analysis of R gene expression and responses to various soybean pathogens.
Objective 4. Evaluation of resistance to various pathogens and mapping of major R genes and QTL.
Objective 5. Development of candidate R-gene-based molecular markers for precision breeding.

Deliverables in year 1:
• A SoyRenSeq platform for NBS-LRR gene capture and enrichment.
• Assembled and mapped NBS-LRR gene cluster sequences from 24 soybean cultivars carrying resistance to the targeted soybean diseases.
• Biparental soybean populations at different generations for mapping or fine mapping of R genes.
• Genome-wide NBS-LRR gene expression patterns in response to infection of targeted pathogens.
• A few candidate R genes of NBS-LRR type underlying specific resistances to Phytophthora, frogeye leaf spot, or brown stem rot.

Updated April 2, 2023:
In this period of the project, we focused on Objective 1, Development of a high-quality RenSeq platform for the soybean research community. We have re-annotated all NBS-LRR genes in ~30 high-quality genomes of diverse varieties including elite cultivars, landraces, and wild soybean accessions. We also conducted comparative genomics analysis for the NBS-LRR gene clusters to understand how NBS-LRR genes evolve over time. We found dramatic copy number and structural variations of NBS-LRR genes among different varieties. These analyses enhanced our understanding about the challenges and strategies for typical disease resistance(R) gene discovery, mapping, and isolation, we well as the importance of design of functional R gene-based molecular markers for marker-assisted selection of R genes in breeding. All the NBS-LRR gene sequences from the ~30 genomes have been extracted for design of a comprehensive set of baits for RenSeq.

Although subcontractors have just received funds to work on this project, they have made some progresses on screening and characterization of new sources of disease resistances and development of R gene mapping population as defined in Objective 4. Nevertheless, we anticipate a need for a 6-months no-cost extension. This would help the subcontractors to complete their research components defined in this project in year 1.

This project will explore, apply, and optimize the game-changing RenSeq and new sequencing technologies for rapid discovery of R genes conferring resistance to prevalent soybean pathogens across the Mid-west region and for efficient deployment of new disease resistance genes into elite soybean cultivars towards more sustainable soybean protection and increased soybean profitability to the Midwestern soybean growers.

Key performance indicators or measures in year 1:
• High quality of the SoyRenSeq platform that can capture and enrich nearly all NBS-LRR gene clusters from any soybean varieties using the data from Williams 82 as a control.
• Well assembled NBS-LRR gene cluster sequences from the 24 genomes using Williams 82.a3 genome assembly as a control.
• Desirable sizes (>500 individuals) of mapping populations for effective R gene mapping and fine mapping.
• Capability of NBS-LRR gene expression for deduction of candidate R genes using Rps1-k in Williams 82 as a control.
• Reliability of the combined SoyRenSeq and SMRT-seq approach for prediction of NBS-LRR type of R genes using Rps1-k in Williams 82 as a control.