Characterizing Allelic Effects and Leveraging Genomic Prediction to Advance Soybean Breeding

Campbell Benjamin W, Falcon Heights MN

Investigators

Abstract

This action funds an NSF National Plant Genome Initiative Postdoctoral Research Fellowship in Biology for FY 2017. The fellowship supports a research and training plan in a host laboratory for the Fellow who also presents a plan to broaden participation in biology. The host institution for the fellowship is the University of Minnesota, and the sponsoring scientist is Dr. Aaron Lorenz. Efforts to increase soybean yield and quality are challenged by the limited genetic diversity available within each maturity zone. However, an increase in this diversity could be achieved if genomic methodologies and tools were developed to enable the utilization of elite genetic variation that has previously been isolated by maturity differences. The broadened available genetic diversity will enable an increase in food production, necessary to sustain the growing world population. The project training will equip the Fellow with expertise in statistical genomics and quantitative genetics. Locally, the Fellow will visit several middle and high schools to promote student involvement in the sciences. Nationally, this project will advance the field of genomics-assisted breeding, will develop tools and databases that will guide the utilization of soybean genetic diversity, and will contribute to basic knowledge of the soybean genome. The development of an understanding of soybean germplasm architecture for grain yield and other agronomic traits will enhance soybean breeding efforts. Yield trial data and genetic marker data from thousands of lines will be analyzed using GWAS and genomic prediction approaches to characterize the germplasm architecture of soybean performance. Computational and informatic tools will then be developed to facilitate the use of this genomic information by breeders. This project will enhance basic research on the soybean genome and will advance applied soybean breeding by enabling breeders to use valuable genetic variation from outside of their maturity zone. The generated data and computational tools will be made publically available at SoyBase.org.