Location: Plant, Soil and Nutrition Research2010 Annual Report
1a. Objectives (from AD-416)
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.
1b. Approach (from AD-416)
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.
3. Progress Report
In collaboration with ARS researchers at Cold Spring Harbor, NY and Columbia, MO, we have been developing the approaches to fully sequence the maize genome. This is especially complicated as nearly 70% of the genome in one maize variety is entirely different from another variety. Bioinformatic, genetic, and molecular approaches are being combined to solve this problem and increase our knowledge to 10-30 fold more regions of the maize genome. Simultaneously, we are developing approaches to reduce the cost of genotyping from $1000s to $10s. We have been successful at using next generation sequencers to accomplish this, which will radically change how we genotype the USDA-ARS germplasm collections and breeding populations. With 2009 next generation sequencing technology, we currently have knowledge of 1.6 million variable regions in the maize genome. Relating this molecular variation to trait variation is a substantial challenge. We have been successful at using genetic design and statistical models that substantially reduce the problem to smaller segments of the genome that can be tackled sequentially. These approaches have already helped us identify the key genes controlling leaf angle, which is central to the current high density planting of maize and maize yield. They are also being used with collaborators to dissect flowering time, photoperiod response, plant height, and several diseases. In collaboration with ARS researchers in Ames, IA, Columbia, MO, and Raleigh, NC, we are evaluating basic growth trait and genotypic diversity for all of the maize inbred lines in the USDA Plant Introduction Station. Together these studies will help pinpoint the genes controlling basic developmental traits, and provide a perspective on the useful genetic variation in the germplasm collection. Additionally, we are making progress in evaluating how a broad spectrum of maize diversity contributes to yield with trials in four locations across the US. This will allow specific hypothesis on hybrid vigor to be tested.
Zhang, Z., Buckler IV, E.S., Casstevens, T., Bradbury, P. 2009. Software engineering the mixed model for genome-wide association studies on large samples. Briefings in Bioinformatics. 10(6):664-675.