NRGene (TASE: NRGN), a worldwide leader in AI-based genomic analysis for research and agricultural breeding, is pleased to announce the launch of Soy SNPro. This is the first product in a line of optimized pre-designed SNP sets for genotyping (DNA tests) of various crops that will significantly improve breeding program efficiency.
Genotyping is widely used in agriculture to allow breeders to identify and select individual crop plants or livestock with favorable genetic content. Many breeding companies and organizations allot a substantial portion of their breeding program budget for genotyping processes.
SNPro is a new off-the-shelf complete genotyping solution that combines low-density genotyping with high-density imputation to reduce genotyping costs, simplify routine workflows, and deliver high-quality genotypic data with fast turnaround times. It was developed based on NRGene’s SNPer solution, launched in 2020, which enables a custom design of an optimized SNP set for any breeding program to maximize genetic information with a minimum number of DNA data points.
Soy SNPro, the first available pre-designed SNP set, can be applied to Northern & Southern US, Canadian and South American germplasm, which together accounts for 92% of total global soy production.
“We are excited to launch SNPro and assist breeding companies and organizations of all sizes to gain a competitive edge by advancing their programs towards genomic selection. This will allow them to develop new elite varieties faster by efficient utilization of their resources,” said Dr. Gil Ronen, NRGene’s CEO & Founder.
Soybean is an increasingly popular source of protein for humans and animals. A highly versatile crop, soy lends itself to widely used cooking oils and plant-based alternative proteins, while also providing a great source for animal feed.
Soy SNPro was validated in an active public soy breeding program at the University of Missouri. The goal of the program is to shift breeding from conventional to molecular methods to provide high-yielding soybean varieties with multiple commercial key traits such as disease resistance and improved seed quality.
“The validation process via the University of Missouri’s (MU) public soybean breeding program included samples from 24 breeding populations comprising 2,256 total progenies,” said Pengyin Chen, David Haggard Endowed Professor in Soybean Breeding at the MU Fisher Delta Research, Extension and Education Center. “Samples were genotyped with the Soy SNPro 500-plex using a proprietary process and algorithms optimized for southern US soy breeding germplasm, with the imputation target of soy 6K plex.”
The validation testing data showed that 98.7% of the samples had an imputation accuracy above 95%. This pilot program combines low-cost genotypic data with imputation methodologies based on parental information to generate high-density SNP coverage.
“The use of genomic tools can speed up breeding pipelines and improve overall efficiency. Data-driven predictive breeding could shorten the breeding cycle and enhance selection accuracy. This information can help breeders to characterize breeding materials, select parental lines for crossing, as well as assess the yield potential of preliminary materials early in the pipeline using predictive analytics at a reduced cost,” said Chen.