Location: Plant, Soil and Nutrition Research
Project Number: 1907-21000-029-00-D
Project Type: In-House Appropriated
Start Date: Mar 1, 2008
End Date: Feb 28, 2013
This project will develop a genetic platform to identify useful variation in a high throughput fashion, and then use this platform to identify those genes and alleles that control kernel quality and tolerance to abiotic stresses. Bioinformatic tools will be developed in conjunction with the above to allow for the rapid analysis of plant germplasm diversity. Although the direct application of these approaches will be in maize and biofuel grasses, many of these genetic, statistical, and bioinformatic approaches could have broad implications for both the plant and animal genetics community at large. Objective 1: Develop statistical, genetic, and genomic approaches for dissecting complex traits in crop plants. Objective 2: Identify key genes and natural allelic variation for improving abiotic stress tolerance, nitrogen use efficiency, nutritional quality, and biofuel potential for maize and related grasses. Objective 3: Development of bioinformatic tools to mine and present functional allelic variation.
This project will use the natural variation inherent in the maize and biofuel grass genomes for the dissection of complex traits and for the identification of superior alleles. Such discovery is important to the development of improved breeding strategies for maize, the number one production crop in the world. First, this project will develop genetic resources that allow for the rapid dissection of any complex trait to the gene level. These resources will involve the creation of germplasm, the genotyping of this germplasm, and the development of statistical analyses. Using this platform, the project will then dissect the quantitative traits of nutritional quality, nitrogen use, biofuel productivity, and aluminum tolerance. The identification of advantageous alleles could allow for marker-assisted improvement of maize’s nutrition profile for humans and animals, increased processing efficiency, lower fertilizer requirements, and better adaptation to acidic soils. Finally, this project will improve access to diversity data and analysis tools for plant breeders and geneticists. We will facilitate the use of these materials by creating analysis tools, user friendly websites, and breeder decision making tools.