STTR Phase I: Advanced Computational System for Assessing Genetic Provenance in Crop Plants and its Practical Applications

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) project will be the development of a highly accurate genotype analysis technique and the computational pipeline for assessing genetic provenance in crop plants. The commercial potential of the proposed products and services is based on the need to increase crop yield and accelerate breeding programs. The proposed technology will revolutionize the field of plant selection by enabling breeders to analyze genotypes of individual plants and optimize crosses. Provided that most of ancestry data on major crops are not publicly available, the platform opens up a range of opportunities for researchers and practitioners. Since it is organism-independent, the software will be applied for analysis of biodiversity and adaptation of species to climate change. These species include, for example, fruit fly, Arabidopsis, Medicago, rat, mouse, as well as endangered plant and animal species such as pandas, bonobos, or whales. Organism-specific pipelines for predicting phenotype using a whole-genome prediction and modeling approach will be established. Free educational versions of the solution will be provided to academic institutions. This STTR Phase I project proposes to develop and test a novel software modeling solution that will be the first-in-class commercial "whole genome" tool in the booming agro-genomics segment, designed to deduce the exact percentages of genetic lines that went into given organism solely based on its genotype. This tool will help farmers to effectively select plants with required traits, and also will automatically identify possible genetic contamination that is a common problem in plant species. The solution will be cloud based with a friendly front end enabling breeders and biologists to easily operate and gain insights from complex plant datasets. This visualization tool will be customizable for various species and types of geographical/climate data. Visual integration of geographical, phenotypical, and genomic data will help breeders deducing relationship among individual plants, strains, and variants. The phenotypes will include yield, height, photosynthetic performances, metabolites concentrations, fitness, drought tolerance, cold tolerance, and quantitative disease resistance. The software will be a revolutionary discovery tool enabling agro-companies to significantly reduce number of breeding experiments, and make the breeding process predictable and efficient.