Principal Investigators:  Daniel Bush, Colorado State University  Hei Leung, International Rice Research Institute, Manila, Philippines  John McKay, Colorado State University  Jan E. Leach, Colorado State University 

Introduction 

Developing a sustainable biofuels program that makes significant contributions to the national energy budget requires unprecedented inputs of biomass for energy conversion. These crops must provide a net energy gain, be environmentally friendly, economically competitive, non-competitive with food supply, and be producible in large quantities. Historically, focus on biofuel plants has been on those which placed more carbon in the harvested grain than on the rest of the plant in order to increase crop yield. The new “energy crops,” however, must be vigorous growers that yield maximum dry biomass per acre. This can be acheived by maximizing growth of vegetative tissues largely composed of cell walls, since the available free energy in multicellular plants is found in the cell walls of vegetative issues. The consensus among biofuel researchers is that perennial grasses will be one of the “new energy” crops developed for biomass production. 

Our goal is to provide the applied biomass research community and industry with information to allow exploitation of the genes and pathways relevant to biomass accumulation in grasses. Our specific objectives include: 

Objective 1: Identify genes involved in biomass accumulation. Using genetic populations for rice lines that exhibit extremes in biomass accumulation, we will (a) map QTL using a phenotyping/genotyping approach, (b) confirm the QTL location and phenotyping effect, and (c) screen a deletion mutant collection to identify large deleted regions corresponding to biomass accumulation. 

Objective 2: Dissect the QTL using an integrated analysis. First, we will use an expedited approach to generating near isogenic lines containing each QTL. Second, we will integrate genome-wide data to refine the QTL regions including (a) association mapping, (b) expression profiling, (c) mutant analysis, (d) fine scale mapping, and (e) comprehensive sequencing of the QTL region. 

Objective 3: Validate the significance of candidate genes or pathways in biomass accumulation by using RNAi, reverse genetic resources (insertion and deletion mutants) and overexpression coupled to a comprehensive analysis or morophology and physiology. 

Objective 4: Integrate expression and mapping data into gene browser/database to allow easy navigation and use of the data. 

To approach these objectives, we are using rice as a model system because of the powerful genetic and genomic resources available for rice. In addition, rice has all of the genes necessary for high biomass productivity. We seek to identify those genes to expedite improvement of productivity in candidate biomass plants (switchgrass, Miscanthus, etc.).