Location: Plant Genetics Research
Project Number: 5070-21000-045-020-R
Project Type: Reimbursable Cooperative Agreement
Start Date: Oct 1, 2024
End Date: Sep 30, 2025
Objective:
1)Develop, validate and deploy a suite of multi-disciplinary data analysis and mining strategies and tools to discover genes and causative alleles underlying trait QTLs.
2)Develop and deploy a new genotyping technology to increase breeding and biological discovery efficiency.
3)Generate and deploy an expandable collection of QTL genes, alleles, germplasm, markers and phenotype prediction model for soybean research and development.
4)Develop a novel algorithm to discover genes and alleles associated with environmental stresses.
Approach:
Recently, the ARS scientists consolidated and analyzed genome sequencing data from 12,000 diverse wild and domesticated soybean accessions, and about 8,000 RNA-seq of soybean tissues in response to diverse environmental changes generated in my lab and available in the public. The ARS lab has been developing a big-data driven technology platform including AI/ML algorithm to translate a massive amount of -omics data for soybean research and development. The platform increases the efficiency of discovering trait genes/candidates and identifying causative alleles and germplasm containing its beneficial alleles by multiple magnitudes. The proposal will integrate those data and future improve our big-data mining approach to discover soybean trait QTL genes and alleles, and develop a functional marker array technology for molecular breeding. The ARS scientists will apply the technology platform to characterize genes and germplasm associated with desirable traits for soybean improvement.