Functional genomics of forage and bioenergy quality traits in the grasses

Biomass from forage and energy crops can provide a renewable source of meat, milk, and wool, or power, heat, transport fuels and platform chemicals, respectively. Whilst in forage grasses some improvements have been made, the potential of energy grasses is limited because plant varieties have not yet been selected for this purpose. There are distinct challenges to determine and improve quality traits which increase ultimate energy yield but experience from forage crops can help. Energy grasses offer the potential to be utilised through either thermal or biological conversion methods with the route chosen being largely determined by the calorific value, moisture content and the ratio of soluble to structural carbohydrates. Plant chemical composition underlies these characteristics, for example whichever way grass feedstocks are converted the major determinates of energy are lignin, cell wall phenolics and the soluble and cell wall carbohydrates. These components affect the efficiency of the energy conversion process to meat, milk, wool, energy, platform chemicals and the end quality of certain liquid fuels such as pyrolysis oils. To associate phenotype to genotype for such underlying chemical composition, it is necessary to develop both DNA based molecular markers and high throughput methods for compositional analysis. The genetic resources available in forage and energy grasses are limited in comparison with several model grasses including maize and for some traits it may be appropriate to work initially on such a model and then translate this research back to the forage or bioenergy crop. However not all traits will be present in the model, and so genetic and genomic resources are and will have to be developed in the crops themselves. As part of the EU project GRASP, SNP based markers have been developed in carbohydrate associated genes which map to soluble carbohydrate QTL in Lolium perenne (perennial ryegrass) and these have been used in association studies in a synthetic population of L. perenne to measure allele shifts. High throughput calibration models have been developed using near infrared reflectance spectroscopy (NIRS) and Fourier transform infrared spectroscopy (FTIR) in the mid-infrared spectral range which allow accurate predictions of a number of composition traits including lignin, cellulose and hemicellulose contents in several forage and energy grasses including Miscanthus, L. perenne and related species. These calibrations have allowed a comparison of chemical composition from different grass genotypes, species and environments. Both tools and genetic resources for the optimisation of biomass as forage and energy feedstocks are therefore being developed to enable association of phenotype with genotype.