320-14 Development of SNP Markers Associated with Biofuel Traits In Alfalfa.



Wednesday, October 19, 2011
Henry Gonzalez Convention Center, Hall C, Street Level

Jiqing Gou, Xuehui Li, Yuanhong Han, Dong-Man Khu, E. Charles Brummer and Maria J. Monteros, Samuel Roberts Noble Foundation, Ardmore, OK
Biofuel crops must maximize the composition and processing efficiency for the production of a renewable source of energy.  Processing of lignocellulosic biomass for conversion to liquid biofuels is affected by the lignin content and composition.  The objectives of this research were to identify single nucleotide polymorphism (SNP) markers associated with lignin content and cell wall composition in alfalfa.  UV microscopy and wet chemistry analysis were used to quantify lignin content and cell wall composition of six parental genotypes used to develop three alfalfa mapping populations. We identified transcription factors that are either positively or negatively regulated during lignification in the model legume and close relative of alfalfa, Medicago truncatula. A BLAST search of alfalfa transcriptome sequences was used to identify SNPs in these transcription factors and in genes known to be involved in lignin biosynthesis and cell wall development in various plant species.  The parental genotypes were used to validate the in silico SNP discovery using high-resolution melting (HRM) analysis.  A similar approach was used to genotype the polymorphic SNP markers in the corresponding mapping populations.  Existing SSR-based genetic linkage maps were used to anchor the SNP markers with putative relevance to biofuel traits. The development of SNPs markers associated with lignin and cell wall content and their integration with traditional field-oriented alfalfa breeding programs will greatly facilitate the development of biofuel-ready alfalfa cultivars with improved forage quality and yield.
See more from this Division: C01 Crop Breeding & Genetics
See more from this Session: Molecular, Statistical and Breeding Tools to Improve Selection Efficiency